Intersectoral collaborations are an integral component of the prevention and control of diseases in a complex health system. On the one hand, One Health (OH) is promoting the establishment of intersectoral collaborations for prevention at the human-animal-environment interface. On the other hand, operationalising OH can only be realized through intersectoral collaborations. This work contributes to broadening the knowledge of the process for operationalising OH by analysing the governance structures behind different initiatives that tackle health problems at the human-animal-environment interface. The cases taken as examples for the analysis are the control and response to rabies and avian influenza under “classical OH”, and the management of floods and droughts for insights into “extended OH”. Data from Ghana and India were collected and compared to identify the key elements that enable ISC for OH. Despite the case studies being heterogeneous in terms of their geographic, economic, social, cultural, and historical contexts, strong similarities were identified on how intersectoral collaborations in OH were initiated, managed, and taken to scale. The actions documented for rabies prevention and control were historically based on one sector being the leader and implementer of activities, while avian influenza management relied more on intersectoral collaborations with clearly defined sectoral responsibilities. The management of the impact of flood and droughts on health provided a good example of intersectoral collaborations achieved by sectoral integration; however, the human health component was only involved in the response stage in the case of Ghana, while for India, there were broader schemes of intersectoral collaborations for prevention, adaptation, and response concerning climate change and disaster.
Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in “high and extremely high” HDI areas and subtropical areas than that in other subgroups (I(2) = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.
BACKGROUND: Temperature and precipitation are known to affect Vibrio cholerae outbreaks. Despite this, the impact of drought on outbreaks has been largely understudied. Africa is both drought and cholera prone and more research is needed in Africa to understand cholera dynamics in relation to drought. METHODS: Here, we analyse a range of environmental and socioeconomic covariates and fit generalised linear models to publicly available national data, to test for associations with several indices of drought and make cholera outbreak projections to 2070 under three scenarios of global change, reflecting varying trajectories of CO(2) emissions, socio-economic development, and population growth. RESULTS: The best-fit model implies that drought is a significant risk factor for African cholera outbreaks, alongside positive effects of population, temperature and poverty and a negative effect of freshwater withdrawal. The projections show that following stringent emissions pathways and expanding sustainable development may reduce cholera outbreak occurrence in Africa, although these changes were spatially heterogeneous. CONCLUSIONS: Despite an effect of drought in explaining recent cholera outbreaks, future projections highlighted the potential for sustainable development gains to offset drought-related impacts on cholera risk. Future work should build on this research investigating the impacts of drought on cholera on a finer spatial scale and potential non-linear relationships, especially in high-burden countries which saw little cholera change in the scenario analysis.
Chronic seasonal crop and livestock loss due to heat stress and rainfall shortages can pose a serious threat to human health, especially in Sub-Saharan Africa where subsistence and small-scale farming dominate. Young children are particularly susceptible to undernutrition when households experience food insecurity because nutritional deficiencies affect their growth and development. The increase in the frequency of extreme climate events, including droughts, can potentially pose serious health impacts on children. However, the evidence is inconclusive and rather limited to small-scale local contexts. Furthermore, little is known about the differential impacts of droughts on the health of population subgroups. This study contributes to the literature by using data from three nationwide Demographic and Health Surveys (DHS) for Ethiopia conducted in 2005, 2011 and 2016 (n = 21,551). Undernutrition, measured as stunting and wasting among children under five, is used as a health indicator. Droughts are identified using the Standardized Precipitation Evapotranspiration Index (SPEI), a multi-scalar drought index. This study found that drought exposure during the main agricultural season (meher) increased the risk of both chronic undernutrition (stunting) and acute undernutrition (wasting) among under-five children in Ethiopia, however, the impacts vary with population subgroups. Boys, children born to uneducated mothers, and those living in the rural area and whose households are engaged in agricultural activities were more likely to be affected. This suggests that nutritional intervention should target these particularly vulnerable groups of the population. (C) 2021 Elsevier Ltd. All rights reserved.
A healthy and a dignified life experience requires adequate water, sanitation, and hygiene (WaSH) coverage. However, inadequate WaSH resources remain a significant public health challenge in many communities in Southern Africa. A systematic search of peer-reviewed Researchs from 2010 -May 2022 was undertaken on Medline, PubMed, EbscoHost and Google Scholar from 2010 to May 2022 was searched using combinations of predefined search terms with Boolean operators. Eighteen peer-reviewed articles from Southern Africa satisfied the inclusion criteria for this review. The general themes that emerged for both barriers and facilitators included geographical inequalities, climate change, investment in WaSH resources, low levels of knowledge on water borne-diseases and ineffective local community engagement. Key facilitators to improved WaSH practices included improved WaSH infrastructure, effective local community engagement, increased latrine ownership by individual households and the development of social capital. Water and sanitation are critical to ensuring a healthy lifestyle. However, many people and communities in Southern Africa still lack access to safe water and improved sanitation facilities. Rural areas are the most affected by barriers to improved WaSH facilities due to lack of WaSH infrastructure compared to urban settings. Our review has shown that, the current WaSH conditions in Southern Africa do not equate to the improved WaSH standards described in SDG 6 on ensuring access to water and sanitation for all. Key barriers to improved WaSH practices identified include rurality, climate change, low investments in WaSH infrastructure, inadequate knowledge on water-borne illnesses and lack of community engagement.
Flooding is the most frequent natural hazard globally, but evidence of its impact on domestic water point contamination remains limited. This study aimed to assess dam-related flooding’s impact on microbiological contamination of rural water points and to evaluate agreement of satellite-derived flood maps with ground-based observations of water point flooding. Fieldwork took place in two Ghanaian districts frequently flooded following dam overspill. Fifty-seven water points were tested for bacterial parameters during and immediately after flooding. Forty water points were resampled in the dry season, with the remainder having run dry. Ground-based observations of flooding were compared with three satellite-derived flood maps. Boreholes were less contaminated than wells or surface waters (geometric mean E. coli = 20.2, 175.6, and 590.7 cfu/100 ml, respectively). Among groundwater points, a Wilcoxon signed-rank test indicated significantly greater median E. coli and thermotolerant coliform contamination during flooding (p = 0.025 and p < 0.001, respectively), but Shigella, salmonella, and intestinal enterococci counts were not significantly different between seasons. In contrast, among surface water points, E. coli, Shigella, and Salmonella counts were significantly greater in dry season samples (p < 0.005 for all parameters), possibly reflecting a "concentration" effect. Satellite-derived flood maps had no or low agreement with ground-based observations of water point flooding. Although groundwater quality deteriorated during and after flooding, surface waters were the most microbiologically contaminated in both seasons. The greatest public health risk thus occurred where households switched to surface water collection during or following flood season. Flood risk should be assessed before borehole installation and existing flood-prone boreholes remediated to mitigate population exposure to contaminated water.
Floods affect the human security conditions of floodplain residents. The aim of this paper is to explore how residents of the Tana River Delta in Kenya become flood insecure. This paper utilises assemblage theory, particularly the principles of rhizomatic multiplicity to explain the concept of becoming flood insecure. It combines these rhizomatic multiplicity principles with disruptions to the pillars of human security which are becoming afraid, becoming wanting and becoming undignified and their composite conditions of human insecurity to create an analytical framework with which to understand becoming flood insecure. The study sources its data from Focus Group Discussions in 10 sampled villages in the Tana River Delta. The results reveal that becoming flood insecure is a rhizomatic multiplicity and that the pillars and conditions of human security that comprise it are heterogenous and interconnected. The results reveal the conditions of human insecurity in the Tana River Delta as personal, food, water, fuel, housing, health, environment, and political. They also reveal that while children become more flood insecure, they are also the most adaptive. Additionally, the results show that there are transitory conditions of human insecurity, food, housing health, to which people attempt to find local solutions and redundant conditions of human insecurity, political, health, water, personal and environment, to which people cannot find local solutions and public action is required.
Leptospirosis is a waterborne zoonosis (60,000 infections and 1 million deaths annually). Knowledge about the disease in the urban context is surprisingly rare, especially in Africa. Here, we provide the first study of leptospires in waters within an African city. A simple centrifugation-based method was developed to screen waterborne leptospires from remote or poorly areas. Major ions, trace elements, stable isotopes and pathogenic Leptospira were then seasonally investigated in 193 water samples from three neighborhoods of Cotonou (Benin) with different socio-environmental and hydrographic characteristics. Firstly, no leptospire was detected in tap waters. Secondly, although surface contamination cannot be excluded, one groundwater well was found leptospire positive. Thirdly, pathogenic Leptospira mainly contaminated surface waters of temporary and permanent ponds (9.5% and 27.3% of total prevalence, respectively). Isotopic signatures suggest that leptospires occurred in pond waters formed at the beginning of the rainy season following low to moderate rainfall events. Nevertheless, Leptospira-containing waters possess physico-chemical characteristics that are similar to the spectrum of waters sampled throughout the three sites, thus suggesting that Cotonou waters are widely compatible with Leptospira survival. The frequent contact with water exposes Cotonou inhabitants to the risk of leptospirosis which deserves more attention from public health authorities.
BACKGROUND: Floods are the most frequently occurring natural disaster and constitute a significant public health risk. Several operational satellite-based flood detection systems quantify flooding extent, but it is unclear how far the choice of satellite-based flood product affects the findings of epidemiological studies of associated public health risks. Few studies of flooding’s health impacts have used mixed methods to enrich understanding of these impacts. This study therefore aims to evaluate the relationship between two satellite-derived flood products with outpatient attendance and diarrhoeal disease in northern Ghana, identifying plausible reasons for observed relationships via qualitative interviews. METHODS: A convergent parallel mixed methods design combined an ecological time series with focus group discussions and key informant interviews. Through an ecological time series component, monthly outpatient attendance and diarrhoea case counts from health facilities in two flood-prone districts for 2016-2020 were integrated with monthly flooding map layers classified via the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite sensors. The relationship between reported diarrhoea and outpatient attendance with flooding was examined using Poisson regression, controlling for seasonality and facility catchment population. Four focus group discussions with affected community members and four key informant interviews with health professionals explored flooding’s impact on healthcare delivery and access. RESULTS: Flooding detected via Landsat better predicted outpatient attendance and diarrhoea than flooding via MODIS. Outpatient attendance significantly reduced as LandSat-derived flood area per facility catchment increased (adjusted Incidence Rate Ratio = 0.78, 95% CI: 0.61-0.99, p < 0.05), whilst reported diarrhoea significantly increased with flood area per facility catchment (adjusted Incidence Rate Ratio = 4.27, 95% CI: 2.74-6.63, p < 0.001). Key informants noted how flooding affected access to health services as patients and health professionals could not reach the health facility and emergency referrals were unable to travel. CONCLUSIONS: The significant reduction in outpatient attendance during flooding suggests that flooding impairs healthcare delivery. The relationship is sensitive to the choice of satellite-derived flood product, so future studies should consider integrating multiple sources of satellite imagery for more robust exposure assessment. Health teams and communities should plan spatially targeted flood mitigation and health system adaptation strategies that explicitly address population and workforce mobility issues.
Climate change threatens the health and well-being of populations. We conducted a risk assessment of two climate-related variables (i.e., temperature and rainfall) and associated water, sanitation and hygiene (WASH)-related exposures and vulnerabilities for people living in Mopani District, Limpopo province, South Africa. Primary and secondary data were applied in a qualitative and quantitative assessment to generate classifications of risk (i.e., low, medium, or high) for components of hazard/threat, human exposure, and human vulnerability. Climate-related threats were likely to impact human health due to the relatively high risk of waterborne diseases and WASH-associated pathogens. Vulnerabilities that increased the susceptibility of the population to these adverse outcomes included environmental, human, physical infrastructure, and political and institutional elements. People of low socio-economic status were found to be least likely to cope with changes in these hazards. By identifying and assessing the risk to sanitation services and water supply, evidence exists to inform actions of government and WASH sector partners. This evidence should also be used to guide disaster risk reduction, and climate change and human health adaptation planning.
This longitudinal flood-relief study assessed the impact of the March 2019 Cyclone Idai flood event on E. coli contamination of hand-pumped boreholes in Mulanje District, Malawi. It established the microbiological water-quality safety of 279 community supplies over three phases, each comprising water-quality survey, rehabilitation and treatment verification monitoring. Phase 1 contamination three months after Idai was moderate, but likely underestimated. Increased contamination in Phase 2 at 9 months and even greater in Phase 3, a year after Idai was surprising and concerning, with 40% of supplies then registering E. coli contamination and 20% of supplies deemed ‘unsafe’. Without donor support for follow-up interventions, this would have been missed by a typical single-phase flood-relief activity. Contamination rebound at boreholes successfully treated months earlier signifies a systemic problem from persistent sources intensified by groundwater levels likely at a decade high. Problem extent in normal, or drier years is unknown due to absence of routine monitoring of water point E. coli in Malawi. Statistical analysis was not conclusive, but was indicative of damaged borehole infrastructure and increased near-borehole pit-latrine numbers being influential. Spatial analysis including groundwater flow-field definition (an overlooked sector opportunity) revealed ‘hit-and-miss’ contamination of safe and unsafe boreholes in proximity. Hydrogeological control was shown by increased contamination near flood-affected area and in more recent recharge groundwater otherwise of good quality. Pit latrines are presented as credible e-coli sources in a conceptual model accounting for heterogeneous borehole contamination, wet season influence and rebound behavior. Critical to establish are groundwater level – flow direction, hand-pump plume draw, multiple footprint latrine sources – ‘skinny’ plumes, borehole short-circuiting and fast natural pathway (e.g. fracture flow) and other source influences. Concerted WASH (Water, Sanitation and Hygiene) sector investment in research and policy driving national water point based E. coli monitoring programs are advocated.
BACKGROUND: Malaria epidemics are a well-described phenomenon after extreme precipitation and flooding, which account for nearly half of global disasters over the past two decades. Yet few studies have examined mitigation measures to prevent post-flood malaria epidemics. METHODS: We conducted an evaluation of a malaria chemoprevention program implemented in response to severe flooding in western Uganda. Children ≤12 years of age from one village were eligible to receive 3 monthly rounds of dihydroartemisinin-piperaquine (DP). Two neighboring villages served as controls. Malaria cases were defined as individuals with a positive rapid diagnostic test result as recorded in health center registers. We performed a difference-in-differences analysis to estimate changes in the incidence and test positivity of malaria between intervention and control villages. RESULTS: A total of 554 children received at least one round of chemoprevention with 75% participating in at least two rounds. Compared to control villages, we estimated a 53.4% reduction (aRR 0.47, 95% CI 0.34 – 0.62, p<.01) in malaria incidence and a 30% decrease in the test positivity rate (aRR=0.70, CI 0.50 - 0.97, p=0.03) in the intervention village in the six months post-intervention. The impact was greatest among children receiving the intervention, but decreased incidence was also observed in older children and adults (aRR=0.57, CI 0.38-0.84, p<.01). CONCLUSIONS: Three rounds of chemoprevention with DP delivered under pragmatic conditions reduced the incidence of malaria after severe flooding in western Uganda. These findings provide a proof-of-concept for the use of malaria chemoprevention to reduce excess disease burden associated with severe flooding.
Climate changes in the eastern part of Sahelian regions will induce an increase in rainfalls and extreme climate events. In this area, due to the intense events and floods, malaria transmission, a climate sensitive disease, is thus slowly extending in time to the drought season and in areas close to the border of the desert. Vectors can as well modify their area of breeding. Control programs must be aware of these changes to adapt their strategies.
Given the crucial role of climate in malaria transmission, many mechanistic models of malaria represent vector biology and the parasite lifecycle as functions of climate variables in order to accurately capture malaria transmission dynamics. Lower dimension mechanistic models that utilize implicit vector dynamics have relied on indirect climate modulation of transmission processes, which compromises investigation of the ecological role played by climate in malaria transmission. In this study, we develop an implicit process-based malaria model with direct climate-mediated modulation of transmission pressure borne through the Entomological Inoculation Rate (EIR). The EIR, a measure of the number of infectious bites per person per unit time, includes the effects of vector dynamics, resulting from mosquito development, survivorship, feeding activity and parasite development, all of which are moderated by climate. We combine this EIR-model framework, which is driven by rainfall and temperature, with Bayesian inference methods, and evaluate the model’s ability to simulate local transmission across 42 regions in Rwanda over four years. Our findings indicate that the biologically-motivated, EIR-model framework is capable of accurately simulating seasonal malaria dynamics and capturing of some of the inter-annual variation in malaria incidence. However, the model unsurprisingly failed to reproduce large declines in malaria transmission during 2018 and 2019 due to elevated anti-malaria measures, which were not accounted for in the model structure. The climate-driven transmission model also captured regional variation in malaria incidence across Rwanda’s diverse climate, while identifying key entomological and epidemiological parameters important to seasonal malaria dynamics. In general, this new model construct advances the capabilities of implicitly-forced lower dimension dynamical malaria models by leveraging climate drivers of malaria ecology and transmission.
In this paper, we present a nonlinear deterministic mathematical model for malaria transmission dynamics incorporating climatic variability as a factor. First, we showed the limited region and nonnegativity of the solution, which demonstrate that the model is biologically relevant and mathematically well-posed. Furthermore, the fundamental reproduction number was determined using the next-generation matrix approach, and the sensitivity of model parameters was investigated to determine the most affecting parameter. The Jacobian matrix and the Lyapunov function are used to illustrate the local and global stability of the equilibrium locations. If the fundamental reproduction number is smaller than one, a disease-free equilibrium point is both locally and globally asymptotically stable, but endemic equilibrium occurs otherwise. The model exhibits forward and backward bifurcation. Moreover, we applied the optimal control theory to describe the optimal control model that incorporates three controls, namely, using treated bed net, treatment of infected with antimalaria drugs, and indoor residual spraying strategy. The Pontryagin’s maximum principle is introduced to obtain the necessary condition for the optimal control problem. Finally, the numerical simulation of optimality system and cost-effectiveness analysis reveals that the combination of treated bed net and treatment is the most optimal and least-cost strategy to minimize the malaria.
BACKGROUND: High altitude settings in Eastern Africa have been reported to experience increased malaria burden due to vector habitat expansion. This study explored possible associations between malaria test positivity rates and its predictors including malaria control measures and meteorological factors at a high-altitude, low malaria transmission setting, south of Mount Kilimanjaro. METHODS: Malaria cases reported at the Tanganyika Plantation Company (TPC) hospital’s malaria registers, meteorological data recorded at TPC sugar factory and data on bed nets distributed in Lower Moshi from 2009 to 2018 were studied. Correlation between bed nets distributed and malaria test positivity rates were explored by using Pearson correlation analysis and the associations between malaria test positivity rates and demographic and meteorological variables were determined by logistic regression and negative binomial regression analyses, respectively. RESULTS: Malaria cases reported at TPC hospital ranged between 0.48 and 2.26% per year and increased slightly at the introduction of malaria rapid diagnostic tests. The risk of testing positive for malaria were significantly highest among individuals aged between 6 and 15 years (OR = 1.65; 1.65 CI = 1.28-2.13; p = 0.001) and 16-30 years (OR = 1.49; CI = 1.17-1.89; p = 0.001) and when adjusted for age, the risk were significantly higher among male individuals when compared to female individuals (OR = 1.54; 1.00-1.31; p = 0.044). Malaria test positivity rates were positively associated with average monthly minimum temperatures and negatively associated with average monthly maximum temperatures (incidence rate ratio (IRR) = 1.37, 95% confidence interval (CI) = 1.05-1.78, p = 0.019 and IRR = 0.72, 95% CI = 0.58-0.91, p = 0.005, respectively). When analysed with one month lag for predictor variables, malaria test positivity rates were still significantly associated with average monthly minimum and maximum temperatures (IRR = 1.67, 95% CI = 1.28-2.19, p = 0.001 and IRR = 0.68, 95% CI = 0.54-0.85, p = 0.001, respectively). Average monthly rainfall and relative humidity with or without a one month lag was not associated with malaria test positivity rates in the adjusted models. Explopring possible associations between distribution of long-lasting insecticidal nets, (LLINs) and malaria test positivity rates showed no apparent correlation between numbers of LLINs distributed in a particular year and malaria test positivity rates. CONCLUSION: In Lower Moshi, the risk of being tested positive for malaria was highest for older children and male individuals. Higher minimum and lower maximum temperatures were the strongest climatic predictors for malaria test positivity rates. In areas with extensive irrigation activity as in Lower Moshi, vector abundance and thus malaria transmission may be less dependent on rainfall patterns and humidity. Mass distribution of LLINs did not have an effect in this area with already very low malaria transmission.
BACKGROUND: Climate variables impact human health and in an era of climate change, there is a pressing need to understand these relationships to best inform how such impacts are likely to change. OBJECTIVES: This study sought to investigate time series of daily admissions from two public hospitals in Limpopo province in South Africa with climate variability and air quality. METHODS: We used wavelet transform cross-correlation analysis to monitor coincidences in changes of meteorological (temperature and rainfall) and air quality (concentrations of PM(2.5) and NO(2)) variables with admissions to hospitals for gastrointestinal illnesses including diarrhoea, pneumonia-related diagnosis, malaria and asthma cases. We were interested to disentangle meteorological or environmental variables that might be associated with underlying temporal variations of disease prevalence measured through visits to hospitals. RESULTS: We found preconditioning of prevalence of pneumonia by changes in air quality and showed that malaria in South Africa is a multivariate event, initiated by co-occurrence of heat and rainfall. We provided new statistical estimates of time delays between the change of weather or air pollution and increase of hospital admissions for pneumonia and malaria that are addition to already known seasonal variations. We found that increase of prevalence of pneumonia follows changes in air quality after a time period of 10 to 15 days, while the increase of incidence of malaria follows the co-occurrence of high temperature and rainfall after a 30-day interval. DISCUSSION: Our findings have relevance for early warning system development and climate change adaptation planning to protect human health and well-being.
Studies about the impact of future climate change on diseases have mostly focused on standard Representative Concentration Pathway climate change scenarios. These scenarios do not account for the non-linear dynamics of the climate system. A rapid ice-sheet melting could occur, impacting climate and consequently societies. Here, we investigate the additional impact of a rapid ice-sheet melting of Greenland on climate and malaria transmission in Africa using several malaria models driven by Institute Pierre Simon Laplace climate simulations. Results reveal that our melting scenario could moderate the simulated increase in malaria risk over East Africa, due to cooling and drying effects, cause a largest decrease in malaria transmission risk over West Africa and drive malaria emergence in southern Africa associated with a significant southward shift of the African rain-belt. We argue that the effect of such ice-sheet melting should be investigated further in future public health and agriculture climate change risk assessments.
In the last decade, many malaria-endemic countries, like Zambia, have achieved significant reductions in malaria incidence among children <5 years old but face ongoing challenges in achieving similar progress against malaria in older age groups. In parts of Zambia, changing climatic and environmental factors are among those suspectedly behind high malaria incidence. Changes and variations in these factors potentially interfere with intervention program effectiveness and alter the distribution and incidence patterns of malaria differentially between young children and the rest of the population. We used parametric and non-parametric statistics to model the effects of climatic and socio-demographic variables on age-specific malaria incidence vis-à-vis control interventions. Linear regressions, mixed models, and Mann-Kendall tests were implemented to explore trends, changes in trends, and regress malaria incidence against environmental and intervention variables. Our study shows that while climate parameters affect the whole population, their impacts are felt most by people aged ≥5 years. Climate variables influenced malaria substantially more than mosquito nets and indoor residual spraying interventions. We establish that climate parameters negatively impact malaria control efforts by exacerbating the transmission conditions via more conducive temperature and rainfall environments, which are augmented by cultural and socioeconomic exposure mechanisms. We argue that an intensified communications and education intervention strategy for behavioural change specifically targeted at ≥5 aged population where incidence rates are increasing, is urgently required and call for further malaria stratification among the ≥5 age groups in the routine collection, analysis and reporting of malaria mortality and incidence data.
Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035-2065) and far future (2071-2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m(-2)), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.
In 2017, diarrheal diseases were responsible for 606 024 deaths in Sub-Saharan Africa. This situation is due to domestic and recreational use of polluted surface waters, deficits in hygiene, access to healthcare and drinking water, and to weak environmental and health monitoring infrastructures. Escherichia coli (E. coli) is an indicator for the enteric pathogens that cause many diarrheal diseases. The links between E. coli, diarrheal diseases and environmental parameters have not received much attention in West Africa, and few studies have assessed health risks by taking into account hazards and socio-health vulnerabilities. This case study, carried out in Burkina Faso (Bagre Reservoir), aims at filling this knowledge gap by analyzing the environmental variables that play a role in the dynamics of E. coli, cases of diarrhea, and by identifying initial vulnerability criteria. A particular focus is given to satellite-derived parameters to assess whether remote sensing can provide a useful tool to assess the health hazard. Samples of surface water were routinely collected to measure E. coli, enterococci and suspended particulate matter (SPM) at a monitoring point (Kapore) during one year. In addition, satellite data were used to estimate precipitation, water level, Normalized Difference Vegetation Index (NDVI) and SPM. Monthly epidemiological data for cases of diarrhea from three health centers were also collected and compared with microbiological and environmental data. Finally, semi-structured interviews were carried out to document the use of water resources, contact with elements of the hydrographic network, health behavior and condition, and water and health policy and prevention, in order to identify the initial vulnerability criteria. A positive correlation between E. coli and enterococci in surface waters was found indicating that E. coli is an acceptable indicator of fecal contamination in this region. E. coli and diarrheal diseases were strongly correlated with monsoonal precipitation, in situ SPM, and Near Infra-Red (NIR) band between March and November. Partial least squares regression showed that E. coli concentration was strongly associated with precipitation, Sentinel-2 reflectance in the NIR and SPM, and that the cases of diarrhea were strongly associated with precipitation, NIR, E. coli, SPM, and to a lesser extent with NDVI. Moreover, E. coli dynamics were reproduced using satellite data alone, particularly from February to mid-December (R2 = 0.60) as were cases of diarrhea throughout the year (R2 = 0.76). This implies that satellite data could provide an important contribution to water quality monitoring. Finally, the vulnerability of the population was found to increase during the rainy season due to reduced accessibility to healthcare and drinking water sources and increased use of water of poor quality. During this period, surface water is used because it is close to habitations, easy to use and free from monetary or political constraints. This vulnerability is aggravated by marginality and particularly affects the Fulani, whose concessions are often close to surface water (river, lake) and far from health centers.
Non-typhoidal Salmonella (NTS) ranks first among causes of bloodstream infection in children under five years old in the Democratic Republic of Congo and has a case fatality rate of 15%. Main host-associated risk factors are Plasmodium falciparum malaria, anemia and malnutrition. NTS transmission in sub-Saharan Africa is poorly understood. NTS bloodstream infections mostly occur during the rainy season, which may reflect seasonal variation in either environmental transmission or host susceptibility. We hypothesized that environment- and host-associated factors contribute independently to the seasonal variation in NTS bloodstream infections in children under five years old admitted to Kisantu referral hospital in 2013-2019. We used remotely sensed rainfall and temperature data as proxies for environmental factors and hospital data for host-associated factors. We used principal component analysis to disentangle the interrelated environment- and host-associated factors. With timeseries regression, we demonstrated a direct association between rainfall and NTS variation, independent of host-associated factors. While the latter explained 17.5% of NTS variation, rainfall explained an additional 9%. The direct association with rainfall points to environmental NTS transmission, which should be explored by environmental sampling studies. Environmental and climate change may increase NTS transmission directly or via host susceptibility, which highlights the importance of preventive public health interventions.
BACKGROUND: Internally displaced persons fleeing their homes due to conflict and drought are particularly at risk of morbidity and mortality from diarrhoeal diseases. Regular handwashing with soap (HWWS) could substantially reduce the risk of these infections, but the behaviour is challenging to practice while living in resource-poor, informal settlements. To mitigate these challenges, humanitarian aid organisations distribute hygiene kits, including soap and handwashing infrastructure. Our study aimed to assess the effect of modified hygiene kits on handwashing behaviours among internally displaced persons in Moyale, Ethiopia. METHODS: The pilot study evaluated three interventions: providing liquid soap; scented soap bar; and the inclusion of a mirror in addition to the standard hygiene kit. The hygiene kits were distributed to four study arms. Three of the arms received one of the interventions in addition to the standard hygiene kit. Three to six weeks after distribution the change in behaviour and perceptions of the interventions were assessed through structured observations, surveys and focus group discussions. RESULTS: HWWS was rare at critical times for all study arms. In the liquid soap arm, HWWS was observed for only 20% of critical times. This result was not indicated significantly different from the control arm which had a prevalence of 17% (p-value = 0.348). In the mirror and scented soap bar intervention arms, HWWS prevalence was 11 and 10%, respectively. This was indicated to be significantly different from the control arm. Participants in the focus group discussions indicated that liquid soap, scented soap bar and the mirror made handwashing more desirable. In contrast, participants did not consider the soap bar normally distributed in hygiene kits as nice to use. CONCLUSION: We found no evidence of an increased prevalence of handwashing with soap following distribution of the three modified hygiene kits. However, our study indicates the value in better understanding hygiene product preferences as this may contribute to increased acceptability and use among crisis-affected populations. The challenges of doing research in a conflict-affected region had considerable implications on this study’s design and implementation. TRIAL REGISTRATION: The trial was registered at www.ClinicalTrials.gov 6 September 2019 (reg no: NCT04078633 ).
BACKGROUND: Environmental Enteric Dysfunction (EED) is a chronic intestinal inflammatory disorder of unclear aetiology prevalent amongst children in low-income settings and associated with stunting. We aimed to characterise development of EED and its putative risk factors amongst rural Kenyan infants. METHODS: In a birth cohort study in Junju, rural coastal Kenya, between August 2015 and January 2017, 100 infants were each followed for nine months. Breastfeeding status was recorded weekly and anthropometry monthly. Acute illnesses and antibiotics were captured by active and passive surveillance. Intestinal function and small intestinal bacterial overgrowth (SIBO) were assessed by monthly urinary lactulose mannitol (LM) and breath hydrogen tests. Faecal alpha-1-antitrypsin, myeloperoxidase and neopterin were measured as EED biomarkers, and microbiota composition assessed by 16S sequencing. FINDINGS: Twenty nine of the 88 participants (33%) that underwent length measurement at nine months of age were stunted (length-for-age Z score <-2). During the rainy season, linear growth was slower and LM ratio was higher. In multivariable models, LM ratio, myeloperoxidase and neopterin increased after cessation of continuous-since-birth exclusive breastfeeding. For LM ratio this only occurred during the rainy season. EED markers were not associated with antibiotics, acute illnesses, SIBO, or gut microbiota diversity. Microbiota diversified with age and was not strongly associated with complementary food introduction or linear growth impairment. INTERPRETATION: Our data suggest that intensified promotion of uninterrupted exclusive breastfeeding amongst infants under six months during the rainy season, where rainfall is seasonal, may help prevent EED. Our findings also suggest that therapeutic strategies directed towards SIBO are unlikely to impact on EED in this setting. However, further development of non-invasive diagnostic methods for SIBO is required. FUNDING: This research was funded in part by the Wellcome Trust (Research Training Fellowship to RJC (103376/Z/13/Z)). EPKP was supported by the MRC/DfID Newton Fund (MR/N006259/1). JAB was supported by the MRC/DFiD/Wellcome Trust Joint Global Health Trials scheme (MR/M007367/1) and the Bill & Melinda Gates Foundation (OPP1131320). HHU was supported by the NIHR Oxford Biomedical Research Centre (IS-BRC-1215-20008).
Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.
Faecal pathogens can be introduced into surface water through open defecation, illegal disposal and inadequate treatment of faecal sludge and wastewater. Despite sanitation improvements, poor countries are progressing slowly towards the United Nation’s Sustainable Development Goal 6 by 2030. Sanitation-associated pathogenic contamination of surface waters impacted by future population growth, urbanization and climate change receive limited attention. Therefore, a model simulating human rotavirus river inputs and concentrations was developed combining population density, sanitation coverage, rotavirus incidence, wastewater treatment and environmental survival data, and applied to Uganda. Complementary surface runoff and river discharge data were used to produce spatially explicit rotavirus outputs for the year 2015 and for two scenarios in 2050. Urban open defecation contributed 87%, sewers 9% and illegal faecal sludge disposal 3% to the annual 15.6 log(10) rotavirus river inputs in 2015. Monthly concentrations fell between -3.7 (Q5) and 2.6 (Q95) log(10) particles per litre, with 1.0 and 2.0 median and mean log(10) particles per litre, respectively. Spatially explicit outputs on 0.0833 × 0.0833° grids revealed hotspots as densely populated urban areas. Future population growth, urbanization and poor sanitation were stronger drivers of rotavirus concentrations in rivers than climate change. The model and scenario analysis can be applied to other locations.
Climate variability is expected to increase the risk of diarrhea diseases, a leading cause of child mortality and morbidity in Sub-Saharan Africa (SSA). The risk of diarrhea is more acute when populations have poor access to improved water and sanitation. This study seeks to determine individual and joint effects of climate variation, water supply and sanitation on the occurrence of diarrhea among children under five in SSA using multilevel mixed-effect Poisson regression including cross-level interaction. We merged 57 Demographic and Health Surveys (DHS) from 25 SSA countries covering the period 2000-2019 with climatic data from the DHS geolocation databases. The results of the research indicate that 77.7% of the variation in the occurrence of diarrhea in Sub-Saharan households is due to climatic differences between clusters. Also, a household residing in a cluster with a high incidence of diarrhea is 1.567 times more likely to have diarrhea cases than a household from a cluster with a low incidence. In addition, when average temperature and rainfall increase, households using unimproved sanitation or unimproved water have more cases of diarrhea. For SSA, the results of the multilevel analysis suggest the adoption at both levels; macro (national) and micro (household), of climate change adaption measures in the water sector to reduce the prevalence of diarrhea.
Temperature constrains the transmission of many pathogens. Interventions that target temperature-sensitive life stages, such as vector control measures that kill intermediate hosts, could shift the thermal optimum of transmission, thereby altering seasonal disease dynamics and rendering interventions less effective at certain times of the year and with global climate change. To test these hypotheses, we integrated an epidemiological model of schistosomiasis with empirically determined temperature-dependent traits of the human parasite Schistosoma mansoni and its intermediate snail host (Biomphalaria spp.). We show that transmission risk peaks at 21.7 °C (T (opt) ), and simulated interventions targeting snails and free-living parasite larvae increased T (opt) by up to 1.3 °C because intervention-related mortality overrode thermal constraints on transmission. This T (opt) shift suggests that snail control is more effective at lower temperatures, and global climate change will increase schistosomiasis risk in regions that move closer to T (opt) Considering regional transmission phenologies and timing of interventions when local conditions approach T (opt) will maximize human health outcomes.
The incidence of most diseases varies greatly with seasons, and global climate change is expected to increase its risk. Predictive models that automatically capture trends between climate and diseases are likely to be beneficial in minimizing disease outbreaks. Machine learning (ML) predictive analytic tools have been popularized across many health-care applications, however the optimal task performance of such ML tools largely depends on manual parameter tuning and calibration. Such manual tuning significantly limits the full potential of ML methods, especially for high-dimensional and complex task domains, as typified by real-world health-care application data-sets. Additionally, the inaccessibility of many health-care data-sets compounds innate problems of method comparison, predictive accuracy and the overall advancement of ML based health-care applications. In this study we investigate the impact of Relevance Estimation and Value Calibration, an evolutionary parameter optimization method applied to automate parameter tuning for comparative ML methods (Deep learning and Support Vector Machines) applied to predict daily diarrhoea cases across various geographic regions. Data-augmentation is also used to complement real-world noisy, sparse and incomplete data-sets with synthetic data-sets for training, validation and testing. Results support the efficacy of evolutionary parameter optimization and data synthesis to boost predictive accuracy in the given task, indicating a significant prediction accuracy boost for the deep-learning models across all data-sets.
Water pollution had become a major problem due to its negative impact on the human health. Effects of humaninduced actions on groundwater quality were examined in this study. The physicochemical, heavy metals and microbial parameters of groundwater, sampled during the two major climatic periods in Nigeria, were measured according to APHA approved procedures. Results obtaned from laboratory tests revealed that anthropogenic IP: 14.98.160.66 On: Fri, 01 Jul 2022 12:43:29 activities had substantial effect on the groundwater quality. The groundwater TDS, nitrate, BOD, chloride and phosphate concentrations varied from 23.93 to 42.32 mg/L, 0.54 to 2.16 mg/L, 2.23 to 4.72 mg/L, 10.78 to Delivered by Ingenta 19.15 mg/L, and 0.22 to 0.36 mg/L respectively. Likewise, Cd concentration fluctuated between 0 and 0.001 mg/L, Cu varied between 0 and 0.149 mg/L, Fe varied between 0 and 0.293 mg/L, Pb varied between 0 to 0.105 mg/mL, Zn varied between from 0 and 0.768 mg/L, while Ni fluctuated between 0 and 0.001 mg/L. The findings revealed that areas with poor sanitary situations had poor groundwater quality, compared to the areas with improved sanitary situations. Regarding the microbial population, the highest Total Bacteria and Fungi Counts recorded in the groundwater were 1.11 x 102 cfu/mL and 1.23 x 102 cfu/mL respectively. Similarly, the highest recorded Enterobacterial spp., Staphylococus arurius, E. coli, Proteus spp. and Shegeela spp. populations were 26.22 x 102 cfu/mL, 1.23 x 102 cfu/mL, 0.41 MPN/100 mL, 0.12 cfu/ml and 0.30 x 102 cfu/mL respectively. Although, the groundwater physicochemical parameters and heavy metals concentrations were within safe drinking water limits; the groundwater was largely contaminated with pathogenic microorganisms, mostly during the rainy season.
This work reports control strategies of the water quality in the city of Souk-Ahras (east Algeria). With the recent development, rapid population growth, and the consequences of climate change, the capacity of water supply reserves becomes more unpredictable in the long term. This has drastically affected the distributed water quantity. A correlation between bacteriological water analysis and the analysis of pollution indicative physicochemical parameters is developed to replace the slow bacteriological analysis, which takes more than two days, by directly accessible physicochemical analysis to anticipate the case-onset of waterborne diseases. A good correlation is found between different combinations of physicochemical pollution parameters: (Turbidity, Nitrates); (Turbidity, Active chlorine) (nitrates, active chlorine); (Ammonium, Chlorine) and (Turbidity, Ammonium) with Spearman rank coefficients of 0.8657, -0.8602 and -0.8531 -0.8227 et 0.7957 respectively. Besides, long term analysis (over several years) revealed a high correlation of more than 0.92 between the analysis of pollution indicative physicochemical parameters and bacteriological analysis. The EPANET software is used to simulate the hydraulic behaviour of the network system over an extended period within pressurized and pressure-deficient conditions. The simulation results of several supply scenarios of daily drinking water pressure in the city center area show that 62% of drinking water distribution system is supplied with a steep slope (80 m), 10% with unsatisfactory pressure and only 23% with acceptable pressure (1-80 m). Therefore, the high working pressure at the mesh, and the interruptions of the water supply are factors that can lead to the occurrence of cross-connection cases. This diagnosis of the defects in the water supply system is combined with a statistical data analysis of physicochemical parameters to set up an effective sampling strategy that takes into account the frequency of analysis and the areas at risk to prevent the risk of waterborne diseases.
In this article we draw on an interdisciplinary study on drinking water quality in Maputo, the capital of Mozambique, to examine the nature, scale, and politics of waterborne diseases. We show how water contamination and related diseases are discursively framed as household risks, thereby concealing the politics of uneven exposure to contaminated water and placing the burden of being healthy on individuals. In contrast, we propose that uneven geographies of waterborne diseases are best understood as the product of Maputo’s urban metabolism, in which attempts at being sanitary and healthy are caught up in relations of power, class, and variegated citizenship. Waterborne diseases are the result of complex and fragmented circulations and intersections of (waste)waters, generated by uneven urban development, heterogeneous infrastructure configurations, and everyday practices to cope with basic service deficits, in conjunction with increasing climatic variability. The latrine-from which ultimately contamination and diseases spread-is an outcome of these processes, rather than the site to be blamed. This article also advances an interdisciplinary framework for analyzing urban metabolism and deepening its explanatory potential. It serves as a demonstration of how interdisciplinary approaches might be taken forward to generate new readings of more-than-human metabolic processes at distinct temporal and spatial scales.
Africa has historically seen several periods of prolonged and extreme droughts across the continent, causing food insecurity, exacerbating social inequity and frequent mortality. A known consequence of droughts and their associated risk factors are infectious disease outbreaks, which are worsened by malnutrition, poor access to water, sanitation and hygiene and population displacement. Cholera is a potential causative agent of such outbreaks. Africa has the highest global cholera burden, several drought-prone regions and high levels of inequity. Despite this, research on cholera and drought in Africa is lacking. Here, we review available research on drought-related cholera outbreaks in Africa and identify a variety of potential mechanisms through which these outbreaks occurred, including poor access to water, marginalization of refugees and nomadic populations, expansion of informal urban settlements and demographic risks. Future climate change may alter precipitation, temperature and drought patterns, resulting in more extremes, although these changes are likely to be spatially heterogeneous. Despite high uncertainty in future drought projections, increases in drought frequency and/or durations have the potential to alter these related outbreaks into the future, potentially increasing cholera burden in the absence of countermeasures (e.g. improved sanitation infrastructure). To enable effective planning for a potentially more drought-prone Africa, inequity must be addressed, research on the health implications of drought should be enhanced, and better drought diplomacy is required to improve drought resilience under climate change.
BACKGROUND: Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia. METHODS: Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence. RESULTS: Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801-1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m. CONCLUSIONS: Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.
BACKGROUND: During the last two decades, researchers have suggested that the changes of malaria cases in African highlands were driven by climate change. Recently, a study claimed that the malaria cases (Plasmodium falciparum) in Oromia (Ethiopia) were related to minimum temperature. Critics highlighted that other variables could be involved in the dynamics of the malaria. The literature mentions that beyond climate change, trends in malaria cases could be involved with HIV, human population size, poverty, investments in health control programmes, among others. METHODS: Population ecologists have developed a simple framework, which helps to explore the contributions of endogenous (density-dependent) and exogenous processes on population dynamics. Both processes may operate to determine the dynamic behaviour of a particular population through time. Briefly, density-dependent (endogenous process) occurs when the per capita population growth rate (R) is determined by the previous population size. An exogenous process occurs when some variable affects another but is not affected by the changes it causes. This study explores the dynamics of malaria cases (Plasmodium falciparum and Plasmodium vivax) in Oromia region in Ethiopia and explores the interaction between minimum temperature, HIV, poverty, human population size and social instability. RESULTS: The results support that malaria dynamics showed signs of a negative endogenous process between R and malaria infectious class, and a weak evidence to support the climate change hypothesis. CONCLUSION: Poverty, HIV, population size could interact to force malaria models parameters explaining the dynamics malaria observed at Ethiopia from 1985 to 2007.
BACKGROUND: Informed decision making is underlined by all tiers in the health system. Poor data record system coupled with under- (over)-reporting of malaria cases affects the country’s malaria elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate the elimination progresses. This study was intended to assess overall reported malaria cases, reporting quality, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia. METHODS: Past 8 years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone, South Ethiopia were extracted. Malaria cases data at each health center with sociodemographic information, between January 2012 and December 2019, were included. Meteorological data were obtained from the national meteorology agency of Ethiopia. The data were analyzed using Stata 13. RESULTS: A total of 485,414 suspected cases were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases with an overall decline during the 8-year period. We noted that 3758 suspected cases and 467 confirmed malaria cases were not captured at the health offices. Based on the health centers records, the proportions of Plasmodium falciparum (49.74%) and P. vivax (47.59%) infection were nearly equivalent (p = 0.795). The former was higher at low altitudes while the latter was higher at higher altitudes. The over 15 years of age group accounted for 11.47% of confirmed malaria cases (p < 0.001). There was high spatiotemporal variation: the highest case record was during Belg (12.52%) and in Dilla town (18,150, 13.17%, p < 0.001) which is located at low altitude. Monthly rainfall and minimum temperature exhibited strong associations with confirmed malaria cases. CONCLUSION: A notable overall decline in malaria cases was observed during the eight-year period. Both P. falciparum and P. vivax were found at equivalent endemicity level; hence control measures should continue targeting both species. The noticed under reporting, the high malaria burden in urban settings, low altitudes and Belg season need spatiotemporal consideration by the elimination program.
Background: In Gabon, a new national malaria control policy was implemented in 2003. It resulted in a decrease in the number of malaria cases in the country. In March 2020, the disruption of routine health services due to the COVID-19 pandemic has led to an increase in cases and deaths due to malaria. However, in Franceville, south-east Gabon, no data on malaria cases recorded before, during and after the COVID-19 epidemic has been published. Thus, the objective of this study was to determine the epidemiological characteristics of malaria in Franceville from 2019 to 2021. Methods: A retrospectively study of malaria cases was performed at the Hopital de l’Amitie Sino-Gabonaise (HASG). Information regarding age, gender, malaria diagnosis by microscopy and hematology cell count were collected from laboratory registers from June 2019 to December 2021. Malaria data were analyzed and correlated with seasonal variations. Results: The data of 12,695 febrile patients were collected from the laboratory registers of the HASG, among which 4252 (33.5%) patients were found positive for malaria. The malaria prevalence was 37.5% in 2020 year. This prevalence was highest compared to the 2019 (29.6%) and 2021 (31.5%) year (p < 0.001). During the short rainy season (October to December), a large increase in malaria cases was observed all three year, from 2019 to 2021 (p > 0.05). Conclusion: The prevalence of malaria in Franceville was very high during COVID-19 pandemic. It is therefore necessary to strengthen existing interventions and implement more effective interventions.
Background: This study investigated malaria transmission under various contrasting settings in the Central Region, a malaria endemic region in Ghana. Methods: This cross-sectional study was carried out in five randomly selected districts in the Central Region of Ghana. Three of the districts were forested, while the rest was coastal. Study participants were selected to coincide with either the regular rainy or dry season. From each study site, hospital attendees were randomly selected with prior consent. Consciously, study participants were selected in both rainy (September and October, 2020) and dry (November and December, 2020) seasons. Clinical data for each patient was checked for clinical malaria suspicion and microscopic confirmation of malaria. Using SPSS Version 24 (Chicago, IL, USA), bivariate analysis was done to determine the association of independent variables (ecological and seasonal variations) with malaria status. When the overall analysis did not yield significant association, further statistical analysis was performed after stratification of variables (into age and gender) to determine whether any or both of them would significantly associate with the dependent variable. Results: Of the 3993 study participants, 62.5% were suspected of malaria whereas 38.2% were confirmed to have clinical falciparum malaria. Data analysis revealed that in both rainy and dry seasons, malaria cases were significantly higher in forested districts ) than coastal districts (x2 = 217.9 vs x2 = 50.9; p < 0.001). Taken together, the risk of malaria was significantly higher in the dry season (COR = 1.471, p < 0.001) and lower in coastal zones (COR = 0.826, p = 0.007). There was significant reduced risk of participants aged over 39 years of malaria (COR=0.657, p < 0.001). Whereas, in general patients between 10 and 19 years were insignificantly less likely to have malaria (COR = 0.911, p = 0.518) compared to participants aged less than < 10 years, the reverse was observed in coastal districts where patients less than 10 years of age in coastal districts were less likely to have malaria (COR=2.440, p = 0.003). In general, gender did not associate with malaria, but when stratified by study district, the risk of female gender to malaria was significantly higher in Agona Swedru (COR = 5.605, p < 0.001), Assin central (COR = 2.172, p < 0.001), Awutu Senya (COR = 2.410, p < 0.001) and Cape Coast (COR = 3.939, p < 0.001) compared to Abura-Asebu-Kwamankese. Conclusion: This study demonstrated that the predictors of malaria differ from one endemic area to another. Therefore, malaria control interventions such as distribution of long-lasting insecticide treated bed nets, residual spraying with insecticide and mass distribution of antimalaria prophylaxis must be intensified in forested districts in all seasons with particular attention on females. (c) 2022 The Author(s). Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. CC_BY_NC_ND_4.0
The Greater Accra Region is the smallest of the 16 administrative regions in Ghana. It is highly populated and characterized by tropical climatic conditions. Although efforts towards malaria control in Ghana have had positive impacts, malaria remains in the top five diseases reported at healthcare facilities within the Greater Accra Region. To further accelerate progress, analysis of regionally generated data is needed to inform control and management measures at this level. This study aimed to examine the climatic drivers of malaria transmission in the Greater Accra Region and identify inter-district variation in malaria burden. Monthly malaria cases for the Greater Accra Region were obtained from the Ghanaian District Health Information and Management System. Malaria cases were decomposed using seasonal-trend decomposition, based on locally weighted regression to analyze seasonality. A negative binomial regression model with a conditional autoregressive prior structure was used to quantify associations between climatic variables and malaria risk and spatial dependence. Posterior parameters were estimated using Bayesian Markov chain Monte Carlo simulation with Gibbs sampling. A total of 1,105,370 malaria cases were recorded in the region from 2015 to 2019. The overall malaria incidence for the region was approximately 47 per 1000 population. Malaria transmission was highly seasonal with an irregular inter-annual pattern. Monthly malaria case incidence was found to decrease by 2.3% (95% credible interval: 0.7-4.2%) for each 1 °C increase in monthly minimum temperature. Only five districts located in the south-central part of the region had a malaria incidence rate lower than the regional average at >95% probability level. The distribution of malaria cases was heterogeneous, seasonal, and significantly associated with climatic variables. Targeted malaria control and prevention in high-risk districts at the appropriate time points could result in a significant reduction in malaria transmission in the Greater Accra Region.
Malaria remains a serious public health challenge in Ghana including the Greater Accra Region. This study aimed to quantify the spatial, temporal and spatio-temporal patterns of malaria in the Greater Accra Region to inform targeted allocation of health resources. Malaria cases data from 2015 to 2019 were obtained from the Ghanaian District Health Information and Management System and aggregated at a district and monthly level. Spatial analysis was conducted using the Global Moran’s I, Getis-Ord Gi*, and local indicators of spatial autocorrelation. Kulldorff’s space-time scan statistics were used to investigate space-time clustering. A negative binomial regression was used to find correlations between climatic factors and sociodemographic characteristics and the incidence of malaria. A total of 1,105,370 malaria cases were reported between 2015 and 2019. Significant seasonal variation was observed, with June and July being the peak months of reported malaria cases. The hotspots districts were Kpone-Katamanso Municipal District, Ashaiman Municipal Districts, Tema Municipal District, and La-Nkwantanang-Madina Municipal District. While La-Nkwantanang-Madina Municipal District was high-high cluster. The Spatio-temporal clusters occurred between February 2015 and July 2017 in the districts of Ningo-Prampram, Shai-Osudoku, Ashaiman Municipal, and Kpone-Katamanso Municipal with a radius of 26.63 km and an relative risk of 4.66 (p < 0.001). Malaria cases were positively associated with monthly rainfall (adjusted odds ratio [AOR] = 1.01; 95% confidence interval [CI] = 1.005, 1.016) and the previous month's cases (AOR = 1.064; 95% CI 1.062, 1.065) and negatively correlated with minimum temperature (AOR = 0.86, 95% CI = 0.823, 0.899) and population density (AOR = 0.996, 95% CI = 0.994, 0.998). Malaria control and prevention should be strengthened in hotspot districts in the appropriate months to improve program effectiveness.
Whilst climate change is expected to tremendously influence the regional transmission of malaria, the available data reveal conflicting results. This study provides contextual evidence. We adopted multi-scale geographically weighted regression (MGWR) modelling approach. AICc and local r(2) were used to evaluate performance of the MGWR.. The MGWR analysis showed that LST (beta = -0.667), maximum temperature (beta = -0.507), mean temperature (beta = -0.480), and distance from streams (beta = -0.487) were negatively associated with malaria prevalence. However, enhanced vegetation index correlated positively with malaria prevalence (beta = 0.663). Our results may be important for public health interventions.
Malaria has a significant impact on the lives of many in Ghana. It is one of the key causes of mortality and morbidity, resulting in 32.5% of outpatient visits and 48.8% of under 5-year-old hospital admissions. Future climate change may impact on this risk. This study aims at estimating the impact of climate variables and health facilities on malaria prevalence in Ghana using regional data from January 2012 to May 2017. This study links data at a regional level on malaria cases with weather data to evaluate the impact that changes in weather may have on malaria prevalence in Ghana. The results of fixed-effect modelling show that the maximum temperature has a statistically significant negative impact on malaria in the context of Ghana, and rainfall with a lag of two months has a positive statistically significant impact. Adapting to climate change in Ghana requires a better understanding of the climate-malaria relationship and this paper attempts to bridge this gap.
CONTEXT: In Mali, malaria transmission is seasonal, exposing children to high morbidity and mortality. A preventative strategy called Seasonal Malaria Chemoprevention (SMC) is being implemented, consisting of the distribution of drugs at monthly intervals for up to 4 months to children between 3 and 59 months of age during the period of the year when malaria is most prevalent. This study aimed to analyze the evolution of the incidence of malaria in the general population of the health districts of Kati, Kadiolo, Sikasso, Yorosso, and Tominian in the context of SMC implementation. METHODS: This is a transversal study analyzing the routine malaria data and meteorological data of Nasa Giovanni from 2016 to 2018. General Additive Model (GAM) analysis was performed to investigate the relationship between malaria incidence and meteorological factors. RESULTS: From 2016 to 2018, the evolution of the overall incidence in all the study districts was positively associated with the relative humidity, rainfall, and minimum temperature components. The average monthly incidence and the relative humidity varied according to the health district, and the average temperature and rainfall were similar. A decrease in incidence was observed in children under five years old in 2017 and 2018 compared to 2016. CONCLUSION: A decrease in the incidence of malaria was observed after the SMC rounds. SMC should be applied at optimal periods.
Malaria is among the greatest public health threats in Mozambique, with over 10 million cases reported annually since 2018. Although the relationship between seasonal trends in environmental parameters and malaria cases is well established, the role of climate in deviations from the annual cycle is less clear. To investigate this and the potential for leveraging inter-annual climate variability to predict malaria outbreaks, weekly district-level malaria incidence spanning 2010-2017 were processed for a cross-analysis with climate data. An empirical orthogonal function analysis of district-level malaria incidence revealed two dominant spatiotemporal modes that collectively account for 81% of the inter-annual variability of malaria: a mode dominated by variance over the southern half of Mozambique (64%), and another dominated by variance in the northern third of the country (17%). These modes of malaria variability are shown to be closely related to precipitation. Linear regression of global sea surface temperatures onto local precipitation indices over these variance maxima links the leading mode of inter-annual malarial variability to the El Nino-Southern Oscillation, such that La Nina leads to wetter conditions over southern Mozambique and, therefore, higher malaria prevalence. Similar analysis of spatiotemporal patterns of precipitation over a longer time period (1979-2019) indicate that the Subtropical Indian Ocean Dipole is both a strong predictor of regional precipitation and the climatic mechanism underlying the second mode of malarial variability. These results suggest that skillful malaria early warning systems may be developed that leverage quasi-predictable modes of inter-annual climate variability in the tropical oceans. Plain Language Summary Malaria is one of the main public health concerns in Mozambique, with millions of reported cases in the country each year. While malaria has been tied to monthly swings in rainfall and temperature, its relationship to year-to-year changes of the climate is less well known. We identified regions where local malaria cases varied together and found two main patterns: a main hotspot over the southern half of Mozambique, and a second hotspot over the northern third of the country. Rainfall drives both of these hotspots. We then tied these patterns to two natural climate phenomena, the El Nino-Southern Oscillation and the Subtropical Indian Ocean Dipole, both of which impact the climate of the region and help drive malaria prevalence. Our results suggest that it may be possible to take advantage of the predictability of these climate phenomena to improve public health planning both in Mozambique and more broadly.
Introduction: Despite the recent progress in the malaria burden, climatic factors are important if the world will achieve the set target of its eradication. Hence, this study determined the impact of climatic conditions on childhood severe malaria in a tertiary health facility in northern Nigeria. Methodology: This was a retrospective descriptive study that involved children with severe malaria managed between July 2016 and August 2017. The diagnosis of severe malaria was according to the World Health Organization 2015 guidelines. We extracted relevant data from case records and obtained the weather information from the Nigerian Meteorological Agency and www.worldweatheronline.com. Data were entered in Microsoft Excel 2013 and analyzed with Statistical Package for the Social Sciences version 20. Results: A total of 483 cases of children with severe malaria were managed. The median age was 4.0 (2.5-8.0) years. Males were 261 (54.0%). In the wet season, 375 (77.6%) cases were recorded, while 108 (22.4%) cases occurred during the dry season. The odds of malaria occurring during the wet season were 2.057 (95% CI, 1.613-2.622). Temperature patterns were not related to malaria cases. Malaria cases showed significant moderate positive cross-correlation at 2- and 3-months lag for the rainfall pattern (best cross-correlation occurred at 3 months lag with a coefficient of 0.598, p = 0.045). Conclusion: This study demonstrated marked seasonality of childhood severe malaria infection with 77% of cases during the wet season. Malaria was associated with only rainfall at a 2 to 3 months lag amongst the climatic variables. We recommend the urgent implementation of seasonal malaria chemoprophylaxis.
Several vector-borne diseases, such as malaria, are sensitive to climate and weather conditions. When unusual conditions prevail, for example, during periods of heavy rainfall, mosquito populations can multiply and trigger epidemics. This study, which consists of better understanding the link between malaria transmission and climate factors at a national level, aims to validate the VECTRI model (VECtor borne disease community model of ICTP, TRIeste) in Senegal. The VECTRI model is a grid-distributed dynamical model that couples a biological model for the vector and parasite life cycles to a simple compartmental Susceptible-Exposed-Infectious-Recovered (SEIR) representation of the disease progression in the human host. In this study, a VECTRI model driven by reanalysis data (ERA-5) was used to simulate malaria parameters, such as the entomological inoculation rate (EIR) in Senegal. In addition to the ERA5-Land daily reanalysis rainfall, other daily rainfall data come from different meteorological products, including the CPC Global Unified Gauge-Based Analysis of Daily Precipitation (CPC for Climate Prediction Center), satellite data from the African Rainfall Climatology 2.0 (ARC2), and the Climate Hazards InfraRed Precipitation with Station data (CHIRPS). Observed malaria data from the National Malaria Control Program in Senegal (PNLP/Programme National de Lutte contre le Paludisme au Senegal) and outputs from the climate data used in this study were compared. The findings highlight the unimodal shape of temporal malaria occurrence, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall, showing a south-north gradient over Senegal. This study showed that the peak of malaria takes place from September to October, with a lag of about one month from the peak of rainfall in Senegal. There is an agreement between observations and simulations about decreasing malaria cases on time. These results indicate that the southern area of Senegal is at the highest risk of malaria spread outbreaks. The findings in the paper are expected to guide community-based early-warning systems and adaptation strategies in Senegal, which will feed into the national malaria prevention, response, and care strategies adapted to the needs of local communities.
Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on aspects of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite’s reproductive cycle inside the mosquito. The rainfall intensity and frequency modulate the mosquito population’s development intensity. In this study, the Liverpool Malaria Model (LMM) was used to simulate the spatiotemporal variation of malaria incidence in Senegal. The simulations were based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and the biased-corrected CMIP6 model data, separating historical simulations and future projections for three Shared Socio-economic Pathways scenarios (SSP126, SSP245, and SSP585). Our results highlight a strong increase in temperatures, especially within eastern Senegal under the SSP245 but more notably for the SSP585 scenario. The ability of the LMM model to simulate the seasonality of malaria incidence was assessed for the historical simulations. The model revealed a period of high malaria transmission between September and November with a maximum reached in October, and malaria results for historical and future trends revealed how malaria transmission will change. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and the extreme scenario. This study is important for the planning, prioritization, and implementation of malaria control activities in Senegal.
BACKGROUND: This study aimed to assess the seasonality of confirmed malaria cases in Togo and to provide new indicators of malaria seasonality to the National Malaria Control Programme (NMCP). METHODS: Aggregated data of confirmed malaria cases were collected monthly from 2008 to 2017 by the Togo’s NMCP and stratified by health district and according to three target groups: children < 5 years old, children ≥ 5 years old and adults, and pregnant women. Time series analysis was carried out for each target group and health district. Seasonal decomposition was used to assess the seasonality of confirmed malaria cases. Maximum and minimum seasonal indices, their corresponding months, and the ratio of maximum/minimum seasonal indices reflecting the importance of malaria transmission, were provided by health district and target group. RESULTS: From 2008 to 2017, 7,951,757 malaria cases were reported in Togo. Children < 5 years old, children ≥ 5 years old and adults, and pregnant women represented 37.1%, 57.7% and 5.2% of the confirmed malaria cases, respectively. The maximum seasonal indices were observed during or shortly after a rainy season and the minimum seasonal indices during the dry season between January and April in particular. In children < 5 years old, the ratio of maximum/minimum seasonal indices was higher in the north, suggesting a higher seasonal malaria transmission, than in the south of Togo. This is also observed in the other two groups but to a lesser extent. CONCLUSIONS: This study contributes to a better understanding of malaria seasonality in Togo. The indicators of malaria seasonality could allow for more accurate forecasting in malaria interventions and supply planning throughout the year.
BACKGROUND: Environmental factors such as temperature, rainfall, and vegetation cover play a critical role in malaria transmission. However, quantifying the relationships between environmental factors and measures of disease burden relevant for public health can be complex as effects are often non-linear and subject to temporal lags between when changes in environmental factors lead to changes in malaria incidence. The study investigated the effect of environmental covariates on malaria incidence in high transmission settings of Uganda. METHODS: This study leveraged data from seven malaria reference centres (MRCs) located in high transmission settings of Uganda over a 24-month period. Estimates of monthly malaria incidence (MI) were derived from MRCs’ catchment areas. Environmental data including monthly temperature, rainfall, and normalized difference vegetation index (NDVI) were obtained from remote sensing sources. A distributed lag nonlinear model was used to investigate the effect of environmental covariates on malaria incidence. RESULTS: Overall, the median (range) monthly temperature was 30 °C (26-47), rainfall 133.0 mm (3.0-247), NDVI 0.66 (0.24-0.80) and MI was 790 per 1000 person-years (73-3973). Temperature of 35 °C was significantly associated with malaria incidence compared to the median observed temperature (30 °C) at month lag 2 (IRR: 2.00, 95% CI: 1.42-2.83) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 8.16 (95% CI: 3.41-20.26) at lag-month 4. Rainfall of 200 mm significantly increased IRR of malaria compared to the median observed rainfall (133 mm) at lag-month 0 (IRR: 1.24, 95% CI: 1.01-1.52) and the increased cumulative IRR of malaria at month lags 1-4, with the highest cumulative IRR of 1.99(95% CI: 1.22-2.27) at lag-month 4. Average NVDI of 0.72 significantly increased the cumulative IRR of malaria compared to the median observed NDVI (0.66) at month lags 2-4, with the highest cumulative IRR of 1.57(95% CI: 1.09-2.25) at lag-month 4. CONCLUSIONS: In high-malaria transmission settings, high values of environmental covariates were associated with increased cumulative IRR of malaria, with IRR peaks at variable lag times. The complex associations identified are valuable for designing strategies for early warning, prevention, and control of seasonal malaria surges and epidemics.
BACKGROUND: There is concern in the international community regarding the influence of climate change on weather variables and seasonality that, in part, determine the rates of malaria. This study examined the role of sociodemographic variables in modifying the association between temperature and malaria in Kanungu District (Southwest Uganda). METHODS: Hospital admissions data from Bwindi Community Hospital were combined with meteorological satellite data from 2011 to 2014. Descriptive statistics were used to describe the distribution of malaria admissions by age, sex, and ethnicity (i.e. Bakiga and Indigenous Batwa). To examine how sociodemographic variables modified the association between temperature and malaria admissions, this study used negative binomial regression stratified by age, sex, and ethnicity, and negative binomial regression models that examined interactions between temperature and age, sex, and ethnicity. RESULTS: Malaria admission incidence was 1.99 times greater among Batwa than Bakiga in hot temperature quartiles compared to cooler temperature quartiles, and that 6-12 year old children had a higher magnitude of association of malaria admissions with temperature compared to the reference category of 0-5 years old (IRR = 2.07 (1.40, 3.07)). DISCUSSION: Results indicate that socio-demographic variables may modify the association between temperature and malaria. In some cases, such as age, the weather-malaria association in sub-populations with the highest incidence of malaria in standard models differed from those most sensitive to temperature as found in these stratified models. CONCLUSION: The effect modification approach used herein can be used to improve understanding of how changes in weather resulting from climate change might shift social gradients in health.
BACKGROUND: Seasonal patterns of malaria cases in many parts of Africa are generally associated with rainfall, yet in the dry seasons, malaria transmission declines but does not always cease. It is important to understand what conditions support these periodic cases. Aerial moisture is thought to be important for mosquito survival and ability to forage, but its role during the dry seasons has not been well studied. During the dry season aerial moisture is minimal, but intermittent periods may arise from the transpiration of peri-domestic trees or from some other sources in the environment. These periods may provide conditions to sustain pockets of mosquitoes that become active and forage, thereby transmitting malaria. In this work, humidity along with other ecological variables that may impact malaria transmission have been examined. METHODS: Negative binomial regression models were used to explore the association between peri-domestic tree humidity and local malaria incidence. This was done using sensitive temperature and humidity loggers in the rural Southern Province of Zambia over three consecutive years. Additional variables including rainfall, temperature and elevation were also explored. RESULTS: A negative binomial model with no lag was found to best fit the malaria cases for the full year in the evaluated sites of the Southern Province of Zambia. Local tree and granary night-time humidity and temperature were found to be associated with local health centre-reported incidence of malaria, while rainfall and elevation did not significantly contribute to this model. A no lag and one week lag model for the dry season alone also showed a significant effect of humidity, but not temperature, elevation, or rainfall. CONCLUSION: The study has shown that throughout the dry season, periodic conditions of sustained humidity occur that may permit foraging by resting mosquitoes, and these periods are associated with increased incidence of malaria cases. These results shed a light on conditions that impact the survival of the common malaria vector species, Anopheles arabiensis, in arid seasons and suggests how they emerge to forage when conditions permit.
The role of climate change on global malaria is often highlighted in World Health Organisation reports. We modelled a Zambian socio-environmental dataset from 2000 to 2016, against malaria trends and investigated the relationship of near-term environmental change with malaria incidence using Bayesian spatio-temporal, and negative binomial mixed regression models. We introduced the diurnal temperature range (DTR) as an alternative environmental measure to the widely used mean temperature. We found substantial sub-national near-term variations and significant associations with malaria incidence-trends. Significant spatio-temporal shifts in DTR/environmental predictors influenced malaria incidence-rates, even in areas with declining trends. We highlight the impact of seasonally sensitive DTR, especially in the first two quarters of the year and demonstrate how substantial investment in intervention programmes is negatively impacted by near-term climate change, most notably since 2010. We argue for targeted seasonally-sensitive malaria chemoprevention programmes.
BACKGROUND: Malaria epidemics are increasing in East Africa since the 1980s, coincident with rising temperature and widening climate variability. A projected 1-3.5 °C rise in average global temperatures by 2100 could exacerbate the epidemics by modifying disease transmission thresholds. Future malaria scenarios for the Lake Victoria Basin (LVB) are quantified for projected climate scenarios spanning 2006-2100. METHODS: Regression relationships are established between historical (1995-2010) clinical malaria and anaemia cases and rainfall and temperature for four East African malaria hotspots. The vector autoregressive moving average processes model, VARMAX (p,q,s), is then used to forecast malaria and anaemia responses to rainfall and temperatures projected with an ensemble of eight General Circulation Models (GCMs) for climate change scenarios defined by three Representative Concentration Pathways (RCPs 2.6, 4.5 and 8.5). RESULTS: Maximum temperatures in the long rainy (March-May) and dry (June-September) seasons will likely increase by over 2.0 °C by 2070, relative to 1971-2000, under RCPs 4.5 and 8.5. Minimum temperatures (June-September) will likely increase by over 1.5-3.0 °C under RCPs 2.6, 4.5 and 8.5. The short rains (OND) will likely increase more than the long rains (MAM) by the 2050s and 2070s under RCPs 4.5 and 8.5. Historical malaria cases are positively and linearly related to the 3-6-month running means of monthly rainfall and maximum temperature. Marked variation characterizes the patterns projected for each of the three scenarios across the eight General Circulation Models, reaffirming the importance of using an ensemble of models for projections. CONCLUSIONS: The short rains (OND), wet season (MAM) temperatures and clinical malaria cases will likely increase in the Lake Victoria Basin. Climate change adaptation and mitigation strategies, including malaria control interventions could reduce the projected epidemics and cases. Interventions should reduce emerging risks, human vulnerability and environmental suitability for malaria transmission.
Dengue is now a major health concern in sub-Saharan Africa. Understanding the influence of local meteorological factors on the incidence of dengue is an important element for better prediction and control of this disease. This study aims to assess the impact of meteorological factors on dengue transmission in the central region of Burkina Faso. We analyzed the lagged effects of meteorological factors on the weekly incidence of dengue from 2017 to 2019 in the central region of Burkina Faso using a General Additive Model. The results show that maximum and minimum temperature, relative humidity, and wind speed have a significant non-linear effect on dengue cases in the region with 83% of case variance explained. The optimal temperature that increases dengue cases was 27°C to 32°C for the maximum temperature and 18°C to 20°C for the minimum temperature with a decrease beyond that. The maximum temperature shifted by six weeks had the best correlation with dengue incidence. The estimated number of dengue cases increases as the maximum relative humidity increases from 15 to 45% and then from 60 to 70%. In general, an increase in daily wind speed is estimated to decrease the number of daily dengue cases. The relationship between rainfall and dengue cases was not significant. This study provides local information about the effect of meteorological factors on dengue that should help improve predictive models of dengue cases in Burkina Faso and contribute to the control of this disease.
Dengue Fever (DF) is an important arthropod-borne viral infection that has repeatedly occurred as outbreaks in eastern and northeastern Ethiopia since 2013. A cross-sectional epidemiological outbreak investigation was carried out from September to November 2019 on febrile patients (confirmed malaria negative) who presented with suspected and confirmed DF at both public and private health facilities in Gewane District, Afar Region, northeastern Ethiopia. Entomological investigation of containers found in randomly selected houses belonging to DF-positive patients was undertaken to survey for the presence of Aedes larvae/pupae. A total of 1185 DF cases were recorded from six health facilities during the 3-month study period. The mean age of DF cases was 27.2 years, and 42.7% of cases were female. The most affected age group was 15−49 years old (78.98%). The total case proportions differed significantly across age groups when compared to the population distribution; there were approximately 15% and 5% higher case proportions among those aged 15−49 years and 49+ years, respectively. A total of 162 artificial containers were inspected from 62 houses, with 49.4% found positive for Aedes aegypti larva/pupae. Aedes mosquitoes were most commonly observed breeding in plastic tanks, tires, and plastic or metal buckets/bowls. World Health Organization entomological indices classified the study site as high risk for dengue virus outbreaks (House Index = 45.2%, Container Index = 49.4%, and Breteau Index = 129). Time series climate data, specifically rainfall, were found to be significantly predictive of AR (p = 0.035). Study findings highlight the importance of vector control to prevent future DF outbreaks in the region. The scarcity of drinking water and microclimatic conditions may have also contributed to the occurrence of this outbreak.
Dengue fever is a systemic viral infection of epidemic proportions in tropical countries. The incidence of dengue fever is ever increasing and has doubled over the last few decades. Estimated 50million new cases are detected each year and close to 10000 deaths occur each year. Epidemics are unpredictable and unprecedented. When epidemics occur, health services are over whelmed leading to overcrowding of hospitals. At present there is no evidence that dengue epidemics can be predicted. Since the breeding of the dengue mosquito is directly influenced by environmental factors, it is plausible that epidemics could be predicted using weather data. We hypothesized that there is a mathematical relationship between incidence of dengue fever and environmental factors and if such relationship exists, new cases of dengue fever in the succeeding months can be predicted using weather data of the current month. We developed a mathematical model using machine learning technique. We used Island wide dengue epidemiology data, weather data and population density in developing the model. We used incidence of dengue fever, average rain fall, humidity, wind speed, temperature and population density of each district in the model. We found that the model is able to predict the incidence of dengue fever of a given month in a given district with precision (RMSE between 18- 35.3). Further, using weather data of a given month, the number of cases of dengue in succeeding months too can be predicted with precision (RMSE 10.4-30). Health authorities can use existing weather data in predicting epidemics in the immediate future and therefore measures to prevent new cases can be taken and more importantly the authorities can prepare local authorities for outbreaks.
Dengue contributes a significant burden on global public health and economies. In Africa, the burden of dengue virus (DENV) infection is not well described. This review was undertaken to determine the prevalence of dengue and associated risk factors. A literature search was done on PubMed/MEDLINE, Scopus, Embase, and Google Scholar databases to identify articles published between 1960 and 2020. Meta-analysis was performed using a random-effect model at a 95% confidence interval, followed by subgroup meta-analysis to determine the overall prevalence. Between 1960 and 2020, 45 outbreaks were identified, of which 17 and 16 occurred in East and West Africa, respectively. Dengue virus serotype 1 (DENV-1) and DENV-2 were the dominant serotypes contributing to 60% of the epidemics. Of 2211 cases reported between 2009 and 2020; 1954 (88.4%) were reported during outbreaks. Overall, the prevalence of dengue was 29% (95% CI: 20-39%) and 3% (95% CI: 1-5%) during the outbreak and non-outbreak periods, respectively. Old age (6/21 studies), lack of mosquito control (6/21), urban residence (4/21), climate change (3/21), and recent history of travel (3/21) were the leading risk factors. This review reports a high burden of dengue and increased risk of severe disease in Africa. Our findings provide useful information for clinical practice and health policy decisions to implement effective interventions.
Human vulnerability to disasters poses a significant concern to water resources management. The present study examined the factors influencing the occurrence of flooding, risk and management strategies in Lagos, Nigeria. A set of questionnaires was administered to 400 respondents in four randomly selected settlements in Lagos State based on perception and observation methods. Descriptive and multivariate statistics and cartographic mapping techniques were employed for data analysis. The result indicates that the majority of the respondents live in a rented room and parlor. The significant flood risks include poor sanitation, a breeding site for mosquitoes, water contamination/waterborne diseases, and mental stress. Factors analysis explains 74.62% of the variance, indicating anthropogenic, natural, and institutional factors influencing flooding in the study area. The dominant flood management measures are clearance of drains, environmental sanitation, public awareness, training/education, while the significant steps taken by the government to ameliorate flooding challenges in the area include awareness, early warning, and education. The study concluded that there exists a significant difference in the factors influencing flooding across the settlements based on the ANOVA result given as: (DWSD F = 19.661, p < 0.05; RI = 41.104, p < 0.05; WIC = 18.123, p < 0.05; HWL = 37.481, p < 0.05; SD = 10.294, p < 0.05). The study contributes to knowledge using cartographic techniques to map the risks of flooding for easy understanding. The study has potential policy implications for planning and interventions in areas vulnerable areas. The study recommended monitoring of construction activities, enforcement of building codes, awareness campaigns, and early warning flood technology for sustainable flood management in the area.
Despite the long-lasting and widespread drought in the Sahel, flood events did punctuate in the past. The concern about floods remains dwarf on the international research and policy agenda compared to droughts. In this paper, we elucidate that floods in the Sahel are now becoming more frequent, widespread, and more devastating. We analyzed gridded daily rainfall data over the period 1981-2020, used photographs and satellite images to depict flood areas and threats, compiled and studied flood-related statistics over the past two decades, and supported the results with peer-reviewed literature. Our analysis revealed that the timing of the maximum daily rainfall occurs from the last week of July to mid-August in the Eastern Sahel, but from the last week of July to the end of August in the Western Sahel. In 2019 and 2020, flash and riverine floods took their toll in Sudan and elsewhere in the region in terms of the number of affected people, direct deaths, destroyed and damaged houses and croplands, contaminated water resources, and disease outbreaks and deaths. Changes in rainfall intensity, human interventions in the physical environment, and poor urban planning play a major role in driving catastrophic floods. Emphasis should be put on understanding flood causes and impacts on vulnerable societies, controlling water-borne diseases, and recognizing the importance of compiling relevant and reliable flood information. Extreme rainfall in this dry region could be an asset for attenuating the regional water scarcity status if well harvested and managed. We hope this paper will induce the hydroclimate scholars to carry out more flood studies for the Sahel. It is only then encumbered meaningful opportunities for flood risk management can start to unveil.
The study examined the effect of heat stress on the well-being of outdoor workers and their coping strategies. A cross-sectional survey study was conducted between September 2019 and December 2019 to collect data from outdoor workers including hawkers and traffic wardens from 13 urban areas (N = 322) and analyzed using SPSS v.23. The results of the study show that most of the outdoor workers were in a good health state based on their self-health assessment. However, the respondents expressed concerns and symptoms of heat stress including heat cramps, heat exhaustion, heat stroke and sleep disorders. The findings also show that male outdoor workers were 1.3 times more likely than females to be affected by heat stress. Respondents in their 20s were more likely to be affected by heat stress, as a result of temperatures and humidity conditions, than those in their 30s (OR = 0.389, CI = 0.158-0962) and 40s (OR = 0.395, CI = 0.147-1.063). Coping strategies identified include the use of breathable cotton attires, drinking a lot of water, hiding under shades and reducing outdoor activity intermittently.
The 2019 and 2020 sporadic outbreaks of yellow fever (YF) in Sub-Saharan African countries had raised a lot of global health concerns. This article aims to narratively review the vector biology, YF vaccination program, environmental factors and climatic changes, and to understand how they could facilitate the reemergence of YF. This study comprehensively reviewed articles that focused on the interplay and complexity of YF virus (YFV) vector diversity/competence, YF vaccine immunodynamics and climatic change impacts on YFV transmission as they influence the 2019/2020 sporadic outbreaks in Sub-Saharan Africa (SSA). Based on available reports, vectorial migration, climatic changes and YF immunization level could be reasons for the re-mergence of YF at the community and national levels. Essentially, the drivers of YFV infection due to spillover are moderately constant. However, changes in land use and landscape have been shown to influence sylvan-to-urban spillover. Furthermore, increased precipitation and warmer temperatures due to climate change are likely to broaden the range of mosquitoes’ habitat. The 2019/2020 YF outbreaks in SSA is basically a result of inadequate vaccination campaigns, YF surveillance and vector control. Consequently, and most importantly, adequate immunization coverage must be implemented and properly achieved under the responsibility of the public health stakeholders.
BACKGROUND: Malaria has continued to be a life-threatening disease among under-five children in sub-Saharan Africa. Recent data indicate rising cases in Rwanda after some years of decline. We aimed at estimating the spatial variations in malaria prevalence at a continuous spatial scale and to quantify locations where the prevalence exceeds the thresholds of 5% and 10% across the country. We also consider the effects of some socioeconomic and climate variables. METHODS: Using data from the 2014-2015 Rwanda Demographic and Health Survey, a geostatistical modeling technique based on stochastic partial differential equation approach was used to analyze the geospatial prevalence of malaria among under-five children in Rwanda. Bayesian inference was based on integrated nested Laplace approximation. RESULTS: The results demonstrate the uneven spatial variation of malaria prevalence with some districts including Kayonza and Kirehe from Eastern province; Huye and Nyanza from Southern province; and Nyamasheke and Rusizi from Western province having higher chances of recording prevalence exceeding 5%. Malaria prevalence was found to increase with rising temperature but decreases with increasing volume for rainfall. The findings also revealed a significant association between malaria and demographic factors including place of residence, mother’s educational level, and child’s age and sex. CONCLUSIONS: Potential intervention programs that focus on individuals living in rural areas, lowest wealth quintile, and the locations with high risks should be reinforced. Variations in climatic factors particularly temperature and rainfall should be taken into account when formulating malaria intervention programs in Rwanda.
BACKGROUND: Floods have affected 2.3 billion people worldwide in the last 20 years, and are associated with a wide range of negative health outcomes. Climate change is projected to increase the number of people exposed to floods due to more variable precipitation and rising sea levels. Vulnerability to floods is highly dependent on economic wellbeing and other societal factors. Therefore, this systematic review synthesizes the evidence on health effects of flood exposure among the population of sub-Saharan Africa. METHODS: We systematically searched two databases, Web of Science and PubMed, to find published articles. We included studies that (1) were published in English from 2010 onwards, (2) presented associations between flood exposure and health indicators, (3) focused on sub-Saharan Africa, and (4) relied on a controlled study design, such as cohort studies, case-control studies, cross-sectional studies, or quasi-experimental approaches with a suitable comparator, for instance individuals who were not exposed to or affected by floods or individuals prior to experiencing a flood. RESULTS: Out of 2306 screened records, ten studies met our eligibility criteria. We included studies that reported the impact of floods on water-borne diseases (n = 1), vector-borne diseases (n = 8) and zoonotic diseases (n = 1). Five of the ten studies assessed the connection between flood exposure and malaria. One of these five evaluated the impact of flood exposure on malaria co-infections. The five non-malaria studies focused on cholera, scabies, taeniasis, Rhodesian sleeping sickness, alphaviruses and flaviviruses. Nine of the ten studies reported significant increases in disease susceptibility after flood exposure. CONCLUSION: The majority of included studies of the aftermath of floods pointed to an increased risk of infection with cholera, scabies, taeniasis, Rhodesian sleeping sickness, malaria, alphaviruses and flaviviruses. However, long-term health effects, specifically on mental health, non-communicable diseases and pregnancy, remain understudied. Further research is urgently needed to improve our understanding of the health risks associated with floods, which will inform public policies to prevent and reduce flood-related health risks.
BACKGROUND: Seasonal malaria chemoprevention (SMC) involves administering antimalarial drugs at monthly intervals during the high malaria transmission period to children aged 3 to 59 months as recommended by the World Health Organization. Typically, a full SMC course is administered over four monthly cycles from July to October, coinciding with the rainy season. However, an analysis of rainfall patterns suggest that the malaria transmission season is longer and starting as early as June in the south of Burkina Faso, leading to a rise in cases prior to the first cycle. This study assessed the acceptability and feasibility of extending SMC from four to five cycles to coincide with the earlier rainy season in Mangodara health district. METHODS: The mixed-methods study was conducted between July and November 2019. Quantitative data were collected through end-of-cycle and end-of-round household surveys to determine the effect of the additional cycle on the coverage of SMC in Mangodara. The data were then compared with 22 other districts where SMC was implemented by Malaria Consortium. Eight focus group discussions were conducted with caregivers and community distributors and 11 key informant interviews with community, programme and national-level stakeholders. These aimed to determine perceptions of the acceptability and feasibility of extending SMC to five cycles. RESULTS: The extension was perceived as acceptable by caregivers, community distributors and stakeholders due to the positive impact on the health of children under five. However, many community distributors expressed concern over the feasibility, mainly due to the clash with farming activities in June. Stakeholders highlighted the need for more evidence on the impact of the additional cycle on parasite resistance prior to scale-up. End-of-cycle survey data showed no difference in coverage between five SMC cycles in Mangodara and four cycles in the 22 comparison districts. CONCLUSIONS: The additional cycle should begin early in the day in order to not coincide with the agricultural activities of community distributors. Continuous sensitisation at community level is critical for the sustainability of SMC and acceptance of an additional cycle, which should actively engage male caregivers. Providing additional support in proportion to the increased workload from a fifth cycle, including timely remuneration, is critical to avoid the demotivation of community distributors. Further studies are required to understand the effectiveness, including cost-effectiveness, of tailoring SMC according to the rainy season. Understanding the impact of an additional cycle on parasite resistance to SPAQ is critical to address key informants’ concerns around the deviation from the current four-cycle policy recommendation.
BACKGROUND: In 2012, the WHO issued a policy recommendation for the use of seasonal malaria chemoprevention (SMC) to children 3-59 months in areas of highly seasonal malaria transmission. Clinical trials have found SMC to prevent around 75% of clinical malaria. Impact under routine programmatic conditions has been assessed during research studies but there is a need to identify sustainable methods to monitor impact using routinely collected data. METHODS: Data from Demographic Health Surveys were merged with rainfall, geographical and programme data in Burkina Faso (2010, 2014, 2017) and Nigeria (2010, 2015, 2018) to assess impact of SMC. We conducted mixed-effects logistic regression to predict presence of malaria infection in children aged 6-59 months (rapid diagnostic test (RDT) and microscopy, separately). RESULTS: We found strong evidence that SMC administration decreases odds of malaria measured by RDT during SMC programmes, after controlling for seasonal factors, age, sex, net use and other variables (Burkina Faso OR 0.28, 95% CI 0.21 to 0.37, p<0.001; Nigeria OR 0.40, 95% CI 0.30 to 0.55, p<0.001). The odds of malaria were lower up to 2 months post-SMC in Burkina Faso (1-month post-SMC: OR 0.29, 95% CI 0.12 to 0.72, p=0.01; 2 months post-SMC: OR: 0.33, 95% CI 0.17 to 0.64, p<0.001). The odds of malaria were lower up to 1 month post-SMC in Nigeria but was not statistically significant (1-month post-SMC 0.49, 95% CI 0.23 to 1.05, p=0.07). A similar but weaker effect was seen for microscopy (Burkina Faso OR 0.38, 95% CI 0.29 to 0.52, p<0.001; Nigeria OR 0.53, 95% CI 0.38 to 0.76, p<0.001). CONCLUSIONS: Impact of SMC can be detected in reduced prevalence of malaria from data collected through household surveys if conducted during SMC administration or within 2 months afterwards. Such evidence could contribute to broader evaluation of impact of SMC programmes.
BACKGROUND: Malaria is a global burden in terms of morbidity and mortality. In the Democratic Republic of Congo, malaria prevalence is increasing due to strong climatic variations. Reductions in malaria morbidity and mortality, the fight against climate change, good health and well-being constitute key development aims as set by the United Nations Sustainable Development Goals (SDGs). This study aims to predict malaria morbidity to 2036 in relation to climate variations between 2001 and 2019, which may serve as a basis to develop an early warning system that integrates monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns. METHODS: Meteorological data were collected at the Mettelsat and the database of the Epidemiological Surveillance Directorate including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria, was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. Malaria prevalence and mortality rates by year and province using direct standardization and mean annual percentage change were calculated using DRC mid-year populations. Time series combining several predictive models were used to forecast malaria epidemic episodes to 2036. Finally, the impact of climatic factors on malaria morbidity was modeled using multivariate time series analysis. RESULTS: The geographical distribution of malaria prevalence from 2001 and 2019 shows strong disparities between provinces with the highest of 7700 cases per 100,000 people at risk for South Kivu. In the northwest, malaria prevalence ranges from 4980 to 7700 cases per 100,000 people at risk. Malaria has been most deadly in Sankuru with a case-fatality rate of 0.526%, followed by Kasai (0.430%), Kwango (0.415%), Bas-Uélé, (0.366%) and Kwilu (0.346%), respectively. However, the stochastic trend model predicts an average annual increase of 6024.07 malaria cases per facility with exponential growth in epidemic waves over the next 200 months of the study. This represents an increase of 99.2%. There was overwhelming evidence of associations between geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at two meters altitude and malaria morbidity (p < 0.0001). CONCLUSIONS: The stochastic trends in our time series observed in this study suggest an exponential increase in epidemic waves over the next 200 months of the study. The increase in new malaria cases is statistically related to population density, average number of rainy days, average wind speed, and unstable and intermediate epidemiological facies. Therefore, the results of this research should provide relevant information for the Congolese government to respond to malaria in real time by setting up a warning system integrating the monitoring of rainfall and temperature trends and early detection of anomalies in weather patterns.
Background: Environmentally related morbidity and mortality still remain high worldwide, although they have decreased significantly in recent decades. This study aims to forecast malaria epidemics taking into account climatic and spatio-temporal variations and therefore identify geo-climatic factors predictive of malaria prevalence from 2001 to 2019 in the Democratic Republic of Congo. Methods: This is a retrospective longitudinal ecological study. The database of the Directorate of Epidemiological Surveillance including all malaria cases registered in the surveillance system based on positive blood test results, either by microscopy or by a rapid diagnostic test for malaria was used to estimate malaria morbidity and mortality by province of the DRC from 2001 to 2019. The impact of climatic factors on malaria morbidity was modeled using the Generalized Poisson Regression, a predictive model with the dependent variable Y the count of the number of occurrences of malaria cases during a period of time adjusting for risk factors. Results: Our results show that the average prevalence rate of malaria in the last 19 years is 13,246 (1,178,383−1,417,483) cases per 100,000 people at risk. This prevalence increases significantly during the whole study period (p < 0.0001). The year 2002 was the most morbid with 2,913,799 (120,9451−3,830,456) cases per 100,000 persons at risk. Adjusting for other factors, a one-day in rainfall resulted in a 7% statistically significant increase in malaria cases (p < 0.0001). Malaria morbidity was also significantly associated with geographic location (western, central and northeastern region of the country), total evaporation under shelter, maximum daily temperature at a two-meter altitude and malaria morbidity (p < 0.0001). Conclusions: In this study, we have established the association between malaria morbidity and geo-climatic predictors such as geographical location, total evaporation under shelter and maximum daily temperature at a two-meter altitude. We show that the average number of malaria cases increased positively as a function of the average number of rainy days, the total quantity of rainfall and the average daily temperature. These findings are important building blocks to help the government of DRC to set up a warning system integrating the monitoring of rainfall and temperature trends and the early detection of anomalies in weather patterns in order to forecast potential large malaria morbidity events.
Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion - 0.25 (BCI: - 0.46, - 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.
A counterargument to the importance of climate change for malaria transmission has been that regions where an effect of warmer temperatures is expected, have experienced a marked decrease in seasonal epidemic size since the turn of the new century. This decline has been observed in the densely populated highlands of East Africa at the center of the earlier debate on causes of the pronounced increase in epidemic size from the 1970s to the 1990s. The turnaround of the incidence trend around 2000 is documented here with an extensive temporal record for malaria cases for both Plasmodium falciparum and Plasmodium vivax in an Ethiopian highland. With statistical analyses and a process-based transmission model, we show that this decline was driven by the transient slowdown in global warming and associated changes in climate variability, especially ENSO. Decadal changes in temperature and concurrent climate variability facilitated rather than opposed the effect of interventions.
BACKGROUND: Malaria is a serious public health problem of most developing countries, including Ethiopia. The burden of malaria is severely affecting the economy and lives of people, particularly among the productive ages of rural society. Thus, this study was targeted to analyze the past five-year retrospective malaria data among the rural setting of Maygaba town, Welkait district, northwest Ethiopia. METHODS: The study was done on 36,219 outpatients attending for malaria diagnosis during January 2015 to 2019. Data was extracted from the outpatient medical database. Chi-square (χ (2)) test and binary logistic regression model were used to analyze the retrospective data. Statistical significance was defined at p < 0.05. RESULTS: Of 36,219 outpatients examined, 7,309 (20.2%) malaria-positive cases were reported during 2015-2019. There was a fluctuating trend in the number of malaria-suspected and -confirmed cases in each year. Male slide-confirmed (61.4%, N = 4,485) were significantly higher than females (38.6%, N = 2,824) (p < 005). Plasmodium falciparum and Plasmodium vivax were the dominant parasites detected, which accounted for 66.1%; N = 4832, 33.9%; N = 2477, respectively. Despite the seasonal abundance of malaria cases, the highest prevalence was recorded in autumn (September to November) in the study area. Binary logistic regression analysis revealed that statistically significant associations were observed between sexes, interseasons, mean seasonal rainfall, and mean seasonal temperature with the prevalence of P. vivax. However, P. falciparum has shown a significant association with interseasons and mean seasonal temperature. CONCLUSIONS: Although the overall prevalence of malaria was continually declined from 2015-2019, malaria remains the major public health problem in the study area. The severe species of P. falciparum was found to be the dominant parasite reported in the study area. A collaborative action between the national malaria control program and its partners towards the transmission, prevention, and control of the two deadly species is highly recommended.
Background. In Sub-Saharan African countries, malaria is a leading cause of morbidity and mortality. In Ethiopia, malaria is found in three-fourths of its land mass with more than 63 million people living in malaria endemic areas. Nowadays, Ethiopia is implementing a malaria elimination program with the goal of eliminating the disease by 2030. To assist this goal, the trends of malaria cases should be evaluated with a function of time in different areas of the country to develop area-specific evidence-based interventions. Therefore, this study was aimed at analysing a five year trend of malaria in Nirak Health Center, Abergele District, Northeast Ethiopia, from 2016 to 2020. Methods. A retrospective study was conducted at Nirak Health Center, Abergele District, Northeast Ethiopia from February to April 2021. Five-year (2016 to 2020) retrospective data were reviewed from the malaria registration laboratory logbook. The sociodemographic and malaria data were collected using a predesigned data collection sheet. Data were entered, cleaned, and analysed using SPSS version 26. Results. In the five-year period, a total of 19,433 malaria suspected patients were diagnosed by microscopic examination. Of these, 6,473 (33.3%) were positive for malaria parasites. Of the total confirmed cases, 5,900 (91.2%) were P. falciparum and 474 (7.2%) were P. vivax. Majority of the cases were males (62.2%) and in the age group of 15-45 years old (52.8%). The findings of this study showed an increasing trend in malaria cases in the past five years (2016-2020). The maximum number of confirmed malaria cases reported was in the year 2020, while the minimum number of confirmed malaria cases registered was in 2016. Regarding the seasonal distribution of malaria, the highest number of malaria cases (55.2%) was observed in Dry season (September to January) and also the least (15.9%) was observed in Autumn (March to May) replaced by the least (21.6%) was observed in Rainy season (June to August), that is, the major malaria transmission season in Ethiopia and the least (15.9%) was observed in autumn (March to May). Conclusion. The trends of malaria in Nirak Health Center showed steadily increasing from the year 2016-2020, and the predominant species isolated was P. falciparum. This showed that the malaria control and elimination strategy in the area were not properly implemented or failed to achieve its designed goal. Therefore, this finding alarms the local governments and other stack holders urgently to revise their intervention strategies and take action in the locality.
BACKGROUND: Land use change has increasingly been expanding throughout the world in the past decades. It can have profound effects on the spatial and temporal distribution of vector borne diseases like malaria through ecological and habitat change. Understanding malaria disease occurrence and the impact of prevention interventions under this intense environmental modification is important for effective and efficient malaria control strategy. METHODS: A descriptive ecological study was conducted by reviewing health service records at Abobo district health office. The records were reviewed to extract data on malaria morbidity, mortality, and prevention and control methods. Moreover, Meteorological data were obtained from Gambella region Meteorology Service Center and National Meteorology Authority head office. Univariate, bivariate and multivariate analysis techniques were used to analyze the data. RESULTS: For the twelve-year time period, the mean annual total malaria case count in the district was 7369.58. The peak monthly malaria incidence was about 57 cases per 1000 people. Only in 2009 and 2015 that zero death due to malaria was recorded over the past 12 years. Fluctuating pattern of impatient malaria cases occurrence was seen over the past twelve years with an average number of 225.5 inpatient cases. The data showed that there is a high burden of malaria in the district. Plasmodium falciparum (Pf) was a predominant parasite species in the district with the maximum percentage of about 90. There was no statistically significant association between season and total malaria case number (F(3,8): 1.982, P:0.195). However, the inter-annual total case count difference was statistically significant (F(11,132): 36.305, p < 0001). Total malaria case count had shown two months lagged carry on effect. Moreover, 3 months lagged humidity had significant positive effect on total malaria cases. Malaria prevention interventions and meteorological factors showed statistically significant association with total malaria cases. CONCLUSION: Malaria was and will remain to be a major public health problem in the area. The social and economic impact of the disease on the local community is clearly pronounced as it is the leading cause of health facility visit and admission including the mortality associated with it. Scale up of effective interventions is quite important. Continuous monitoring of the performance of the vector control tools needs to be done.
BACKGROUND: Rainfall is one of the climate variables most studied as it affects malaria occurrence directly. OBJECTIVE: This study aimed to describe how monthly rainfall variability affects malaria incidence in different years. METHODS: A total of 7 years (2013/14-2019/20) retrospective confirmed and treated malaria cases in Gondar Zuria district were used for analysis in addition to five (2013/14-2017/18) years retrospective data from Dembia district. RESULTS: The annual rainfalls in the study years showed no statistically significant difference (p = 0. 78). But, variations in rainfalls of the different months (p = 0.000) of the different years were the source of variations for malaria count (incidences) in the different years. Malaria was transmitted throughout the year with the highest peak in November (mean count = 1468.7 ± 697.8) and followed by May (mean count = 1253.4 ± 1391.8), after main Kiremt/Summer and minor Bulg/Spring rains respectively. The lowest transmission was occurred in February (338 ± 240.3) when the rivers were the only source of mosquito vectors. Year 2013/14 (RF = 2351.12 mm) and 2019/20 (RF = 2278.80 mm) with no statistically significant difference (p = 0.977) in annual rainfalls produced 10, 702 (49.2%) and 961 (20%) malaria counts for the Bulg (spring) season respectively due to 581.92 mm (24.8%) higher total Bulg/Spring rain in 2013/14 compared to 124.1 mm (5.45%) in 2019/20. Generally, above normal rainfalls in Bulg/Spring season increased malaria transmission by providing more aquatic habitats supporting the growth of the immature stages. But heavy rains in Summer/Kiremt produced low malaria counts due to the high intensity of the rainfalls which could kill the larvae and pupae. Spearman’s correlation analysis indicated that the mean rainfalls of current month (RF) (0 lagged month) (P = 0.025), previous month (RF1) (1 month lagged) (p = 0.000), before previous months (RF2) (2 months lagged) (p = 0.001) and mean RF + RF1 + RF2 (P = 0.001) were positive significantly correlated with mean monthly malaria counts compared to negative significant correlations for temperature variables. Temperature variables negative correlations were interpreted as confounding effects because decreased malaria counts in dry months were due to a decrease in rainfalls. Conclusion: rainfall distribution in different months of a year affects malaria occurrences.
Objective Respiratory viruses are the most important cause of lower respiratory tract infections (LRTI) in children. Meteorological factors can influence viral outbreaks. The objective of this study was to determine the association between climate variables and respiratory virus detection. Methods Multicenter prospective 1-year surveillance was conducted among children hospitalized for LRTI in Tunisia. Nasopharyngeal aspirates were tested by direct immunofluorescence assay (DIFA) for the detection of respiratory syncytial virus (RSV); adenovirus (AdV); influenza virus (IFV) A and B; and parainfluenza virus 1, 2, and 3 (PIV1/2/3). Samples were further analyzed by reverse-transcription polymerase chain reaction for the detection of human metapneumovirus (hMPV). Monthly meteorological data were determined by consulting the National Institute of Meteorology and the World Weather Online Meteorological Company websites. Pearson’s correlation tests were used to determine the statistical association between the detection of respiratory viruses and climatic characteristics. Results Among 572 patients, 243 (42.5%) were positive for at least one virus. The most frequently detected viruses by DIFA were RSV (30.0%), followed by IFVA (3.8%), IFVB (3.5%), PIV (0.9%), and AdV (0.9%). HMPV was detected in 13 RSV-negative samples (3.3%). Dual infections were detected in seven cases (1.2%). Monthly global respiratory viruses and RSV detections correlated significantly with temperature, rainfall, cloud cover, wind speed, wind temperature, and duration of sunshine. Monthly IFV detection significantly correlated with rainfall, wind speed, wind temperature, and duration of sunshine. HMPV detection significantly correlated with temperature and wind temperature. Conclusion Respiratory viral outbreaks are clearly related to meteorological factors in Tunisia.
Since the outbreak of COVID-19, its effects on different aspects of life have been subject to much research, including food security, a domain that has been of special concern in many low-income countries. Ethiopia has been facing many challenges related to food security for decades via drought, famine, and conflict. Within this context, this case study assessed the impact of the COVID-19 pandemic on food security in Ethiopia. Results show that the ongoing pandemic has negatively impacted different regions and at-risk groups in a heterogeneous manner. This has been mainly through disruptions in the Ethiopian food value chain and the relative failure of social security programmes to address the losses generated by COVID-19. The population in the capital city, Addis Ababa, was able to maintain the same level of food security despite income losses caused by the COVID-19 pandemic. However, at-risk groups such as refugees, internally displaced persons (IDPs), and conflict affected regions were seen to suffer significantly from food insecurity exacerbated by COVID-19. Furthermore, this paper particularly emphasizes the importance of considering contextual factors other than COVID-19, such as conflicts or climate change, when discussing the state of food security in Ethiopia.
The African Sahel countries are inherently fragile, environmentally insecure and economically weak. This paper underscores the compounded impacts brought about by the COVID-19 pandemic on resource supply security and, hence, the long-term development of the region. It outlines the Sahel-specific COVID-19 scenario by firstly highlighting the underlying vulnerabilities and later linking the health sector outcomes to increased political instability and environmental insecurity, particularly the deterioration of food security. In this sense, this paper shows from a region-wide perspective how COVID-19 in the Sahel is associated with enlarged sociopolitical developmental perils. Lower remittance sent by expatriates, violent conflicts, increased cross-border terrorism and migration, discriminant mobility restrictions of people and goods, weak national healthcare infrastructures, bottlenecks in international aid, pressures on the education system and recent climate extremes are some revealing examples of aggravators of the impacts on the supply of vital resources, such as food. This paper also shows the importance of considering the close interlinks between health, food and political stability in the Sahel. There is a paramount need for more comprehensive approaches linking human health to other sectors, and for re-considering local sustainable agriculture. To avoid prolonged or recurrent humanitarian crises, the Sahel countries need to strengthen response capacities through public sector-led responses. Examples of these responses include reinforced national disaster programs for the vulnerable, support to sustainable agriculture and food markets, improved performance and communication of public sector relief, state-based cooperation, building of regional alliances and peacemaking efforts.
The aim of this study is to simulate the impact of mineral dust emissions from the Sahel-Saharan zone on temperature extremes over the Sahel. To achieve this goal, we performed two numerical simulations: one with the standard version of the regional climate model RegCM4 (no dust run) and another one with the same version of this model incorporating a dust module (dust run). The difference between both versions of the model allowed to isolate the impacts of mineral dust emissions on temperature extremes. The results show that the accumulation of mineral dust into the atmosphere leads to a decrease of the frequency of warm days, very warm days, and warm nights over the Sahel. This decrease is higher during the MAM (March-April-May) and JJA (June-July-August) periods especially in the northern and western parts of the Sahel. The impact of the mineral dust emissions is also manifested by a decrease of the frequency of tropical nights especially during MAM in the northern Sahel. When considering the warm spells, mineral particles tend to weaken them especially in MAM and JJA in the northern Sahel. To estimate the potential impacts of the mineral dust accumulation on heat stress, the heat index and the humidex are used. The analysis of the heat index shows that the dust impact is to reduce the health risks particularly in the northern Sahel during the MAM period, in the western Sahel during JJA, and in the southern and the northeastern parts of the Sahel during the SON (September-October-November) period. As for the humidex, it is characterized by a decrease especially in the northern Sahel for all seasons. This reduction of the occurrence of thermal extremes may have a positive effect on the energy demand for cooling and on global health. However, the accumulation of dust particles in the atmosphere may also increase the meningitis incidence and prevalence.
BACKGROUND: Onchocerciasis is a neglected tropical filarial disease transmitted by the bites of blackflies, causing blindness and severe skin lesions. The change in focus for onchocerciasis management from control to elimination requires thorough mapping of pre-control endemicity to identify areas requiring interventions and to monitor progress. Onchocerca volvulus nodule prevalence in sub-Saharan Africa is spatially continuous and heterogeneous, and highly endemic areas may contribute to transmission in areas of low endemicity or vice-versa. Ethiopia is one such onchocerciasis-endemic country with heterogeneous O. volvulus nodule prevalence, and many districts are still unmapped despite their potential for onchocerciasis transmission. METHODOLOGY/PRINCIPLE FINDINGS: A Bayesian geostatistical model was fitted for retrospective pre-intervention nodule prevalence data collected from 916 unique sites and 35,077 people across Ethiopia. We used multiple environmental, socio-demographic, and climate variables to estimate the pre-intervention prevalence of O. volvulus nodules across Ethiopia and to explore their relationship with prevalence. Prevalence was high in southern and northwestern Ethiopia and low in Ethiopia’s central and eastern parts. Distance to the nearest river (RR: 0.9850, 95% BCI: 0.9751-0.995), precipitation seasonality (RR: 0.9837, 95% BCI: 0.9681-0.9995), and flow accumulation (RR: 0.9586, 95% BCI: 0.9321-0.9816) were negatively associated with O. volvulus nodule prevalence, while soil moisture (RR: 1.0218, 95% BCI: 1.0135-1.0302) was positively associated. The model estimated the number of pre-intervention cases of O. volvulus nodules in Ethiopia to be around 6.48 million (95% BCI: 3.53-13.04 million). CONCLUSIONS/SIGNIFICANCE: Nodule prevalence distribution was correlated with habitat suitability for vector breeding and associated biting behavior. The modeled pre-intervention prevalence can be used as a guide for determining priorities for elimination mapping in regions of Ethiopia that are currently unmapped, most of which have comparatively low infection prevalence.
Malaria is a severe public health problem in the Amhara region, Ethiopia. A retrospective study was conducted to model and interpret the effects of climate variability and environmental factors on the monthly malaria surveillance data of 152 districts in the region. The data were analyzed using the Bayesian generalized Poisson spatiotemporal model. Malaria incidence had significant seasonal, temporal, and spatial variations in the region. The risk of malaria incidence was decreased by 24% per 100 m increase in altitude. Monthly minimum temperature decreases the risk of malaria by 2.2% per a 1 °C increment. The risk of malaria transmission was increased by 8% per 100 mm rise in the total monthly rainfall of districts. Besides, long-lasting insecticidal net coverage significantly reduces malaria risk by a factor of 0.8955. The finding suggests that malaria transmission was higher in northern and western districts. Hence, concerned bodies should consider seasonal, temporal, and spatial variations and effects of climate and environmental factors for intervention and elimination.
Background: Insecticide treated bed nets (ITN) are considered a core malaria vector control tool by the WHO and are the main contributor to the large decline in malaria burden in sub-Saharan Africa over the past 20 years, but they are less effective if they are not broadly and regularly used. ITN use may depend on factors including temperature, relative humidity, mosquito density, seasonality, as well as ideational or psychosocial factors including perceptions of nets and perceptions of net use behaviours.Methods: A cross-sectional household survey was conducted as part of a planned randomized controlled trial in Magoe District, Mozambique. Interviewers captured data on general malaria and ITN perceptions including ideational factors related to perceived ITN response efficacy, self-efficacy to use an ITN, and community norms around ITN using a standardized questionnaire. Only households with sufficient ITNs present for all children to sleep under (at least one ITN for every two children under the age of five years) were eligible for inclusion in the study. Additional questions were added about seasonality and frequency of ITN use.Results: One-thousand six hundred sixteen mother-child dyads were interviewed. Responses indicated gaps in use of existing nets and net use was largely independent of ideational factors related to ITNs. Self-reported ITN use varied little by season nor meaningfully when different methods were used to solicit responses on net use behaviour. Mothers’ perceived response efficacy of ITNS was negatively associated with net use (high perceived response efficacy reduced the log-odds of net use by 0.27 (95% CI – 0.04 to – 0.51), implying that stronger beliefs in the effectiveness of ITNs might result in reduced net use among their children.Conclusions: In this context, ITN use among children was not clearly related to mothers’ ideational factors measured in the study. Scales used in solicitation of ideation around ITN use and beliefs need careful design and testing across a broader range of populations in order to identify ideational factors related to ITN use among those with access.
Malaria is a major public health concern in tropics and subtropics. Accurate malaria prediction is critical for reporting ongoing incidences of infection and its control. Hence, the purpose of this investigation was to evaluate the performances of different models of predicting malaria incidence in Marodijeh region, Somaliland. The study used monthly historical data from January 2011 to December 2020. Five deterministic and stochastic models, i.e. Seasonal Autoregressive Moving Average (SARIMA), Holt-Winters’ Exponential Smoothing, Harmonic Model, Seasonal and Trend Decomposition using Loess (STL) and Artificial Neural Networks (ANN), were fitted to the malaria incidence data. The study employed Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE) to measure the accuracy of each model. The results indicated that the artificial neural network (ANN) model outperformed other models in terms of the lowest values of RMSE (39.4044), MAE (29.1615), MAPE (31.3611) and MASE (0.6618). The study also incorporated three meteorological variables (Humidity, Rainfall and Temperature) into the ANN model. The incorporation of these variables into the model enhanced the prediction of malaria incidence in terms of achieving better prediction accuracy measures (RMSE = 8.6565, MAE = 6.1029, MAPE = 7.4526 and MASE = 0.1385). The 2-year generated forecasts based on the ANN model implied a significant increasing trend. The study recommends the ANN model for forecasting malaria cases and for taking the steps to reduce malaria incidence during the times of year when high incidence is reported in the Marodijeh region.
In the Maasai Steppe, public health and economy are threatened by African Trypanosomiasis, a debilitating and fatal disease to livestock (African Animal Trypanosomiasis -AAT) and humans (Human African Trypanosomiasis-HAT), if not treated. The tsetse fly is the primary vector for both HAT and AAT and climate is an important predictor of their occurrence and the parasites they carry. While understanding tsetse fly distribution is essential for informing vector and disease control strategies, existing distribution maps are old and were based on coarse spatial resolution data, consequently, inaccurately representing vector and disease dynamics necessary to design and implement fit-for-purpose mitigation strategies. Also, the assertion that climate change is altering tsetse fly distribution in Tanzania lacks empirical evidence. Despite tsetse flies posing public health risks and economic hardship, no study has modelled their distributions at a scale needed for local planning. This study used MaxEnt species distribution modelling (SDM) and ecological niche modeling tools to predict potential distribution of three tsetse fly species in Tanzania’s Maasai Steppe from current climate information, and project their distributions to midcentury climatic conditions under representative concentration pathways (RCP) 4.5 scenarios. Current climate results predicted that G. m. morsitans, G. pallidipes and G swynnertoni cover 19,225 km2, 7,113 km2 and 32,335 km2 and future prediction indicated that by the year 2050, the habitable area may decrease by up to 23.13%, 12.9% and 22.8% of current habitable area, respectively. This information can serve as a useful predictor of potential HAT and AAT hotspots and inform surveillance strategies. Distribution maps generated by this study can be useful in guiding tsetse fly control managers, and health, livestock and wildlife officers when setting surveys and surveillance programs. The maps can also inform protected area managers of potential encroachment into the protected areas (PAs) due to shrinkage of tsetse fly habitats outside PAs.
In mountain communities like Sebei, Uganda, which are highly vulnerable to emerging and re-emerging infectious diseases, community-based surveillance plays an important role in the monitoring of public health hazards. In this survey, we explored capacities of village health teams (VHTs) in Sebei communities of Mount Elgon in undertaking surveillance tasks for emerging and re-emerging infectious diseases in the context of a changing climate. We used participatory epidemiology techniques to elucidate VHTs’ perceptions on climate change and public health and assessed their capacities to conduct surveillance for emerging and re-emerging infectious diseases. Overall, VHTs perceived climate change to be occurring with wider impacts on public health. However, they had inadequate capacities in collecting surveillance data. The VHTs lacked transport to navigate through their communities and had insufficient capacities in using mobile phones for sending alerts. They did not engage in reporting other hazards related to the environment, wildlife, and domestic livestock that would accelerate infectious disease outbreaks. Records were not maintained for disease surveillance activities and the abilities of VHTs to analyze data were also limited. However, VHTs had access to platforms that could enable them to disseminate public health information. The VHTs thus need to be retooled to conduct their work effectively and efficiently through equipping them with adequate logistics and knowledge on collecting, storing, analyzing, and relaying data, which will improve infectious disease response and mitigation efforts.
This study used a mixed-methods research design to examine the sensitivity of vector-borne disease (VBD) patterns to the changes in rainfall and temperature trends. The research focused on malaria in Masvingo Province, Zimbabwe. The study interfaced the climate action, health and sustainable cities and communities with sustainable development goals (SDGs). Historical climate and epidemiological data were used to compute the correlations and determine the possible modifications of disease patterns. Clustered random and chain-referral sampling approaches were used to select study sites and respondents. Primary data were gathered through a questionnaire survey (n = 191), interviews and focus group discussions, with Mann-Kendal trend tests performed using XLSTAT 2020. The results show a positive correlation between malaria prevalence rates and temperature-related variables. A decline in precipitation-related variables, specifically mean monthly precipitation (MMP), was associated with an increase in malaria prevalence. These observations were confirmed by the views of the respondents, which show that climate change has a bearing on malaria spatial and temporal dynamics in Masvingo Province. The study concludes that climate change plays a contributory role in VBD dynamics, thereby impeding the attainment of the 2030 Agenda for Sustainable Development, especially SDG 3, which deals with health. The study recommends further research into appropriate adaptation mechanisms to increase the resilience of rural and urban communities against the negative transmutations associated with weather and climatic pressures.
BACKGROUND: Understanding the response of vector habitats to climate change is essential for vector management. Increasingly, there is fear that climate change may cause vectors to be more important for animal husbandry in the future. Therefore, knowledge about the current and future spatial distribution of vectors, including ticks (Ixodida), is progressively becoming more critical to animal disease control. METHODS: Our study produced present (2018) and future (2050) bont tick (Amblyomma hebraeum) niche models for Mashonaland Central Province, Zimbabwe. Specifically, our approach used the Ensemble algorithm in Biomod2 package in R 3.4.4 with a suite of physical and anthropogenic covariates against the tick’s presence-only location data obtained from cattle dipping facilities. RESULTS: Our models showed that currently (the year 2018) the bont tick potentially occurs in 17,008 km(2), which is 60% of Mashonaland Central Province. However, the models showed that in the future (the year 2050), the bont tick will occur in 13,323 km(2), which is 47% of Mashonaland Central Province. Thus, the models predicted an ~ 13% reduction in the potential habitat, about 3685 km(2) of the study area. Temperature, elevation and rainfall were the most important variables explaining the present and future potential habitat of the bont tick. CONCLUSION: Results of our study are essential in informing programmes that seek to control the bont tick in Mashonaland Central Province, Zimbabwe and similar environments.
Glossina morsitans is a vector for Human African Trypanosomiasis (HAT), which is mainly distributed in sub-Saharan Africa at present. Our objective was to project the historical and future potentially suitable areas globally and explore the influence of climatic factors. The maximum entropy model (MaxEnt) was utilized to evaluate the contribution rates of bio-climatic factors and to project suitable habitats for G. morsitans. We found that Isothermality and Precipitation of Wettest Quarter contributed most to the distribution of G. morsitans. The predicted potentially suitable areas for G. morsitans under historical climate conditions would be 14.5 million km(2), including a large area of Africa which is near and below the equator, small equatorial regions of southern Asia, America, and Oceania. Under future climate conditions, the potentially suitable areas are expected to decline by about -5.38 ± 1.00% overall, under all shared socioeconomic pathways, compared with 1970-2000. The potentially suitable habitats of G. morsitans may not be limited to Africa. Necessary surveillance and preventive measures should be taken in high-risk regions.
Genetic evolution of Rift Valley fever virus (RVFV) in Africa has been shaped mainly by environmental changes such as abnormal rainfall patterns and climate change that has occurred over the last few decades. These gradual environmental changes are believed to have effected gene migration from macro (geographical) to micro (reassortment) levels. Presently, 15 lineages of RVFV have been identified to be circulating within the Sub-Saharan Africa. International trade in livestock and movement of mosquitoes are thought to be responsible for the outbreaks occurring outside endemic or enzootic regions. Virus spillover events contribute to outbreaks as was demonstrated by the largest epidemic of 1977 in Egypt. Genomic surveillance of the virus evolution is crucial in developing intervention strategies. Therefore, we have developed a computational tool for rapidly classifying and assigning lineages of the RVFV isolates. The computational method is presented both as a command line tool and a web application hosted at https://www.genomedetective.com/app/typingtool/rvfv/ . Validation of the tool has been performed on a large dataset using glycoprotein gene (Gn) and whole genome sequences of the Large (L), Medium (M) and Small (S) segments of the RVFV retrieved from the National Center for Biotechnology Information (NCBI) GenBank database. Using the Gn nucleotide sequences, the RVFV typing tool was able to correctly classify all 234 RVFV sequences at species level with 100% specificity, sensitivity and accuracy. All the sequences in lineages A (n = 10), B (n = 1), C (n = 88), D (n = 1), E (n = 3), F (n = 2), G (n = 2), H (n = 105), I (n = 2), J (n = 1), K (n = 4), L (n = 8), M (n = 1), N (n = 5) and O (n = 1) were also correctly classified at phylogenetic level. Lineage assignment using whole RVFV genome sequences (L, M and S-segments) did not achieve 100% specificity, sensitivity and accuracy for all the sequences analyzed. We further tested our tool using genomic data that we generated by sequencing 5 samples collected following a recent RVF outbreak in Kenya. All the 5 samples were assigned lineage C by both the partial (Gn) and whole genome sequence classifiers. The tool is useful in tracing the origin of outbreaks and supporting surveillance efforts.Availability: https://github.com/ajodeh-juma/rvfvtyping.
BACKGROUND: Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies. METHODS: We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models. RESULTS: We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria. CONCLUSION: We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.
Aims: This study examines the dynamics of malaria as influenced by meteorological factors in French Guiana from 2005 to 2019. It explores spatial hotspots of malaria transmission and aims to determine the factors associated with variation of hotspots with time. Methods: Data for individual malaria cases came from the surveillance system of the Delocalized Centers for Prevention and Care (CDPS) (n = 17) from 2005-2019. Meteorological data was acquired from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) database. The Box-Jenkins autoregressive integrated moving average (ARIMA) model tested stationarity of the time series, and the impact of meteorological indices (issued from principal component analysis-PCA) on malaria incidence was determined with a general additive model. Hotspot characterization was performed using spatial scan statistics. Results: The current sample includes 7050 eligible Plasmodium vivax (n = 4111) and Plasmodium falciparum (n = 2939) cases from health centers across French Guiana. The first and second PCA-derived meteorological components (maximum/minimum temperature/minimum humidity and maximum humidity, respectively) were significantly negatively correlated with total malaria incidence with a lag of one week and 10 days, respectively. Overall malaria incidence decreased across the time series until 2017 when incidence began to trend upwards. Hotspot characterization revealed a few health centers that exhibited spatial stability across the entire time series: Saint Georges de l’Oyapock and Antecume Pata for P. falciparum, and Saint Georges de l’Oyapock, Antecume Pata, Régina and Camopi for P. vivax. Conclusions: This study highlighted changing malaria incidence in French Guiana and the influences of meteorological factors on transmission. Many health centers showed spatial stability in transmission, albeit not temporal. Knowledge of the areas of high transmission as well as how and why transmission has changed over time can inform strategies to reduce the transmission of malaria in French Guiana. Hotspots should be further investigated to understand other influences on local transmission, which will help to facilitate elimination.
BACKGROUND: Diarrhea and typhoid, ancient water-borne diseases which are highly connected to rainfall are serious public health challenges in the blocks of Kalahandi district of Odisha, India. OBJECTIVES: Corroboration of rainfall and waterborne diseases are available in abundance; therefore, the objective of this article is to calculate the climate and disease vulnerability index (CDVI) value for each block of Kalahandi. METHODS: We have applied the livelihood vulnerability index with some modifications and classify the three major categories, i.e., exposure, sensitivity, and adaptive capacity into six subcategories. These six subcategories are further divided into 26 vulnerability indicators based on a detailed literature review. RESULTS: The result indicated that the Thuamul Rampur block, the southernmost part of the district is highly exposed to the annual and seasonal mean rainfall, and the Madanpur Rampur block lies in the northernmost part of the district is highly exposed to diarrhea and typhoid. Based on the calculation of the final CDVI value, nearly 50% of blocks of the Kalahandi district fall in the category of very high to high vulnerable zones. Furthermore, it has been observed that factors such as rainfall and disease distribution, vulnerable population and infrastructure, and education and health-care capacities had a notable influence on vulnerability. CONCLUSION: It is rare to find a health vulnerability-related study in India at this microlevel based on the suitable indicators selected for a tribal and backward region.
Melioidosis is a seasonal infectious disease in tropical and subtropical areas caused by the soil bacterium Burkholderia pseudomallei. In many parts of the world, including South West India, most cases of human infections are reported during times of heavy rainfall, but the underlying causes of this phenomenon are not fully understood. India is among the countries with the highest predicted melioidosis burden globally, but there is very little information on the environmental distribution of B. pseudomallei and its determining factors. The present study aimed (i) to investigate the prevalence of B. pseudomallei in soil in South West India, (ii) determine geochemical factors associated with B. pseudomallei presence and (iii) look for potential seasonal patterns of B. pseudomallei soil abundance. Environmental samplings were performed in two regions during the monsoon and post-monsoon season and summer from July 2016 to November 2018. We applied direct quantitative real time PCR (qPCR) together with culture protocols to overcome the insufficient sensitivity of solely culture-based B. pseudomallei detection from soil. A total of 1,704 soil samples from 20 different agricultural sites were screened for the presence of B. pseudomallei. Direct qPCR detected B. pseudomallei in all 20 sites and in 30.2% (517/1,704) of all soil samples, whereas only two samples from two sites were culture-positive. B. pseudomallei DNA-positive samples were negatively associated with the concentration of iron, manganese and nitrogen in a binomial logistic regression model. The highest number of B. pseudomallei-positive samples (42.6%, p < 0.0001) and the highest B. pseudomallei loads in positive samples [median 4.45 × 10(3) genome equivalents (GE)/g, p < 0.0001] were observed during the monsoon season and eventually declined to 18.9% and a median of 1.47 × 10(3) GE/g in summer. In conclusion, our study from South West India shows a wide environmental distribution of B. pseudomallei, but also considerable differences in the abundance between sites and within single sites. Our results support the hypothesis that nutrient-depleted habitats promote the presence of B. pseudomallei. Most importantly, the highest B. pseudomallei abundance in soil is seen during the rainy season, when melioidosis cases occur.
Climate change is adversely impacting the burden of diarrheal diseases. Despite significant reduction in global prevalence, diarrheal disease remains a leading cause of morbidity and mortality among young children in low- and middle-income countries. Previous studies have shown that diarrheal disease is associated with meteorological conditions but the role of large-scale climate phenomena such as El Niño-Southern Oscillation (ENSO) and monsoon anomaly is less understood. We obtained 13 years (2002-2014) of diarrheal disease data from Nepal and investigated how the disease rate is associated with phases of ENSO (El Niño, La Niña, vs. ENSO neutral) monsoon rainfall anomaly (below normal, above normal, vs. normal), and changes in timing of monsoon onset, and withdrawal (early, late, vs. normal). Monsoon season was associated with a 21% increase in diarrheal disease rates (Incident Rate Ratios [IRR]: 1.21; 95% CI: 1.16-1.27). El Niño was associated with an 8% reduction in risk while the La Niña was associated with a 32% increase in under-5 diarrheal disease rates. Likewise, higher-than-normal monsoon rainfall was associated with increased rates of diarrheal disease, with considerably higher rates observed in the mountain region (IRR 1.51, 95% CI: 1.19-1.92). Our findings suggest that under-5 diarrheal disease burden in Nepal is significantly influenced by ENSO and changes in seasonal monsoon dynamics. Since both ENSO phases and monsoon can be predicted with considerably longer lead time compared to weather, our findings will pave the way for the development of more effective early warning systems for climate sensitive infectious diseases.
Introduction: The incidence of diarrhea, a leading cause of morbidity and mortality in low-income countries such as Nepal, is temperature-sensitive, suggesting it could be associated with climate change. With climate change fueled increases in the mean and variability of temperature and precipitation, the incidence of water and food-borne diseases are increasing, particularly in sub-Saharan Africa and South Asia. This national-level ecological study was undertaken to provide evidence linking weather and climate with diarrhea incidence in Nepal. Method: We analyzed monthly diarrheal disease count and meteorological data from all districts, spanning 15 eco-development regions of Nepal. Meteorological data and monthly data on diarrheal disease were sourced, respectively, from the Department of Hydrology and Meteorology and Health Management Information System (HMIS) of the Government of Nepal for the period from 2002 to 2014. Time-series log-linear regression models assessed the relationship between maximum temperature, minimum temperature, rainfall, relative humidity, and diarrhea burden. Predictors with p-values < 0.25 were retained in the fitted models. Results: Overall, diarrheal disease incidence in Nepal significantly increased with 1 °C increase in mean temperature (4.4%; 95% CI: 3.95, 4.85) and 1 cm increase in rainfall (0.28%; 95% CI: 0.15, 0.41). Seasonal variation of diarrheal incidence was prominent at the national level (11.63% rise in diarrheal cases in summer (95% CI: 4.17, 19.61) and 14.5% decrease in spring (95% CI: −18.81, −10.02) compared to winter season). Moreover, the effects of temperature and rainfall were highest in the mountain region compared to other ecological regions of Nepal. Conclusion: Our study provides empirical evidence linking weather factors and diarrheal disease burden in Nepal. This evidence suggests that additional climate change could increase diarrheal disease incidence across the nation. Mountainous regions are more sensitive to climate variability and consequently the burden of diarrheal diseases. These findings can be utilized to allocate necessary resources and envision a weather-based early warning system for the prevention and control of diarrheal diseases in Nepal.
Surface water quality is among the significant challenges in the Sutlej River basin, passing through Pakistan’s most densely populated province. Currently, the overall surface water quality is grossly polluted, mainly due to the direct discharge of wastewater from the urban areas to the Sutlej River directly or through stream networks. Escherichia coli concentrations vary under extreme weather events like floods and droughts and socioeconomic circumstances like urbanization, population growth, and treatment options. This paper assesses the future E. coli load and concentrations using the Soil and Water Assessment Tool (SWAT) along with scenarios based on Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) developed by the Intergovernmental Panel on Climate Change (IPCC). E. coli concentrations according to a more polluted scenario disclose a near and mid future increase by 108% and 173%, and far future increases up to 251% compared to the reference period (baseline) concentrations. The E. coli concentration is reduced by - 54%, - 68%, and - 81% for all the projected time steps compared to the baseline concentrations. While highly improved sewerage and manure management options are adapted, the concentration is further reduced by - 96%, - 101%, and - 105%, respectively, compared to the baseline. Our modeling and scenario matrix study shows that reducing microbiological concentrations in the surface water is possible. Still, it requires rigorous sanitation and treatment options, and socioeconomic variables play an essential role besides climate change to determine the microbiological concentration of water resources and be included in future studies whenever water quality and health risks are considered.
In tropical countries such as Sri Lanka, where leptospirosis-a deadly disease with a high mortality rate-is endemic, prediction is required for public health planning and resource allocation. Routinely collected meteorological data may offer an effective means of making such predictions. This study included monthly leptospirosis and meteorological data from January 2007 to April 2019 from Sri Lanka. Factor analysis was first used with rainfall data to classify districts into meteorological zones. We used a seasonal autoregressive integrated moving average (SARIMA) model for univariate predictions and an autoregressive distributed lag (ARDL) model for multivariable analysis of leptospirosis with monthly average rainfall, temperature, relative humidity (RH), solar radiation (SR), and the number of rainy days/month (RD). Districts were classified into wet (WZ) and dry (DZ) zones, and highlands (HL) based on the factor analysis of rainfall data. The WZ had the highest leptospirosis incidence; there was no difference in the incidence between the DZ and HL. Leptospirosis was fluctuated positively with rainfall, RH and RD, whereas temperature and SR were fluctuated negatively. The best-fitted SARIMA models in the three zones were different from each other. Despite its known association, rainfall was positively significant in the WZ only at lag 5 (P = 0.03) but was negatively associated at lag 2 and 3 (P = 0.04). RD was positively associated for all three zones. Temperature was positively associated at lag 0 for the WZ and HL (P < 0.009) and was negatively associated at lag 1 for the WZ (P = 0.01). There was no association with RH in contrast to previous studies. Based on altitude and rainfall data, meteorological variables could effectively predict the incidence of leptospirosis with different models for different climatic zones. These predictive models could be effectively used in public health planning purposes.
Cholera is a water-borne infectious disease that affects 1.3 to 4 million people, with 21,000 to 143,000 reported fatalities each year worldwide. Outbreaks are devastating to affected communities and their prospects for development. The key to support preparedness and public health response is the ability to forecast cholera outbreaks with sufficient lead time. How Vibrio cholerae survives in the environment outside a human host is an important route of disease transmission. Thus, identifying the environmental and climate drivers of these pathogens is highly desirable. Here, we elucidate for the first time a mechanistic link between climate variability and cholera (Satellite Water Marker; SWM) index in the Bengal Delta, which allows us to predict cholera outbreaks up to two seasons earlier. High values of the SWM index in fall were associated with above-normal summer monsoon rainfalls over northern India. In turn, these correlated with the La Niña climate pattern that was traced back to the summer monsoon and previous spring seasons. We present a new multi-linear regression model that can explain 50% of the SWM variability over the Bengal Delta based on the relationship with climatic indices of the El Niño Southern Oscillation, Indian Ocean Dipole, and summer monsoon rainfall during the decades 1997-2016. Interestingly, we further found that these relationships were non-stationary over the multi-decadal period 1948-2018. These results bear novel implications for developing outbreak-risk forecasts, demonstrating a crucial need to account for multi-decadal variations in climate interactions and underscoring to better understand how the south Asian summer monsoon responds to climate variability.
BACKGROUND: Pakistan has been experiencing intervals of sporadic cases and localized outbreaks in the last two decades. No proper study has been carried out in order to find out the environmental burden of toxigenic V. cholerae as well as how temporal and environmental factors associated in driving cholera across the country. METHODS: We tested waste water samples from designated national environment surveillance sites in Pakistan with RT-PCR assay. Multistage sampling technique were utilized for samples collection and for effective sample processing Bag-Mediated Filtration system, were employed. Results were analysed by district and month wise to understand the geographic distribution and identify the seasonal pattern of V. cholera detection in Pakistan. RESULTS: Between May 2019, and February 2020, we obtained and screened 160 samples in 12 districts across Pakistan. Out of 16 sentinel environmental surveillance sites, 15 sites showed positive results against cholera toxigenic gene with mostly lower CT value (mean, 34??2) and have significant difference (p < 0.05). The highest number of positive samples were collected from Sindh in month of November, then in June it is circulating in different districts of Pakistan including four Provinces respectively. CONCLUSION: V. cholera detection do not follow a clear seasonal pattern. However, the poor sanitation problems or temperature and rainfall may potentially influence the frequency and duration of cholera across the country. Occurrence of toxigenic V. cholerae in the environment samples showed that cholera is endemic, which is an alarming for a potential future cholera outbreaks in the country.
We examine the impact of flooding in Pakistan on child health using satellite data and two household datasets. Flooding may influence child health, as measured by weight-for-height z-score, through two key channels. First, excessive flood waters can catalyze the spread of diarrheal disease, negatively impacting child health. Second, excessive flood waters – even when damaging in some areas – provide water to rice paddies and other agriculture, increasing food availability in the post-flood period. This may positively influence child health. In Pakistan, we find evidence of both channels: floods increase incidence of morbidity (diarrhea and fever) as well as meal frequency in the post flood season. We also find that floods increase dietary diversity, but only in districts with high rice harvesting intensity where flooding may predict favorable growing conditions. Because these mechanisms (disease incidence and dietary adequacy) act against one another, we find weak overall impact of floods on child health. (c) 2021 Elsevier Ltd. All rights reserved.
BACKGROUND: Chengannur, a town in the south Indian state of Kerala, was 1 of the worst affected towns during the floods of 2018. Post-flood, Kerala state was under the threat of many infectious diseases including leptospirosis, but did not report any leptospirosis infections. OBJECTIVES: This study was conducted with the following objectives: (1) Assess the knowledge, attitude and practices regarding the prevention of leptospirosis among the flood affected population and Accredited Social Health Activists (ASHAs) of Chengannur; and (2) Analyze the factors responsible for and contributing to leptospirosis control in the area post flood. METHODOLOGY: A cross-sectional questionnaire based observational study was conducted among 2 groups: the flood affected population, and ASHA. The questionnaire was divided into 3 parts. Part A contained the socio-demographic information. Part B contained questions on assessment of knowledge, attitude, and practices regarding the prevention, and control of leptospirosis. Part C was only for the ASHA involved. RESULTS: The final sample size was 331 (244 from the general population and 87 ASHAs). With respect to knowledge, attitude, and practice, the responses were dichotomized into correct and wrong responses. The mean knowledge score was 9.01 ± 1.08 (maximum score of 10), mean attitude score was of 3.61 ± 0.55 (maximum score of 4) and the mean practice score was 4.12 ± 1.05 (maximum score of 5). CONCLUSION: Knowledge and attitude scores did not significantly differ between the general population and ASHA, but the practice score showed a higher score among the ASHA, all of which could have probably contributed to the prevention of a leptospirosis outbreak in the region.
Neonates and children are more vulnerable to the negative impact of flood-related changes and may have a variety of detrimental negative impacts on their health. They are more prone to get various infectious diseases. They are also more vulnerable to malnutrition during floods. Flooding limits access to clean water as sewage overflows and contaminates nearby water sources. The polluted setting in the flood-affected area makes it difficult to ensure the hygiene of feeding equipment used to prepare infant formula. Breastfeeding may also become less effective due to the lack of privacy for women to breastfeed their kids while living in temporary shelters with other flood victims. In addition, milk production decreases and might even cease due to mothers’ reduced food intake and increased stress levels. Flooding may also cause supplemental feeding to deteriorate. The mothers and other primary caregivers usually lack the resources in affected areas to prepare supplemental diets for their kids, which further harm the babies. There is mounting evidence that children are more likely to develop clogged noses, itchy eyes, hoarseness, skin complications, and sneezing while living in humid areas.
The recent monsoon rains in Pakistan were unprecedented and caused flooding all over Pakistan, especially in Sindh and Balochistan. Following this national disaster, various water-borne and contagious diseases started erupting all over the country. In such a calamity-struck city of Jacobabad, we started receiving cases with a peculiar set of ocular complaints mimicking viral keratoconjunctivitis. Failure to respond to traditional treatment and the unique appearance of these corneal opacities led to a rare diagnosis of Microsporidial Keratoconjunctivitis, which was later confirmed by microscopy and staining of corneal scrapings of the most affected case. In line with published literature, all cases were treated with topical fluoroquinolone and topical anti-fungal therapy, following which the disease was cleared within a week. The disease has seen an upward trend the world over, especially among Asia. To the best of our knowledge, no such cases have been reported in Pakistan as yet. In this case series, we highlight the strong correlation of emergence of microsporidial keratitis in patients following exposure to pooled water bodies after the monsoon rainy season and floods. Moreover, this report will help create awareness in eye professionals regarding the prevention, timely diagnosis and treatment of these rare and emerging cases. Key Words: Keratitis, Spores, Water-borne diseases, Microsporidia.
BACKGROUND: Current literatures seem devoted only on relating climate change with malaria. Overarching all possible environmental determinants of malaria prevalence addressed by scanty literature in Nepal is found apposite research at this moment. This study aims to explore the environmental determinants of malaria prevalence in western Nepal. METHODS: Cross-sectional data collected from community people were used to identify the environmental determinants of malaria prevalence in western Nepal. Probit and logistic regressions are used for identifying determinants. RESULTS: The results reveal that environmental variables: winter temperature (aOR: 2.14 [95% CI: 1.00-4.56]), flooding (aOR: 2.45 [CI: 1.28-4.69]), heat waves (aOR: 3.14 [CI: 1.16-8.46]) and decreasing river water level (aOR: 0.25 [CI: 0.13-0.47]) are found major factors to influence malaria prevalence in western Nepal. Besides, pipeline drinking water (aOR: 0.37 [0.17-0.81]), transportation facility (aOR: 1.18 [1.07-1.32]) and awareness programs (aOR: 2.62 [0.03-6.65]) are exigent social issues to influence malaria prevalence in Nepal. To be protected from disease induced by environmental problems, households have used extra season specific clothes, iron nets and mosquito nets, use of insecticide in cleaning toilet and so on. CONCLUSIONS: Adaptation mechanism against these environmental issues together with promoting pipeline drinking water, transportation facility and awareness programs are the important in malaria control in Nepal. Government initiation with incentivized adaptation mechanism for the protection of environment with caring household attributes possibly help control malaria in western Nepal.
The Uttarakhand State, known for its Himalayan Mountains, is a territory in Northern India that is extremely vulnerable to earthquakes, landslides, and floods. Currently, due to the COVID-19 outbreak, India is facing the dual challenge of containing a pandemic and responding to natural disasters. This situation can have a negative impact on the health and the economic development of the region, leading to a long-lasting humanitarian crisis that can disrupt even more, the already overburdened health service. In addition, it can pose serious threats to the wellbeing of the population as it complicates physical distancing and other COVID-19 prevention measures. It is of utmost importance to analyse the impact of floods, landslides, and COVID-19 pandemic on the health system of the Uttarakhand State, and how these crises interact with each other.
Cyclone Amphan battered the coastal communities in the southwestern part of Bangladesh in 2020 during the COVID-19 pandemic. These coastal communities were experiencing such a situation for the first time and faced the dilemma of whether to stay at home and embrace the cyclone or be exposed to the COVID-19 virus in the cyclone shelters by evacuating. This article intends to explore individuals’ decisions regarding whether to evacuate in response to cyclone Amphan and in light of the risks of the COVID-19 pandemic. Consequently, this study investigated evacuation behaviors among the households and explored the impacts of COVID-19 during the evacuation procedures. We conducted household surveys to collect primary information and undertook 378 samples for interviews at a precision level of 0.05 in fourteen villages. Despite the utmost effort of the government, the results demonstrated that 96.6% of people in the coastal area received a cyclone evacuation order before the cyclone’s landfall, and only 42% of people followed the evacuation order. The majority of households chose to stay at home because of fear of COVID-19 exposure in the crowded shelters. Although half of the evacuees were housed in cyclone shelters, COVID-19 preventive measures were apparently not set in place. Thus, this study will assist in crafting future government policies to enhance disaster evacuation plans by providing insights from the pandemic that can inform disaster management plans in the Global South.
Background The world has been battling several vector-borne diseases since time immemorial. Socio-economic marginality, precipitation variations and human behavioral attributes play a major role in the proliferation of these diseases. Lockdown and social distancing have affected social behavioral aspects of human life and somehow impact on the spread of vector borne diseases. This article sheds light into the relationship between COVID-19 lockdown and global dengue burden with special focus on India. It also focuses on the interconnection of the COVID-19 pandemic (waves 1 and 2) and the alteration of human behavioral patterns in dengue cases. Methods We performed a systematic search using various resources from different platforms and websites, such as Medline; Pubmed; PAHO; WHO; CDC; ECDC; Epidemiology Unit Ministry of Health (Sri Lanka Government); NASA; NVBDCP from 2015 until 2021. We have included many factors, such as different geographical conditions (tropical climate, semitropic and arid conditions); GDP rate (developed nations, developing nations, and underdeveloped nations). We also categorized our data in order to conform to COVID-19 duration from 2019 to 2021. Data was extracted for the complete duration of 10 years (2012 to 2021) from various countries with different geographical region (arid region, semitropic/semiarid region and tropical region). Results There was a noticeable reduction in dengue cases in underdeveloped (70-85%), developing (50-90%), and developed nations (75%) in the years 2019 and 2021. The dengue cases drastically reduced by 55-65% with the advent of COVID-19 s wave in the year 2021 across the globe. Conclusions At present, we can conclude that COVID-19 and dengue show an inverse relationship. These preliminary, data-based observations should guide clinical practice until more data are made public and basis for further medical research.
Climate change is a concerning matter nowadays. It has a long-term effect on human health by spreading vector-borne diseases throughout the world, and West Bengal is not an exception. Vector-borne diseases are life-threatening risk for human; approximately 27,437 people have been infected (2016) every year by this giant killer in West Bengal of India. Temperature and rainfall, two important parameters, have directly influenced the vector-borne diseases. An association between vector-borne diseases and climatic conditions has been established by using geographically weighted regression (GWR) technique. GWR resulted overall r square value more than 0.523 in every case of diseases signifies that the climatic parameters (temperature and rainfall) and vector-borne diseases (Dengue, Malaria, Japanese Encephlities) are strongly correlated. The climatic parameters and positive cases of diseases were mapped out by using inverse distance weight (IDW) interpolation technique in this study. Artificial neural network (ANN) was performed to predict and forecast the climatic condition. The predicted findings have been validated by root mean square error (RMSE) (temperature: 0.301; rainfall: 0.380, i.e., acceptable). This study revealed an insight between climate variables and vector-borne cases in different districts of West Bengal to better understand the effects of climate variability on these diseases. A novel approach of this study is to forecast the spreading of vector-borne diseases for incoming day in West Bengal. After a critical analysis, temperature and rainfall were found to be potent factors for the development of vectors (Aedes Aegypti and Aedes albopictus), and based on this, the risk of vector-borne diseases has been predicted for upcoming years. Forecasted climatic parameters showed that almost all the districts of West Bengal would be reached in a climatic condition where there would be a chance of spreading of vector-borne diseases.
BACKGROUND: In the climate change discourse, a body of scholarship focusing on how people perceive climate change and its impact is increasing. However, in the Indian context, such scholarship is limited. OBJECTIVE: This paper aims to describe the perceptions of people on climate change and its health impacts, which were captured as part of a larger study. METHODOLOGY: A cross-sectional study was conducted in randomly selected 983 households in four districts spread across Madhya Pradesh and Jammu and Kashmir. A semi-structured questionnaire was used to collect the data. RESULTS: For 72% of respondents, the perception was not related to climate change per se. Their perceptions were contextual and were based on the anomalies which are observed in the immediate weather conditions. The health impacts of climate change were also not understood at the first place, but with probing 64% of respondents were able to report seasonal diseases. CONCLUSION: Perceptions of the people regarding climate change are more linked to their own experiences with their local weather conditions rather than the overall concept. This also explains their lack of comprehension about the health impact of climate change, but a sound understanding of seasonal diseases.
OBJECTIVES: Dakshina Kannada is one of the districts of Karnataka state of India with high incidences of mosquito-borne diseases, especially malaria and dengue. The larval stages of the mosquitoes are very important in determining the prevalence of adult mosquitoes and associated diseases. Hence, the occurrence of mosquito species was investigated by sampling different water bodies present in the Dakshina Kannada district from June 2014 to May 2017. METHODS: Random sampling was carried out from permanent and temporary, artificial and natural water bodies belonging to 11 types of microhabitats using dippers and suction pumps. RESULTS: A maximum of 37 mosquito species belonging to 12 genera were recorded with the dominant genera being Culex. Most species have been recorded from temporary bodies of water with the highest number of species in receptacles. Monsoon is the most productive season, both in terms of occurrence and abundance followed by post-monsoon and pre-monsoon. The abundance of mosquito larvae was significantly higher in temporary water bodies compared to the permanent. INTERPRETATION & CONCLUSION: Abundant rainfall in the study area which produces many natural and domestic temporary water bodies accounts for mosquito breeding throughout the year.
BACKGROUND & OBJECTIVES: In India, Kyasanur Forest Disease has been reported from the states of Karnataka, Kerala, Goa, and Maharashtra. The relationship between climatic factors and transmission of KFD remains untouched, therefore, the present study was undertaken. METHODS: Based on the occurrence of cases, Shivamogga district (Karnataka) and Wayanad district in Kerala and northern Goa (Goa state) were selected for the study. Data on the incidence of KFD and climate factors were collected from concerned authorities. To determine the relationship between dependent and independent variables, spearman’s correlation was calculated for monthly as well as with lag months. RESULTS: KFD cases and temperature (°C) were found significantly correlated up to 1 months’ lag period (p<0.05) while with precipitation relationship was found negatively significant for 0-3 months' lag. The range of suitable temperature for KFD in Shivamogga, Goa and Wayanad was found as 20-31°C, 25-29°C and 27-31°C respectively. The cumulative precipitation during transmission months (November-May) ranged from <150-500mm, while in non-transmission months (June-October) from >1100-2400mm. INTERPRETATION & CONCLUSION: The analysis of three sites revealed that with the increase in temperature, the intensity of KFD transmission decreases as corroborated by the seasonal fluctuations in Shivamogga, Goa and Wayanad. High precipitation from June to October rovides suitable ecology to tick vector and sets in transmission season from November to May when cumulative precipitation is <500 mm.
The effects of climate change on infectious diseases are a topic of considerable interest and discussion. We studied West Nile virus (WNV) in New York (NY) and Connecticut (CT) using a Weather Research and Forecasting (WRF) model climate change scenario, which allows us to examine the effects of climate change and variability on WNV risk at county level. We chose WNV because it is well studied, has caused over 50,000 reported human cases, and over 2200 deaths in the United States. The ecological impacts have been substantial (e.g., millions of avian deaths), and economic impacts include livestock deaths, morbidity, and healthcare-related expenses. We trained two Random Forest models with observational climate data and human cases to predict future levels of WNV based on future weather conditions. The Regional Model used present-day data from NY and CT, whereas the Analog Model was fit for states most closely matching the predicted future conditions in the region. Separately, we predicted changes to mosquito-based WNV risk using a trait-based thermal biology approach (Mosquito Model). The WRF model produced control simulations (present day) and pseudo-global warming simulations (future). The Regional and Analog Models predicted an overall increase in human cases of WNV under future warming. However, the Analog Model did not predict as strong of an increase in the number of human cases as the Regional Model, and predicted a decrease in cases in some counties that currently experience high numbers of WNV cases. The Mosquito Model also predicted a decrease in risk in current high-risk areas, with an overall reduction in the population-weighted relative risk (but an increase in area-weighted risk). The Mosquito Model supports the Analog Model as making more realistic predictions than the Regional Model. All three models predicted a geographic increase in WNV cases across NY and CT.
Vector-borne disease risk assessment is crucial to optimize surveillance, preventative measures (vector control), and resource allocation (medical supplies). High arthropod abundance and host interaction strongly correlate to vector-borne pathogen transmission. Increasing host density and movement increases the possibility of local and long-distance pathogen transmission. Therefore, we developed a risk-assessment framework using climate (average temperature and rainfall) and host demographic (host density and movement) data, particularly suitable for regions with unreported or underreported incidence data. This framework consisted of a spatiotemporal network-based approach coupled with a compartmental disease model and nonhomogeneous Gillespie algorithm. The correlation of climate data with vector abundance and host-vector interactions is expressed as vectorial capacity-a parameter that governs the spreading of infection from an infected host to a susceptible one via vectors. As an example, the framework is applied for dengue in Bangladesh. Vectorial capacity is inferred for each week throughout a year using average monthly temperature and rainfall data. Long-distance pathogen transmission is expressed with human movement data in the spatiotemporal network. We have identified the spatiotemporal suitability of dengue spreading in Bangladesh as well as the significant-incidence window and peak-incidence period. Analysis of yearly dengue data variation suggests the possibility of a significant outbreak with a new serotype introduction. The outcome of the framework comprised spatiotemporal suitability maps and probabilistic risk maps for spatial infection spreading. This framework is capable of vector-borne disease risk assessment without historical incidence data and can be a useful tool for preparedness with accurate human movement data.
Introduction: Dengue is a mosquito borne viral disease. found in tropical and subtropical countries. Dengue virus (DENV) infected mosquitoes of Aedes species are crucial for the transmission of disease. It has emerged as a threat to the public health systems. Dengue is endemic in many parts of India but still the status of dengue cases in Rewa Madhya Pradesh is not reported convincingly. Aim: To investigate the presence of dengue in Rewa district of Madhya Pradesh. Materials and Methods: This cross-sectional study was conducted in the Department of Microbiology at Shyam Shah Medical college Rewa under National Vector Borne Disease Control Programme (NVBDCP), Rewa, Madhya Pradesh, India, including 1113 Outpatient/Inpatient Department samples received during March 2021 to October 2021. Blood samples were collected from patients having febrile illness and after serum separation, serum were subjected to NS1 Enzyme Linked Immunosorbent Assay (ELISA) test. Descriptive statistics and Chi-square tests were applied for data analysis. Results: A total of 1113 sample were received and tested for dengue NS1 out of that 108 sample were found NS1 positive by ELISA. The cases of dengue started from the month of July 2021. But in the month of October dengue positivity was highest in number. Dengue cases reported were 297 (6.73%) in the rainy season (July-August), but the dengue positivity increased (713, 9.3%) in the post rainy season (September-October). Overall prevalence of dengue was higher in the 21-30 years (34.3%) age group followed by 11-20 years (24.1%), 31-40 years (18.5%), 41-50 years (18.5%), 51-60 years (7.4%) and >60 years (3.70%) age groups with respect to total positive cases. The prevalence of dengue was higher in male (12.94%) in comparison to females (5.54%). Conclusion: This study warrants the dengue virus infection as one of the important causes of fever during rainy and post rainy season in this region. Early diagnosis and reporting of cases are important for the better management of disease.
In recent decades, dengue has been expanding rapidly in the tropical cities. Even though environmental factors and landscape features profoundly impact dengue vector abundance and disease epidemiology, significant gaps exist in understanding the role of local environmental heterogeneity on dengue epidemiology in India. In this study, we assessed the role of remotely sensed climatic factors (rainfall, temperature and humidity) and landscape variables (land use pattern, vegetation and built up density) on dengue incidence (2012-2019) in Bhopal city, Central India. Dengue hotspots in the city were assessed through geographical information system based spatial statistics. Dengue incidence increased from 0.59 cases in 2012 to 9.11 cases in 2019 per 10,000 inhabitants, and wards located in Southern Bhopal were found to be dengue hotspots. Distributed lag non-linear model combined with quasi Poisson regression was used to assess the exposure-response association, relative risk (RR), and delayed effects of environmental factors on dengue incidence. The analysis revealed a non-linear relationship between meteorological variables and dengue cases. The model shows that the risk of dengue cases increases with increasing mean temperature, rainfall and absolute humidity. The highest RR of dengue cases (~2.0) was observed for absolute humidity ≥60 g/m3 with a 5-15 week lag. Rapid urbanization assessed by an increase in the built-up area (a 9.1% increase in 2020 compared to 2014) could also be a key factor driving dengue incidence in Bhopal city. The study sheds important insight into the synergistic effects of both the landscape and climatic factors on the transmission dynamics of dengue. Furthermore, the study provides key baseline information on the climatic variables that can be used in the micro-level dengue prediction models in Bhopal and other cities with similar climatic conditions.
INTRODUCTION: The study aimed to develop a reproducible, open-source, and scalable framework for extracting climate data from satellite imagery, understanding dengue’s decadal trend in India, and estimating the relationship between dengue occurrence and climatic factors. MATERIALS AND METHODS: A framework was developed in the Open Source Software, and it was empirically tested using reported annual dengue occurrence data in India during 2010-2019. Census 2011 and population projections were used to calculate incidence rates. Zonal statistics were performed to extract climate parameters. Correlation coefficients were calculated to estimate the relationship of dengue with the annual average of daily mean and minimum temperature and rainy days. RESULTS: Total 818,973 dengue cases were reported from India, with median annual incidence of 6.57 per lakh population; it was high in 2019 and 2017 (11.80 and 11.55 per lakh) and the Southern region (8.18 per lakh). The highest median annual dengue incidence was observed in Punjab (24.49 per lakh). Daily climatic data were extracted from 1164 coordinate locations across the country for the decadal period (4,249,734 observations). The annual average of daily temperature and rainy days positively correlated with dengue in India (r = 0.31 and 0.06, at P < 0.01 and 0.30, respectively). CONCLUSION: The study provides a reproducible algorithm for bulk climatic data extraction from research-level satellite imagery. Infectious disease models can be used to understand disease epidemiology and strengthen disease surveillance in the country.
India has witnessed a five-fold increase in dengue incidence in the past decade. However, the nation-wide distribution of dengue vectors, and the impacts of climate change are not known. In this study, species distribution modeling was used to predict the baseline and future distribution of Aedine vectors in India on the basis of biologically relevant climatic indicators. Known occurrences of Aedes aegypti and Aedes albopictus were obtained from the Global Biodiversity Information Facility database and previous literature. Bio-climatic variables were used as the potential predictors of vector distribution. After eliminating collinear and low contributing predictors, the baseline and future prevalence of Aedes aegypti and Aedes albopictus was determined, under three Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5), using the MaxEnt species distribution model. Aedes aegypti was found prevalent in most parts of the southern peninsula, the eastern coastline, north eastern states and the northern plains. In contrast, Aedes albopictus has localized distribution along the eastern and western coastlines, north eastern states and in the lower Himalayas. Under future scenarios of climate change, Aedes aegypti is projected to expand into unsuitable regions of the Thar desert, whereas Aedes albopictus is projected to expand to the upper and trans Himalaya regions of the north. Overall, the results provide a reliable assessment of vectors prevalence in most parts of the country that can be used to guide surveillance efforts, despite minor disagreements with dengue incidence in Rajasthan and the north east, possibly due to behavioral practices and sampling efforts. Plain Language Summary Climatic parameters derived from temperature and humidity affect the development and survival of mosquitoes that spread diseases. In the past decade, India has witnessed an alarming rise in dengue, a viral disease that spreads through the bite of the mosquitoes Aedes aegypti and Aedes albopictus. We used machine learning based modeling algorithm to predict the present and future abundance of these mosquitoes in India, based on biologically relevant climatic factors. The results project expansion of Aedes aegypti in the hot arid regions of the Thar Desert and Aedes albopictus in cold upper Himalayas as a result of future climatic changes. The results provide a useful guide for strengthening efforts for entomological and dengue surveillance.
Dengue fever is a mosquito-borne viral disease caused by the dengue virus of the Flaviviridae family and is responsible for colossal health and economic burden worldwide. This study aimed to investigate the effect of environmental, seasonal, and spatial variations on the spread of dengue fever in Sri Lanka. The study used secondary data of monthly dengue infection and the monthly average of environmental parameters of 26 Sri Lankan regions from January 2015 to December 2019. Besides the descriptive measurements, Kendall’s tau_b, Spearman’s rho, and Kruskal-Wallis H test have been performed as bivariate analyses. The multivariate generalized linear negative binomial regression model was applied to determine the impacts of meteorological factors on dengue transmission. The aggregate negative binomial regression model disclosed that precipitation (odds ratio: 0.97, p < 0.05), humidity (odds ratio: 1.05, p < 0.01), and air pressure (odds ratio: 1.46, p < 0.01) were significantly influenced the spread of dengue fever in Sri Lanka. The bioclimatic zone is the vital factor that substantially affects the dengue infection, and the wet zone (odds ratio: 6.41, p < 0.05) was more at-risk than the dry zone. The climate season significantly influenced dengue fever transmission, and a higher infection rate was found (odds ratio: 1.46, p < 0.01) in the northeast monsoon season. The findings of this study facilitate policymakers to improve the existing dengue control strategies focusing on the meteorological condition in the local as well as global perspectives.
BACKGROUND: Dengue, transmitted by Aedes mosquitoes, is a major public health problem in Sri Lanka. Weather affects the abundance, feeding patterns, and longevity of Aedes vectors and hence the risk of dengue transmission. We aimed to quantify the effect of weather variability on dengue vector indices in ten Medical Officer of Health (MOH) divisions in Kalutara, Sri Lanka. METHODS: Monthly weather variables (rainfall, temperature, and Oceanic Niño Index [ONI]) and Aedes larval indices in each division in Kalutara were obtained from 2010 to 2018. Using a distributed lag non-linear model and a two-stage hierarchical analysis, we estimated and compared division-level and overall relationships between weather and premise index, Breteau index, and container index. FINDINGS: From Jan 1, 2010, to Dec 31, 2018, three El Niño events (2010, 2015-16, and 2018) occurred. Increasing monthly cumulative rainfall higher than 200 mm at a lag of 0 months, mean temperatures higher than 31·5°C at a lag of 1-2 months, and El Niño conditions (ie, ONI >0·5) at a lag of 6 months were associated with an increased relative risk of premise index and Breteau index. Container index was found to be less sensitive to temperature and ONI, and rainfall. The associations of rainfall and temperature were rather homogeneous across divisions. INTERPRETATION: Both temperature and ONI have the potential to serve as predictors of vector activity at a lead time of 1-6 months, while the amount of rainfall could indicate the magnitude of vector prevalence in the same month. This information, along with knowledge of the distribution of breeding sites, is useful for spatial risk prediction and implementation of effective Aedes control interventions. FUNDING: None.
Dengue is endemic in Bangladesh and is an important cause of morbidity and mortality. Suppressing the mosquito vector activity at the optimal time annually is a practical strategy to control dengue outbreaks. The objective of this study was to estimate the monthly growth factor (GF) of dengue cases over the past 12 years as a means to identify the optimal time for a vector-control programme in Bangladesh. We reviewed the monthly cases reported by the Institute of Epidemiology, Disease Control and Research of Bangladesh during the period of January 2008-December 2019. We calculated the GF of dengue cases between successive months during this period and report means and 95% confidence intervals (CI). The median number of patients admitted to the hospital with dengue fever per year was 1554 (range: 375-101,354). The mean monthly GF of dengue cases was 1.2 (95% CI: 0.4-2.4). The monthly GF lower CI between April and July was > 1, whereas from September to November and January the upper CI was <1. The highest GF of dengue was recorded in June (mean: 2.4; 95% CI: 1.7-3.5) and lowest in October (mean: 0.43; 95% CI: 0.24-0.73). More than 81% (39/48) months between April and July for the period 2008-2019 had monthly GF >1 compared to 20% (19/96) months between August and March of the same period. The monthly GF was significantly correlated with monthly rainfall (r = 0.39) and monthly mean temperature (r = 0.30). The growth factor of the dengue cases over the last 12 years appeared to follow a marked periodicity linked to regional rainfall patterns. The increased transmission rate during the months of April-July, a seasonally determined peak suggests the need for strengthening a range of public health interventions, including targeted vector control efforts and community education campaigns.
Numerous studies on climate change and variability have revealed that these phenomena have noticeable influence on the epidemiology of dengue fever, and such relationships are complex due to the role of the vector—the Aedes mosquitoes. By undertaking a step-by-step approach, the present study examined the effects of climatic factors on vector abundance and subsequent effects on dengue cases of Dhaka city, Bangladesh. Here, we first analyzed the time-series of Stegomyia indices for Aedes mosquitoes in relation to temperature, rainfall and relative humidity for 2002–2013, and then in relation to reported dengue cases in Dhaka. These data were analyzed at three sequential stages using the generalized linear model (GLM) and generalized additive model (GAM). Results revealed strong evidence that an increase in Aedes abundance is associated with the rise in temperature, relative humidity, and rainfall during the monsoon months, that turns into subsequent increases in dengue incidence. Further we found that (i) the mean rainfall and the lag mean rainfall were significantly related to Container Index, and (ii) the Breteau Index was significantly related to the mean relative humidity and mean rainfall. The relationships of dengue cases with Stegomyia indices and with the mean relative humidity, and the lag mean rainfall were highly significant. In examining longitudinal (2001–2013) data, we found significant evidence of time lag between mean rainfall and dengue cases.
This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.
Meeting global and national malaria elimination targets requires identifying challenges as early as possible so that strategies can be modified to stay on track. This qualitative study of stakeholders who have a major influence on malaria programs across the Southeast Asian region, including those at a state level in India and at a national level in Cambodia, Myanmar, Thailand and Vietnam, shows that most believe Plasmodium falciparum malaria elimination targets are attainable, but are less optimistic for meeting Plasmodium vivax targets. Across these countries, stakeholders reported large variations in access to malaria diagnosis and treatment; the effectiveness of strategies for reaching migrants and hardto-serve populations; and securing sufficient numbers of skilled workers for both diagnosis and compliance with artemisinin-combination treatments and the need to optimise use of insecticides. Additionally, there was optimism about coordinated surveillance and response, but this was counterbalanced with a sense that national and regional collaboration opportunities have been missed. Climate change impacts were seen as a potential threat by all stakeholders in this study and in need of further research.
BACKGROUND: Climate and climate change affect the spatial pattern and seasonality of malaria risk. Season lengths and spatial extents of mapped current and future malaria transmission suitability predictions for Nepal were assessed for a combination of malaria vector and parasites: Anopheles stephensi and Plasmodium falciparum (ASPF) and An. stephensi and Plasmodium vivax (ASPV) and compared with observed estimates of malaria risk in Nepal. METHODS: Thermal bounds of malaria transmission suitability for baseline (1960-1990) and future climate projections (RCP 4.5 and RCP 8.5 in 2030 and 2050) were extracted from global climate models and mapped for Nepal. Season length and spatial extent of suitability between baseline and future climate scenarios for ASPF and ASPV were compared using the Warren’s I metric. Official 2010 DoHS risk districts (DRDs) and 2021 DoHS risk wards (DRWs), and spatiotemporal incidence trend clusters (ITCs) were overlaid on suitability season length and extent maps to assess agreement, and potential mismatches. RESULTS: Shifts in season length and extent of malaria transmission suitability in Nepal are anticipated under both RCP 4.5 and RCP 8.5 scenarios in 2030 and 2050, compared to baseline climate. The changes are broadly consistent across both future climate scenarios for ASPF and ASPV. There will be emergence of suitability and increasing length of season for both ASPF and ASPV and decreasing length of season for ASPV by 2050. The emergence of suitability will occur in low and no-risk DRDs and outside of high and moderate-risk DRWs, season length increase will occur across all DRD categories, and outside of high and moderate-risk DRWs. The high and moderate risk DRWs of 2021 fall into ITCs with decreasing trend. CONCLUSIONS: The study identified areas of Nepal where malaria transmission suitability will emerge, disappear, increase, and decrease in the future. However, most of these areas are anticipated outside of the government’s current and previously designated high and moderate-risk areas, and thus outside the focus of vector control interventions. Public health officials could use these anticipated changing areas of malaria risk to inform vector control interventions for eliminating malaria from the country, and to prevent malaria resurgence.
The common cold is a leading cause of morbidity and contributes significantly to the health costs in Bhutan. The study utilized multivariate Zero-inflated Poisson regression in a Bayesian framework to identify climatic variability and spatial and temporal patterns of the common cold in Bhutan. There were 2,480,509 notifications of common cold between 2010 and 2018. Children aged < 15 years were twice (95% credible interval [CrI] 2.2, 2.5) as likely to get common cold than adults, and males were 12.4% (95 CrI 5.5%, 18.7%) less likely to get common cold than females. A 10 mm increase in rainfall lagged one month, and each 1 °C increase of maximum temperature was associated with a 5.1% (95% CrI 4.2%, 6.1%) and 2.6% (95% CrI 2.3%, 2.8%) increase in the risk of cold respectively. An increase in elevation of 100 m and 1% increase in relative humidity lagged three months were associated with a decrease in risk of common cold by 0.1% (95% CrI 0.1%, 0.2%) and 0.3% (95% CrI 0.2%, 0.3%) respectively. Seasonality and spatial heterogeneity can partly be explained by the association of common cold to climatic variables. There was statistically significant residual clustering after accounting for covariates. The finding highlights the influence of climatic variables on common cold and suggests that prioritizing control strategies for acute respiratory infection program to subdistricts and times of the year when climatic variables are associated with common cold may be an effective strategy.
Maria made a landfall in Puerto Rico on September 20, 2017 as a category 4 hurricane, causing severe flooding, widespread electricity outages, damage to infrastructure, and interruptions in water and wastewater treatment. Small rural community water systems face unique challenges in providing drinking water, which intensify after natural disasters. The purpose of this study was to evaluate the functionality of six very small rural public water systems and one large regulated system in Puerto Rico six months after Maria and survey a broad sweep of fecal, zoonotic, and opportunistic pathogens from the source to tap. Samples were collected from surface and groundwater sources, after water treatment and after distribution to households. Genes indicative of pathogenic Leptospira spp. were detected by polymerase chain reaction (PCR) in all systems reliant on surface water sources. Salmonella spp. was detected in surface and groundwater sources and some distribution system water both by culture and PCR. Legionella spp. and Mycobacteria spp. gene numbers measured by quantitative PCR were similar to nonoutbreak conditions in the continental U.S. Amplicon sequencing provided a nontarget screen for other potential pathogens of concern. This study aids in improving future preparedness, assessment, and recovery operations for small rural water systems after natural disasters.
Hurricanes often flood homes and industries, spreading pathogens. Contact with pathogen-contaminated water can result in diarrhea, vomiting, and/or nausea, known collectively as acute gastrointestinal illness (AGI). Hurricanes Matthew and Florence caused record-breaking flooding in North Carolina (NC) in October 2016 and September 2018, respectively. To examine the relationship between hurricane flooding and AGI in NC, we first calculated the percent of each ZIP code flooded after Hurricanes Matthew and Florence. Rates of all-cause AGI emergency department (ED) visits were calculated from NC’s ED surveillance system data. Using controlled interrupted time series, we compared AGI ED visit rates during the three weeks after each hurricane in ZIP codes with a third or more of their area flooded to the predicted rates had these hurricanes not occurred, based on AGI 2016-2019 ED trends, and controlling for AGI ED visit rates in unflooded areas. We examined alternative case definitions (bacterial AGI) and effect measure modification by race and age. We observed an 11% increase (rate ratio (RR): 1.11, 95% CI: 1.00, 1.23) in AGI ED visit rates after Hurricanes Matthew and Florence. This effect was particularly strong among American Indian patients and patients aged 65 years and older after Florence and elevated among Black patients for both hurricanes. Florence’s effect was more consistent than Matthew’s effect, possibly because little rain preceded Florence and heavy rain preceded Matthew. When restricted to bacterial AGI, we found an 85% (RR: 1.85, 95% CI: 1.37, 2.34) increase in AGI ED visit rate after Florence, but no increase after Matthew. Hurricane flooding is associated with an increase in AGI ED visit rate, although the strength of effect may depend on total storm rainfall or antecedent rainfall. American Indians and Black people-historically pushed to less desirable, flood-prone land-may be at higher risk for AGI after storms.
BACKGROUND: While most prior research has focused on extreme heat, few assessed the immediate health effects of winter storms and associated power outages (PO), although severe storms have become more frequent. This study evaluates the joint and independent health effects of winter storms and PO, snow versus ice-storm, effects by time window (peak timing, winter/transitional months) and the impacts on critical care indicators including numbers of comorbidity, procedure, length of stay and cost. METHODS: We use distributed lag nonlinear models to assess the impacts of winter storm/PO on hospitalizations due to cardiovascular, lower respiratory diseases (LRD), respiratory infections, food/water-borne diseases (FWBD) and injuries in New York State on 0-6 lag days following storm/PO compared with non-storm/non-PO periods (references), while controlling for time-varying factors and PM(2.5). The storm-related hospitalizations are described by time window. We also calculate changes in critical care indicators between the storm/PO and control periods. RESULTS: We found the joint effects of storm/PO are the strongest (risk ratios (RR) range: 1.01-1.90), followed by that of storm alone (1.02-1.39), but not during PO alone. Ice storms have stronger impacts (RRs: 1.04-3.15) than snowstorms (RRs: 1.03-2.21). The storm/PO-health associations, which occur immediately, and some last a whole week, are stronger in FWBD, October/November, and peak between 3:00-8:00 p.m. Comorbidity and medical costs significantly increase after storm/PO. CONCLUSION: Winter storms increase multiple diseases, comorbidity and medical costs, especially when accompanied by PO or ice storms. Early warnings and prevention may be critical in the transitional months and afternoon rush hours.
BACKGROUND: Climate variability influences the population dynamics of the Aedes aegypti mosquito that transmits the viruses that cause dengue, chikungunya and Zika. In recent years these diseases have grown considerably. Dengue is now the fastest-growing mosquito-transmitted disease worldwide, putting 40 per cent of the global population at risk. With no effective antiviral treatments or vaccines widely available, controlling mosquito population remains one of the most effective ways to prevent epidemics. This paper analyses the temporal and spatial dynamics of dengue in Mexico during 2000-2020 and that of chikungunya and Zika since they first appeared in the country in 2014 and 2015, respectively. This study aims to evaluate how seasonal climatological variability affects the potential risk of transmission of these mosquito-borne diseases. Mexico is among the world’s most endemic countries in terms of dengue. Given its high incidence of other mosquito-borne diseases and its size and wide range of climates, it is a good case study. METHODS: We estimate the recently proposed mosquito-borne viral suitability index P, which measures the transmission potential of mosquito-borne pathogens. This index mathematically models how humidity, temperature and precipitation affect the number of new infections generated by a single infected adult female mosquito in a host population. We estimate this suitability index across all Mexico, at small-area level, on a daily basis during 2000-2020. RESULTS: We find that the index P predicted risk transmission is strongly correlated with the areas and seasons with a high incidence of dengue within the country. This correlation is also high enough for chikungunya and Zika in Mexico. We also show the index P is sensitive to seasonal climatological variability, including extreme weather shocks. CONCLUSIONS: The paper shows the dynamics of dengue, chikungunya and Zika in Mexico are strongly associated with seasonal climatological variability and the index P. This potential risk of transmission index, therefore, is a valuable tool for surveillance for mosquito-borne diseases, particularly in settings with varied climates and limited entomological capacity.
Aedes aegypti mosquitoes are the main vector of dengue viruses globally and are present throughout much of the state of Florida (FL) in the United States of America. However, local transmission of dengue viruses in FL has mainly occurred in the southernmost counties; specifically Monroe and Miami-Dade counties. To get a better understanding of the ecologic risk factors for dengue fever incidence throughout FL, we collected and analyzed numerous environmental factors that have previously been connected to local dengue cases in disease-endemic regions. We analyzed these factors for each county-year in FL, between 2009-2019, using negative binomial regression. Monthly minimum temperature of 17.5-20.8 °C, an average temperature of 26.1-26.7 °C, a maximum temperature of 33.6-34.7 °C, rainfall between 11.4-12.7 cm, and increasing numbers of imported dengue cases were associated with the highest risk of dengue incidence per county-year. To our knowledge, we have developed the first predictive model for dengue fever incidence in FL counties and our findings provide critical information about weather conditions that could increase the risk for dengue outbreaks as well as the important contribution of imported dengue cases to local establishment of the virus in Ae. aegypti populations.
Rodents can transmit infectious diseases directly to humans and other animals via bites and exposure to infectious salivary aerosols and excreta. Arthropods infected while blood-feeding on rodents can also transmit rodent-borne pathogens indirectly to humans and animals. Environmental events, such as wet winters, cooler summers, heavy rains, and flooding, have precipitated regional rodent-borne infectious disease outbreaks; these outbreaks are now increasing with climate change. The objectives of this review are to inform wilderness medicine providers about the environmental conditions that can precipitate rodent-borne infectious disease outbreaks; to describe the regional geographic distributions of rodent-borne infectious diseases in North America; and to recommend prophylactic treatments and effective prevention and control strategies for rodent-borne infectious diseases. To meet these objectives, Internet search engines were queried with keywords to identify scientific articles on outbreaks of the most common regional rodent-borne infectious diseases in North America. Wilderness medicine providers should maintain high levels of suspicion for regional rodent-borne diseases in patients who develop febrile illnesses after exposure to contaminated freshwater after heavy rains or floods and after swimming, rafting, or paddling in endemic areas. Public health education strategies should encourage limiting human contact with rodents; avoiding contact with or safely disposing of rodent excreta; avoiding contact with contaminated floodwaters, especially contact with open wounds; securely containing outdoor food stores; and modifying wilderness cabins and campsites to deter rodent colonization.
Amblyomma maculatum (Gulf Coast tick), and Dermacentor andersoni (Rocky Mountain wood tick) are two North American ticks that transmit spotted fevers associated Rickettsia. Amblyomma maculatum transmits Rickettsia parkeri and Francisella tularensis, while D. andersoni transmits R. rickettsii, Anaplasma marginale, Coltivirus (Colorado tick fever virus), and F. tularensis. Increases in temperature causes mild winters and more extreme dry periods during summers, which will affect tick populations in unknown ways. Here, we used ecological niche modeling (ENM) to assess the potential geographic distributions of these two medically important vector species in North America under current condition and then transfer those models to the future under different future climate scenarios with special interest in highlighting new potential expansion areas. Current model predictions for A. maculatum showed suitable areas across the southern and Midwest United States, and east coast, western and southern Mexico. For D. andersoni, our models showed broad suitable areas across northwestern United States. New potential for range expansions was anticipated for both tick species northward in response to climate change, extending across the Midwest and New England for A. maculatum, and still farther north into Canada for D. andersoni.
Aedes aegypti and Aedes albopictus are important pathogen-carrying vectors that broadly exhibit similar habitat suitability, but that differ at fine spatial scales in terms of competitive advantage and tolerance to urban driven environmental parameters. This study evaluated how spatial and temporal patterns drive the assemblages of these competing species in cemeteries of New Orleans, LA, applying indicators of climatic variability, vegetation, and heat that may drive habitat selection at multiple scales. We found that Ae. aegypti was well predicted by urban heat islands (UHI) at the cemetery scale and by canopy cover directly above the cemetery vase. As predicted, UHI positively correlate to Ae. aegypti, but contrary to predictions, Ae. aegypti, was more often found under the canopy of trees in high heat cemeteries. Ae. albopictus was most often found in low heat cemeteries, but this relationship was not statistically significant, and their overall abundances in the city were lower than Ae. aegypti. Culex quinquefasciatus, another important disease vector, was also an abundant mosquito species during the sampling year, but we found that it was temporally segregated from Aedes species, showing a negative association to the climatic variables of maximum and minimum temperature, and these factors positively correlated to its more direct competitor Ae. albopictus. These findings help us understand the mechanism by which these three important vectors segregate both spatially and temporally across the city. Our study found that UHI at the cemetery scale was highly predictive of Ae. aegypti and strongly correlated to income level, with low-income cemeteries having higher UHI levels. Therefore, the effect of excessive heat, and the proliferation of the highly competent mosquito vector, Ae. aegypti, may represent an unequal disease burden for low-income neighborhoods of New Orleans that should be explored further. Our study highlights the importance of considering socioeconomic aspects as indirectly shaping spatial segregation dynamics of urban mosquito species.
Ixodes cookei Packard, the groundhog tick or woodchuck tick, is the main known vector of Powassan virus (POWV) disease in North America and an ectoparasite that infests diverse small- and mid-size mammals for blood meals to complete its life stages. Since I. cookei spends much of its life cycle off the host and needs hosts for a blood meal in order to pass to the next life stage, it is susceptible to changes in environmental conditions. We used a maximum-entropy approach to ecological niche modeling that incorporates detailed model-selection routes to link occurrence data to climatic variables to assess the potential geographic distribution of I. cookei under current and likely future climate conditions. Our models identified suitable areas in the eastern United States, from Tennessee and North Carolina north to southern Canada, including Nova Scotia, New Brunswick, eastern Newfoundland and Labrador, southern Quebec, and Ontario; suitable areas were also in western states, including Washington and Oregon and restricted areas of northern Idaho, northwestern Montana, and adjacent British Columbia, in Canada. This study produces the first maps of the potential geographic distribution of I. cookei. Documented POWV cases overlapped with suitable areas in the northeastern states; however, the presence of this disease in areas classified by our models as not suitable by our models but with POWV cases (Minnesota and North Dakota) requires more study.
How weather affects tick development and behavior and human Lyme disease remains poorly understood. We evaluated relations of temperature and humidity during critical periods for the tick lifecycle with human Lyme disease. We used electronic health records from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014. Lyme disease cases (n = 9657) were frequency-matched (5:1) by year, age, and sex. Using daily weather data at ~4 km(2) resolution, we created cumulative metrics hypothesized to promote (warm and humid) or inhibit (hot and dry) tick development or host-seeking during nymph development (March 1-May 31), nymph activity (May 1-July 30), and prior year larva activity (Aug 1-Sept 30). We estimated odds ratios (ORs) of Lyme disease by quartiles of each weather variable, adjusting for demographic, clinical, and other weather variables. Exposure-response patterns were observed for higher cumulative same-year temperature, humidity, and hot and dry days (nymph-relevant), and prior year hot and dry days (larva-relevant), with same-year hot and dry days showing the strongest association (4th vs. 1st quartile OR = 0.40; 95% confidence interval [CI] = 0.36, 0.43). Changing temperature and humidity could increase or decrease human Lyme disease risk.
Biting insects have a long-standing reputation for being an extreme presence in the Arctic, but it is unclear how they are responding to the rapid environmental changes currently taking place in the region. We review recent advances in our understanding of climate change responses by several key groups of biting insects, including mosquitoes, blackflies, and warble/botflies, and we highlight the significant knowledge gaps on this topic. We also discuss how changes in biting insect populations could impact humans and wildlife, including disease transmission and the disruption of culturally and economically important activities. Future work should integrate scientific with local and traditional ecological knowledge to better understand global change responses by biting insects in the Arctic and the associated consequences for the environmental security of Arctic communities.
Climate change is already impacting the North American Great Lakes ecosystem and understanding the relationship between climate events and public health, such as waterborne acute gastrointestinal illnesses (AGIs), can help inform needed adaptive capacity for drinking water systems (DWSs). In this study, we assessed a harmonized binational dataset for the effects of extreme precipitation events (≥90th percentile) and preceding dry periods, source water turbidity, total coliforms, and protozoan AGIs – cryptosporidiosis and giardiasis – in the populations served by four DWSs that source surface water from Lake Ontario (Hamilton and Toronto, Ontario, Canada) and Lake Michigan (Green Bay and Milwaukee, Wisconsin, USA) from January 2009 through August 2014. We used distributed lag non-linear Poisson regression models adjusted for seasonality and found extreme precipitation weeks preceded by dry periods increased the relative risk of protozoan AGI after 1 and 3-5 weeks in three of the four cities, although only statistically significant in two. Our results suggest that the risk of protozoan AGI increases with extreme precipitation preceded by a dry period. As extreme precipitation patterns become more frequent with climate change, the ability to detect changes in water quality and effectively treat source water of varying quality is increasingly important for adaptive capacity and protection of public health.
With a focus on five sites in an impaired, densely populated area in the New Orleans area, we investigated the temporal and spatial variability of standard FIB and a marker of human-associated pollution (Bacteroides HF183). With all sites combined, only a weak positive correlation (r = 0.345; p = 0.001) was observed between E. coli and HF183. Also, specific conductivity (r = - 0.374; p < 0.0001) and dissolved oxygen (r = - 0.390; p < 0.0001) were observed to show a weak moderate correlation with E. coli. These correlations increased to moderately negative when HF183 was correlated with specific conductivity (r = - 0.448; p < 0.0001) and dissolved oxygen (r = - 0.455; p < 0.0001). E. coli contamination was generally highest at the sites in the canal that are situated in the most densely populated part of the watershed while HF183 was frequently detected across all sites. E. coli concentrations were significantly higher (p < 0.05) when HF183 was present. HF183 was detected at significantly higher concentrations in samples that exceeded the EPA water quality standard (WQS) than those that did not (p < 0.05). Dissolved oxygen and specific conductivity were significantly lower when E. coli WQS was exceeded or when HF183 was present (p < 0.05). Rainfall impacted E. coli concentrations and HF183 differently at the study sites. While HF183 and E. coli concentrations levels were significantly higher (p < 0.05) if the days prior to sampling had been wet, the frequency of detection of HF183 was unimpacted, as comparable detection rates were recorded during wet and dry weather conditions. Without testing for HF183, it would have been assumed, based on testing for E. coli alone, that human fecal pollution was only associated with densely populated areas and rainfall events. E. coli alone may not be an effective indicator of sewage pollution at the study sites across all weather conditions and may need to be complemented with HF183 enumeration to optimize human fecal pollution identification and management at the watershed level.
Despite the widely acknowledged public health impacts of surface water fecal contamination, there is limited understanding of seasonal effects on (i) fate and transport processes and (ii) the mechanisms by which they contribute to water quality impairment. Quantifying relationships between land use, chemical parameters, and fecal bacterial concentrations in watersheds can help guide the monitoring and control of microbial water quality and explain seasonal differences. The goals of this study were to (i) identify seasonal differences in Escherichia coli and Bacteroides thetaiotaomicron concentrations, (ii) evaluate environmental drivers influencing microbial contamination during baseflow, snowmelt, and summer rain seasons, and (iii) relate seasonal changes in B. thetaiotaomicron to anticipated gastrointestinal infection risks. Water chemistry data collected during three hydroclimatic seasons from 64 Michigan watersheds were analyzed using seasonal linear regression models with candidate variables including crop and land use proportions, prior precipitation, chemical parameters, and variables related to both wastewater treatment and septic usage. Adaptive least absolute shrinkage and selection operator (LASSO) linear regression with bootstrapping was used to select explanatory variables and estimate coefficients. Regardless of season, wastewater treatment plant and septic system usage were consistently selected in all primary models for B. thetaiotaomicron and E. coli. Chemistry and precipitation-related variable selection depended upon season and organism. These results suggest a link between human pollution (e.g., septic systems) and microbial water quality that is dependent on flow regime. IMPORTANCE In this study, a data set of 64 Michigan watersheds was utilized to gain insights into fecal contamination sources, drivers, and chemical correlates across seasons for general E. coli and human-specific fecal indicators. Results reaffirmed a link between human-specific sources (e.g., septic systems) and microbial water quality. While the importance of human sources of fecal contamination and fate and transport variables (e.g., precipitation) remain important across seasons, this study provides evidence that fate and transport mechanisms vary with seasonal hydrologic condition and microorganism source. This study contributes to a body of research that informs prioritization of fecal contamination source control and surveillance strategy development to reduce the public health burden of surface water fecal contamination.
Vibrio vulnificus is an opportunistic pathogen indigenous to estuarine and marine environments and associated with aquatic organisms. Vibrio vulnificus is of utmost importance because it causes 95% of the seafood-related deaths in the United States due to rapid progression of septicemia. Changes in environmental parameters associated with climate change and coastal population expansion are altering geographical constraints, resulting in increased Vibrio spread, exposure, and rates of infection. In addition, coastal population expansion is resulting in increased input of treated municipal sewage into areas that are also experiencing increased Vibrio proliferation. This study aimed to better understand the influence of treated sewage effluent on effluent-receiving microbial communities using Vibrio as a model of an opportunistic pathogen. Integrated transcriptomic approaches were used to analyze the changes in overall gene expression of V. vulnificus NBRC 15645 exposed to wastewater treatment plant (WWTP) effluent for a period of 6h using a modified seawater yeast extract media that contained 0, 50, and 100% filtered WWTP effluent. RNA-seq reads were mapped, annotated, and analyzed to identify differentially expressed genes using the Pathosystems Resource Integration Center analysis tool. The study revealed that V. vulnificus responds to wastewater effluent exposure by activating cyclic-di-GMP-influenced biofilm development. Also, genes involved in crucial functions, such as nitrogen metabolism and bacterial attachment, were upregulated depending on the presence of treated municipal sewage. This altered gene expression increased V. vulnificus growth and proliferation and enhanced genes and pathways involved in bacterial survival during the early stages of infection in a host. These factors represent a potential public health risk due to exposure to environmental reservoirs of potentially Vibrio strains with enhanced virulence profiles in coastal areas.
Dynamic recharge events related to extreme rainfall or snowmelt are becoming more common due to climate change. The vulnerability of public supply wells to water quality degradation may temporarily increase during these types of events. The Walkerton, ON, Canada, tragedy (2000) highlighted the threat to human health associated with the rapid transport of microbial pathogens to public supply wells during dynamic recharge events. Field research at the Thornton (Woodstock, ON, Canada) and Mannheim West (Kitchener, ON, Canada) well fields, situated in glacial overburden aquifers, identified a potential increase in vulnerability due to event-based recharge phenomena. Ephemeral surface water flow and local ponding containing microbial pathogen indicator species were observed and monitored within the capture zones of public supply wells following heavy rain and/or snowmelt. Elevated recharge rates beneath these temporary surface water features were estimated to range between 40 and 710 mm over two-week periods using analytical and numerical modelling based on the water level, soil moisture, and temperature data. Modelling also suggested that such events could reduce contaminant travel times to a supply well, increasing vulnerability to water quality degradation. These studies suggest that event-based recharge processes occurring close to public supply wells may enhance the vulnerability of the wells to surface-sourced contaminants.
Urban floods can be contaminated with fecal material and pathogens. Evidence on infection risks associated with exposure to waterborne pathogens in urban floods is lacking. We address this gap by assessing the risk of infection from exposure to Giardia lamblia in urban flood water samples in Mexico City using a QMRA. Historical flood data was used to build severity indices and to test for correlations with risk of infection estimates. Results indicate similar maximal pathogen densities in urban flood water samples to those from wastewater treatment plants. Significant positive correlations between risk of G. lamblia infection and severity indices suggest that floods could act as an important source of pathogen transmission in Mexico City. Risk of infection to G. lamblia is greater in the city’s periphery, which is characterized by high marginalization levels. We argue that these risks should be managed by engaging citizens, water, and health authorities in decision making.
Low elevation coastal zones (LECZ) are extensive throughout the southeastern United States. LECZ communities are threatened by inundation from sea level rise, storm surge, wetland degradation, land subsidence, and hydrological flooding. Communication among scientists, stakeholders, policy makers and minority and poor residents must improve. We must predict processes spanning the ecological, physical, social, and health sciences. Communities need to address linkages of (1) human and socioeconomic vulnerabilities; (2) public health and safety; (3) economic concerns; (4) land loss; (5) wetland threats; and (6) coastal inundation. Essential capabilities must include a network to assemble and distribute data and model code to assess risk and its causes, support adaptive management, and improve the resiliency of communities. Better communication of information and understanding among residents and officials is essential. Here we review recent background literature on these matters and offer recommendations for integrating natural and social sciences. We advocate for a cyber-network of scientists, modelers, engineers, educators, and stakeholders from academia, federal state and local agencies, non-governmental organizations, residents, and the private sector. Our vision is to enhance future resilience of LECZ communities by offering approaches to mitigate hazards to human health, safety and welfare and reduce impacts to coastal residents and industries.
Sea level rise has increased the frequency of tidal flooding even without accompanying precipitation in many coastal areas worldwide. As the tide rises, inundates the landscape, and then recedes, it can transport organic and inorganic matter between terrestrial systems and adjacent aquatic environments. However, the chemical and biological effects of tidal flooding on urban estuarine systems remain poorly constrained. Here, we provide the first extensive quantification of floodwater nutrient concentrations during a tidal flooding event and estimate the nitrogen (N) loading to the Lafayette River, an urban tidal sub-tributary of the lower Chesapeake Bay (USA). To enable the scale of synoptic sampling necessary to accomplish this, we trained citizen-scientist volunteers to collect 190 flood water samples during a perigean spring tide to measure total dissolved N (TDN), dissolved inorganic N (DIN) and phosphate concentrations, and Enterococcus abundance from the retreating ebb tide while using a phone application to measure the extent of tidal inundation. Almost 95% of Enterococcus results had concentrations that exceeded the standard established for recreational waters (104 MPN 100 mL(-1)). Floodwater dissolved nutrient concentrations were higher than concentrations measured in natural estuarine waters, suggesting floodwater as a source of dissolved nutrients to the estuary. However, only DIN concentrations were statistically higher in floodwater samples than in the estuary. Using a hydrodynamic model to calculate the volume of water inundating the landscape, and the differences between the median DIN concentrations in floodwaters and the estuary, we estimate that 1,145 kg of DIN entered the Lafayette River during this single, blue sky, tidal flooding event. This amount exceeds the annual N load allocation for overland flow established by federal regulations for this segment of the Chesapeake Bay by 30%. Because tidal flooding is projected to increase in the future as sea levels continue to rise, it is crucial we quantify nutrient loading from tidal flooding in order to set realistic water quality restoration targets for tidally influenced water bodies.
Urban floodwater could lead to significant risk for public and environmental health from mobilization of microbial pathogens and overflow of wastewater treatment systems. Here, we attempted to assess this risk by obtaining metagenomic profiles of antibiotic resistance genes (ARGs), virulence factors (VFs) and pathogens present in floodwater samples collected in urban Atlanta, GA that were categorized in two distinct groups: floods that occurred after periods of drought and those after regular (seasonal) rain events. Even though no major (known) pathogens were present at the limit of detection of our sequencing effort (~3 Gbp/sample), we observed that floodwaters after drought showed a 2.5-fold higher abundance of both ARGs and VFs compared to floodwater after rainy days. These differences were mainly derived by several novel species of the Pseudomonas genus, which were more dominant in the former versus the latter samples and carried several genes to cope with osmotic stress in addition to ARGs and VFs. These results revealed that there are previously undescribed species that become mobilized after flooding events in the Southeast US urban settings and could represent an increased public health risk, especially after periods of drought, which warrants further attention.
Wastewater treatment plants (WWTPs) are among the most important infrastructures, especially in coastal cities with a risk of flooding. During intense floods, runoff volume may exceed the capacity of a WWTP causing plant failures. This paper investigates the impacts of flooding on combined sewer overflows (CSOs) in a WWTP in New York City. The impacts of CSOs after flooding are classified into four terms of health, economic, social, and environmental factors. Different factors are defined to evaluate impacts of CSOs using multi-criteria decision-making of Preference Ranking Organization Method For Enrichment Evaluation and fuzzy technique for order performance by similarity to ideal solution. Since volume and depth were found the most significant factors for the CSO impact assessment, the Gridded Surface Subsurface Hydrologic Analysis model was run to compute flood depth and CSO volume under three treatment plant failure scenarios considering the hurricane Sandy information. Sensitivity analysis revealed that the TSS, BOD, and dissolved oxygen have the highest impacts on CSO. Uncertainty analysis was applied to investigate CSO impact variation. Results show that evaluating the impacts of CSOs in different aspects can give a good idea for flood planning and management with higher efficiency during storms.
Hurricanes and associated stormwater runoff events are expected to greatly impact coastal marine water quality, yet little is known about their immediate effects on microbiological quality of near-shore water. This study sampled Hilo Bay immediately after the impact of Hurricane Lane to understand the spatial and temporal variations of the abundance and diversity of fecal indicator enterococci, common fecal pathogens, and antibiotic resistance genes (ARGs). Water samples from seven sampling sites over 7 days were collected and analyzed, which showed that the overall microbiological water quality parameters [enterococci geometric mean (GM): 6-22 cfu/100 mL] fell within water quality standards and that the temporal dynamics indicated continuing water quality recovery. However, considerable spatial variation was observed, with the most contaminated site exhibiting impaired water quality (GM = 144 cfu/100 mL). The Enterococcus population also showed distinct genotypic composition at the most contaminated site. Although marker genes for typical fecal pathogens (invA for Salmonella, hipO for Campylobacter, mip for Legionella pneumophila, and eaeA for enteropathogenic Escherichia coli) were not detected, various ARGs (ermB, qurS, tetM, blaTEM, and sul1) and integron-associated integrase intI1 were detected at high levels. Understanding the temporal and spatial variation of microbiological water quality at fine granularity is important for balancing economic and recreational uses of coastal water and the protection of public health post the impact of major hurricane events.
Densely populated, low-lying coastal areas are most at-risk for negative impacts from increasing intensity of storm-induced flooding. Due to the effects of global warming and subsequent climate change, coastal temperatures and the magnitude of storm-induced flooding are projected to increase, creating a hospitable environment for the aquatic Vibrio spp. bacteria. A relative risk model analysis was used to determine which census block groups in coastal South Carolina have the highest risk of Vibrio spp. exposure using storm surge flooding as a proxy. Coastal block groups with dense vulnerable sub-populations exposed to storm surge have the highest relative risk, while inland block groups away from riverine-mediated storm surge have the lowest relative risk. As Vibriosis infections may be extremely severe or even deadly, the best methods of infection control will be regular standardized coastal and estuarine water monitoring for Vibrio spp. to enable more informed and timely public health advisories and help prevent future exposure.
Everglades virus (EVEV) is a subtype (II) of Venezuelan equine encephalitis virus (VEEV), endemic in southern Florida, USA. EVEV has caused clinical encephalitis in humans, and antibodies have been found in a variety of wild and domesticated mammals. Over 29,000 Culex cedecei females, the main vector of EVEV, were collected in 2017 from Big Cypress and Fakahatchee Strand Preserves in Florida and pool-screened for the presence of EVEV using reverse transcription real-time polymerase chain reaction. The entire 1 E1 protein gene was successfully sequenced from fifteen positive pools. Phylogenetic analysis showed that isolates clustered, based on the location of sampling, into two monophyletic clades that diverged in 2009. Structural analyses revealed two mutations of interest, A116V and H441R, which were shared among all isolates obtained after its first isolation of EVEV in 1963, possibly reflecting adaptation to a new host. Alterations of the Everglades ecosystem may have contributed to the evolution of EVEV and its geographic compartmentalization. This is the first report that shows in detail the evolution of EVEV in South Florida. This zoonotic pathogen warrants inclusion into routine surveillance given the high natural infection rate in the vectors. Invasive species, increasing urbanization, the Everglades restoration, and modifications to the ecosystem due to climate change and habitat fragmentation in South Florida may increase rates of EVEV spillover to the human population.
Temperature plays a significant role in the vector competence, extrinsic incubation period, and intensity of infection of arboviruses within mosquito vectors. Most laboratory infection studies use static incubation temperatures that may not accurately reflect daily temperature ranges (DTR) to which mosquitoes are exposed. This could potentially compromise the application of results to real world scenarios. We evaluated the effect of fluctuating DTR versus static temperature treatments on the infection, dissemination, and transmission rates and viral titers of Culex tarsalis and Culex quinquefasciatus mosquitoes for West Nile virus. Two DTR regimens were tested including an 11 and 15 °C range, both fluctuating around an average temperature of 28 ??C. Overall, no significant differences were found between DTR and static treatments for infection, dissemination, or transmission rates for either species. However, significant treatment differences were identified for both Cx. tarsalis and Cx. quinquefasciatus viral titers. These effects were species-specific and most prominent later in the infection. These results indicate that future studies on WNV infections in Culex mosquitoes should consider employing realistic DTRs to reflect interactions most accurately between the virus, vector, and environment.
Leishmaniasis, a chronic and persistent intracellular protozoal infection caused by many different species within the genus Leishmania, is an unfamiliar disease to most North American providers. Clinical presentations may include asymptomatic and symptomatic visceral leishmaniasis (so-called Kala-azar), as well as cutaneous or mucosal disease. Although cutaneous leishmaniasis (caused by Leishmania mexicana in the United States) is endemic in some southwest states, other causes for concern include reactivation of imported visceral leishmaniasis remotely in time from the initial infection, and the possible long-term complications of chronic inflammation from asymptomatic infection. Climate change, the identification of competent vectors and reservoirs, a highly mobile populace, significant population groups with proven exposure history, HIV, and widespread use of immunosuppressive medications and organ transplant all create the potential for increased frequency of leishmaniasis in the U.S. Together, these factors could contribute to leishmaniasis emerging as a health threat in the U.S., including the possibility of sustained autochthonous spread of newly introduced visceral disease. We summarize recent data examining the epidemiology and major risk factors for acquisition of cutaneous and visceral leishmaniasis, with a special focus on implications for the United States, as well as discuss key emerging issues affecting the management of visceral leishmaniasis.
Seasonal variations in environmental conditions lead to changing infectious disease epidemic risks at different times of year. The probability that early cases initiate a major epidemic depends on the season in which the pathogen enters the population. The instantaneous epidemic risk (IER) can be tracked. This quantity is straightforward to calculate, and corresponds to the probability of a major epidemic starting from a single case introduced at time t=t(0), assuming that environmental conditions remain identical from that time onwards (i.e. for all t≥t(0)). However, the threat when a pathogen enters the population in fact depends on changes in environmental conditions occurring within the timescale of the initial phase of the outbreak. For that reason, we compare the IER with a different metric: the case epidemic risk (CER). The CER corresponds to the probability of a major epidemic starting from a single case entering the population at time t=t(0), accounting for changes in environmental conditions after that time. We show how the IER and CER can be calculated using different epidemiological models (the stochastic Susceptible-Infectious-Removed model and a stochastic host-vector model that is parameterised using temperature data for Miami) in which transmission parameter values vary temporally. While the IER is always easy to calculate numerically, the adaptable method we provide for calculating the CER for the host-vector model can also be applied easily and solved using widely available software tools. In line with previous research, we demonstrate that, if a pathogen is likely to either invade the population or fade out on a fast timescale compared to changes in environmental conditions, the IER closely matches the CER. However, if this is not the case, the IER and the CER can be significantly different, and so the CER should be used. This demonstrates the need to consider future changes in environmental conditions carefully when assessing the risk posed by emerging pathogens.
This case study examined current trends in the prevalence of vector-borne diseases and the impact of climate change on disease distribution. Our findings indicate that the dynamics of the Anopheles mosquito population in particular has changed dramatically in the past decade and now poses an increasing threat to human populations previously at low risk for malaria transmission. Given their geographic location and propensity for sustaining vector-borne disease outbreaks, southeastern states are particularly vulnerable to climate-induced changes in vector populations. We demonstrate the need to strengthen our hospital and laboratory infrastructure prior to further increases in the incidence of vector-borne diseases by discussing a case of uncomplicated malaria in a patient who arrived in one of our hospitals in Louisiana. This case exemplifies a delay in diagnosis and obtaining appropriate treatment in a timely manner, which suggests that our current health care infrastructure, especially in areas heavily affected by climate change, may not be adequately prepared to protect patients from vector-borne diseases. We conclude our discussion by examining current laboratory protocols in place with suggestions for future actions to combat this increasing threat to public health in the United States.
The Greater Everglades Region of South Florida is one of the largest natural wetlands and the only subtropical ecosystem found in the continental United States. Mosquitoes are seasonally abundant in the Everglades where several potentially pathogenic mosquito-borne arboviruses are maintained in natural transmission cycles involving vector-competent mosquitoes and reservoir-competent vertebrate hosts. The fragile nature of this ecosystem is vulnerable to many sources of environmental change, including a wetlands restoration project, climate change, invasive species and residential development. In this study, we obtained baseline data on the distribution and abundance of both mosquitos and arboviruses occurring in the southern Everglades region during the summer months of 2013, when water levels were high, and in 2014, when water levels were low. A total of 367,060 mosquitoes were collected with CO2-baited CDC light traps at 105 collection sites stratified among the major landscape features found in Everglades National Park, Big Cypress National Preserve, Fakahatchee State Park Preserve and Picayune State Forest, an area already undergoing restoration. A total of 2,010 pools of taxonomically identified mosquitoes were cultured for arbovirus isolation and identification. Seven vertebrate arboviruses were isolated: Everglades virus, Tensaw virus, Shark River virus, Gumbo Limbo virus, Mahogany Hammock virus, Keystone virus, and St. Louis encephalitis virus. Except for Tensaw virus, which was absent in 2013, the remaining viruses were found to be most prevalent in hardwood hammocks and in Fakahatchee, less prevalent in mangroves and pinelands, and absent in cypress and sawgrass. In contrast, in the summer of 2014 when water levels were lower, these arboviruses were far less prevalent and only found in hardwood hammocks, but Tensaw virus was present in cypress, sawgrass, pinelands, and a recently burned site. Major environmental changes are anticipated in the Everglades, many of which will result in increased water levels. How these might lead to the emergence of arboviruses potentially pathogenic to both humans and wildlife is discussed.
From 2016 to 2018, Hidalgo County observed the emergence of Zika virus (ZIKV) infections along with sporadic cases of Dengue virus (DENV) and West Nile virus (WNV). Due to the emergence of ZIKV and the historical presence of other mosquito-borne illnesses, Hidalgo County obtained funding to enhance mosquito surveillance and educate residents on arboviruses and travel risks. During this time period, Hidalgo County mosquito surveillance efforts increased by 1.275%. This increase resulted in >8000 mosquitoes collected, and 28 mosquito species identified. Aedes aegypti, Ae albopictus and Culex quinquefasciatus made up approximately two-thirds of the mosquitoes collected in 2018 (4122/6171). Spatiotemporal shifts in vector species composition were observed as the collection period progressed. Significantly, temperature variations (p < 0.05) accounted for associated variations in vector abundance, whereas all other climate variables were not significant.
BACKGROUND: West Nile virus (WNV), a global arbovirus, is the most prevalent mosquito-transmitted infection in the United States. Forecasts of WNV risk during the upcoming transmission season could provide the basis for targeted mosquito control and disease prevention efforts. We developed the Arbovirus Mapping and Prediction (ArboMAP) WNV forecasting system and used it in South Dakota from 2016 to 2019. This study reports a post hoc forecast validation and model comparison. OBJECTIVES: Our objective was to validate historical predictions of WNV cases with independent data that were not used for model calibration. We tested the hypothesis that predictive models based on mosquito surveillance data combined with meteorological variables were more accurate than models based on mosquito or meteorological data alone. METHODS: The ArboMAP system incorporated models that predicted the weekly probability of observing one or more human WNV cases in each county. We compared alternative models with different predictors including a) a baseline model based only on historical WNV cases, b) mosquito models based on seasonal patterns of infection rates, c) environmental models based on lagged meteorological variables, including temperature and vapor pressure deficit, d) combined models with mosquito infection rates and lagged meteorological variables, and e) ensembles of two or more combined models. During the WNV season, models were calibrated using data from previous years and weekly predictions were made using data from the current year. Forecasts were compared with observed cases to calculate the area under the receiver operating characteristic curve (AUC) and other metrics of spatial and temporal prediction error. RESULTS: Mosquito and environmental models outperformed the baseline model that included county-level averages and seasonal trends of WNV cases. Combined models were more accurate than models based only on meteorological or mosquito infection variables. The most accurate model was a simple ensemble mean of the two best combined models. Forecast accuracy increased rapidly from early June through early July and was stable thereafter, with a maximum AUC of 0.85. The model predictions captured the seasonal pattern of WNV as well as year-to-year variation in case numbers and the geographic pattern of cases. DISCUSSION: The predictions reached maximum accuracy early enough in the WNV season to allow public health responses before the peak of human cases in August. This early warning is necessary because other indicators of WNV risk, including early reports of human cases and mosquito abundance, are poor predictors of case numbers later in the season. https://doi.org/10.1289/EHP10287.
Eastern equine encephalitis (EEE) is a rare but lethal mosquito-borne zoonotic disease. Recent years have seen incursion into new areas of the USA, and in 2019 the highest number of human cases in decades. Due to the low detection rate of EEE, previous studies were unable to quantify large-scale and recent EEE ecological dynamics. We used Bayesian spatial generalized-linear mixed model to quantify the spatiotemporal dynamics of human EEE incidence in the northeastern USA. In addition, we assessed whether equine EEE incidence has predictive power for human cases, independently from other environmental variables. The predictors of the model were selected based on variable importance. Human incidence increased with temperature seasonality, but decreased with summer temperature, summer, fall, and winter precipitation. We also found EEE transmission in equines strongly associated with human infection (OR: 1.57; 95% CI: 1.52-1.60) and latitudes above 41.9 °N after 2018. The study designed for sparse dataset described new and known relationships between human and animal EEE and environmental factors, including geographical directionality. Future models must include equine cases as a risk factor when predicting human EEE risks. Future work is still necessary to ascertain the establishment of EEE in northern latitudes and the robustness of the available data.
Human granulocytic anaplasmosis (HGA) and human babesiosis are tick-borne diseases spread by the blacklegged tick (Ixodes scapularis Say, Acari: Ixodidae) and are the result of infection with Anaplasma phagocytophilum and Babesia microti, respectively. In New York State (NYS), incidence rates of these diseases increased concordantly until around 2013, when rates of HGA began to increase more rapidly than human babesiosis, and the spatial extent of the diseases diverged. Surveillance data of tick-borne pathogens (2007 to 2018) and reported human cases of HGA (n = 4,297) and human babesiosis (n = 2,986) (2013-2018) from the New York State Department of Health (NYSDOH) showed a positive association between the presence/temporal emergence of each pathogen and rates of disease in surrounding areas. Incidence rates of HGA were higher than human babesiosis among White and non-Hispanic/non-Latino individuals, as well as all age and sex groups. Human babesiosis exhibited higher rates among non-White individuals. Climate, weather, and landscape data were used to build a spatially weighted zero-inflated negative binomial (ZINB) model to examine and compare associations between the environment and rates of HGA and human babesiosis. HGA and human babesiosis ZINB models indicated similar associations with forest cover, forest land cover change, and winter minimum temperature; and differing associations with elevation, urban land cover change, and winter precipitation. These results indicate that tick-borne disease ecology varies between pathogens spread by I. scapularis.
Odocoileus virginianus (white-tailed deer) is the primary host of adult Ixodes scapularis (deer tick). Most of the research into I. scapularis has been geographically restricted to the northeastern United States, with limited interest in Oklahoma until recently as the I. scapularis populations spread due to climate change. Ticks serve as a vector for pathogenic bacteria, protozoans, and viruses that pose a significant human health risk. To date, there has been limited research to determine what potential tick-borne pathogens are present in I. scapularis in central Oklahoma. Using a one-step multiplex real-time reverse transcription-PCR, I. scapularis collected from white-tailed deer was screened for Anaplasma phagocytophilum, Borrelia burgdorferi, Borrelia miyamotoi, Babesia microti, and deer tick virus (DTV). Ticks (n = 394) were pooled by gender and life stage into 117 samples. Three pooled samples were positive for B. miyamotoi and five pooled samples were positive for DTV. This represents a minimum infection rate of 0.8% and 1.2%, respectively. A. phagocytophilum, B. burgdorferi, and B. microti were not detected in any samples. This is the first report of B. miyamotoi and DTV detection in Oklahoma I. scapularis ticks. This demonstrates that I. scapularis pathogens are present in Oklahoma and that further surveillance of I. scapularis is warranted.
Rhipicephalus sanguineus is a species complex of ticks that vector disease worldwide. Feeding primarily on dogs, members of the complex also feed incidentally on humans, potentially transmitting disease agents such as Rickettsia rickettsii, R. conorii, and Ehrlichia species. There are two genetic Rh. sanguineus lineages in North America, designated as the temperate and tropical lineages, which had occurred in discrete locations, although there is now range overlap in parts of California and Arizona. Rh. sanguineus in Europe are reportedly more aggressive toward humans during hot weather, increasing the risk of pathogen transmission to humans. The aim of this study was to assess the impact of hot weather on choice between humans and dog hosts among tropical and temperate lineage Rh. sanguineus individuals. Ticks in a two-choice olfactometer migrated toward a dog or human in trials at room (23.5°C) or high temperature (38°C). At 38°C, 2.5 times more tropical lineage adults chose humans compared with room temperature, whereas temperate lineage adults demonstrated a 66% reduction in preference for dogs and a slight increase in preference for humans. Fewer nymphs chose either host at 38°C than at room temperature in both lineages. These results demonstrate that risk of disease transmission to humans may be increased during periods of hot weather, where either lineage is present, and that hot weather events associated with climatic change may result in more frequent rickettsial disease outbreaks.
The range of ticks in North America has been steadily increasing likely, in part, due to climate change. Along with it, there has been a rise in cases of tick-borne disease. Among those medically important tick species of particular concern are Ixodes scapularis Say (Acari: Ixodidae), Dermacentor variabilis Say (Acari: Ixodidae), and Amblyomma americanum Linneaus (Acari: Ixodidae). The aim of this study was to determine if climate factors explain existing differences in abundance of the three aforementioned tick species between two climatically different regions of Illinois (Central and Southern), and if climate variables impact each species differently. We used both zero-inflated regression approaches and Bayesian network analyses to assess relationships among environmental variables and tick abundance. Results suggested that the maximum average temperature and total precipitation are associated with differential impact on species abundance and that this difference varied by region. Results also reinforced a differential level of resistance to desiccation among these tick species. Our findings help to further define risk periods of tick exposure for the general public, and reinforce the importance of responding to each tick species differently.
In recent decades, Lyme disease has been expanding to previous nonendemic areas. We hypothesized that infected I. scapularis nymphs that retain host-seeking behavior under optimal environmental conditions are fit to fulfil their transmission role in the enzootic cycle of B. burgdorferi. We produced nymphal ticks in the laboratory under controlled temperature (22-25°C), humidity (80-90%), and natural daylight cycle conditions to allow them to retain host-seeking/questing behavior for 1 year. We then analyzed differences in B. burgdorferi infection prevalence in questing and diapause nymphs at 6 weeks postmolting (prime questing) as well as differences in infection prevalence of questing nymphs maintained under prolonged environmental induced questing over 12 months (prolonged questing). Lastly, we analyzed the fitness of nymphal ticks subjected to prolonged questing in transmission of B. burgdorferi to naive mice over the course of the year. B. burgdorferi infected unfed I. scapularis nymphal ticks maintained under optimal environmental conditions in the laboratory not only survived for a year in a developmental state of prolonged questing (host-seeking), but they retained an infection prevalence sufficient to effectively fulfil transmission of B. burgdorferi to uninfected mice after tick challenge. Our study is important for understanding and modeling Lyme disease expansion into former nonendemic regions due to climate change. IMPORTANCE Lyme disease is rapidly spreading from its usual endemic areas in the Northeast, Midwest, and Midatlantic states into neighboring areas, which could be due to changing climate patterns. Our study shows that unfed I. scapularis nymphal ticks kept under optimal environmental conditions in the laboratory survived for a year while exhibiting aggressive host-seeking behavior, and they maintained a B. burgdorferi infection prevalence which was sufficient to infect naive reservoir hosts after tick challenge. Our study raises important questions regarding prolonged survival of B. burgdorferi infected host-seeking nymphal I. scapularis ticks that can potentially increase the risk of Lyme disease incidence, if conditions of temperature and humidity become amenable to the enzootic cycle of B. burgdorferi in regions currently classified as nonendemic.
In the western United States, Ixodes pacificus Cooley & Kohls (Acari: Ixodidae) is the primary vector of the agents causing Lyme disease and granulocytic anaplasmosis in humans. The geographic distribution of the tick is associated with climatic variables that include temperature, precipitation, and humidity, and biotic factors such as the spatial distribution of its primary vertebrate hosts. Here, we explore (1) how climate change may alter the geographic distribution of I. pacificus in California, USA, during the 21(st) century, and (2) the spatial overlap among predicted changes in tick habitat suitability, land access, and ownership. Maps of potential future suitability for I. pacificus were generated by applying climate-based species distribution models to a multi-model ensemble of climate change projections for the Representative Concentration Pathway (RCP) 4.5 (moderate emission) and 8.5 (high emission) scenarios for two future periods: mid-century (2026-2045) and end-of-century (2086-2099). Areas climatically-suitable for I. pacificus are projected to expand by 23% (mid-century RCP 4.5) to 86% (end-of-century RCP 8.5) across California, compared to the historical period (1980-2014), with future estimates of total suitable land area ranging from about 88 to 133 thousand km(2), or up to about a third of California. Regions projected to have the largest area increases in suitability by end-of-century are in northwestern California and the south central and southern coastal ranges. Over a third of the future suitable habitat is on lands currently designated as open access (i.e. publicly available), and by 2100, the amount of these lands that are suitable habitat for I. pacificus is projected to more than double under the most extreme emissions scenario (from ~23,000 to >51,000 km(2)). Of this area, most is federally-owned (>45,000 km(2)). By the end of the century, 26% of all federal land in the state is predicted to be suitable habitat for I. pacificus. The resulting maps may facilitate regional planning and preparedness by informing public health and vector control decision-makers.
Ixodes pacificus Cooley & Kohls is the primary vector of Lyme disease spirochetes to humans in the western United States. Although not native to Alaska, this tick species has recently been found on domestic animals in the state. Ixodes pacificus has a known native range within the western contiguous United States and southwest Canada; therefore, it is not clear if introduced individuals can successfully survive and reproduce in the high-latitude climate of Alaska. To identify areas of suitable habitat within Alaska for I. pacificus, we used model parameters from two existing sets of ensemble habitat distribution models calibrated in the contiguous United States. To match the model input covariates, we calculated climatic and land cover covariates for the present (1980-2014) and future (2070-2100) climatologies in Alaska. The present-day habitat suitability maps suggest that the climate and land cover in Southeast Alaska and portions of Southcentral Alaska could support the establishment of I. pacificus populations. Future forecasts suggest an increase in suitable habitat with considerable uncertainty for many areas of the state. Repeated introductions of this non-native tick to Alaska increase the likelihood that resident populations could become established.
Human cases of tick-borne diseases have been increasing in the United States. In particular, the incidence of Lyme disease, the major vector-borne disease in Rhode Island, has risen, along with cases of babesiosis and anaplasmosis, all vectored by the blacklegged tick. These increases might relate, in part, to climate change, although other environmental changes in the northeastern U.S. (land use as it relates to habitat; vertebrate host populations for tick reproduction and enzootic cycling) also contribute. Lone star ticks, formerly southern in distribution, have been spreading northward, including expanded distributions in Rhode Island. Illnesses associated with this species include ehrlichiosis and alpha-gal syndrome, which are expected to increase. Ranges of other tick species have also been expanding in southern New England, including the Gulf Coast tick and the introduced Asian longhorned tick. These ticks can carry human pathogens, but the implications for human disease in Rhode Island are unclear.
BACKGROUND: Dengue fever is one of the most important arboviral diseases. Surface temperature versus dengue burden in tropical environments can provide valuable information that can be adapted in future measurements to improve health policies. METHODS: A methodological approach using Daymet-V3 provided estimates of daily weather parameters. A Python code developed by us extracted the median temperature from the urban regions of Colima State (207.3 km(2)) in Mexico. JointPoint regression models computed the mean temperature-adjusted average annual percentage of change (AAPC) in disability-adjusted life years (DALY) rates (per 100,000) due to dengue in Colima State among school-aged (5-14 years old) children. RESULTS: Primary outcomes were average temperature in urban areas and cumulative dengue burden in DALYs in the school-aged population. A model from 1990 to 2017 medium surface temperature with DALY rates was performed. The increase in DALYs rate was 64% (95% CI, 44-87%), and it seemed to depend on the 2000-2009 estimates (AAPC = 185%, 95% CI 18-588). CONCLUSION: From our knowledge, this is the first study to evaluate surface temperature and to model it through an extensive period with health economics calculations in a specific subset of the Latin-American endemic population for dengue epidemics.
Lyme disease (LD) risk is emerging rapidly in Canada due to range expansion of its tick vectors, accelerated by climate change. The risk of contracting LD varies geographically due to variability in ecological characteristics that determine the hazard (the densities of infected host-seeking ticks) and vulnerability of the human population determined by their knowledge and adoption of preventive behaviors. Risk maps are commonly used to support public health decision-making on Lyme disease, but the ability of the human public to adopt preventive behaviors is rarely taken into account in their development, which represents a critical gap. The objective of this work was to improve LD risk mapping using an integrated social-behavioral and ecological approach to: (i) compute enhanced integrated risk maps for prioritization of interventions and (ii) develop a spatially-explicit assessment tool to examine the relative contribution of different risk factors. The study was carried out in the Estrie region located in southern Québec. The blacklegged tick, Ixodes scapularis, infected with the agent of LD is widespread in Estrie and as a result, regional LD incidence is the highest in the province. LD knowledge and behaviors in the population were measured in a cross-sectional health survey conducted in 2018 reaching 10,790 respondents in Estrie. These data were used to create an index for the social-behavioral component of risk in 2018. Local Empirical Bayes estimator technique were used to better quantify the spatial variance in the levels of adoption of LD preventive activities. For the ecological risk analysis, a tick abundance model was developed by integrating data from ongoing long-term tick surveillance programs from 2007 up to 2018. Social-behavioral and ecological components of the risk measures were combined to create vulnerability index maps and, with the addition of human population densities, prioritization index maps. Map predictions were validated by testing the association of high-risk areas with the current spatial distribution of human cases of LD and reported tick exposure. Our results demonstrated that social-behavioral and ecological components of LD risk have markedly different distributions within Estrie. The occurrence of human LD cases or reported tick exposure in a municipality was positively associated with tick density and the prioritization risk index (p < 0.001). This research is a second step towards a more comprehensive integrated LD risk assessment approach, examining social-behavioral risk factors that interact with ecological risk factors to influence the management of emerging tick-borne diseases, an approach that could be applied more widely to vector-borne and zoonotic diseases.
Dengue is one of the major health problems in the state of Chiapas. Consequently, spatial information on the distribution of the disease can optimize directed control strategies. Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. This is an ecological study that uses data from a range of sources. Dengue cases occurred from January to August 2019. The data analysis used the spatial correlation of dengue cases (DCs), which was calculated with the Moran index statistic, and a generalized linear spatial model (GLSM) within a Bayesian framework, which was considered to model the spatial distribution of DCs in the state of Chiapas. We selected the climatological, geographic, and sociodemographic variables related to the study area. A prediction of the model on Chiapas maps was carried out based on the places where the cases were registered. We find a spatial correlation of 0.115 (p value=0.001)between neighboring municipalities using the Moran index. The variables that have an effect on the number of confirmed cases of dengue are the maximum temperature (Coef=0.110; 95% CrI: 0.076 – 0.215), rainfall (Coef=0.013; 95% CrI:0.008 – 0.028), and altitude (Coef=0.00045; 95% CrI:0.00002 – 0.00174) of each municipality. The predicting power is notably better in regions that have a greater number of municipalities where DCs are registered. The model shows the importance of considering these variables to prevent future DCs in vulnerable areas.
Influenza typically causes mild infection but can lead to severe outcomes for those with compromised lung health. Flooding, a seasonal problem in Iowa, can expose many Iowans to molds and allergens shown to alter lung inflammation, leading to asthma attacks and decreased viral clearance. Based on this, the hypothesis for this research was that there would be geographically specific positive associations in locations with flooding with influenza diagnosis. An ecological study was performed using influenza diagnoses and positive influenza polymerase chain reaction tests from a de-identified large private insurance database and Iowa State Hygienic Lab. After adjustment for multiple confounding factors, Poisson regression analysis resulted in a consistent 1% associated increase in influenza diagnoses per day above flood stage (95% confidence interval: 1.00-1.04). This relationship remained after removal of the 2009-2010 influenza pandemic year. There was no associated risk between flooding and influenza-like illness as a nonspecific diagnosis. Associated risks between flooding and increased influenza diagnoses were geographically specific, with the greatest risk in the most densely populated areas. This study indicates that populations who live, work, or volunteer in flooded environments should consider preventative measures to avoid environmental exposures to mitigate illness from influenza in the following year.
INTRODUCTION: Cryptococcus gattii (C. gatti) is a rare cause of meningitis in the United States. Outbreaks in new geographic distributions in the past few decades raise concern that climate change may be contributing to a broader distribution of this pathogen. We review a case of C. gattii in a 23-year-old woman in Northern California who was diagnosed via lumbar puncture after six weeks of headache, blurred vision, and tinnitus. CASE REPORT: A 23-year-old previously healthy young woman presented to the emergency department (ED) after multiple visits to primary care, other EDs, and neurologists, for several weeks of headache, nausea, tinnitus, and blurred vision. On examination the patient was found to have a cranial nerve VI palsy (impaired abduction of the left eye) and bilateral papilledema on exam. Lumbar puncture had a significantly elevated opening pressure. Cerebrospinal fluid studies were positive for C. gattii. The patient was treated with serial lumbar punctures, followed by lumbar drain, as well as amphotericin and flucytosine. The patient had improvement in headache and neurologic symptoms and was discharged to another facility that specializes in management of this disease to undergo further treatment with immunomodulators and steroids. CONCLUSION: Fungal meningitis is uncommon in the US, particularly among immunocompetent patients. Due to climate change, C. gattii may be a new pathogen to consider. This finding raises important questions to the medical community about the way global climate change affects day to day medical care now, and how it may change in the future.
ABSTRACT: Consumption of seeds has increased in recent years due to their high nutrient content. However, Salmonella outbreaks associated with the consumption of low-water-activity food items have also increased, although these food items do not support microbial growth. The main goal of this study was to quantify microbial indicators and to determine the prevalence and content of Salmonella in chia, amaranth, and sesame seeds obtained from Mexican retail outlets. In addition, the behavior of this pathogen on seeds was evaluated. One hundred samples of each product (chia, amaranth, and sesame seeds) were collected from Queretaro City markets. Aerobic plate count, coliforms, and Escherichia coli bacteria were quantified, and the presence and number of Salmonella pathogens were also determined. Chia, amaranth, and sesame seeds (1 kg each) were inoculated with a cocktail of five Salmonella strains (∼6 log CFU mL-1) and stored at ambient temperature, and then populations of Salmonella were quantified. The median aerobic plate count contents in chia, amaranth, and sesame seeds were 2.1, 2.4, and 3.8 log CFU g-1, respectively, and the content of coliforms on the seeds ranged from 0.48 to 0.56 log most probable number (MPN) per g. E. coli was present at low concentrations in the three types of seeds. Salmonella was detected in chia (31%), amaranth (15%), and sesame (12%) seeds, and the population ranged from 0.48 to 0.56 log MPN g-1. Salmonella levels decreased through 240 days of storage, showing inactivation rates of 0.017, 0.011, and 0.016 log CFU h-1 in chia, amaranth, and sesame seeds, respectively. The high prevalence of Salmonella in the seeds highlights potential risks for consumers, particularly given that seeds are generally consumed without treatments guaranteeing pathogen inactivation.
BACKGROUND: This paper represents, to our knowledge, the first national-level (United States) estimate of the economic impacts of vibriosis cases as exacerbated by climate change. Vibriosis is an illness contracted through food- and waterborne exposures to various Vibrio species (e.g., nonV. cholerae O1 and O139 serotypes) found in estuarine and marine environments, including within aquatic life, such as shellfish and finfish. OBJECTIVES: The objective of this study was to project climate-induced changes in vibriosis and associated economic impacts in the United States related to changes in sea surface temperatures (SSTs). METHODS: For our analysis to identify climate links to vibriosis incidence, we constructed three logistic regression models by Vibrio species, using vibriosis data sourced from the Cholera and Other Vibrio Illness Surveillance system and historical SSTs. We relied on previous estimates of the cost-per-case of vibriosis to estimate future total annual medical costs, lost income from productivity loss, and mortality-related indirect costs throughout the United States. We separately reported results for V. parahaemolyticus, V. vulnificus, V. alginolyticus, and “V. spp.,” given the different associated health burden of each. RESULTS: By 2090, increases in SST are estimated to result in a 51% increase in cases annually relative to the baseline era (centered on 1995) under Representative Concentration Pathway (RCP) 4.5, and a 108% increase under RCP8.5. The cost of these illnesses is projected to reach $5.2 billion annually under RCP4.5, and $7.3 billion annually under RCP8.5, relative to $2.2 billion in the baseline (2018 U.S. dollars), equivalent to 140% and 234% increases respectively. DISCUSSION: Vibriosis incidence is likely to increase in the United States under moderate and unmitigated climate change scenarios through increases in SST, resulting in a substantial burden of morbidity and mortality, and costing billions of dollars. These costs are mostly attributable to deaths, primarily from exposure to V. vulnificus. Evidence suggests that other factors, including sea surface salinity, may contribute to further increases in vibriosis cases in some regions of the United States and should also be investigated. https://doi.org/10.1289/EHP9999a.
The Pacific Northwest (PNW) is one of the largest commercial harvesting areas for Pacific oysters (Crassostrea gigas) in the United States. Vibrio parahaemolyticus, a bacterium naturally present in estuarine waters accumulates in shellfish and is a major cause of seafood-borne illness. Growers, consumers, and public-health officials have raised concerns about rising vibriosis cases in the region. Vibrio parahaemolyticus genetic markers (tlh, tdh, and trh) were estimated using an most-probable-number (MPN)-PCR technique in Washington State Pacific oysters regularly sampled between May and October from 2005 to 2019 (N = 2,836); environmental conditions were also measured at each sampling event. Multilevel mixed-effects regression models were used to assess relationships between environmental measures and genetic markers as well as genetic marker ratios (trh:tlh, tdh:tlh, and tdh:trh), accounting for variation across space and time. Spatial and temporal dependence were also accounted for in the model structure. Model fit improved when including environmental measures from previous weeks (1-week lag for air temperature, 3-week lag for salinity). Positive associations were found between tlh and surface water temp, specifically between 15 and 26°C, and between trh and surface water temperature up to 26°C. tlh and trh were negatively associated with 3-week lagged salinity in the most saline waters (> 27 ppt). There was also a positive relationship between tissue temperature and tdh, but only above 20°C. The tdh:tlh ratio displayed analogous inverted non-linear relationships as tlh. The non-linear associations found between the genetic targets and environmental measures demonstrate the complex habitat suitability of V. parahaemolyticus. Additional associations with both spatial and temporal variables also suggest there are influential unmeasured environmental conditions that could further explain bacterium variability. Overall, these findings confirm previous ecological risk factors for vibriosis in Washington State, while also identifying new associations between lagged temporal effects and pathogenic markers of V. parahaemolyticus.
Diverse snail species serve as intermediate hosts of the parasitic nematode Angiostrongylus cantonensis, the etiological agent of human neuroangiostrongyliasis. However, levels of A. cantonensis infection prevalence and intensity vary dramatically among these host species. Factors contributing to this variation are largely unknown. Environmental factors, such as precipitation and temperature, have been correlated with overall A. cantonensis infection levels in a locale, but the influence of environment on infection in individual snail species has not been addressed. We identified levels of A. cantonensis prevalence and intensity in 16 species of snails collected from 29 sites along an environmental gradient on the island of Oahu, Hawaii. The relationship between infection levels of individual species and their environment was evaluated using AIC model selection of Generalized Linear Mixed Models incorporating precipitation, temperature, and vegetation cover at each collection site. Our results indicate that different mechanisms drive parasite prevalence and intensity in the intermediate hosts. Overall, snails from rainy, cool, green sites had higher infection levels than snails from dry, hot sites with less green vegetation. Intensity increased at the same rate along the environmental gradient in all species, though at different levels, while the relation between prevalence and environmental variables depended on species. These results have implications for zoonotic transmission, as human infection is a function of infection in the intermediate hosts, ingestion of which is the main pathway of transmission. The probability of human infection is greater in locations with higher rainfall, lower temperature and more vegetation cover because of higher infection prevalence in the gastropod hosts, but this depends on the host species. Moreover, severity of neuroangiostrongyliasis symptoms is likely to be greater in locations with higher rainfall, lower temperature, and more vegetation because of the higher numbers of infectious larvae (infection intensity) in all infected snail species. This study highlights the variation of infection prevalence and intensity in individual gastropod species, the individualistic nature of interactions between host species and their environment, and the implications for human neuroangiostrongyliasis in different environments.
The Canadian Arctic has a long history with diarrheal disease, including outbreaks of campylobacteriosis, giardiasis, and salmonellosis. Due to climate change, the Canadian Arctic is experiencing rapid environmental transformation, which not only threatens the livelihood of local Indigenous Peoples, but also supports the spread, frequency, and intensity of enteric pathogen outbreaks. Advances in diagnostic testing and detection have brought to attention the current burden of disease due to Cryptosporidium, Campylobacter, and Helicobacter pylori. As climate change is known to influence pathogen transmission (e.g., food and water), Arctic communities need support in developing prevention and surveillance strategies that are culturally appropriate. This review aims to provide an overview of how climate change is currently and is expected to impact enteric pathogens in the Canadian Arctic.
Freshwater can support the survival of the enteric pathogen Salmonella, though temporal Salmonella diversity in a large watershed has not been assessed. At 28 locations within the Susquehanna River basin, 10-liter samples were assessed in spring and summer over 2 years. Salmonella prevalence was 49%, and increased river discharge was the main driver of Salmonella presence. The amplicon-based sequencing tool, CRISPR-SeroSeq, was used to determine serovar population diversity and detected 25 different Salmonella serovars, including up to 10 serovars from a single water sample. On average, there were three serovars per sample, and 80% of Salmonella-positive samples contained more than one serovar. Serovars Give, Typhimurium, Thompson, and Infantis were identified throughout the watershed and over multiple collections. Seasonal differences were evident: serovar Give was abundant in the spring, whereas serovar Infantis was more frequently identified in the summer. Eight of the ten serovars most commonly associated with human illness were detected in this study. Crucially, six of these serovars often existed in the background, where they were masked by a more abundant serovar(s) in a sample. Serovars Enteritidis and Typhimurium, especially, were masked in 71 and 78% of samples where they were detected, respectively. Whole-genome sequencing-based phylogeny demonstrated that strains within the same serovar collected throughout the watershed were also very diverse. The Susquehanna River basin is the largest system where Salmonella prevalence and serovar diversity have been temporally and spatially investigated, and this study reveals an extraordinary level of inter- and intraserovar diversity.IMPORTANCE Salmonella is a leading cause of bacterial foodborne illness in the United States, and outbreaks linked to fresh produce are increasing. Understanding Salmonella ecology in freshwater is of importance, especially where irrigation practices or recreational use occur. As the third largest river in the United States east of the Mississippi, the Susquehanna River is the largest freshwater contributor to the Chesapeake Bay, and it is the largest river system where Salmonella diversity has been studied. Rainfall and subsequent high river discharge rates were the greatest indicators of Salmonella presence in the Susquehanna and its tributaries. Several Salmonella serovars were identified, including eight commonly associated with foodborne illness. Many clinically important serovars were present at a low frequency within individual samples and so could not be detected by conventional culture methods. The technologies employed here reveal an average of three serovars in a 10-liter sample of water and up to 10 serovars in a single sample.
Currently, Salmonella spp. is the bacterium causing the highest number of food-borne diseases (FADs) in the world. It is primarily associated with contaminated water used to that irrigates crops from intensive livestock farming. However, literature emphasizes that the reservoirs for Salmonella spp. remain in wildlife and there are unconventional sources or secondary reservoirs, such as soil. Human soil-borne diseases have not been modeled in spatial scenarios, and therefore it is necessary to consider soil and other climatic factors to anticipate the emergence of new strains or serotypes with potential threat to public and animal health. The objective of this research was to investigate whether edaphic and climatic factors are associated with the occurrence and prevalence of Salmonella spp. in Northwestern Mexico. We estimated the potential distribution of Salmonella spp. with an interpolation method of unsampled kriging areas for 15 environmental variables, considering that these factors have a seasonal dynamic of change during the year and modifications in longer periods. Subsequently, a database was generated with human salmonellosis cases reported in the epidemiological bulletins of the National System of Epidemiological Surveillance (SIVE). For the Northwest region, there were 30,595 human cases of paratyphoid and other salmonellosis reported have been reported in Baja California state, 71,462 in Chihuahua, and 16,247 in Sonora from 2002 to 2019. The highest prevalence was identified in areas with higher temperatures between 35 and 37 °C, and precipitation greater than 1000 mm. The edaphic variables limited the prevalence and geographical distribution of Salmonella spp., because the region is characterized by presenting a low percentage of organic matter (≤4.3), and most of the territory is classified as aridic and xeric, which implies that the humidity comprises ≤ 180 days a year. Finally, the seasonal time series indicated that in the states of Baja California and Chihuahua the rainy quarter of the year is 18.7% and 17.01% above a typical quarter respectively, while for Sonora the warmest quarter is 23.3%. It is necessary to deepen the relationship between different soil characteristics and climate elements such as temperature and precipitation, which influence the distribution of different soil-transmitted diseases.
BACKGROUND: Infections with nontyphoidal Salmonella cause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence of Salmonella in soil and food. However, the impact of extreme weather events on Salmonella infection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions. METHODS: To address this knowledge gap, we obtained Salmonella case data for S. Enteriditis, S. Typhimurium, S. Newport, and S. Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95(th) percentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates. RESULTS: We observed that extreme heat exposure was associated with increased rates of infection with S. Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broiler chickens and cattle). Extreme precipitation events were also associated with increased rates of S. Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate of S. Newport infections in Maryland associated with extreme precipitation events. CONCLUSIONS: Overall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection with Salmonella serovars that persist in environmental or plant-based reservoirs, such as S. Javiana and S. Newport, appear to be of particular significance regarding increased heat and rainfall events.
Vibrio (V), a genus of marine bacteria, are common inhabitants of warm coastal waters and estuaries. Vibrio includes V. parahaemolyticus and V. vulnificus species that can cause human infections through the consumption of contaminated shellfish (as bivalve molluscs). The growth of pathogenic Vibrio is related to ambient water temperature and seems to increase at 15 degrees C and over. The expansion of Vibrio infection outbreak is increasing worldwide due to the increase of the sea surface temperature as a result of ocean warming. Canada’s coast is not an exception to this worldwide Vibrio spread. Faced with this issue, this study focuses on modelling the future potential Vibrio growth risk along the coasts of the St. Lawrence Gulf and Estuary, where the shellfish industry is well developed. This is done using the adequate machine learning model with explanatory variables that include air temperature and wind speed for predicting future water temperatures. Based on the predicted future water temperature scenarios and a threshold of 15 degrees C to determine the conditions favorable to the growth of Vibrio bacteria, we modelled the Vibrio growth risk indicator, i.e. the number of days exceeding the minimum temperature for Vibrio pathogenic growth (15 degrees C), in the horizon 2040-2100. Simulations show that the number of days, where the minimum temperature (15 degrees C) will be reached, will increase spatially and even seasonally and all the shellfish beds would meet the temperature condition for Vibrio growth regardless of the climate scenario (optimistic and pessimistic).
As the nation’s public health leader, the Centers for Disease Control and Prevention (CDC) is actively engaged in a national effort to protect the public’s health from the harmful effects of climate change. Scientists from CDC’s National Center for Emerging and Zoonotic Infectious Diseases (NCEZID) are at the forefront of many of these efforts. This report highlights some of that work and also looks ahead to the important work yet to come. Lyme disease, West Nile virus disease, and Valley fever. These are just some of the infectious diseases that are on the rise and spreading to new areas of the United States. Milder winters, warmer summers, and fewer days of frost make it easier for these and other infectious diseases to expand into new geographic areas and infect more people. To understand climate change’s impact, it’s important to look at some of the common ways these diseases spread—through mosquito and tick bites, contact with animals, fungi, and water.
Canadians face an emerging threat of Lyme disease due to the northward expansion of the tick vector, Ixodes scapularis. We evaluated the degree of I. scapularis population establishment and Borrelia burgdorferi occurrence in the city of Ottawa, Ontario, Canada from 2017-2019 using active surveillance at 28 sites. We used a field indicator tool developed by Clow et al. to determine the risk of I. scapularis establishment for each tick cohort at each site using the results of drag sampling. Based on results obtained with the field indicator tool, we assigned each site an ecological classification describing the pattern of tick colonization over two successive cohorts (cohort 1 was comprised of ticks collected in fall 2017 and spring 2018, and cohort 2 was collected in fall 2018 and spring 2019). Total annual site-specific I. scapularis density ranged from 0 to 16.3 ticks per person-hour. Sites with the highest density were located within the Greenbelt zone, in the suburban/rural areas in the western portion of the city of Ottawa, and along the Ottawa River; the lowest densities occurred at sites in the suburban/urban core. B. burgdorferi infection rates exhibited a similar spatial distribution pattern. Of the 23 sites for which data for two tick cohorts were available, 11 sites were classified as “high-stable”, 4 were classified as “emerging”, 2 were classified as “low-stable”, and 6 were classified as “non-zero”. B. burgdorferi-infected ticks were found at all high-stable sites, and at one emerging site. These findings suggest that high-stable sites pose a risk of Lyme disease exposure to the community as they have reproducing tick populations with consistent levels of B. burgdorferi infection. Continued surveillance for I. scapularis, B. burgdorferi, and range expansion of other tick species and emerging tick-borne pathogens is important to identify areas posing a high risk for human exposure to tick-borne pathogens in the face of ongoing climate change and urban expansion.
Some species of Culicoides Latreille (Diptera: Ceratopogonidae) can be pests as well as pathogen vectors, but data on their distribution in Ontario, Canada, are sparse. Collecting this baseline data is important given ongoing, accelerated alterations in global climate patterns that may favor the establishment of some species in northern latitudes. Culicoides spp. were surveyed using UV light traps over two seasons in 2017 and 2018 at livestock farms in southern Ontario, Canada. Two Culicoides spp. not previously recorded in Canada were identified, C. bergi and C. baueri, representing new country and provincial records. Unlike some congenerics, these two species are not currently recognized as vectors of pathogens that pose a health risk to humans, livestock or wildlife in North America. However, the possibility that these Culicoides species may have recently expanded their geographic range, potentially in association with climate and/or landscape changes, warrants ongoing attention and research. Furthermore, our results provoke the question of the potential undocumented diversity of Culicoides spp. in Ontario and other parts of Canada, and whether other Culicoides spp. may be undergoing range expansion. The current and future distributions of Culicoides spp., and other potential vectors of human, agricultural, and wildlife health significance, are important to identify for proper disease risk assessment, mitigation, and management.
Given the climate crisis and its cumulative impacts on public health, effective communication strategies that engage the public in adaptation and mitigation are critical. Many have argued that a health frame increases engagement, as do visual methodologies including online and interactive platforms, yet to date there has been limited research on audience responses to health messaging using visual interventions. This study explores public attitudes regarding communication tools focused on climate change and climate-affected Lyme disease through six focus groups (n = 61) in rural and urban southern Manitoba, Canada. The results add to the growing evidence of the efficacy of visual and storytelling methods in climate communications and argues for a continuum of mediums: moving from video, text, to maps. Findings underscore the importance of tailoring both communication messages and mediums to increase uptake of adaptive health and environmental behaviours, for some audiences bridging health and climate change while for others strategically decoupling them.
The tick vector of Lyme disease, Ixodes scapularis, is currently expanding its geographical distribution northward into southern Canada driving emergence of Lyme disease in the region. Despite large-scale studies that attributed different factors such as climate change and changes in land use to the geographical expansion of the tick, a comprehensive understanding of local patterns of tick abundance is still lacking in that region. Using a newly endemic periurban nature park located in Quebec (Canada) as a model, we explored intra-habitat patterns in tick distribution and their relationship with biotic and abiotic factors. We verified the hypotheses that (1) there is spatial heterogeneity in tick densities at the scale of the park and (2) these patterns can be explained by host availability, habitat characteristics and microclimatic conditions. During tick activity season in three consecutive years, tick, deer, rodent and bird abundance, as well as habitat characteristics and microclimatic conditions, were estimated at thirty-two sites. Patterns of tick distribution and abundance were investigated by spatial analysis. Generalised additive mixed models were constructed for each developmental stage of the tick and the relative importance of significant drivers on tick abundance were derived from final models. We found fine-scale spatial heterogeneity in densities of all tick stages across the park, with interannual variability in the location of hotspots. For all stages, the local density was related to the density of the previous stage in the previous season, in keeping with the tick’s life cycle. Adult tick density was highest where drainage was moderate (neither waterlogged nor dry). Microclimatic conditions influenced the densities of immature ticks, through the effects of weather at the time of tick sampling (ambient temperature and relative humidity) and of the seasonal microclimate at the site level (degree-days and number of tick adverse moisture events). Seasonal phenology patterns were generally consistent with expected curves for the region, with exceptions in some years that may be attributable to founder events. This study highlights fine scale patterns of tick population dynamics thus providing fundamental knowledge in Lyme disease ecology and information applicable to the development of well-targeted prevention and control strategies for public natural areas affected by this growing problem in southern Canada.
BACKGROUND: Despite scientific evidence that climate change has profound and far reaching implications for public health, translating this knowledge in a manner that supports citizen engagement, applied decision-making, and behavioural change can be challenging. This is especially true for complex vector-borne zoonotic diseases such as Lyme disease, a tick-borne disease which is increasing in range and impact across Canada and internationally in large part due to climate change. This exploratory research aims to better understand public risk perceptions of climate change and Lyme disease in order to increase engagement and motivate behavioural change. METHODS: A focus group study involving 61 participants was conducted in three communities in the Canadian Prairie province of Manitoba in 2019. Focus groups were segmented by urban, rural, and urban-rural geographies, and between participants with high and low levels of self-reported concern regarding climate change. RESULTS: Findings indicate a broad range of knowledge and risk perceptions on both climate change and Lyme disease, which seem to reflect the controversy and complexity of both issues in the larger public discourse. Participants in high climate concern groups were found to have greater climate change knowledge, higher perception of risk, and less skepticism than those in low concern groups. Participants outside of the urban centre were found to have more familiarity with ticks, Lyme disease, and preventative behaviours, identifying differential sources of resilience and vulnerability. Risk perceptions of climate change and Lyme disease were found to vary independently rather than correlate, meaning that high climate change risk perception did not necessarily indicate high Lyme disease risk perception and vice versa. CONCLUSIONS: This research contributes to the growing literature framing climate change as a public health issue, and suggests that in certain cases climate and health messages might be framed in a way that strategically decouples the issue when addressing climate skeptical audiences. A model showing the potential relationship between Lyme disease and climate change perceptions is proposed, and implications for engagement on climate change health impacts are discussed.
Environmental changes triggered by deforestation, urban expansion and climate change are present-day drivers of the emergence and reemergence of leishmaniasis. This review describes the current epidemiological scenario and the feasible influence of environmental changes on disease occurrence in the state of Yucatan, Mexico. Relevant literature was accessed through different databases, including PubMed, Scopus, Google, and Mexican official morbidity databases. Recent LCL autochthonous cases, potential vector sandflies and mammal hosts/reservoirs also have been reported in several localities of Yucatan without previous historical records of the disease. The impact of deforestation, urban expansion and projections on climate change have been documented. The current evidence of the relationships between the components of the transmission cycle, the disease occurrence, and the environmental changes on the leishmaniasis emergence in the state shows the need for strength and an update to the intervention and control strategies through a One Health perspective.
Ticks rank high among arthropod vectors in terms of numbers of infectious agents that they transmit to humans, including Lyme disease, Rocky Mountain spotted fever, Colorado tick fever, human monocytic ehrlichiosis, tularemia, and human granulocytic anaplasmosis. Increasing temperature is suspected to affect tick biting rates and pathogen developmental rates, thereby potentially increasing risk for disease incidence. Tick distributions respond to climate change, but how their geographic ranges will shift in future decades and how those shifts may translate into changes in disease incidence remain unclear. In this study, we have assembled correlative ecological niche models for eight tick species of medical or veterinary importance in North America (Ixodes scapularis, I. pacificus, I. cookei, Dermacentor variabilis, D. andersoni, Amblyomma americanum, A. maculatum, and Rhipicephalus sanguineus), assessing the distributional potential of each under both present and future climatic conditions. Our goal was to assess whether and how species’ distributions will likely shift in coming decades in response to climate change. We interpret these patterns in terms of likely implications for tick-associated diseases in North America.
In California, the western blacklegged tick, Ixodes pacificus Cooley and Kohls, is the principal vector of the Borrelia burgdorferi sensu lato (sl) complex (Spirochaetales: Spirochaetaceae, Johnson et al.), which includes the causative agent of Lyme disease (B. burgdorferi sensu stricto). Ixodes pacificus nymphs were sampled from 2015 to 2017 at one Sierra Nevada foothill site to evaluate our efficiency in collecting this life stage, characterize nymphal seasonality, and identify environmental factors affecting their abundance and infection with B. burgdorferi sl. To assess sampling success, we compared the density and prevalence of I. pacificus nymphs flagged from four questing substrates (logs, rocks, tree trunks, leaf litter). Habitat characteristics (e.g., canopy cover, tree species) were recorded for each sample, and temperature and relative humidity were measured hourly at one location. Generalized linear mixed models were used to assess environmental factors associated with I. pacificus abundance and B. burgdorferi sl infection. In total, 2,033 substrates were sampled, resulting in the collection of 742 I. pacificus nymphs. Seasonal abundance of nymphs was bimodal with peak activity occurring from late March through April and a secondary peak in June. Substrate type, collection year, month, and canopy cover were all significant predictors of nymphal density and prevalence. Logs, rocks, and tree trunks had significantly greater nymphal densities and prevalences than leaf litter. Cumulative annual vapor pressure deficit was the only significant climatic predictor of overall nymphal I. pacificus density and prevalence. No associations were observed between the presence of B. burgdorferi sl in nymphs and environmental variables.
AIM: The purpose of this retrospective, correlational pilot study was to explore the relationship between historical weekly weather data including temperature, dew point, humidity, barometric pressure, visibility, and cloud cover compared to weekly influenza-like illness reports over a four year period. BACKGROUND: Climate and weather-related conditions may affect the viral activity and transmission of influenza, although this relationship has not been widely studied in nursing. Some research suggests that there are causal links between cold temperatures, low indoor humidity, minimal sun exposure, and influenza outbreaks. Additionally, rapid weather variability in a warming climate can increase influenza epidemic risk. METHODS: Data from a local public health district were extracted and used to correlate with weekly weather averages for the area. RESULTS: Findings showed that current influenza reports are significantly associated with temperature and visibility, both lagged two weeks. CONCLUSIONS: Though more research is needed, nurses must understand, recognize, and act upon weather and climate factors that affect the health of populations. With a greater understanding of the relationship between weather and influenza-like illness, nurses and other healthcare providers can potentially work to respond to and mitigate the consequences of weather-related illness as well as anticipate and prepare for increased flu burden. Furthermore, nurses can remain engaged in climate protective initiatives and policy development at their local community and/or organizational levels to underscore and advocate for the needs of populations and groups they serve.
The soil-borne fungal disease coccidioidomycosis (Valley fever) is prevalent across the southwestern United States (US). Previous studies have suggested that the occurrence of this infection is associated with anomalously wet or dry soil moisture states described by the “grow and blow” hypothesis. The growth of coccidioidomycosis is favored by moist conditions both at the surface and in the root zone. A statistical analysis identified two areas in Arizona and central California, with a moderate-to-high number of coccidioidomycosis cases. A Wavelet Transform Coherence (WTC) analysis between El Nino Southern Oscillation (ENSO), coccidioidomycosis cases, surface soil moisture (SSM; 0 to 5 cm) from European Space Agency-Climate Change Initiative (ESA-CCI), and shallow root zone soil moisture (RZSM; 0 to 40 cm depth) from Soil MERGE (SMERGE) was executed for twenty-four CA and AZ counties. In AZ, only SSM was modulated by ENSO. When case values were adjusted for overreporting between 2009 to 2012, a moderate but significant connection between ENSO and cases was observed at a short periodicity (2.1 years). In central CA, SSM, RZSM, and cases all had a significant link to ENSO at longer periodicities (5-to-7 years). This study provides an example of how oceanic-atmospheric teleconnections can impact human health.
Legionellosis is an infection acquired through inhalation of aerosols that are contaminated with environmental bacteria Legionella spp. The bacteria require warm temperature for proliferation in bodies of water and moist soil. The legionellosis incidence in the United States has been rising rapidly in the past two decades without a clear explanation. In the meantime, the US has recorded consecutive years of above-norm temperature since 1997 and precipitation surplus since 2008. The present study analyzed the legionellosis incidence in the US during the 20-year period of 1999 to 2018 and correlated with concurrent temperature, precipitation, solar ultraviolet B (UVB) radiation, and vehicle mileage data. The age-adjusted legionellosis incidence rates rose exponentially from 0.40/100,000 in 1999 (with 1108 cases) to 2.69/100,000 in 2018 (with 9933 cases) at a calculated annual increase of 110%. In regression analyses, the rise correlated with an increase in vehicle miles driven and with temperature and precipitation levels that have been above the 1901-2000 mean since 1997 and 2008, respectively, suggesting more road exposure to traffic-generated aerosols and promotive effects of anomalous climate. Remarkably, the regressions with cumulative anomalies of temperature and precipitation were robust (R2 ≥ 0.9145, P ≤ 4.7E-11), implying possible changes to microbial ecology in the terrestrial and aquatic environments. An interactive synergy between annual precipitation and vehicle miles was also found in multiple regressions. Meanwhile, the bactericidal UVB radiation has been decreasing, which also contributed to the rising incidence in an inverse correlation. The 2018 legionellosis incidence peak corresponded to cumulative effects of the climate anomalies, vast vehicle miles (3,240 billion miles, 15904 km per capita), record high precipitation (880.1 mm), near record low UVB radiation (7488 kJ/m2), and continued above-norm temperature (11.96°C). These effects were examined and demonstrated in California, Florida, New Jersey, Ohio, and Wisconsin, states that represent diverse incidence rates and climates. The incidence and above-norm temperature both rose most in cold Wisconsin. These results suggest that warming temperature and precipitation surplus have likely elevated the density of Legionella bacteria in the environment, and together with road exposure explain the rapidly rising incidence of legionellosis in the United States. These trends are expected to continue, warranting further research and efforts to prevent infection.
Bacteria in the air present patterns in space and time produced by different sources and environmental factors. Few studies have focused on the link between airborne pathogenic bacteria in densely populated cities, and the risk to the population’s health. Bacteria associated with particulate matter (PM) were monitored from the air of Mexico City (Mexico). We employed a metagenomic approach to characterise bacteria using the 16S rRNA gene. Airborne bacteria sampling was carried out in the north, centre, and south of Mexico City, with different urbanisation rates, during 2017. Bacteria added to the particles were sampled using high-volume PM10 samplers. To ascertain significant differences in bacterial diversity between zones and seasons, the Kruskal-Wallis, Wilcoxon tests were done on alpha diversity parameters. Sixty-three air samples were collected, and DNA was sequenced using next-generation sequencing. The results indicated that the bacterial phyla in the north and south of the city were Firmicutes, Cyanobacteria, Proteobacteria, and Actinobacteria, while in the central zone there were more Actinobacteria. There were no differences in the alpha diversity indices between the sampled areas. According to the OTUs, the richness of bacteria was higher in the central zone. Alpha diversity was higher in the rainy season than in the dry season; the Shannon index and the OTUs observed were higher in the central zone in the dry season. Pathogenic bacteria such as Kocuria, Paracoccus, and Micrococcus predominated in both seasonal times, while Staphylococcus, Corynebacterium, and Nocardioides were found during the rainy season, with a presence in the central zone. (C) Higher Education Press 2022
Elevated ambient temperatures and extreme weather events have increased the incidence of wildfires world-wide resulting in increased wood smoke particle (WSP). Epidemiologic data suggests that WSP exposure associates with exacerbations of respiratory diseases, and with increased respiratory viral infections. To assess the impact of WSP exposure on host response to viral pneumonia, we performed WSP exposures in rodents followed by infection with mouse adapted influenza (HINI-PR8). C57BL/6 male mice aged 6-8 weeks were challenged with WSP or PBS by oropharyngeal aspiration in acute (single dose) or sub-acute exposures (day 1, 3, 5, 7 and 10). Additional groups underwent sub-acute exposure followed by infection by influenza or heat-inactivated (HI) virus. Following exposures/infection, bronchoalveolar lavage (BAL) was performed to assess for total cell counts/differentials, total protein, protein carbonyls and hyaluronan. Lung tissue was assessed for viral counts by real time PCR. When compared to PBS, acute WSP exposure associated with an increase in airspace macrophages. Alternatively, sub-acute exposure resulted in a dose dependent increase in airspace neutrophils. Sub-acute WSP exposure followed by influenza infection was associated with improved respiratory viral outcomes including reduced weight loss and increased blood oxygen saturation, and decreased protein carbonyls and viral titers. Flow cytometry demonstrated dynamic changes in pulmonary macrophage and T cell subsets based on challenge with WSP and influenza. This data suggests that sub-acute WSP exposure can improve host response to acute influenza infection.
Big Events are periods during which abnormal large-scale events like war, economic collapse, revolts, or pandemics disrupt daily life and expectations about the future. They can lead to rapid change in health-related norms, beliefs, social networks and behavioural practices. The world is undergoing such Big Events through the interaction of COVID-19, a large economic downturn, massive social unrest in many countries, and ever-worsening effects of global climate change. Previous research, mainly on HIV/AIDS, suggests that the health effects of Big Events can be profound, but are contingent: Sometimes Big Events led to enormous outbreaks of HIV and associated diseases and conditions such as injection drug use, sex trading, and tuberculosis, but in other circumstances, Big Events did not do so. This paper discusses and presents hypotheses about pathways through which the current Big Events might lead to better or worse short and long term outcomes for various health conditions and diseases; considers how pre-existing societal conditions and changing ‘pathway’ variables can influence the impact of Big Events; discusses how to measure these pathways; and suggests ways in which research and surveillance might be conducted to improve human capacity to prevent or mitigate the effects of Big Events on human health.
Racial and ethnic minority and lower-income groups are disproportionately affected by environmental hazards and suffer worse health outcomes than other groups in the United States. Relative to whites and higher-income groups, racial-ethnic minority and lower-income Americans also frequently express greater concern about high-profile global environmental threats like climate change, but they are widely misperceived as being less concerned about these issues than white and higher-income Americans. We use new survey research to explore public perceptions of COVID-19-another global threat marked by substantial racial, ethnic, and class disparities-finding a distinct pattern of misperceptions regarding groups’ concerns. We then discuss how these misperceptions represent a unique form of social misinformation that may pose a threat to science and undermine the cooperation and trust needed to address collective problems.
This research aims to look at the link between environmental pollutants and the coronavirus disease (COVID-19) outbreak in California. To illustrate the COVID-19 outbreak, weather, and environmental pollution, we used daily confirmed cases of COVID-19 patients, average daily temperature, and air quality Index, respectively. To evaluate the data from March 1 to May 24, 2020, we used continuous wavelet transform and then applied partial wavelet coherence (PWC), wavelet transform coherence (WTC), and multiple wavelet coherence (MWC). Empirical estimates disclose a significant association between these series at different time-frequency spaces. The COVID-19 outbreak in California and average daily temperature show a negative (out phase) coherence. Similarly, the air quality index and COVID-19 also show a negative association circle during the second week of the observed period. Our findings will serve as policy implications for state and health officials and regulators to combat the COVID-19 outbreak.
INTRODUCTION: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. METHODOLOGY: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. RESULTS: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. CONCLUSIONS: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.
This article considers the optimal structure of institutions that respond to existential threats such as climate change and pandemics. While science must play a central role in guiding policy responses, there are many values at stake that ought to be reflected in institutional design. There is a distinction between risk assessment, a science-driven analysis in these contexts, and risk management, in which trade-offs are considered in responding to the threats. Moreover, the nature of these threats depends on complex, uncertain and fluid scientific knowledge that requires institutions to be sensitive to communication challenges. Finally, institutions should consider collective action problems and defer or delegate to jurisdictions and institutions whose scope of mandate is appropriate. We assess the Canadian response to the COVID-19 pandemic from an institutional perspective and conclude that, amongst other things, it was insufficiently multidisciplinary, which risked marginalizing the non-public health costs of policy responses to the pandemic.
Understanding whether and how wildfires exacerbate COVID-19 outcomes is important for assessing the efficacy and design of public sector responses in an age of more frequent and simultaneous natural disasters and extreme events. Drawing on environmental and emergency management literatures, we investigate how wildfire smoke (PM(2.5)) impacted COVID-19 infections and deaths during California’s 2020 wildfire season and how public housing resources and hospital capacity moderated wildfires’ effects on COVID-19 outcomes. We also hypothesize and empirically assess the differential impact of wildfire smoke on COVID-19 infections and deaths in counties exhibiting high and low social vulnerability. To test our hypotheses concerning wildfire severity and its disproportionate impact on COVID-19 outcomes in socially vulnerable communities, we construct a county-by-day panel dataset for the period April 1 to November 30, 2020, in California, drawing on publicly available state and federal data sources. This study’s empirical results, based on panel fixed effects models, show that wildfire smoke is significantly associated with increases in COVID-19 infections and deaths. Moreover, wildfires exacerbated COVID-19 outcomes by depleting the already scarce hospital and public housing resources in local communities. Conversely, when wildfire smoke doubled, a one percent increase in the availability of hospital and public housing resources was associated with a 2 to 7 percent decline in COVID-19 infections and deaths. For California communities exhibiting high social vulnerability, the occurrence of wildfires worsened COVID-19 outcomes. Sensitivity analyses based on an alternative sample size and different measures of social vulnerability validate this study’s main findings. An implication of this study for policymakers is that communities exhibiting high social vulnerability will greatly benefit from local government policies that promote social equity in housing and healthcare before, during, and after disasters.
The impacts of wildfires on the health of children are becoming a more urgent matter as wildfires become more frequent, intense and affecting, not only forested areas, but also urban locations. It is important that medical professionals be prepared to provide information to patients and families on how to minimize the adverse health effects on children of wildfire smoke and ash from wildfires. (C) 2021 Elsevier Inc. All rights reserved.
The 2020 California wildfire season coincided with the peak of the COVID-19 pandemic affecting many counties in California, with impacts on air quality. We quantitatively analyzed the short-term effect of air pollution on COVID-19 transmission using county-level data collected during the 2020 wildfire season. Using time-series methodology, we assessed the relationship between short-term exposure to particulate matter (PM(2.5)), carbon monoxide (CO), nitrogen dioxide (NO(2)), and Air Quality Index (AQI) on confirmed cases of COVID-19 across 20 counties impacted by wildfires. Our findings indicate that PM(2.5), CO, and AQI are positively associated with confirmed COVID-19 cases. This suggests that increased air pollution could worsen the situation of a health crisis such as the COVID-19 pandemic. Health policymakers should make tailored policies to cope with situations that may increase the level of air pollution, especially during a wildfire season.
Background: Air pollution has been linked to increased susceptibility to SARS-CoV-2. Thus, it has been suggested that wildfire smoke events may exacerbate the COVID-19 pandemic. Objectives: Our goal was to examine whether wildfire smoke from the 2020 wildfires in the western United States was associated with an increased rate of SARS-CoV-2 infections in Reno, Nevada. Methods: We conducted a time-series analysis using generalized additive models to examine the relationship between the SARS-CoV-2 test positivity rate at a large regional hospital in Reno and ambient PM2.5 from 15 May to 20 Oct 2020. Results: We found that a 10 µg/m3 increase in the 7-day average PM2.5 concentration was associated with a 6.3% relative increase in the SARS-CoV-2 test positivity rate, with a 95% confidence interval (CI) of 2.5 to 10.3%. This corresponded to an estimated 17.7% (CI: 14.4-20.1%) increase in the number of cases during the time period most affected by wildfire smoke, from 16 Aug to 10 Oct. Significance: Wildfire smoke may have greatly increased the number of COVID-19 cases in Reno. Thus, our results substantiate the role of air pollution in exacerbating the pandemic and can help guide the development of public preparedness policies in areas affected by wildfire smoke, as wildfires are likely to coincide with the COVID-19 pandemic in 2021.
Individually, both droughts and pandemics cause disruptions to global food supply chains. The 21st century has seen the frequent occurrence of both natural and human disasters, including droughts and pandemics. Together their impacts can be compounded, leading to severe economic stress and malnutrition, particularly in developing countries. Understanding how droughts and pandemics interact, and identifying appropriate policies to address them together and separately, is important for maintaining a robust global food supply. Herein we assess the impacts of each of these disasters in the context of food and agriculture, and then discuss their compounded effect. We discuss the implications for policy, and suggest opportunities for future research.
BACKGROUND: In March 2020, the Louisiana Department of Health activated the Medical Monitoring Station (MMS) in downtown New Orleans. This alternative care site is designed to decompress hospitals and nursing homes overwhelmed by the coronavirus disease 2019 (COVID-19) pandemic. Given the city’s historic vulnerability to hurricanes, planning for possible tropical weather events has been a priority for MMS leadership. METHODS: The planning process incorporated input from all sectors/agencies working at the facility, to ensure consistency and cohesion. The MMS Shelter-in-Place Plan (MSIPP) was created, and a comprehensive tabletop exercise was conducted. RESULTS: Six planning topics emerged as a result of the planning process and were used to create a comprehensive plan for sheltering-in-place. These topics address hurricane preparedness for patient care, interfacility coordination, wrap-around services, medical logistics, essential staffing, and incident command during a shelter-in-place scenario. CONCLUSIONS: The MSIPP created by the MMS helped to maximize patient safety and continuity of operations during a real-world event. Select pieces of the plan were activated to meet the needs and threat level of Tropical Storm Cristobal. This experience reinforced the need for originality, scalability, and flexibility in building emergency operations plans in the midst of an unprecedented pandemic.
BACKGROUND: Drought is an understudied driver of infectious disease dynamics. Amidst the ongoing southwestern North American megadrought, California (USA) is having the driest multi-decadal period since 800 CE, exacerbated by anthropogenic warming. In this study, we aimed to examine the influence of drought on coccidioidomycosis, an emerging infectious disease in southwestern USA. METHODS: We analysed California census tract-level surveillance data from 2000 to 2020 using generalised additive models and distributed monthly lags on precipitation and temperature. We then developed an ensemble prediction algorithm of incident cases of coccidioidomycosis per census tract to estimate the counterfactual incidence that would have occurred in the absence of drought. FINDINGS: Between April 1, 2000, and March 31, 2020, there were 81 448 reported cases of coccidioidomycosis throughout California. An estimated 1467 excess cases of coccidioidomycosis were observed in California in the 2 years following the drought that occurred between 2007 and 2009, and an excess 2649 drought-attributable cases of coccidioidomycosis were observed in the 2 years following the drought that occurred between 2012 and 2015. These increased numbers of cases more than offset the declines in cases that occurred during drought. An IQR increase in summer temperatures was associated with 2·02 (95% CI 1·84-2·22) times higher incidence in the following autumn (September to November), and an IQR increase in precipitation in the winter was associated with 1·45 (1·36-1·55) times higher incidence in the autumn. The effect of winter precipitation was 36% (25-48) stronger when preceded by two dry, rather than average, winters. Incidence in arid counties was most sensitive to precipitation fluctuations, while incidence in wetter counties was most sensitive to temperature. INTERPRETATION: In California, multi-year cycles of dry conditions followed by a wet winter increases transmission of coccidioidomycosis, especially in historically wetter areas. With anticipated increasing frequency of drought in southwestern USA, continued expansion of coccidioidomycosis, along with more intense seasons, is expected. Our results motivate the need for heightened precautions against coccidioidomycosis in seasons that follow major droughts. FUNDING: National Institutes of Health.
From hampering the ability of water utilities to fill their reservoirs to leaving forests parched and ready to burn, drought is a unique natural hazard that impacts many human and natural systems. A great deal of research and synthesis to date has been devoted to understanding how drought conditions harm agricultural operations, leaving other drought-vulnerable sectors relatively under-served. This review aims to fill in such gaps by synthesizing literature from a diverse array of scientific fields to detail how drought impacts nonagricultural sectors of the economy: public water supply, recreation and tourism, forest resources, and public health. We focus on the Intermountain West region of the United States, where the decadal scale recurrence of severe drought provides a basis for understanding the causal linkages between drought conditions and impacts. This article is categorized under: Human Water & Value of Water Science of Water & Water Extremes.
BACKGROUND: Research on temperature and respiratory hospitalizations is lacking in the southeastern U.S. where cold weather is relatively rare. This retrospective study examined the association between cold waves and pneumonia and influenza (P&I) emergency department (ED) visits and hospitalizations in three metro-Atlanta hospitals. METHODS: We used a case-crossover design, restricting data to the cooler seasons of 2009-2019, to determine whether cold waves influenced ED visits and hospitalizations. This analysis considered effects by race/ethnicity, age, sex, and severity of comorbidities. We used generalized additive models and distributed lag non-linear models to examine these relationships over a 21-day lag period. RESULTS: The odds of a P&I ED visit approximately one week after a cold wave were increased by as much as 11%, and odds of an ED visit resulting in hospitalization increased by 8%. For ED visits on days with minimum temperatures >20 °C, there was an increase of 10-15% in relative risk (RR) for short lags (0-2 days), and a slight decrease in RR (0-5%) one week later. For minimum temperatures <0 °C, RR decreased at short lags (5-10%) before increasing (1-5%) one week later. Hospital admissions exhibited a similar, but muted, pattern. CONCLUSION: Unusually cold weather influenced ED visits and admissions in this population.
Coccidioidomycosis, or valley fever, is an infectious fungal disease currently endemic to the southwestern United States. Symptoms of valley fever range in severity from flu-like illness to severe morbidity and mortality. Warming temperatures and changes in precipitation patterns may cause the area of endemicity to expand northward throughout the western United States, putting more people at risk for contracting valley fever. This may increase the health and economic burdens from this disease. We developed an approach to describe the relationship between climate conditions and valley fever incidence using historical data and generated projections of future incidence in response to both climate change and population trends using the Climate Change Impacts and Risk Analysis (CIRA) framework developed by the U.S. Environmental Protection Agency. We also developed a method to estimate economic impacts of valley fever that is based on case counts. For our 2000-15 baseline time period, we estimated annual medical costs, lost income, and economic welfare losses for valley fever in the United States were $400,000 per case, and the annual average total cost was $3.9 billion per year. For a high greenhouse gas emission scenario and accounting for population growth, we found that total annual costs for valley fever may increase up to 164% by year 2050 and up to 380% by 2090. By the end of the twenty-first century, valley fever may cost $620,000 per case and the annual average total cost may reach $18.5 billion per year. This work contributes to the broader effort to monetize climate change-attributable damages in the United States.
Five-year retrospective analysis of respiratory viruses in children less than 18 years old at Tripler Army Medical Center and outlying clinics in Oahu. Respiratory syncytial virus and influenza A showed pronounced seasonality with peaks from September to December and December to March, respectively. Results provide a better understanding of the timing of viral preventive strategies in Oahu.
Climate change affects human health, and severe acute respiratory syndrome (SARS) incidence is one of the health impacts of climate change. This study is a retrospective cohort study. Data have been collected from the Iranian Ministry of Health and Medical Education between 17 February 2016 and17 February 2018. The Neural Network Model has been used to predict SARS infection. Based on the results of the multivariate Poisson regression and the analysis of the coexistence of the variables, the minimum daily temperature was positively associated with the risk of SARS in men and women. The risk of SARS has increased in women and men with increasing daily rainfall. According to the result, by changes in bioclimatic parameters, the number of SARS patients will be increased in cities of Iran. Our study has shown a significant relationship between SARS and the climatic variables by the type of climate and gender. The estimates suggest that hospital admissions for climate-related respiratory diseases in Iran will increase by 36% from 2020 to 2050. This study demonstrates one of the health impacts of climate change. Policymakers can control the risks of climate change by mitigation and adaptation strategists.
BACKGROUND: Cutaneous leishmaniasis (CL) has been reported in recent years in South Khorasan Province, a desert region of eastern Iran, where the main species is Leishmania tropica. Little is known of the influence of geography and climate on its distribution, and so this study was conducted to determine geo-climatic factors by using geographic information system. METHODS: The home addresses of patients with CL patients who were diagnosed and notified from 2009 to 2017 were retrieved from the provincial health center and registered on the village/town/city point layer. The effects of mean annual rainfall (MAR) and mean annual humidity (MAH), mean annual temperature (MAT), maximum annual temperature (MaxMAT), minimum annual temperature (MinMAT), mean annual number of high-velocity wind days (MAWD), mean annual frosty days (MAFD) and snowy days (MASD), elevation, soil type and land cover on CL distribution were examined. The geographical analysis was done using ArcMap software, and univariate and multivariate binary logistic regression were applied to determine the factors associated with CL. RESULTS: A total of 332 CL patients were identified: 197 (59.3%) male and 135 (40.7%) female. Their mean age was 29.3 ± 2.1 years, with age ranging from 10 months to 98 years. CL patients came from a total of 86 villages/towns/cities. By multivariate analysis, the independent factors associated with increased CL were urban setting (OR = 52.102), agricultural land cover (OR = 3.048), and MAWD (OR = 1.004). Elevation was a protective factor only in the univariate analysis (OR = 0.999). Soil type, MAH, MAT, MinMAT, MaxMAT, and MAFD did not influence CL distribution in eastern Iran. CONCLUSIONS: The major risk zones for CL in eastern Iran were urban and agricultural areas with a higher number of windy days at lower altitudes. Control strategies to reduce human vector contact should be focused in these settings.
Rodents play a significant role in the balance of a terrestrial ecosystem; they are considered prey for many predators like owls and snakes. However, they present a high risk to agriculture (damaging crops) and health. These rodents are the main reservoirs of some vector-borne diseases like leishmaniasis. Meriones shawi (MS) and Psammomys obesus (PO) are the primary Zoonotic cutaneous leishmaniasis (ZCL) reservoirs in the Middle East and North Africa (MENA). A review on the MS and PO at the MENA scale was explored. A database of about 1500 papers was used. 38 sites were investigated as foci for MS and 36 sites for PO, and 83 sites of Phlebotomus papatasi (Pp) in the studied region. An updated map at the regional scale and the trend of the reservoir distribution was carried out using a performing proper density analysis. In this paper, climatic conditions and habitat characteristics of these two reservoirs were reviewed. The association of rodent density with some climatic variables is another aspect explored in a case study from Tunisia in the period 2009-2015 using Pearson correlation. Lastly, the protection and control measures of the reservoir were analyzed. The high concentration of the MS, PO, and Pp can be used as an indicator to identify the high-risk area of leishmaniasis infection.
BACKGROUND/OBJECTIVE: Natural disasters (NDs) are calamitous phenomena that can increase the risk of infections in disaster-affected regions. This study aimed to evaluate the frequency of malaria and cutaneous leishmaniasis (CL) before and after earthquakes, floods, and droughts during the past four decades in Iran. METHODS: Malaria and CL data were obtained from the reports of the Ministry of Health and Medical Education in Iran for the years 1983 through 2017. The data of NDs were extracted from the Centre for Research on the Epidemiology of Disasters (CRED). Interrupted time series analysis with linear regression modeling was used to estimate time trends of mentioned diseases in pre- and post-disaster conditions. RESULTS: For the periods preceding the disasters drought and flood, a decreasing time trend for malaria and CL was found over time. The time trend of malaria rate preceding the 1990 earthquake was stable, a downward trend was found after 1990 disaster until 1997 (β coefficient: -10.7; P = .001), and this declining trend was continued after 1997 disaster (β coefficient: -2.7; P = .001). The time trend of CL rate preceding the 1990 earthquake had a declining trend, an upward trend was found after 1990 earthquake until 1999 (β coefficient: +8.7; P = .293), and a slight upward trend had also appeared after 1999 earthquake (β coefficient: +0.75; P = .839). CONCLUSION: The results of the current study indicated the occurrence of earthquakes, floods, and droughts has no significant effect on the frequency of malaria and CL in Iran.
The anthropogenic activities if not sensibly managed put enormous pressure on water resources of any country. Water quality of Kabul River has severely been polluted by rapid urbanization and industrialization. The sub lethal organic pollution is caused by discharge of effluents and other wastes into the river. The effluents from multiple leather processing units, and various other industries along with human feces and livestock manure are polluting the river ecology at an alarming rate. Climate is further impacting the quality of river and diminutive work has been done on climate change impacts on water quality. Integrated efforts are required to improve the water quality to reduce the morbidity and mortality rate in Pakistan and Afghanistan. In this review, water quality situation of Kabul River in Pakistan and Afghanistan along with potential impacts on health, agriculture and aquatic life under the changing climate scenario are presented. Water quality indices and modelling approaches for different parameters are suggested under the changing climate scenario which is expected to increase in the region to find the fate and transport of pollutants in the Kabul Rivers basin. Finally, recommendations were made to improve water quality of Kabul River and to decrease its adverse impacts.
BACKGROUND: Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates. METHODS: The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR). RESULTS: The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar-e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92-2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03-1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90-0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island. CONCLUSION: Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.
The purpose of this research is to evaluate the hygienic quality of spring and well water used mainly for drinking and domestic activities for some districts in the municipality of Al-Hoceima city. In the rainy season of November to April 2018-2019, a total of fifty-two groundwater samples were collected under appropriate conditions and analyzed according to Moroccan standards, for coliform bacteria (BC), Escherichia coli (E. Coli), and intestinal Enterococcus (IE). The sample locations were identified from the physiochemical details and the nature of nearby pollution. The physical parameters of temperature, pH, dissolved oxygen O-2, oxygen saturation, electrical conductivity (EC), total dissolved solids (TDS) and salinity were measured on site. The results revealed that quality of water from all springs and wells, in the area of study, did not meet the World Health Organization guideline as well as Morocco standard for drinking water of zero (0) coliform forming unit (CFU) per 100 mL for CB, E. Coli and IE, respectively. Furthermore, fecal contamination of groundwater is indicated, the high bacteria count in samples could be attributed to their closeness septic effluent, the infiltration of wastewater into groundwater, and to the inadequate treatment of sewage. It is recommended that the water should be treated properly before consumption.
The Gaza Strip is one of the world’s most fragile states and faces substantial public health and development challenges. Climate change is intensifying existing environmental problems, including increased water stress. We provide the first published assessment of climate impacts on diarrhoeal disease in Gaza and project future health burdens under climate change scenarios. Over 1 million acute diarrhoea cases presenting to health facilities during 2009−2020 were linked to weekly temperature and rainfall data and associations assessed using time-series regression analysis employing distributed lag non-linear models (DLNMs). Models were applied to climate projections to estimate future burdens of diarrhoeal disease under 2 °C and 1.5 °C global warming scenarios. There was a significantly raised risk of diarrhoeal disease associated with both mean weekly temperature above 19 °C and total weekly rainfall below 6 mm in children 0−3 years. A heat effect was also present in subjects aged > 3 years. Annual diarrhoea cases attributable to heat and low rainfall was 2209.0 and 4070.3, respectively, in 0−3-year-olds. In both age-groups, heat-related cases could rise by over 10% under a 2 °C global warming level compared to baseline, but would be limited to below 2% under a 1.5 °C scenario. Mean rises of 0.9% and 2.7% in diarrhoea cases associated with reduced rainfall are projected for the 1.5 °C and 2 °C scenarios, respectively, in 0−3-year-olds. Climate change impacts will add to the considerable development challenges already faced by the people of Gaza. Substantial health gains could be achieved if global warming is limited to 1.5 °C.
Though instances of arthropod-borne (arbo)virus co-infection have been documented clinically, the overall incidence of arbovirus co-infection and its drivers are not well understood. Now that dengue, Zika and chikungunya viruses are all in circulation across tropical and subtropical regions of the Americas, it is important to understand the environmental and biological conditions that make co-infections more likely to occur. To understand this, we developed a mathematical model of co-circulation of two arboviruses, with transmission parameters approximating dengue, Zika and/or chikungunya viruses, and co-infection possible in both humans and mosquitoes. We examined the influence of seasonal timing of arbovirus co-circulation on the extent of co-infection. By undertaking a sensitivity analysis of this model, we examined how biological factors interact with seasonality to determine arbovirus co-infection transmission and prevalence. We found that temporal synchrony of the co-infecting viruses and average temperature were the most influential drivers of co-infection incidence. Our model highlights the synergistic effect of co-transmission from mosquitoes, which leads to more than double the number of co-infections than would be expected in a scenario without co-transmission. Our results suggest that appreciable numbers of co-infections are unlikely to occur except in tropical climates when the viruses co-occur in time and space.
BACKGROUND: Estimates of the geographical distribution of Culex mosquitoes in the Americas have been limited to state and provincial levels in the United States and Canada and based on data from the 1980s. Since these estimates were made, there have been many more documented observations of mosquitoes and new methods have been developed for species distribution modeling. Moreover, mosquito distributions are affected by environmental conditions, which have changed since the 1980s. This calls for updated estimates of these distributions to understand the risk of emerging and re-emerging mosquito-borne diseases. METHODS: We used contemporary mosquito data, environmental drivers, and a machine learning ecological niche model to create updated estimates of the geographical range of seven predominant Culex species across North America and South America: Culex erraticus, Culex nigripalpus, Culex pipiens, Culex quinquefasciatus, Culex restuans, Culex salinarius, and Culex tarsalis. RESULTS: We found that Culex mosquito species differ in their geographical range. Each Culex species is sensitive to both natural and human-influenced environmental factors, especially climate and land cover type. Some prefer urban environments instead of rural ones, and some are limited to tropical or humid areas. Many are found throughout the Central Plains of the USA. CONCLUSIONS: Our updated contemporary Culex distribution maps may be used to assess mosquito-borne disease risk. It is critical to understand the current geographical distributions of these important disease vectors and the key environmental predictors structuring their distributions not only to assess current risk, but also to understand how they will respond to climate change. Since the environmental predictors structuring the geographical distribution of mosquito species varied, we hypothesize that each species may have a different response to climate change.
Amblyomma mixtum is a Neotropical generalist tick of medical and veterinary importance which is widely distributed from United States of America to Ecuador. The aim of this study was to evaluate changes in the geographic projections of the ecological niche models of A. mixtum in climate change scenarios in America. We constructed a database of published scientific publications, personal collections, personal communications, and online databases. Ecological niche modelling was performed with 15 Bioclimatic variables using kuenm in R and was projected to three time periods (Last Glacial Maximum, Current and 2050) for America. Our model indicated a wide distribution for A. mixtum, with higher probability of occurrence along the Gulf of Mexico and occurring in a lesser proportion in the Pacific states, Central America, and the northern part of South America. The areas of new invasion are located mainly on the border of Mexico with Guatemala and Belize, some regions of Central America and Colombia. We conclude that the ecological niche modelling are effective tools to infer the potential distribution of A. mixtum in America, in addition to helping to propose future measures of epidemiological control and surveillance in the new potential areas of invasion.
BACKGROUND: The application of molecular diagnostics has identified enteric group adenovirus serotypes 40 and 41 as important causes of diarrhea in children. However, many aspects of the epidemiology of adenovirus 40/41 diarrhea have not been described. METHODS: We used data from the 8-site Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project birth cohort study to describe site- and age-specific incidence, risk factors, clinical characteristics, and seasonality. RESULTS: The incidence of adenovirus 40/41 diarrhea was substantially higher by quantitative polymerase chain reaction than enzyme immunoassay and peaked at ∼30 episodes per 100 child-years in children aged 7-15 months, with substantial variation in incidence between sites. A significant burden was also seen in children 0-6 months of age, higher than other viral etiologies with the exception of rotavirus. Children with adenovirus 40/41 diarrhea were more likely to have a fever than children with norovirus, sapovirus, and astrovirus (adjusted odds ratio [aOR], 1.62; 95% CI, 1.16-2.26) but less likely than children with rotavirus (aOR, 0.66; 95% CI, 0.49-0.91). Exclusive breastfeeding was strongly protective against adenovirus 40/41 diarrhea (hazard ratio, 0.64; 95% CI, 0.48-0.85), but no other risk factors were identified. The seasonality of adenovirus 40/41 diarrhea varied substantially between sites and did not have clear associations with seasonal variations in temperature or rainfall. CONCLUSIONS: This study supports the situation of adenovirus 40/41 as a pathogen of substantial importance, especially in infants. Fever was a distinguishing characteristic in comparison to other nonrotavirus viral etiologies, and promotion of exclusive breastfeeding may reduce the high observed burden in the first 6 months of life.
Objectives: Sandstorms are natural climate calamities causing severe weather changes and health prob-lems. The sandstorm allied issues are of significant apprehension worldwide, mainly in the present pan-demic. This study aims to examine the “sandstorm impact on environmental pollution particulate matter (PM2.5), carbon monoxide (CO), ozone (O3), and daily new cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) ” in Kuwait. Methods: The two incidences of sandstorms occurred in Kuwait, dated 13 March 2021 and 13 June 2021. The data on “PM2.5, CO, NO2, and O-3, and SARS-CoV-2 cases and deaths ” were documented three weeks before and after both incidences of the sandstorm. For the first incidence, the data was recorded from 18 February to 12 March 2021; and from 13 March to 2 April 2021. However, for the second incidence of sandstorms, data were documented from 23 May to 12 June 2021; and from 13 June to 3 July 2021. The daily “PM2.5, CO, NO2, and O-3 levels ” were recorded from “Air Quality Index-AQI, metrological web, and data on COVID-19 daily cases and deaths were recorded from the World Health Organization “. Results: After the first and second sandstorm incidence, the air contaminants PM2.5 was increased by 26.62%, CO 22.08%, and O-3 increased 18.10% compared to before the sandstorm. SARS-CoV-2 cases were markedly amplified by (21.25%), and deaths were increased by (61.32%) after the sandstorm. Conclusions: Sandstorm events increase air pollutants PM2.5, CO, and O-3 levels, and these pollutants increase the SARS-COV-2 daily cases and deaths in Kuwait. The findings have a meaningful memorandum to healthcare representatives to advise the public about the health hazards of the sandstorm and its link-age with SARS-CoV-2 cases and deaths. (C) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.
BACKGROUND: Leishmaniases are a vector-borne disease, re-emerging in several regions of the world posing a burden on public health. As other vector-borne diseases, climate change is a crucial factor affecting the evolution of leishmaniasis. In Morocco, anthroponotic cutaneous leishmaniasis (ACL) is widespread geographically as many foci across the country, mainly in central Morocco. The objective of this study is to evaluate the potential impacts of climate change on the distribution of ACL due to Leishmania tropica, and its corresponding vector Phlebotomus sergenti in Morocco. METHODS: Using Ecological Niche Modeling (ENM) tool, the estimated geographical range shift of L. tropica and P. sergenti by 2050 was projected under two Representative’s Concentration’s Pathways (RCPs) to be 2.6 and RCP 8.5 respectively. P. sergenti records were obtained from field collections of the laboratory team and previously published entomological observations, while, epidemiological data for L. tropica were obtained from Moroccan Ministry of Health reports. RESULTS: Our models under present-day conditions indicated a probable expansion for L. tropica as well as for its vector in Morocco, P. sergenti. It showed a concentrated distribution in the west-central and northern area of Morocco. Future predictions anticipate expansion into areas not identified as suitable for P. sergenti under present conditions, particularly in northern and southeastern areas of Morocco. L. tropica is also expected to have high expansion in southern areas for the next 30 years in Morocco. CONCLUSION: This indicates that L. tropica and P. sergenti will continue to find suitable climate conditions in the future. A higher abundance of P. sergenti may indeed result in a higher transmission risk of ACL. This information is essential in developing a control plan for ACL in Morocco. However, future investigations on L. tropica reservoirs are needed to confirm our predictions.
BACKGROUND AND AIM: Rift Valley fever virus (RVFV) is a vector-borne virus that causes RVF in humans and ruminants. The clinical symptoms in humans and animals are non-specific and often misdiagnosed, but abortions in ruminants and high mortality in young animals are characteristic. Since the initial outbreak in the Rift Valley area in Kenya, the disease has spread to most African countries and the Middle East. The presence and epidemiological status of RVFV in humans and animals in Palestine are unknown. This study aimed to investigate the presence and risk factors for RVF seroprevalence in veterinarians, as occupational hazard professionals, and sheep, as highly susceptible animals, in Northern Palestine. MATERIALS AND METHODS: A cross-sectional study was conducted. Data and blood samples of 280 Assaf sheep and 100 veterinarians in close occupational contact with sheep were collected between August and September 2020 using an indirect enzyme-linked immunosorbent assay. RESULTS: No evidence of RVF antibodies was found in any human or animal sample. CONCLUSION: Our results suggest that RVFV has not circulated in livestock in Northern Palestine, yet. Surveillance and response capabilities and cooperation with the nearby endemic regions are recommended. The distribution of competent vectors in Palestine, associated with global climate change and the role of wild animals, might be a possible route for RVF spreading to Palestine from neighboring countries.
OBJECTIVES: To study the epidemiology of dengue incidence and understand the dynamics of dengue transmission in Makkah, Kingdom of Saudi Arabia (KSA), between 2017-2019. METHODS: This is a cross-sectional study. Health and demographic data was obtained for all confirmed dengue cases in Makkah, KSA, in the years 2017-2019 from the Vector-Borne and Zoonotic Diseases Administration (VBZDA) in Makkah and the Makkah Regional Laboratory, KSA. In addition, entomological data about Aedes density was obtained from the VBZDA. Descriptive epidemiological methods were used to determine the occurrence and distribution of dengue cases. RESULTS: Laboratory-confirmed dengue cases were higher in 2019 as compared to 2017 and 2018, suggesting an outbreak of dengue in Makkah, KSA, in 2019. The incidence of confirmed dengue cases was 204 in 2017, 163 in 2018 and 748 in 2019. Dengue mostly affected people in the 25-44 age group, accounting for approximately half of the annual dengue cases each year. Men were at a higher dengue incidence risk when compared to women, and Saudi women had a higher risk rate for dengue cases when compared to non-Saudi women in all 3 years studied. There was no dengue related death in these 3 years. CONCLUSION: The dengue incidence increased in Makkah, KSA, in 2019 as compared to the previous 2 years, owing to heavy rainfall in 2019. Post-rainfall Vector control efforts may help contain the disease in Makkah, KSA.
OBJECTIVE: Increased temperature and humidity across the world and emergence of mosquito-borne diseases, notably dengue both continue to present public health problems, but their relationship is not clear as conflicting evidence abound on the association between climate conditions and risk of dengue fever. This characterization is important as mitigation of climate change-related variables will contribute toward efficient planning of health services. The purpose of this study was to determine whether humidity in addition to high temperatures increase the risk of dengue transmission. METHODS: We have assessed the joint association between temperature and humidity with the incidence of dengue fever at Jeddah City in Saudi Arabia. We obtained weekly data from Jeddah City on temperature and humidity between 2006 and 2009 for 200 weeks starting week 1/2006 and ending week 53/2009. We also collected incident case data on dengue fever in Jeddah City. RESULTS: The cross-tabulated analysis showed an association between temperature or humidity conditions and incident cases of dengue. Our data found that hot and dry conditions were associated with a high risk of dengue incidence in Jeddah City. CONCLUSION: Hot and dry conditions are risk factors for dengue fever.
Rift Valley Fever (RVF) and West Nile virus (WNV) are two important emerging Arboviruses transmitted by Aedes and Culex mosquitoes, typically Ae. caspius, Ae. detritus and Cx. pipiens in temperate regions. In Morocco, several outbreaks of WNV (1996, 2003 and 2010), affecting horses mostly, have been reported in north-western regions resulting in the death of 55 horses and one person cumulatively. Serological evidence of WNV local circulation, performed one year after the latest outbreak, revealed WNV neutralizing bodies in 59 out of 499 tested participants (El Rhaffouli et al., 2012). The country also shares common borders with northern Mauritania, where RVF is often documented. Human movement, livestock trade, climate changes and the availability of susceptible mosquito vectors are expected to increase the spread of these diseases in the country. Thus, in this study, we gathered a data set summarizing occurrences of Ae. caspius, Ae. detritus and Cx. pipiens in the country, and generated model prediction for their potential distribution under both current and future (2050) climate conditions, as a proxy to identify regions at-risk of RVF and WNV probable expansion. We found that the north-western regions (where the population is most concentrated), specifically along the Atlantic coastline, are highly suitable for Ae. caspius, Ae. detritus and Cx. pipiens, under present-day conditions. Future model scenarios anticipated possible range changes for the three mosquitoes under all climatic assumptions. All of the studied species are prospected to gain new areas that are currently not suitable, even under the most optimist scenario, thus placing additional human populations at risk. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes. Public health officials, entomological surveillance and control delegation must augment efforts and continuously monitor these areas to reduce and minimize human infection risk.
The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999-2014 was used for training and the three subsequent years of incidence 2015-2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.
Dengue fever has been endemic in Paraguay since 2009 and is a major cause of public-health-management-related burdens. However, Paraguay still lacks information on the association between climate factors and dengue fever. We aimed to investigate the association between climatic factors and dengue fever in Asuncion. Cumulative dengue cases from January 2014 to December 2020 were extracted weekly, and new cases and incidence rates of dengue fever were calculated. Climate factor data were aggregated weekly, associations between dengue cases and climate factors were analyzed, and variables were selected to construct our model. A generalized additive model was used, and the best model was selected based on Akaike information criteria. Piecewise regression analyses were performed for non-linear climate factors. Wind and relative humidity were negatively associated with dengue cases, and minimum temperature was positively associated with dengue cases when the temperature was less than 21.3 °C and negatively associated with dengue when greater than 21.3 °C. Additional studies on dengue fever in Asuncion and other cities are needed to better understand dengue fever.
Climate is considered an important factor in the temporal and spatial distribution of vector-borne diseases. Dengue transmission involves many factors: although it is not yet fully understood, climate is a critical factor as it facilitates risk analysis of epidemics. This study analyzed the effect of seasonal factors and the relationship between climate variables and dengue risk in the municipality of Campo Grande, from 2008 to 2018. Generalized linear models with negative binomial and Poisson distribution were used. The most appropriate model was the one with “minimum temperature” and “precipitation”, both lagged by one month, controlled by “year”. In this model, a 1 degrees C rise in the minimum temperature of one month led to an increase in dengue cases the following month, while a 10 mm increase in precipitation led to an increase in dengue cases the following month.
Dengue fever is a serious and growing public health problem in Latin America and elsewhere, intensified by climate change and human mobility. This paper reviews the approaches to the epidemiological prediction of dengue fever using the One Health perspective, including an analysis of how Machine Learning techniques have been applied to it and focuses on the risk factors for dengue in Latin America to put the broader environmental considerations into a detailed understanding of the small-scale processes as they affect disease incidence. Determining that many factors can act as predictors for dengue outbreaks, a large-scale comparison of different predictors over larger geographic areas than those currently studied is lacking to determine which predictors are the most effective. In addition, it provides insight into techniques of Machine Learning used for future predictive models, as well as general workflow for Machine Learning projects of dengue fever.
BACKGROUND: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. METHODS: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. FINDINGS: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0-3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41-1·73]) and 3-5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22-1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32-2·37] at 0 months lag vs maximum RR 1·58 [1·39-1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33-1·92] vs 1·15 [1·08-1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages. INTERPRETATION: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods. FUNDING: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico. TRANSLATION: For the Portuguese translation of the abstract see Supplementary Materials section.
The expansion of the invasive mosquito Aedes aegypti L. (Diptera: Culicidae) towards temperate regions in the Americas is causing concern because of its public health implications. As for other insects, the distribution limits of Ae. aegypti have been suggested to be related to minimum temperatures and to be controlled mainly by cold tolerance. The aim of this study was to assess the daily mortality of immature stages of Ae. aegypti under natural winter conditions in Buenos Aires, Argentina, in relation to preceding thermal conditions. The experiment was performed outdoors, and one cohort of larvae was started each week for 16 weeks, and reared up to the emergence of the adults. Three times a week, larvae, pupae and emerged adults were counted, and these data were used to calculate the daily mortality of larvae, pupae and adults and to analyze their relationship with thermal conditions. The results showed that mortality was generally low, with a few peaks of high mortality after cold front events. The mortality of pupae and larvae showed a higher correlation with the cooling degree hours of previous days than with the minimum, maximum or mean temperatures. Pupae and adults showed to be more vulnerable to low temperatures than larvae. A delay in mortality was observed in relation to the low temperature events, with a proportion of individuals dying in a later stage after the end of the cold front. These results suggest that thermal conditions during cold fronts in Buenos Aires are close to the tolerance limit of the local Ae. aegypti population. The wide range of responses of different individuals suggests that low winter temperatures may constitute a selective force, leading the population to a higher tolerance to low temperatures, which might favor the further expansion of this species towards colder regions.
Leishmaniasis is a public health problem worldwide. We aimed to predict ecological niche models (ENMs) for visceral (VL) and cutaneous (CL) leishmaniasis and the sand flies involved in the transmission of leishmaniasis in São Paulo, Brazil. Phlebotomine sand flies were collected between 1985 and 2015. ENMs were created for each sand fly species using Maximum Entropy Species Distribution Modeling software, and 20 climatic variables were determined. Nyssomyia intermedia (Lutz & Neiva, 1912) and Lutzomyia longipalpis (Lutz & Neiva, 1912), the primary vectors involved in CL and VL, displayed the highest suitability across the various regions, climates, and topographies. L. longipalpis was found in the border of Paraná an area currently free of VL. The variables with the greatest impact were temperature seasonality, precipitation, and altitude. Co-presence of multiple sand fly species was observed in the cuestas and coastal areas along the border of Paraná and in the western basalt areas along the border of Mato Grosso do Sul. Human CL and VL were found in 475 of 546 (86.7%) and 106 of 645 (16.4%) of municipalities, respectively. Niche overlap between N. intermedia and L. longipalpis was found with 9208 human cases of CL and 2952 cases of VL. ENMs demonstrated that each phlebotomine sand fly species has a unique geographic distribution pattern, and the occurrence of the primary vectors of CL and VL overlapped. These data can be used by public authorities to monitor the dispersion and expansion of CL and VL vectors in São Paulo state.
INTRODUCTION: Zika virus (ZIKV) is primarily transmitted byAedes aegypti and Aedes albopictus mosquitoes between humans and non-human primates. Climate change may enhance virus reproduction in Aedes spp. mosquito populations, resulting in intensified ZIKV outbreaks. The study objective was to explore how an outbreak similar to the 2016 ZIKV outbreak in Brazil might unfold with projected climate change. METHODS: A compartmental infectious disease model that included compartments for humans and mosquitoes was developed to fit the 2016 ZIKV outbreak data from Brazil using least squares optimization. To explore the impact of climate change, published polynomial relationships between temperature and temperature-sensitive mosquito population and virus transmission parameters (mosquito mortality, development rate, and ZIKV extrinsic incubation period) were used. Projections for future outbreaks were obtained by simulating transmission with effects of projected average monthly temperatures on temperature-sensitive model parameters at each of three future time periods: 2011-2040, 2041-2070, and 2071-2100. The projected future climate was obtained from an ensemble of regional climate models (RCMs) obtained from the Co-Ordinated Regional Downscaling Experiment (CORDEX) that used Representative Concentration Pathways (RCP) with two radiative forcing values, RCP4.5 and RCP8.5. A sensitivity analysis was performed to explore the impact of temperature-dependent parameters on the model outcomes. RESULTS: Climate change scenarios impacted the model outcomes, including the peak clinical case incidence, cumulative clinical case incidence, time to peak incidence, and the duration of the ZIKV outbreak. Comparing 2070-2100 to 2016, using RCP4.5, the peak incidence was 22,030 compared to 10,473; the time to epidemic peak was 12 compared to 9 weeks, and the outbreak duration was 52 compared to 41 weeks. Comparing 2070-2100 to 2016, using RCP8.5, the peak incidence was 21,786 compared to 10,473; the time to epidemic peak was 11 compared to 9 weeks, and the outbreak duration was 50 compared to 41weeks. The increases are due to optimal climate conditions for mosquitoes, with the mean temperature reaching 28 °C in the warmest months. Under a high emission scenario (RCP8.5), mean temperatures extend above optimal for mosquito survival in the warmest months. CONCLUSION: Outbreaks of ZIKV in locations similar to Brazil are expected to be more intense with a warming climate. As climate change impacts are becoming increasingly apparent on human health, it is important to quantify the effect and use this knowledge to inform decisions on prevention and control strategies.
In Brazil, approximately 99% of malaria cases are concentrated in the Amazon region. An acute febrile infectious disease, malaria is closely related to climatic and hydrological factors. Environmental variables such as rainfall, flow, level, and color of rivers, the latter associated with the suspended sediment concentration, are important factors that can affect the dynamics of the incidence of some infectious diseases, including malaria. This study explores the possibility that malaria incidence is influenced by precipitation, fluctuations in river levels, and suspended sediment concentration. The four studied municipalities are located in two Brazilian states (Amazonas and Para) on the banks of rivers with different hydrological characteristics. The results suggest that precipitation and river level fluctuations modulate the seasonal pattern of the disease and evidence the existence of delayed effects of river floods on malaria incidence. The seasonality of the disease has a different influence in each municipality studied. However, municipalities close to rivers with the same characteristic color of waters (as a function of the concentration of suspended sediments) have similar responses to the disease.
BACKGROUND: Human leptospirosis is responsible for great losses and deaths, especially in developing countries, which can be mitigated by knowing the correct health indicators and climate influence on the disease. METHODS: Leptospirosis cases and deaths, population and precipitation were recovered from different databases (2007-2019). Annual incidence, mortality and case fatality rates (CFRs) of human leptospirosis and average precipitation were calculated for Brazil and its regions. Time series analysis using an moving average with external variable (ARMAX) model was used to analyse the monthly contribution and precipitation influence over leptospirosis cases for each Brazilian region and for the whole country. A forecast model to predict cases for 2020 was created for Brazil. RESULTS: Human leptospirosis exhibited heterogeneous distribution among Brazilian regions, with most cases occurring during the rainy season and precipitation influenced the disease occurrence in all regions but the South. The forecast model predicted 3276.99 cases for 2020 (mean absolute percentage error 14.680 and root mean square error 53.013). Considering the annual average for the period, the leptospirosis incidence was 1913 cases per 100 000 inhabitants, mortality was 0.168 deaths per 100 000 inhabitants and the CFR was 8.83%. CONCLUSIONS: The models built can be useful for planning leptospirosis surveillance and control actions for the whole country and its regions and, together with the health indicators, revealed no uniform epidemiological situation of leptospirosis in Brazil.
Leptospirosis is a zoonosis with epidemic potential, especially after heavy rainfall causing river, urban and flash floods. Certain features of Santa Catarina’s coastal region influence these processes. Using negative binomial regression, we investigated trends in the incidence of leptospirosis in the six municipalities with the highest epidemic peaks between 2000 and 2015 and the climatic and environmental variables associated with the occurrence of the disease. Incidence was highest in 2008 and 2011, and peaks occurred in the same month or month after disasters. Incidence showed a strong seasonal trend, being higher in summer months. There was a decrease trend in incidence across the six municipalities (3.21% per year). The climatic and environmental factors that showed the strongest associations were number of rainy days, maximum temperature, presence of flash floods, and river flooding. The impact of these variables varied across the municipalities. Significant interactions were found, indicating that the effect of river flooding on incidence is not the same across all municipalities and differences in incidence between municipalities depend on the occurrence of river flooding.
Leptospirosis is an acute febrile disease that mainly affects developing countries with tropical climates. The complexity and magnitude of this disease is attributed to socioeconomic, climatic, and environmental conditions. In this study, in a 10-year period from 2008 to 2017, the relationship between human leptospirosis cases and climatic factors in Cartagena de Indias, Colombia were evaluated. Monthly leptospirosis cases, climatic variables, and macroclimatic phenomena (El Nino and La Nina) were obtained from public datasets. Local climatic factors included temperature (maximum, average, and minimum), relative humidity, precipitation, and the number of precipitation days. Time series graphs were drawn and correlations between cases of leptospirosis and climatic variables considering lags from 0 to 10 months were examined. A total of 360 cases of leptospirosis were reported in Cartagena during the study period, of which 192 (53.3%) were systematically notified between October and December. Several correlations were detected between the number of cases, local climatic variables, and macroclimatic phenomena. Mainly, the increase of cases correlated with increased precipitation and humidity during the La Nina periods. Herein, seasonal patterns and correlations suggest that the climate in Cartagena could favor the incidence of leptospirosis. Our findings suggest that prevention and control of human leptospirosis in Cartagena should be promoted and strengthened, especially in the last quarter of the year.
The way newspapers frame infectious disease outbreaks and their connection to the environmental determinants of disease transmission matter because they shape how we understand and respond to these major events. In 2017, following an unexpected climatic event named “El Niño Costero,” a dengue epidemic in Peru affected over seventy-five thousand people. This paper examines how the Peruvian news media presented dengue, a climate-sensitive disease, as a public health problem by analyzing a sample of 265 news stories on dengue from two major newspapers published between January 1st and December 31st of 2017. In analyzing the construction of responsibility for the epidemic, I find frames that blamed El Niño Costero’s flooding and Peru’s poorly prepared cities and public health infrastructure as the causes of the dengue outbreak. However, when analyzing frames that offer solutions to the epidemic, I find that news articles call for government-led, short-term interventions (e.g., fogging) that fail to address the decaying public health infrastructure and lack of climate-resilient health systems. Overall, news media tended to over-emphasize dengue as requiring technical solutions that ignore the root causes of health inequality and environmental injustice that allow dengue to spread in the first place. This case speaks to the medicalization of public health and to a long history of disease-control programs in the Global South that prioritized top-down technical approaches, turning attention away from the social and environmental determinants of health, which are particularly important in an era of climate change.
The 2030 Agenda for Sustainable Development is a plan of action for people, planet and prosperity. Thousands of years and centuries of colonisation have passed the precarious housing conditions, food insecurity, lack of sanitation, the limitation of surveillance, health care programs and climate change. Chagas disease continues to be a public health problem. The control programs have been successful in many countries in reducing transmission by T. cruzi; but the results have been variable. WHO makes recommendations for prevention and control with the aim of eliminating Chagas disease as a public health problem. Climate change, deforestation, migration, urbanisation, sylvatic vectors and oral transmission require integrating the economic, social, and environmental dimensions of sustainable development, as well as the links within and between objectives and sectors. While the environment scenarios change around the world, native vector species pose a significant public health threat. The man-made atmosphere change is related to the increase of triatomines’ dispersal range, or an increase of the mobility of the vectors from their sylvatic environment to man-made constructions, or humans getting into sylvatic scenarios, leading to an increase of Chagas disease infection. Innovations with the communities and collaborations among municipalities, International cooperation agencies, local governmental agencies, academic partners, developmental agencies, or environmental institutions may present promising solutions, but sustained partnerships, long-term commitment, and strong regional leadership are required. A new world has just opened up for the renewal of surveillance practices, but the lessons learned in the past should be the basis for solutions in the future.
The transmission of leptospirosis is conditioned by climatic variables. In northeastern Argentina leptospirosis outbreaks occur mainly in coincidence with periods of abundant precipitation and high hydrometric level. A Susceptible-Infectious-Recovered Epidemiological Model (SIR) is proposed, which incorporates hydroclimatic variables for the three most populated cities in the area (Santa Fe, Paraná and Rosario), during the 2009-2018 period. Results obtained by solving the proposed SIR model for the 2010 outbreak are in good agreement with the actual data, capturing the dynamics of the leptospirosis outbreak wave. However, the model does not perform very well in the last months of the year when isolated cases appear outside the outbreak periods, probably due to non- climatic factors not explicitly considered in the present version of the model. Nevertheless, the dynamic modeling of infectious diseases considering hydroclimatic variables constitutes a climatic service for the public health system, not yet available in Argentina.
The relationship between hydrometeorological disasters and the health of affected populations is still hardly discussed in Rio Grande do Sul (RS), Brazil. Hepatitis A is a disease that involves health and urban environment issue and is an avoidable disease. This study aims to analyze the relationship between flood areas and waterborne diseases, in this case, Hepatitis A. A database of confirmed cases of Hepatitis A and flood events in the municipality of Encantado-RS, Brazil between 2012 and 2014 was structured. These data were analyzed spatially from the kernel estimator of the occurrence points of Hepatitis A cases and correlated to the urban perimeter. It was verified that 44 cases were registered in the three months following the occurrence of flood, an increase of almost 300% in the records of Hepatitis A. The results identified that all the confirmed cases are in the urban area located in the floodplain. This reaffirms the importance of encouraging the formulation and implementation of policies to prevent outbreaks of waterborne diseases post hydrometeorological disaster.
Arboviruses transmitted by Aedes aegypti (e.g., dengue, chikungunya, Zika) are of major public health concern on the arid coastal border of Ecuador and Peru. This high transit border is a critical disease surveillance site due to human movement-associated risk of transmission. Local level studies are thus integral to capturing the dynamics and distribution of vector populations and social-ecological drivers of risk, to inform targeted public health interventions. Our study examines factors associated with household-level Ae. aegypti presence in Huaquillas, Ecuador, while accounting for spatial and temporal effects. From January to May of 2017, adult mosquitoes were collected from a cohort of households (n = 63) in clusters (n = 10), across the city of Huaquillas, using aspirator backpacks. Household surveys describing housing conditions, demographics, economics, travel, disease prevention, and city services were conducted by local enumerators. This study was conducted during the normal arbovirus transmission season (January-May), but during an exceptionally dry year. Household level Ae. aegypti presence peaked in February, and counts were highest in weeks with high temperatures and a week after increased rainfall. Univariate analyses with proportional odds logistic regression were used to explore household social-ecological variables and female Ae. aegypti presence. We found that homes were more likely to have Ae. aegypti when households had interruptions in piped water service. Ae. aegypti presence was less likely in households with septic systems. Based on our findings, infrastructure access and seasonal climate are important considerations for vector control in this city, and even in dry years, the arid environment of Huaquillas supports Ae. aegypti breeding habitat.
Vector-borne diseases are some of the leading public health problems in the tropics, and their association with climatic anomalies is well known. The current study aimed to evaluate the trend of American cutaneous leishmaniasis cases in the municipality of Manaus, Amazonas-Brazil, and its relationship with climatic extremes (ENSO). The study was carried out using a series of secondary data from notifications on the occurrence of several American cutaneous leishmaniasis cases in the municipality of Manaus between 1990 and 2017 obtained through the Sistema de Informação de Agravos de Notificação. Data regarding temperature, relative humidity, and precipitation for this municipality were derived from the Instituto Nacional de Meteorologia (INMET) and the National Oceanic and Atmospheric Administration (NOAA) websites. Coherence and wavelet phase analysis was conducted to measure the degree of relationship of the occurrence of the cases of cutaneous leishmaniasis and the El Niño-Southern Oscillation (ENSO). The results show that during La Niña events, an increase in American cutaneous leishmaniasis (ACL) cases is anticipated after the increase in rainfall from November, resulting in a more significant number of cases in January, February, and March. It was observed that in the municipality of Manaus, the dynamics of ACL cases are directly influenced by ENSO events that affect environmental variables such as precipitation, temperature, and humidity. Therefore, climatic variations consequently change the ACL incidence dynamics, leading to subsequent increases or decreases in the incidence of ACL cases in the area.
Due to the global increase in mosquito-borne diseases outbreaks it is recommended to increase surveillance and monitoring of vector species to respond swiftly and with early warning indicators. Usually, however, the information about vector presence and activity seems to be insufficient to implement timely and effective control strategies. Here we present an improved mathematical model of Aedes aegypti population dynamics with the aim of making the Dengue surveillance system more proactive. The model considers the four life stages of the mosquito: egg, larva, pupa and adult. As driving factors, it incorporates temperature which affects development and mortality rates at certain stages, and precipitation which is known to affect egg submergence and hatching, as well as larval mortality associated with desiccation. Our mechanistic model is implemented as a free and stand-alone system that automatically retrieves all needed inputs, runs a simulation and shows the results. A major improvement in our implementation is the capacity of the system to predict the population dynamics of Ae. aegypti in the near future, given that it uses gridded weather forecast data. Hence, it is independent by meteorological station proximity. The model predictions are compared with field data from C ‘ ordoba City, Argentina. Although field data have high variability, an overall accordance has been observed. The comparison of results obtained using observed weather data, with the simulations based on forecasts, suggests that the modeled dynamics are accurate up to 15 days in advance. Preliminary results of Ae. aegypti population dynamics for a consecutive three-year period, spanning different eco-regions of Argentina, are presented, and demonstrate the flexibility of the system.
Dengue is steadily increasing worldwide and expanding into higher latitudes. Current non-endemic areas are prone to become endemic soon. To improve understanding of dengue transmission in these settings, we assessed the spatiotemporal dynamics of the hitherto largest outbreak in the non-endemic metropolis of Buenos Aires, Argentina, based on detailed information on the 5,104 georeferenced cases registered during summer-autumn of 2016. The highly seasonal dengue transmission in Buenos Aires was modulated by temperature and triggered by imported cases coming from regions with ongoing outbreaks. However, local transmission was made possible and consolidated heterogeneously in the city due to housing and socioeconomic characteristics of the population, with 32.8% of autochthonous cases occurring in slums, which held only 6.4% of the city population. A hierarchical spatiotemporal model accounting for imperfect detection of cases showed that, outside slums, less-affluent neighborhoods of houses (vs. apartments) favored transmission. Global and local spatiotemporal point-pattern analyses demonstrated that most transmission occurred at or close to home. Additionally, based on these results, a point-pattern analysis was assessed for early identification of transmission foci during the outbreak while accounting for population spatial distribution. Altogether, our results reveal how social, physical, and biological processes shape dengue transmission in Buenos Aires and, likely, other non-endemic cities, and suggest multiple opportunities for control interventions.
Cutaneous Leishmaniasis (CL) is the most prevalent form of Leishmaniasis and is widely endemic in the Americas. Several species of Leishmania are responsible for CL, a severely neglected tropical disease and the treatment of CL vary according to the different species of Leishmania. We proposed to map the distribution of the Leishmania species reported in French Guiana (FG) using a biogeographic approach based on environmental predictors. We also measured species endemism i.e., the uniqueness of species to a defined geographic location. Our results show that the distribution patterns varied between Leishmania spp. and were spatially dependent on climatic covariates. The species distribution modelling of the eco-epidemiological spatial patterns of Leishmania spp. is the first to measure endemism based on bioclimatic factors in FG. The study also emphasizes the impact of tree cover loss and climate on the increasing distribution of L. (Viannia) braziliensis in the most anthropized regions. Detection of high-risk regions for the different between Leishmania spp. is essential for monitoring and active surveillance of the vector. As climate plays a major role in the spatial distribution of the vector and reservoir and the survival of the pathogen, climatic covariates should be included in the analysis and mapping of vector-borne diseases. This study underscores the significance of local land management and the urgency of considering the impact of climate change in the development of vector-borne disease management strategies at the global scale.
BACKGROUND: West Nile virus (WNV) is a vector-borne pathogen of global relevance and is currently the most widely distributed flavivirus causing encephalitis worldwide. Climate conditions have direct and indirect impacts on vector abundance and virus dynamics within the mosquito. The significance of environmental variables as drivers in WNV epidemiology is increasing under the current climate change scenario. In this study we used a machine learning algorithm to model WNV distributions in South America. METHODS: Our model evaluated eight environmental variables for their contribution to the occurrence of WNV since its introduction in South America in 2004. RESULTS: Our results showed that environmental variables can directly alter the occurrence of WNV, with lower precipitation and higher temperatures associated with increased virus incidence. High-risk areas may be modified in the coming years, becoming more evident with high greenhouse gas emission levels. Countries such as Bolivia, Paraguay and several Brazilian areas, mainly in the northeast and midwest regions and the Pantanal biome, will be greatly affected, drastically changing the current WNV distribution. CONCLUSIONS: Understanding the linkages between climatological and ecological change as determinants of disease emergence and redistribution will help optimize preventive strategies. Increased virus surveillance, integrated modelling and the use of geographically based data systems will provide more anticipatory measures by the scientific community.
Dengue is an endemic disease in more than 100 countries, but there are few studies about the effects of hydroclimatic variability on dengue incidence (DI) in tropical dryland areas. This study investigates the association between hydroclimatic variability and DI (2008-2018) in a large tropical dryland area. The area studied comprehends seven municipalities with populations ranging from 32,879 to 2,545,419 inhabitants. First, the precipitation and temperature impacts on interannual and seasonal DI were investigated. Then, the monthly association between DI and hydroclimatic variables was analyzed using generalized least squares (GLS) regression. The model’s capability to reproduce DI given the current hydroclimatic conditions and DI seasonality over the entire time period studied were assessed. No association between the interannual variation of precipitation and DI was found. However, seasonal variation of DI was shaped by precipitation and temperature. February-July was the main dengue season period. A precipitation threshold, usually above 100 mm, triggers the rapid DI rising. Precipitation and minimum air temperature were the main explanatory variables. A two-month-lagged predictor was relevant for modeling, occurring in all regressions, followed by a non-lagged predictor. The climate predictors differed among the regression models, revealing the high spatial DI variability driven by hydroclimatic variability. GLS regressions were able to reproduce the beginning, development, and end of the dengue season, although we found underestimation of DI peaks and overestimation of low DI. These model limitations are not an issue for climate change impact assessment on DI at the municipality scale since historical DI seasonality was well simulated. However, they may not allow seasonal DI forecasting for some municipalities. These findings may help not only public health policies in the studied municipalities but also have the potential to be reproducible for other dryland regions with similar data availability.
In the last 20 years yellow fever (YF) has seen dramatic changes to its incidence and geographic extent, with the largest outbreaks in South America since 1940 occurring in the previously unaffected South-East Atlantic coast of Brazil in 2016-2019. While habitat fragmentation and land-cover have previously been implicated in zoonotic disease, their role in YF has not yet been examined. We examined the extent to which vegetation, land-cover, climate and host population predicted the numbers of months a location reported YF per year and by each month over the time-period. Two sets of models were assessed, one looking at interannual differences over the study period (2003-2016), and a seasonal model looking at intra-annual differences by month, averaging over the years of the study period. Each was fit using hierarchical negative-binomial regression in an exhaustive model fitting process. Within each set, the best performing models, as measured by the Akaike Information Criterion (AIC), were combined to create ensemble models to describe interannual and seasonal variation in YF. The models reproduced the spatiotemporal heterogeneities in YF transmission with coefficient of determination (R2) values of 0.43 (95% CI 0.41-0.45) for the interannual model and 0.66 (95% CI 0.64-0.67) for the seasonal model. For the interannual model, EVI, land-cover and vegetation heterogeneity were the primary contributors to the variance explained by the model, and for the seasonal model, EVI, day temperature and rainfall amplitude. Our models explain much of the spatiotemporal variation in YF in South America, both seasonally and across the period 2003-2016. Vegetation type (EVI), heterogeneity in vegetation (perhaps a proxy for habitat fragmentation) and land cover explain much of the trends in YF transmission seen. These findings may help understand the recent expansions of the YF endemic zone, as well as to the highly seasonal nature of YF.
In the last two decades dengue cases increased significantly throughout the world, giving place to more frequent outbreaks in Latin America. In the non-endemic city of San Ramón de la Nueva Orán, located in Northwest Argentina, large dengue outbreaks alternate with several years of smaller ones. This pattern, as well as the understanding of the underlying mechanisms, could be essential to design proper strategies to reduce epidemic size. We develop a stochastic model that includes climate variables, social structure, and mobility between a non-endemic city and an endemic area. Climatic variables were input of a mosquito population ecological model, which in turn was coupled to a meta-population, spatially explicit, epidemiological model. Human mobility was included into the model given the high border crossing to the northern country of Bolivia, where dengue transmission is sustained during the whole year. We tested different hypotheses regarding people mobility as well as climate variability by fitting numerical simulations to weekly clinical data reported from 2009 to 2016. After assessing the number of imported cases that triggered the observed outbreaks, our model allows to explain the observed epidemic pattern. We found that the number of vectors per host and the effective reproductive number are proxies for large epidemics. Both proxies are related with climate variability such as rainfall and temperature, opening the possibility to test these meteorological variables for forecast purposes.
Dengue is a re-emerging disease, currently considered the most important mosquito-borne arbovirus infection affecting humankind, taking into account both its morbidity and mortality. Brazil is considered an endemic country for dengue, such that more than 1,544,987 confirmed cases were notified in 2019, which means an incidence rate of 735 for every 100 thousand inhabitants. Climate is an important factor in the temporal and spatial distribution of vector-borne diseases, such as dengue. Thus, rainfall and temperature are considered macro-factors determinants for dengue, since they directly influence the population density of Aedes aegypti, which is subject to seasonal fluctuations, mainly due to these variables. This study examined the incidence of dengue fever related to the climate influence by using temperature and rainfall variables data obtained from remote sensing via artificial satellites in the metropolitan region of Rio de Janeiro, Brazil. The mathematical model that best fits the data is based on an auto-regressive moving average with exogenous inputs (ARMAX). It reproduced the values of incidence rates in the study period and managed to predict with good precision in a one-year horizon. The approach described in present work may be replicated in cities around the world by the public health managers, to build auxiliary operational tools for control and prevention tasks of dengue, as well of other arbovirus diseases.
Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and processing multi-source data to generate risk predictors and consuming significant time and computational resources to train and validate models locally. In this context, this study proposed a framework for dengue risk prediction by integrating big geospatial data cloud computing based on Google Earth Engine (GEE) platform and artificial intelligence modeling on the Google Colab platform. It enables defining the epidemiological calendar, delineating the predominant area of dengue transmission in cities, generating the data of risk predictors, and defining multi-date ahead prediction scenarios. We implemented the experiments based on weekly dengue cases during 2013-2020 in the Federal District and Fortaleza, Brazil to evaluate the performance of the proposed framework. Four predictors were considered, including total rainfall (R(sum)), mean temperature (T(mean)), mean relative humidity (RH(mean)), and mean normalized difference vegetation index (NDVI(mean)). Three models (i.e., random forest (RF), long-short term memory (LSTM), and LSTM with attention mechanism (LSTM-ATT)), and two modeling scenarios (i.e., modeling with or without dengue cases) were set to implement 1- to 4-week ahead predictions. A total of 24 models were built, and the results showed in general that LSTM and LSTM-ATT models outperformed RF models; modeling could benefit from using historical dengue cases as one of the predictors, and it makes the predicted curve fluctuation more stable compared with that only using climate and environmental factors; attention mechanism could further improve the performance of LSTM models. This study provides implications for future dengue risk prediction in terms of the effectiveness of GEE-based big geospatial data processing for risk predictor generation and Google Colab-based risk modeling and presents the benefits of using historical dengue data as one of the input features and the attention mechanism for LSTM modeling.
Dengue fever is re-emerging worldwide, however the reasons of this new emergence are not fully understood. Our goal was to report the incidence of dengue in one of the most populous States of Brazil, and to assess the high-risk areas using a spatial and spatio-temporal annual models including geoclimatic, demographic and socioeconomic characteristics. An ecological study with both, a spatial and a temporal component was carried out in Sao Paulo State, Southeastern Brazil, between January 1st, 2007 and December 31st, 2019. Crude and Bayesian empirical rates of dengue cases following by Standardized Incidence Ratios (SIR) were calculated considering the municipalities as the analytical units and using the Integrated Nested Laplace Approximation in a Bayesian context. A total of 2,027,142 cases of dengue were reported during the studied period. The spatial model allocated the municipalities in four groups according to the SIR values: (I) SIR<0.8; (II) SIR 0.8<1.2; (III) SIR 1.2<2.0 and SIR>2.0 identified the municipalities with higher risk for dengue outbreaks. “Hot spots” are shown in the thematic maps. Significant correlations between SIR and two climate variables, two demographic variables and one socioeconomical variable were found. No significant correlations were found in the spatio-temporal model. The incidence of dengue exhibited an inconstant and unpredictable variation every year. The highest rates of dengue are concentrated in geographical clusters with lower surface pressure, rainfall and altitude, but also in municipalities with higher degree of urbanization and better socioeconomic conditions. Nevertheless, annual consolidated variations in climatic features do not influence in the epidemic yearly pattern of dengue in southeastern Brazil.
BACKGROUND: This research addresses two questions: (1) how El Niño Southern Oscillation (ENSO) affects climate variability and how it influences dengue transmission in the Metropolitan Region of Recife (MRR), and (2) whether the epidemic in MRR municipalities has any connection and synchronicity. METHODS: Wavelet analysis and cross-correlation were applied to characterize seasonality, multiyear cycles, and relative delays between the series. This study was developed into two distinct periods. Initially, we performed periodic dengue incidence and intercity epidemic synchronism analyses from 2001 to 2017. We then defined the period from 2001 to 2016 to analyze the periodicity of climatic variables and their coherence with dengue incidence. RESULTS: Our results showed systematic cycles of 3-4 years with a recent shortening trend of 2-3 years. Climatic variability, such as positive anomalous temperatures and reduced rainfall due to changes in sea surface temperature (SST), is partially linked to the changing epidemiology of the disease, as this condition provides suitable environments for the Aedes aegypti lifecycle. CONCLUSION: ENSO may have influenced the dengue temporal patterns in the MRR, transiently reducing its main way of multiyear variability (3-4 years) to 2-3 years. Furthermore, when the epidemic coincided with El Niño years, it spread regionally and was highly synchronized.
Tropical countries face urgent public health challenges regarding epidemic control of Dengue. Since effective vector-control efforts depend on the timing in which public policies take place, there is an enormous demand for accurate prediction tools. In this work, we improve upon a recent approach of coarsely predicting outbreaks in Brazilian urban centers based solely on their yearly climate data. Our methodological advancements encompass a judicious choice of data pre-processing steps and usage of modern computational techniques from signal-processing and manifold learning. Altogether, our results improved earlier prediction accuracy scores from 0.72 to 0.80, solidifying manifold learning on climate data alone as a viable way to make (coarse) dengue outbreak prediction in large urban centers. Ultimately, this approach has the potential of radically simplifying the data required to do outbreak analysis, as municipalities with limited public health funds may not monitor a large number of features needed for more extensive machine learning approaches.
This study investigated a model to assess the role of climate fluctuations on dengue (DENV) dynamics from 2010 to 2019 in four Brazilian municipalities. The proposed transmission model was based on a preexisting SEI-SIR model, but also incorporates the vector vertical transmission and the vector’s egg compartment, thus allowing rainfall to be introduced to modulate egg-hatching. Temperature and rainfall satellite data throughout the decade were used as climatic model inputs. A sensitivity analysis was performed to understand the role of each parameter. The model-simulated scenario was compared to the observed dengue incidence and the findings indicate that the model was able to capture the observed seasonal dengue incidence pattern with good accuracy until 2016, although higher deviations were observed from 2016 to 2019. The results further demonstrate that vertical transmission fluctuations can affect attack transmission rates and patterns, suggesting the need to investigate the contribution of vertical transmission to dengue transmission dynamics in future assessments. The improved understanding of the relationship between different environment variables and dengue transmission achieved by the proposed model can contribute to public health policies regarding mosquito-borne diseases.
Environmental changes are among the main factors that contribute to the emergence or re-emergence of viruses of public health importance. Here, we show the impact of environmental modifications on cases of infections by the dengue, chikungunya and Zika viruses in humans in the state of Tocantins, Brazil, between the years 2010 and 2019. We conducted a descriptive and principal component analysis (PCA) to explore the main trends in environmental modifications and in the cases of human infections caused by these arboviruses in Tocantins. Our analysis demonstrated that the occurrence of El Niño, deforestation in the Cerrado and maximum temperatures had correlations with the cases of infections by the Zika virus between 2014 and 2016. El Niño, followed by La Niña, a gradual increase in precipitation and the maximum temperature observed between 2015 and 2017 were shown to have contributed to the infections by the chikungunya virus. La Niña and precipitation were associated with infections by the dengue virus between 2010 and 2012 and El Niño contributed to the 2019 outbreak observed within the state. By PCA, deforestation, temperatures and El Niño were the most important variables related to cases of dengue in humans. We conclude from this analysis that environmental changes (deforestation and climate change) presented a strong influence on the human infections caused by the dengue, chikungunya and Zika viruses in Tocantins from 2010 to 2019.
Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we developed a model for predicting monthly dengue cases in Brazilian cities 1 month ahead, using data from 2007-2019. We compared different machine learning algorithms and feature selection methods using epidemiologic and meteorological variables. We found that different models worked best in different cities, and a random forests model trained on monthly dengue cases performed best overall. It produced lower errors than a seasonal naive baseline model, gradient boosting regression, a feed-forward neural network, or support vector regression. For each city, we computed the mean absolute error between predictions and true monthly numbers of dengue cases on the test data set. The median error across all cities was 12.2 cases. This error was reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities.
According to the World Health Organization, dengue is a neglected tropical disease. Latin America, specifically Colombia is in alert regarding this arbovirosis as there was a spike in the number of reported dengue cases at the beginning of 2019. Although there has been a worldwide decrease in the number of reported dengue cases, Colombia has shown a growing trend over the past few years. This study performed a Poisson multilevel analysis with mixed effects on STATA® version 16 and R to assess sociodemographic, climatic, and entomological factors that may influence the occurrence of dengue in three municipalities for the period 2010-2015. Information on dengue cases and their sociodemographic variables was collected from the National Public Health Surveillance System (SIVIGILA) records. For climatic variables (temperature, relative humidity, and precipitation), we used the information registered by the weather stations located in the study area, which are managed by the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (IDEAM) or the Corporación Autónoma Regional (CAR). The entomological variables (house index, container index, and Breteau index) were provided by the Health office of the Cundinamarca department. SIVIGILA reported 1921 dengue cases and 56 severe dengue cases in the three municipalities; of them, three died. One out of four cases occurred in rural areas. The age category most affected was adulthood, and there were no statistical differences in the number of cases between sexes. The Poisson multilevel analysis with the best fit model explained the presentation of cases were temperature, relative humidity, precipitation, childhood, live in urban area and the contributory healthcare system. The temperature had the biggest influence on the presentation of dengue cases in this region between 2010 and 2015.
Dengue virus (DENV) is an endemic disease in the hot and humid low-lands of Colombia. We characterize the association of monthly series of dengue cases with indices of El Niño/Southern Oscillation (ENSO) at the tropical Pacific and local climatic variables in Colombia during the period 2007-2017 at different temporal and spatial scales. For estimation purposes, we use lagged cross-correlations (Pearson test), cross-wavelet analysis (wavelet cross spectrum, and wavelet coherence), as well as a novel nonlinear causality method, PCMCI, that allows identifying common causal drivers and links among high dimensional simultaneous and time-lagged variables. Our results evidence the strong association of DENV cases in Colombia with ENSO indices and with local temperature and rainfall. El Niño (La Niña) phenomenon is related to an increase (decrease) of dengue cases nationally and in most regions and departments, with maximum correlations occurring at shorter time lags in the Pacific and Andes regions, closer to the Pacific Ocean. This association is mainly explained by the ENSO-driven increase in temperature and decrease in rainfall, especially in the Andes and Pacific regions. The influence of ENSO is not stationary, given the reduction of DENV cases since 2005, and that local climate variables vary in space and time, which prevents to extrapolate results from one region to another. The association between DENV and ENSO varies at national and regional scales when data are disaggregated by seasons, being stronger in DJF and weaker in SON. Overall, the Pacific and Andes regions control the relationship between dengue dynamics and ENSO at national scale. Cross-wavelet analysis indicates that the ENSO-DENV relation in Colombia exhibits a strong coherence in the 12 to 16-months frequency band, which implies the frequency locking between the annual cycle and the interannual (ENSO) timescales. Results of nonlinear causality metrics reveal the complex concomitant effects of ENSO and local climate variables, while offering new insights to develop early warning systems for DENV in Colombia.
BACKGROUND: The influence of climate on the epidemiology of dengue has scarcely been studied in Cartagena. METHODS: The relationship between dengue cases and climatic and macroclimatic factors was explored using an ecological design and bivariate and time-series analyses during lag and non-lag months. Data from 2008-2017 was obtained from the national surveillance system and meteorological stations. RESULTS: Cases correlated only with climatic variables during lag and non-lag months. Decreases in precipitation and humidity and increases in temperature were correlated with an increase in cases. CONCLUSIONS: Our findings provide useful information for establishing and strengthening dengue prevention and control strategies.
BACKGROUND: Respiratory Syncytial Virus (RSV) is the main cause of pediatric morbidity and mortality. The complex evolution of RSV creates a need for worldwide surveillance, which may assist in the understanding of multiple viral aspects. OBJECTIVES: This study aimed to investigate RSV features under the Brazilian Influenza Surveillance Program, evaluating the role of viral load and genetic diversity in disease severity and the influence of climatic factors in viral seasonality. METHODOLOGY: We have investigated the prevalence of RSV in children up to 3 years of age with severe acute respiratory infection (SARI) in the state of Espirito Santo (ES), Brazil, from 2016 to 2018. RT-qPCR allowed for viral detection and viral load quantification, to evaluate association with clinical features and mapping of local viral seasonality. Gene G sequencing and phylogenetic reconstruction demonstrated local genetic diversity. RESULTS: Of 632 evaluated cases, 56% were caused by RSV, with both subtypes A and B co-circulating throughout the years. A discrete inverse association between average temperature and viral circulation was observed. No correlation between viral load and disease severity was observed, but children infected with RSV-A presented a higher clinical severity score (CSS), stayed longer in the hospital, and required intensive care, and ventilatory support more frequently than those infected by RSV-B. Regarding RSV diversity, some local genetic groups were observed within the main genotypes circulation RSV-A ON1 and RSV-B BA, with strains showing modifications in the G gene amino acid chain. CONCLUSION: Local RSV studies using the Brazilian Influenza Surveillance Program are relevant as they can bring useful information to the global RSV surveillance. Understanding seasonality, virulence, and genetic diversity can aid in the development and suitability of antiviral drugs, vaccines, and assist in the administration of prophylactic strategies.
OBJECTIVE: The frequency and seasonality of viruses in tropical regions are scarcely reported. We estimated the frequency of seven respiratory viruses and assessed seasonality of respiratory syncytial virus (RSV) and influenza viruses in a tropical city. METHODS: Children (age ≤ 18 years) with acute respiratory infection were investigated in Salvador, Brazil, between July 2014 and June 2017. Respiratory viruses were searched by direct immunofluorescence and real-time polymerase chain reaction for detection of RSV, influenza A virus, influenza B virus, adenovirus (ADV) and parainfluenza viruses (PIV) 1, 2 and 3. Seasonal distribution was evaluated by Prais-Winsten regression. Due to similar distribution, influenza A and influenza B viruses were grouped to analyse seasonality. RESULTS: The study group comprised 387 cases whose median (IQR) age was 26.4 (10.5-50.1) months. Respiratory viruses were detected in 106 (27.4%) cases. RSV (n = 76; 19.6%), influenza A virus (n = 11; 2.8%), influenza B virus (n = 7; 1.8%), ADV (n = 5; 1.3%), PIV 1 (n = 5; 1.3%), PIV 3 (n = 3; 0.8%) and PIV 2 (n = 1; 0.3%) were identified. Monthly count of RSV cases demonstrated seasonal distribution (b3 = 0.626; P = 0.003). More than half (42/76 [55.3%]) of all RSV cases were detected from April to June. Monthly count of influenza cases also showed seasonal distribution (b3 = -0.264; P = 0.032). Influenza cases peaked from November to January with 44.4% (8/18) of all influenza cases. CONCLUSIONS: RSV was the most frequently detected virus. RSV and influenza viruses showed seasonal distribution. These data may be useful to plan the best time to carry out prophylaxis and to increase the number of hospital beds.
The emergence of the COVID-19 pandemic reinforced the central role of the One Health (OH) approach, as a multisectoral and multidisciplinary perspective, to tackle health threats at the human-animal-environment interface. This study assessed Brazilian preparedness and response to COVID-19 and zoonoses with a focus on the OH approach and equity dimensions. We conducted an environmental scan using a protocol developed as part of a multi-country study. The article selection process resulted in 45 documents: 79 files and 112 references on OH; 41 files and 81 references on equity. The OH and equity aspects are poorly represented in the official documents regarding the COVID-19 response, either at the federal and state levels. Brazil has a governance infrastructure that allows for the response to infectious diseases, including zoonoses, as well as the fight against antimicrobial resistance through the OH approach. However, the response to the pandemic did not fully utilize the resources of the Brazilian state, due to the lack of central coordination and articulation among the sectors involved. Brazil is considered an area of high risk for emergence of zoonoses mainly due to climate change, large-scale deforestation and urbanization, high wildlife biodiversity, wide dry frontier, and poor control of wild animals’ traffic. Therefore, encouraging existing mechanisms for collaboration across sectors and disciplines, with the inclusion of vulnerable populations, is required for making a multisectoral OH approach successful in the country.
The 2020 Atlantic hurricane season was notable for a record-setting 30 named storms while, contemporaneously, the COVID-19 pandemic was circumnavigating the globe. The active spread of COVID-19 complicated disaster preparedness and response actions to safeguard coastal and island populations from hurricane hazards. Major hurricanes Eta and Iota, the most powerful storms of the 2020 Atlantic season, made November landfalls just two weeks apart, both coming ashore along the Miskito Coast in Nicaragua’s North Caribbean Coast Autonomous Region. Eta and Iota bore the hallmarks of climate-driven storms, including rapid intensification, high peak wind speeds, and decelerating forward motion prior to landfall. Hurricane warning systems, combined with timely evacuation and sheltering procedures, minimized loss of life during hurricane impact. Yet these protective actions potentially elevated risks for COVID-19 transmission for citizens sharing congregate shelters during the storms and for survivors who were displaced post-impact due to severe damage to their homes and communities. International border closures and travel restrictions that were in force to slow the spread of COVID-19 diminished the scope, timeliness, and effectiveness of the humanitarian response for survivors of Eta and Iota. Taken together, the extreme impacts from hurricanes Eta and Iota, compounded by the ubiquitous threat of COVID-19 transmission, and the impediments to international humanitarian response associated with movement restrictions during the pandemic, acted to exacerbate harms to population health for the citizens of Nicaragua.
Foodborne diseases are a neglected research area, and despite the existence of many tools for diagnosis and genetic studies, very little is known about the effect of the landscape on the genetic diversity and presence of parasites. One of these foodborne disease is paragonimiasis, caused by trematodes of the genus Paragonimus, which is responsible for a high number of infections in humans and wild animals. The main Paragonimus sp reported in Mesoamerica is Paragonimus mexicanus, yet there are doubts about its correct identification as a unique species throughout the region. This, together with a lack of detailed knowledge about their ecology, evolution and differentiation, may complicate the implementation of control strategies across the Mesoamerican region. We had the goal of delimiting the species of P. mexicanus found throughout Mesoamerica and determining the effect of landscape and geology on the diversity and presence of the parasite. We found support for the delimitation of five genetic groups. The genetic differentiation among these groups was positively affected by elevation and the isolation of river basins, while the parasite’s presence was affected negatively only by the presence of human settlements. These results suggest that areas with lower elevation, connected rivers basins, and an absence of human settlements have low genetic differentiation and high P. mexicanus presence, which may increase the risk of Paragonimus infection. These demonstrate the importance of accurate species delimitation and consideration of the effect of landscape on Paragonimus in the proposal of adequate control strategies. However, other landscape variables cannot be discarded, including temperature, rainfall regime, and spatial scale (local, landscape and regional). These additional variables were not explored here, and should be considered in future studies.
BACKGROUND: The burden of gastrointestinal infections related to hot ambient temperature remains largely unexplored in low-to-middle income countries which have most of the cases globally and are experiencing the greatest impact from climate change. The situation is particularly true in Brazil. OBJECTIVES: Using medical records covering over 78 % of population, we quantify the association between high temperature and risk of hospitalization for gastrointestinal infection in Brazil between 2000 and 2015. METHODS: Data on hospitalization for gastrointestinal infection and weather conditions were collected from 1814 Brazilian cities during the 2000-2015 hot seasons. A time-stratified case-crossover design was used to estimate the association. Stratified analyses were performed by region, sex, age-group, type of infection and early/late study period. RESULTS: For every 5 °C increase in mean daily temperature, the cumulative odds ratio (OR) of hospitalization over 0-9 days was 1.22 [95 % confidence interval (CI): 1.21, 1.23] at the national level, reaching its maximum in the south and its minimum in the north. The strength of association tended to decline across successive age-groups, with infants < 1 year most susceptible. The effect estimates were similar for men and women. Waterborne and foodborne infections were more associated with high temperature than the 'others' and 'idiopathic' groups. There was no substantial change in the association over the 16-year study period. DISCUSSION: Our findings indicate that exposure to high temperature is associated with increased risk of hospitalization for gastrointestinal infection in the hot season, with the strength varying by region, population subgroup and infection type. There was no evidence to indicate adaptation to heat over the study duration.
We evaluated species richness, abundance, alpha diversity, and true diversity of Phlebotominae sand flies temporal changes in domiciles within the northern Argentina city of Corrientes. A total of 16 sampling nights were conducted seasonally throughout the years 2012-2014 through light traps supplemented with CO2. Meteorological and remote sensing environmental factors were used to assessed for vectors implications in disease transmission through Generalized Mixt Models. Lutzomyia longipalpis was the most abundant and common species, followed by Nyssomyia neivai and Migonemyia migonei. Lutzomyia longipalpis was more abundant in urban areas, Ny. neivai was associated with vegetation in periurban areas, both were found all sampling years with higher abundance during the rainy season. Positive association of Lu. longipalpis with precipitation and relative humidity and negative association with temperature were observed. Models showed humidity and vegetation as making effects on Lu. longipalpis abundance. Precipitation was significant for Mg. migonei models, with higher abundance in periurban and periurban-rural environments. For Ny. neivai models, relative humidity was the most important variable, followed by precipitation frequency. Our findings led to identify high risk areas and develop predictive models. These are useful for public health stakeholders giving tolls to optimized resources aim to prevent leshmaniasis transmission on the area.
The Atlantic Forest is home to several arboviruses potentially pathogenic to humans. Therefore, it is crucial to assess the effects of seasonality on mosquito populations circulating in this domain. We evaluated the influence of seasonal variation on the oviposition activity of epidemiologically important mosquito populations in an Environmental Protection Area in Rio de Janeiro, Brazil. Mosquito eggs were collected using ovitraps for 1 year. During the sampling period, 1,086 eggs were collected. Of these, 39 (3.6%) did not hatch, and 1,047 (96.4%) reached the adult stage. Aedes albopictus (44.8%), Ae. terrens (6.4%), and Haemagogus leucocelaenus (48.8%) eggs and adults were identified. The changes in this community over the seasons were also analyzed. Season influence on the collections was significant. The highest numbers of collected eggs were collected in the summer and autumn, with Hg. leucocelaenus dominant in the summer and Ae. albopictus in the autumn. These two seasons were more similar to each other in terms of the composition of the collected mosquito community, forming a separate cluster from winter and spring groups. Summer, autumn, and winter presented values of Dominance (D), Shannon Diversity (H), and Evenness (J) closer to each other than spring. Climatic factors recorded throughout the collection period were not associated with egg abundance, except for temperature, which was positively correlated with Ae. albopictus presence. Finally, seasonality seemed to influence the oviposition activity of the three species recorded. Summer and autumn were the most critical seasons due to Ae. albopictus and Hg. leucocelaenus circulation. These findings should be considered in prophylaxis and implementation of entomological control strategies in the study area.
BACKGROUND: Phlebotomines are a group of insects which include vectors of the Leishmania parasites that cause visceral leishmaniasis (VL) and cutaneous leishmaniasis (CL), diseases primarily affecting populations of low socioeconomic status. VL in Brazil is caused by Leishmania infantum, with transmission mainly attributed to Lutzomyia longipalpis, a species complex of sand fly, and is concentrated mainly in the northeastern part of the country. CL is distributed worldwide and occurs in five regions of Brazil, at a higher incidence in the north and northeast regions, with etiological agents, vectors, reservoirs and epidemiological patterns that differ from VL. The aim of this study was to determine the composition, distribution and ecological relationships of phlebotomine species in an Atlantic Forest conservation unit and nearby residential area in northeastern Brazil. METHODS: Centers for Disease Control and Shannon traps were used for collections, the former at six points inside the forest and in the peridomestic environment of surrounding residences, three times per month for 36 months, and the latter in a forest area, once a month for 3 months. The phlebotomines identified were compared with climate data using simple linear correlation, Pearson’s correlation coefficient and cross-correlation. The estimate of ecological parameters was calculated according to the Shannon-Wiener diversity index, standardized index of species abundance and the dominance index. RESULTS: A total of 75,499 phlebotomines belonging to 11 species were captured in the CDC traps, the most abundant being Evandromyia walkeri, Psychodopygus wellcomei and Lu. longipalpis. Evandromyia walkeri abundance was most influenced by temperature at collection time and during the months preceding collection and rainfall during the months preceding collection. Psychodopygus wellcomei abundance was most affected by rainfall and relative humidity during the collection month and the month immediately preceding collection time. Lutzomyia longipalpis abundance showed a correlation with temperature and the rainfall during the months preceding collection time. The Shannon trap contained a total of 3914 phlebotomines from these different species. Psychodopygus wellcomei, accounting for 91.93% of the total, was anthropophilic and active mainly at night. CONCLUSIONS: Most of the species collected in the traps were seasonal and exhibited changes in their composition and population dynamics associated with local adaptions. The presence of vectors Ps. wellcomei and Lu. longipalpis underscore the epidemiological importance of these phlebotomines in the conservation unit and surrounding anthropized areas. Neighboring residential areas should be permanently monitored to prevent VL or CL transmission and outbreaks.
According to the World Health Organization, more than 80% of the world’s population lives in areas at risk of vector-borne diseases transmission. The Aedes aegypti mosquito is through its bite the responsible vector for transmitting many diseases, such as dengue, Zika, and chikungunya fever, with 50-100 million estimated cases of dengue fever yearly worldwide. The vector control is the recommended action to mitigate the transmission, but public health organizations face limitations on budget, mainly in emerging countries. In that sense, the efficiency in vector control with fewer costs becomes reasonably desirable. The present work aims to develop an optimization procedure on a new rainfall dependent nonlinear dynamic population model, which is adjusted by the data obtained from females captured in traps. Thus, we can find solutions that contribute to reduce the vector infestation and minimize both the social and economic costs involved. The problem is approached over two different strategies: simultaneous step size control (SSC) and simultaneous descending control (SDC). Control strategies may vary according to the type of control, the time, and the application period throughout the year. Numerical simulations consider the case for the city of Lavras, Minas Gerais State, Brazil, during the spring and summer. The Real-Biased Genetic Algorithm was used in a mono-objective optimization problem to find optimal intervention solutions. The findings indicate policy solutions with a low total cost and a high efficiency, reflecting the decline in vector populations according to the weather. (c) 2020 Elsevier Inc. All rights reserved.
Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010-2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.
Climate change is projected to intensify drought conditions, which may increase the risk of diarrheal diseases in children. We constructed log-binomial generalized linear mixed models to examine the association between diarrhea risk, ascertained from global-scale nationally representative Demographic and Health Surveys, and drought, represented by the standardized precipitation evapotranspiration index, among children under five in 51 low- and middle-income countries (LMICs). Exposure to 6-month mild or severe drought was associated with an increased diarrhea risk of 5% (95% confidence interval 3-7%) or 8% (5-11%), respectively. The association was stronger among children living in a household that needed longer time to collect water or had no access to water or soap/detergent for handwashing. The association for 24-month drought was strong in dry zones but weak or null in tropical or temperate zones, whereas that for 6-month drought was only observed in tropical or temperate zones. In this work we quantify the associations between exposure to long-term drought and elevated diarrhea risk among children under five in LMICs and suggest that the risk could be reduced through improved water, sanitation, and hygiene practices, made more urgent by the likely increase in drought due to climate change.
Naegleria fowleri (N. fowleri) is a free-living, unicellular, opportunistic protist responsible for the fatal central nervous system infection, primary amoebic meningoencephalitis (PAM). Given the increase in temperatures due to global warming and climate change, it is estimated that the cases of PAM are on the rise. However, there is a current lack of awareness and effective drugs, meaning there is an urgent need to develop new therapeutic drugs. In this study, the target compounds were synthesized and tested for their anti-amoebic properties against N. fowleri. Most compounds exhibited significant amoebicidal effects against N. fowleri; for example, 1h, 1j, and 1q reduced N. fowleri’s viability to 15.14%, 17.45% and 28.78%, respectively. Furthermore, the majority of the compounds showed reductions in amoeba-mediated host death. Of interest are the compounds 1f, 1k, and 1v, as they were capable of reducing the amoeba-mediated host cell death to 52.3%, 51%, and 56.9% from 100%, respectively. Additionally, these compounds exhibit amoebicidal properties as well; they were found to decrease N. fowleri’s viability to 26.41%, 27.39%, and 24.13% from 100%, respectively. Moreover, the MIC(50) values for 1e, 1f, and 1h were determined to be 48.45 µM, 60.87 µM, and 50.96 µM, respectively. Additionally, the majority of compounds were found to exhibit limited cytotoxicity, except for 1l, 1o, 1p, 1m, 1c, 1b, 1zb, 1z, 1y, and 1x, which exhibited negligible toxicity. It is anticipated that these compounds may be developed further as effective treatments against these devastating infections due to brain-eating amoebae.
Harmful algal blooms (HABs) in freshwater lakes and oceans date back to as early as the 19th century, which can cause the death of aquatic and terrestrial organisms. However, it was not until the end of the 20th century that researchers had started to pay attention to the hazards and causes of HABs. In this study, we analyzed 5720 published literatures on HABs studies in the past 30 years. Our review presents the emerging trends in the past 30 years on HABs studies, the environmental and human health risks, prevention and control strategies and future developments. Therefore, this review provides a global perspective of HABs and calls for immediate responses.
Water supply and wastewater systems are essential infrastructure affected by floods. Additional risk is posed in developing countries, where access to sanitation is not universal. Few studies assess the flood risk to the sanitation-health nexus. Therefore, this study aims to present a theoretical and general framework for assessing the resilience of flood-sanitation-public health nexus in urban environments, composed by risk estimation and risk management assessment. The framework was developed from a system analysis approach focusing on central supply systems. Regarding risk assessment, the main vulnerability and exposure factors identified were land use, social vulnerability, coverage of sanitation systems, occurrence of waterborne diseases, number of people affected by floods and intersection with the flood map. From the risk management assessment stage three main typologies of trade-offs and synergies were identified: urban territorial planning versus runoff control, water quality versus sanitation infrastructure and flood management policy versus social behavior.
Modelling urban inundation and its associated health implications is numerous in its many applications. Flood modelling research contains a broad wealth of material, and microbial risk assessment has gained more popularity over the last decade. However, there is still a relative lack of understanding of how the microbial risk can be quantified from urban sewer flooding. This article intends to review the literature encompassing contemporary urban flood modelling approaches. Hydrodynamic and microbial models that can be applied for quantitative microbial risk assessment will be discussed. Consequently, urban sewer flooding will be the focus. This review found that the literature contains a variety of different hazards posed by urban flooding. Yet, far fewer examples encompass microbial risk from sewer system exceedance. To date, there is no evidence of a perfect model or technique, to carry out a quantitative microbial risk assessment from hydrodynamic simulations. The literature details many different methods. We intend to detail the advantages and limitations of each method. Along similar lines, hydraulic data constitutes a large part of the uncertainty which is inherent to this research field. Many studies in the literature detail data paucity and uncertainty in input data. As such, any advancement in this discipline will very likely aid future research.
Leptospirosis is a quintessential one health disease of humans and animals caused by pathogenic spirochetes of the genus Leptospira. Intra- and interspecies transmission is dependent on 1) reservoir host animals in which organisms replicate and are shed in urine over long periods of time, 2) the persistence of spirochetes in the environment, and 3) subsequent human-animal-environmental interactions. The combination of increased flooding events due to climate change, changes in human-animal-environmental interactions as a result of the pandemic that favor a rise in the incidence of leptospirosis, and under-recognition of leptospirosis because of nonspecific clinical signs and severe signs that resemble COVID-19 represents a “perfect storm” for resurgence of leptospirosis in people and domestic animals. Although often considered a disease that occurs in warm, humid climates with high annual rainfall, pathogenic Leptospira spp have recently been associated with disease in animals and humans that reside in semiarid regions like the southwestern US and have impacted humans that have a wide spectrum of socioeconomic backgrounds. Therefore, it is critical that physicians, veterinarians, and public health experts maintain a high index of suspicion for the disease regardless of geographic and socioeconomic circumstances and work together to understand outbreaks and implement appropriate control measures. Over the last decade, major strides have been made in our understanding of the disease because of improvements in diagnostic tests, molecular epidemiologic tools, educational efforts on preventive measures, and vaccines. These novel approaches are highlighted in the companion Currents in One Health by Sykes et al, AJVR, September 2022.
No abstract available.
BACKGROUND: Climate change is expected to increase the frequency of flooding events. Although rainfall is highly correlated with mosquito-borne diseases (MBD) in humans, less research focuses on understanding the impact of flooding events on disease incidence. This lack of research presents a significant gap in climate change-driven disease forecasting. OBJECTIVES: We conducted a scoping review to assess the strength of evidence regarding the potential relationship between flooding and MBD and to determine knowledge gaps. METHODS: PubMed, Embase, and Web of Science were searched through 31 December 2020 and supplemented with review of citations in relevant publications. Studies on rainfall were included only if the operationalization allowed for distinction of unusually heavy rainfall events. Data were abstracted by disease (dengue, malaria, or other) and stratified by post-event timing of disease assessment. Studies that conducted statistical testing were summarized in detail. RESULTS: From 3,008 initial results, we included 131 relevant studies (dengue n = 45, malaria n = 61, other MBD n = 49). Dengue studies indicated short-term ( < 1 month) decreases and subsequent (1-4 month) increases in incidence. Malaria studies indicated post-event incidence increases, but the results were mixed, and the temporal pattern was less clear. Statistical evidence was limited for other MBD, though findings suggest that human outbreaks of Murray Valley encephalitis, Ross River virus, Barmah Forest virus, Rift Valley fever, and Japanese encephalitis may follow flooding. DISCUSSION: Flooding is generally associated with increased incidence of MBD, potentially following a brief decrease in incidence for some diseases. Methodological inconsistencies significantly limit direct comparison and generalizability of study results. Regions with established MBD and weather surveillance should be leveraged to conduct multisite research to a) standardize the quantification of relevant flooding, b) study nonlinear relationships between rainfall and disease, c) report outcomes at multiple lag periods, and d) investigate interacting factors that modify the likelihood and severity of outbreaks across different settings. https://doi.org/10.1289/EHP8887.
In recent years, natural disasters such as hurricanes and floods have become more frequent, which usually leads to the pollution of drinking water. Drinking contaminated water may cause public health emergencies. The demand for healthy drinking water in disaster-affected areas is huge and urgent. Therefore, it is necessary to develop a simple water treatment technology suitable for emergencies. Inspired by nature, a fractional spray method was used to prepare graded purification material under mild conditions. The material consists of a calcium alginate isolation layer and a functional layer composed of calcium alginate, polyethylenimine, and water-based polyurethane, which can purify complex pollutants in water such as heavy metals, oils, pathogens, and micro/nano plastics through percolation. It does not require additional energy and can purify polluted water only under gravity. A disposable paper cup model was also designed, which can be used to obtain purified water by immersing in polluted water directly without other filtering devices. The test report shows that the water obtained from the paper cup was deeply purified. This design makes the material user-friendly and has the potential as a strategic material. This discovery can effectively improve the safety of drinking water after disasters and improve people’s quality of life.
West Nile virus (WNV, Flaviviridae, Flavivirus) is a mosquito-borne flavivirus introduced to North America in 1999. Since 1999, the Earth’s average temperature has increased by 0.6 °C. Mosquitoes are ectothermic organisms, reliant on environmental heat sources. Temperature impacts vector-virus interactions which directly influence arbovirus transmission. RNA viral replication is highly error-prone and increasing temperature could further increase replication rates, mutation frequencies, and evolutionary rates. The impact of temperature on arbovirus evolutionary trajectories and fitness landscapes has yet to be sufficiently studied. To investigate how temperature impacts the rate and extent of WNV evolution in mosquito cells, WNV was experimentally passaged 12 times in Culex tarsalis cells, at 25 °C and 30 °C. Full-genome deep sequencing was used to compare genetic signatures during passage, and replicative fitness was evaluated before and after passage at each temperature. Our results suggest adaptive potential at both temperatures, with unique temperature-dependent and lineage-specific genetic signatures. Further, higher temperature passage was associated with significantly increased replicative fitness at both temperatures and increases in nonsynonymous mutations. Together, these data indicate that if similar selective pressures exist in natural systems, increases in temperature could accelerate emergence of high-fitness strains with greater phenotypic plasticity.
The threats, both real and perceived, surrounding the development of new and emerging infectious diseases of humans are of critical concern to public health and well-being. Among these risks is the potential for zoonotic transmission to humans of species of the malaria parasite, Plasmodium, that have been considered historically to infect exclusively non-human hosts. Recently observed shifts in the mode, transmission, and presentation of malaria among several species studied are evidenced by shared vectors, atypical symptoms, and novel host-seeking behavior. Collectively, these changes indicate the presence of environmental and ecological pressures that are likely to influence the dynamics of these parasite life cycles and physiological make-up. These may be further affected and amplified by such factors as increased urban development and accelerated rate of climate change. In particular, the extended host-seeking behavior of what were once considered non-human malaria species indicates the specialist niche of human malaria parasites is not a limiting factor that drives the success of blood-borne parasites. While zoonotic transmission of non-human malaria parasites is generally considered to not be possible for the vast majority of Plasmodium species, failure to consider the feasibility of its occurrence may lead to the emergence of a potentially life-threatening blood-borne disease of humans. Here, we argue that recent trends in behavior among what were hitherto considered to be non-human malaria parasites to infect humans call for a cross-disciplinary, ecologically-focused approach to understanding the complexities of the vertebrate host/mosquito vector/malaria parasite triangular relationship. This highlights a pressing need to conduct a multi-species investigation for which we recommend the construction of a database to determine ecological differences among all known Plasmodium species, vectors, and hosts. Closing this knowledge gap may help to inform alternative means of malaria prevention and control.
Two recent initiatives, the World Health Organization (WHO) Strategic Advisory Group on Malaria Eradication and the Lancet Commission on Malaria Eradication, have assessed the feasibility of achieving global malaria eradication and proposed strategies to achieve it. Both reports rely on a climate-driven model of malaria transmission to conclude that long-term trends in climate will assist eradication efforts overall and, consequently, neither prioritize strategies to manage the effects of climate variability and change on malaria programming. This review discusses the pathways via which climate affects malaria and reviews the suitability of climate-driven models of malaria transmission to inform long-term strategies such as an eradication programme. Climate can influence malaria directly, through transmission dynamics, or indirectly, through myriad pathways including the many socioeconomic factors that underpin malaria risk. These indirect effects are largely unpredictable and so are not included in climate-driven disease models. Such models have been effective at predicting transmission from weeks to months ahead. However, due to several well-documented limitations, climate projections cannot accurately predict the medium- or long-term effects of climate change on malaria, especially on local scales. Long-term climate trends are shifting disease patterns, but climate shocks (extreme weather and climate events) and variability from sub-seasonal to decadal timeframes have a much greater influence than trends and are also more easily integrated into control programmes. In light of these conclusions, a pragmatic approach is proposed to assessing and managing the effects of climate variability and change on long-term malaria risk and on programmes to control, eliminate and ultimately eradicate the disease. A range of practical measures are proposed to climate-proof a malaria eradication strategy, which can be implemented today and will ensure that climate variability and change do not derail progress towards eradication.
The rearing temperature of the immature stages can have a significant impact on the life-history traits and the ability of adult mosquitoes to transmit diseases. This review assessed published evidence of the effects of temperature on the immature stages, life-history traits, insecticide susceptibility, and expression of enzymes in the adult Anopheles mosquito. Original articles published through 31 March 2021 were systematically retrieved from Scopus, Google Scholar, Science Direct, PubMed, ProQuest, and Web of Science databases. After applying eligibility criteria, 29 studies were included. The review revealed that immature stages of An. arabiensis were more tolerant (in terms of survival) to a higher temperature than An. funestus and An. quadriannulatus. Higher temperatures resulted in smaller larval sizes and decreased hatching and pupation time. The development rate and survival of An. stephensi was significantly reduced at a higher temperature than a lower temperature. Increasing temperatures decreased the longevity, body size, length of the gonotrophic cycle, and fecundity of Anopheles mosquitoes. Higher rearing temperatures increased pyrethroid resistance in adults of the An. arabiensis SENN DDT strain, and increased pyrethroid tolerance in the An. arabiensis SENN strain. Increasing temperature also significantly increased Nitric Oxide Synthase (NOS) expression and decreased insecticide toxicity. Both extreme low and high temperatures affect Anopheles mosquito development and survival. Climate change could have diverse effects on Anopheles mosquitoes. The sensitivities of Anopeheles mosquitoes to temperature differ from species to species, even among the same complex. Notwithstanding, there seem to be limited studies on the effects of temperature on adult life-history traits of Anopheles mosquitoes, and more studies are needed to clarify this relationship.
One of the most important vector-borne disease in humans is malaria, caused by Plasmodium parasite. Seasonal temperature elements have a major effect on the life development of mosquitoes and the development of parasites. In this paper, we establish and analyze a reaction-diffusion model, which includes seasonality, vector-bias, temperature-dependent extrinsic incubation period (EIP) and maturation delay in mosquitoes. In order to get the model threshold dynamics, a threshold parameter, the basic reproduction number R-0 is introduced, which is the spectral radius of the next generation operator. Quantitative analysis indicates that when R-0 < 1, there is a globally attractive disease-free omega-periodic solution; disease is uniformly persistent in humans and mosquitoes if R-0 > 1. Numerical simulations verify the results of the theoretical analysis and discuss the effects of diffusion and seasonality. We study the relationship between the parameters in the model and R-0. More importantly, how to allocate medical resources to reduce the spread of disease is explored through numerical simulations. Last but not least, we discover that when studying malaria transmission, ignoring vector-bias or assuming that the maturity period is not affected by temperature, the risk of disease transmission will be underestimate.
This paper mainly explores the complex impacts of spatial heterogeneity, vector-bias effect, multiple strains, temperature-dependent extrinsic incubation period (EIP) and seasonality on malaria transmission. We propose a multi-strain malaria transmission model with diffusion and periodic delays and define the reproduction numbers Ri and R^i (i = 1, 2). Quantitative analysis indicates that the disease-free ω-periodic solution is globally attractive when Ri < 1, while if Ri > 1 > Rj (i ≠ j, i, j = 1, 2), then strain i persists and strain j dies out. More interestingly, when R1 and R2 are greater than 1, the competitive exclusion of the two strains also occurs. Additionally, in a heterogeneous environment, the coexistence conditions of the two strains are R^1 > 1 and R^2 > 1. Numerical simulations verify the analytical results and reveal that ignoring vector-bias effect or seasonality when studying malaria transmission will underestimate the risk of disease transmission.
As a long-standing public health issue, malaria still severely affects many parts of the world, especially Africa. With greenhouse gas emissions, temperatures continue to rise. Based on diverse shared socioeconomic pathways (SSPs), future temperatures can be estimated. However, the impacts of climate change on malaria infection rates in all epidemic regions are unknown. Here, we estimate the differences in global malaria infection rates predicted under different SSPs during several periods as well as malaria infection case changes (MICCs) resulting from those differences. Our results indicate that the global MICCs resulting from the conversion from SSP1-2.6 to SSP2-4.5, to SSP3-7.0, and to SSP5-8.5 are 6.506 (with a 95% uncertainty interval [UI] of 6.150-6.861) million, 3.655 (3.416-3.894) million, and 2.823 (2.635-3.012) million, respectively, from 2021 to 2040; these values represent increases of 2.699%, 1.517%, and 1.171%, respectively, compared to the 241 million infection cases reported in 2020. Temperatures increases will adversely affect malaria the most in Africa during the 2021-2040 period. From 2081 to 2100, the MICCs obtained for the three scenario shifts listed above are -79.109 (-83.626 to -74.591) million, -238.337 (-251.920 to -0.141) million, and -162.692 (-174.628 to -150.757) million, corresponding to increases of -32.825%, -98.895%, and -67.507%, respectively. Climate change will increase the danger and risks associated with malaria in the most vulnerable regions in the near term, thus aggravating the difficulty of eliminating malaria. Reducing GHG emissions is a potential pathway to protecting people from malaria.
Beach sand and water have both shown relevance for human health and their microbiology have been the subjects of study for decades. Recently, the World Health Organization recommended that recreational beach sands be added to the matrices monitored for enterococci and Fungi. Global climate change is affecting beach microbial contamination, via changes to conditions like water temperature, sea level, precipitation, and waves. In addition, the world is changing, and humans travel and relocate, often carrying endemic allochthonous microbiota. Coastal areas are amongst the most frequent relocation choices, especially in regions where desertification is taking place. A warmer future will likely require looking beyond the use of traditional water quality indicators to protect human health, in order to guarantee that waterways are safe to use for bathing and recreation. Finally, since sand is a complex matrix, an alternative set of microbial standards is necessary to guarantee that the health of beach users is protected from both sand and water contaminants. We need to plan for the future safer use of beaches by adapting regulations to a climate-changing world.
Marine phycotoxins are a multiplicity of bioactive compounds which are produced by microalgae and bioaccumulate in the marine food web. Phycotoxins affect the ecosystem, pose a threat to human health, and have important economic effects on aquaculture and tourism worldwide. However, human health and food safety have been the primary concerns when considering the impacts of phycotoxins. Phycotoxins toxicity information, often used to set regulatory limits for these toxins in shellfish, lacks traceability of toxicity values highlighting the need for predefined toxicological criteria. Toxicity data together with adequate detection methods for monitoring procedures are crucial to protect human health. However, despite technological advances, there are still methodological uncertainties and high demand for universal phycotoxin detectors. This review focuses on these topics, including uncertainties of climate change, providing an overview of the current information as well as future perspectives.
Nontuberculous mycobacterial (NTM) infections are costly, difficult to treat, and increasing in prevalence. Given this, there is a desire to understand the potential relationships between NTM in water sources and climate change stressors. To address this need, a critical literature review was performed. Connections were made between NTM fate and transport, climate change, engineering decisions, and societal changes, and uncertainties highlighted. Environmental conditions discussed with respect to NTM risk included changing temperature, humidity, salinity, rainfall, and extreme weather events. NTM risk was then considered under climate/societal scenarios described by Intergovernmental Panel on Climate Change (IPCC) scientists. Findings indicate that the resilience of NTM under a variety of environmental conditions (e.g., warm temperatures, eutrophication) may increase their net prevalence in water environments under climate change, increasing exposure. Water management decisions may also influence exposure to NTM as water scarcity is expected to result in increased reliance on reclaimed water. Water managers may control risk of exposure through innovative water treatment processes and equitable water management decisions, turning towards an integrated One Water approach to reduce and/or mitigate the impacts of de facto reuse. Future research recommendations are provided including studies into potential changes to NTM fate and transport in uniquely impacted climates (e.g., boreal regions), and investigations into the relative risk of managed aquifer recharge as compared to no action.
PURPOSE OF REVIEW: Melioidosis, caused by the soil-dwelling bacterium Burkholderia pseudomallei, is a tropical infection associated with high morbidity and mortality. This review summarizes current insights into melioidosis’ endemicity, focusing on epidemiological transitions, zoonosis, and climate change. RECENT FINDINGS: Estimates of the global burden of melioidosis affirm the significance of hot-spots in Australia and Thailand. However, it also highlights the paucity of systematic data from South Asia, The Americas, and Africa. Globally, the growing incidence of diabetes, chronic renal and (alcoholic) liver diseases further increase the susceptibility of individuals to B. pseudomallei infection. Recent outbreaks in nonendemic regions have further exposed the hazard from the trade of animals and products as potential reservoirs for B. pseudomallei. Lastly, global warming will increase precipitation, severe weather events, soil salinity and anthrosol, all associated with the occurrence of B. pseudomallei. SUMMARY: Epidemiological transitions, zoonotic hazards, and climate change are all contributing to the emergence of novel melioidosis-endemic areas. The adoption of the One Health approach involving multidisciplinary collaboration is important in unraveling the real incidence of B. pseudomallei, as well as reducing the spread and associated mortality.
Intermittent water supply systems (IWSSs) are prevalent in most developing countries and some developed ones. Their usage is driven by necessity rather than as a principal objective, mostly due to technical and economic deficiencies. Major health risks and socio-economic inequities are associated with such systems. Their impacts are aggravated by climate changes and the COVID-19 crisis. These are likely to have profound implications on progress toward advancing sustainable development goals (SDGs). Motivated by providing a comprehensive overview of global knowledge on IWSSs, the present work proposed to track and analyze research works on IWSSs utilizing bibliometric techniques and visual mapping tools. This includes investigating the trends and growth trajectories of research works on IWSSs and analyzing the various approaches proposed to expand our understanding with respect to the management, modeling, optimization, and impacts of IWSSs. The national and international contributions and collaboration figures are further analyzed at country, institution, author, and source levels. This analysis indicates that research works conducted on IWSSs have certain expectations in terms of productivity (total global productivity; 197 documents). The United States was the best country in terms of productivity (58 documents; 29.4%), while the Water Switzerland journal was the most productive journal (19 documents; 9.6%). The impacts of IWSSs on health and well-being have attracted considerable attention. The outcomes showed deep and justified worries in relation to the transition from intermittent to continuous supply, equity, and mitigating the health risks associated with IWSSs in the foreseen future. The utilization of artificial intelligence techniques and expert systems will drive and shape future IWSS-related research activities. Therefore, investments in this regard are crucial.
The quality of global water resources is increasingly strained by socio-economic developments and climate change, threatening both human livelihoods and ecosystem health. With inadequately managed wastewater being a key driver of deterioration, Sustainable Development Goal (SDG) 6.3 was established to halve the proportion of untreated wastewater discharged to the environment by 2030. Yet, the impact of achieving SDG6.3 on global ambient water quality is unknown. Addressing this knowledge gap, we develop a high-resolution surface water quality model for salinity as indicated by total dissolved solids, organic pollution as indicated by biological oxygen demand and pathogen pollution as indicated by fecal coliform. Our model includes a novel spatially-explicit approach to incorporate wastewater treatment practices, a key determinant of in-stream pollution. We show that achieving SDG6.3 reduces water pollution, but is still insufficient to improve ambient water quality to below key concentration thresholds in several world regions. Particularly in the developing world, reductions in pollutant loadings are locally effective but transmission of pollution from upstream areas still leads to water quality issues downstream. Our results highlight the need to go beyond the SDG-target for wastewater treatment in order to achieve the overarching goal of clean water for all. SGD 6.3 targets to half the proportion of untreated wastewater discharged to the environment by 2030 will substantially improve water quality globally, but a high-resolution surface water quality model suggests key thresholds will still not be met in regions with limited existing wastewater treatment.
Climate change is altering the habits of the population. Extensive drought periods and overuse of potable water led to significant water shortages in many different places. Therefore, new water sources are necessary for usage in applications where the microbiological and chemical water quality demands are less stringent, as for agriculture. In this study, we planted, germinated, and grew vegetables/fruits (cherry tomato, lettuce, and carrot) using three types of potential waters for irrigation: secondary-treated wastewater, chlorine-treated wastewater, and green wall-treated greywater, to observe potential health risks of foodstuff consumption. In this study the waters and crops were analyzed for three taxonomic groups: bacteria, enteric viruses, and protozoa. Enteric viruses, human Norovirus I (hNoVGI) and Enterovirus (EntV), were detected in tomato and carrots irrigated with secondary-treated and chlorine-treated wastewater, in concentrations as high as 2.63 log genome units (GU)/g. On the other hand, Aichi viruses were detected in lettuce. Bacteria and protozoa remained undetected in all fresh produce although being detected in both types of wastewaters. Fresh produce irrigated with green wall-treated greywater were free from the chosen pathogens. This suggests that green wall-treated greywater may be a valuable option for crop irrigation, directly impacting the cities of the future vision, and the circular and green economy concepts. On the other hand, this work demonstrates that further advancement is still necessary to improve reclaimed water to the point where it no longer constitutes risk of foodborne diseases and to human health.
Due to the growing and diverse demands on water supply, exploitation of non-conventional sources of water has received much attention. Since water consumption for irrigation is the major contributor to total water withdrawal, the utilization of non-conventional sources of water for the purpose of irrigation is critical to assuring the sustainability of water resources. Although numerous studies have been conducted to evaluate and manage non-conventional water sources, little research has reviewed the suitability of available water technologies for improving water quality, so that water reclaimed from non-conventional supplies could be an alternative water resource for irrigation. This article provides a systematic overview of all aspects of regulation, technology and management to enable the innovative technology, thereby promoting and facilitating the reuse of non-conventional water. The study first reviews the requirements for water quantity and quality (i.e., physical, chemical, and biological parameters) for agricultural irrigation. Five candidate sources of non-conventional water were evaluated in terms of quantity and quality, namely rainfall/stormwater runoff, industrial cooling water, hydraulic fracturing wastewater, process wastewater, and domestic sewage. Water quality issues, such as suspended solids, biochemical/chemical oxygen demand, total dissolved solids, total nitrogen, bacteria, and emerging contaminates, were assessed. Available technologies for improving the quality of non-conventional water were comprehensively investigated. The potential risks to plants, human health, and the environment posed by non-conventional water reuse for irrigation are also discussed. Lastly, three priority research directions, including efficient collection of non-conventional water, design of fit-for-purpose treatment, and deployment of energy-efficient processes, were proposed to provide guidance on the potential for future research.
Monitoring of fecal indicator bacteria at recreational waters is an important public health measure to minimize water-borne disease, however traditional culture methods for quantifying bacteria can take 18-24 hours to obtain a result. To support real-time notifications of water quality, models using environmental variables have been created to predict indicator bacteria levels on the day of sampling. We conducted a systematic review of predictive models of fecal indicator bacteria at freshwater recreational sites in temperate climates to identify and describe the existing approaches, trends, and their performance to inform beach water management policies. We conducted a comprehensive search strategy, including five databases and grey literature, screened abstracts for relevance, and extracted data using structured forms. Data were descriptively summarized. A total of 53 relevant studies were identified. Most studies (n = 44, 83%) were conducted in the United States and evaluated water quality using E. coli as fecal indicator bacteria (n = 46, 87%). Studies were primarily conducted in lakes (n = 40, 75%) compared to rivers (n = 13, 25%). The most commonly reported predictive model-building method was multiple linear regression (n = 37, 70%). Frequently used predictors in best-fitting models included rainfall (n = 39, 74%), turbidity (n = 31, 58%), wave height (n = 24, 45%), and wind speed and direction (n = 25, 47%, and n = 23, 43%, respectively). Of the 19 (36%) studies that measured accuracy, predictive models averaged an 81.0% accuracy, and all but one were more accurate than traditional methods. Limitations identifed by risk-of-bias assessment included not validating models (n = 21, 40%), limited reporting of whether modelling assumptions were met (n = 40, 75%), and lack of reporting on handling of missing data (n = 37, 70%). Additional research is warranted on the utility and accuracy of more advanced predictive modelling methods, such as Bayesian networks and artificial neural networks, which were investigated in comparatively fewer studies and creating risk of bias tools for non-medical predictive modelling.
Globally, waterborne gastroenteritis attributable to rotaviruses is on the increase due to the rapid increase in population growth, poor socioeconomic conditions, and drastic changes in climatic conditions. The burden of diarrhea is quite alarming in developing nations where the majority of the populations still rely on untreated surface water that is usually polluted for their immediate water needs. Humans and animals of all ages are affected by rotaviruses. In humans, the preponderance of cases occurs in children under 5 years. Global efforts in advancing water/wastewater treatment technologies have not yet realized the objective of complete viral removal from wastewater. Most times, surface waters are impacted heavily by inadequately treated wastewater run-offs thereby exposing people or animals to preventable health risks. The relative stability of rotaviruses in aquatic matrices during wastewater treatment, poor correlation of bacteriological indicators with the presence of rotaviruses, and their infectiousness at a low dose informed the proposal for inclusion in the routine microbiological water screening panel. Environmental monitoring data have been shown to provide early warnings that can complement clinical data used to monitor the impact of current rotavirus vaccination in a community. This review was therefore undertaken to critically appraise rotavirus excretion and emission pathways, and the existence, viability and persistence in the receiving aquatic milieu. The efficiency of the current wastewater treatment modality for rotavirus removal, correlation of the current bacteriological water quality assessment strategy, public health risks and current laboratory methods for an epidemiological study were also discussed.
Climate change driven seawater temperature (SWT) increases results in greater abundance and geographical expansion of marine pathogens, among which Vibrio parahaemolyticus (Vp) causes serious economic and health issues. In addition, plastic pollution in the ocean constitutes a vector for harmful pathogens dissemination. We investigate the effect of elevated SWT on the expression of genes implicated in adhesion and biofilm formation on abiotic surfaces in the clinical Vp strain RIMD2210633, which expresses hemolysins. Among the genes studied, the multivalent adhesion molecule-7 and the GlcNAc-binding protein A were involved in the adhesion of Vp to abiotic and biotic surfaces, whereas the type IV pili, the mannose-sensitive hemagglutinin, and the chitin-regulated pilins facilitate attachment and biofilm formation. Data presented here show that at 21°C, Vp is still viable but does not either proliferate or express the virulence factors studied. Interestingly, at 27°C and as early as 1 h of incubation, all factors are transiently expressed in free-living bacteria only and even more upregulated at 31°C. These results clearly show that increased SWT has an important impact on the adhesion properties of free-living Vp to plastic support and thus emphasize the role of climate change in the spread of this pathogenic bacteria.
BACKGROUND: Infections caused by non-cholera Vibrio species have undergone a global expansion over the past few decades reaching new areas of the world that were previously considered adverse for these organisms. The geographical extent of the expansion has not been uniform, and some areas have shown a rapid increase in infections. METHODS: We applied a new generation of models combining climate, population, and socioeconomic projections to map future scenarios of distribution and season suitability for pathogenic Vibrio. We used the Coupled Model Intercomparison Project 6 framework. Three datasets were used: Geophysical Fluid Dynamics Laboratory’s CM4.0 sea surface temperature and sea surface salinity; the coastline length dataset from the World Resources Institute; and Inter-Sectoral Impact Model Intercomparison Project 2b annual global population data. Future projections were used up to the year 2100 and historical simulations from 1850 to 2014. We also project human population at risk under different shared socioeconomic pathways worldwide. FINDINGS: Projections showed that coastal areas suitable for Vibrio could cover 38000 km of new coastal areas by 2100 under the most unfavourable scenario with an expansion rate of season suitability in these regions of around 1 month every 30 years. Population at risk in suitable regions almost doubled from 1980 to 2020 (from 610 million to 1100 million under the scenario of medium challenges to mitigation and adaptation, shared socioeconomic pathway 2-4.5), although the increment will be more moderate in the future and stabilises after 2050 at 1300 million. Finally, we provide the first global estimate for Vibrio infections, with values around half a million of cases worldwide in 2020. INTERPRETATION: Our projections anticipated an expansion of both the temporal and spatial disease burden for Vibrio infections, in particular at high latitudes of the northern hemisphere. However, the largest extent occurred from 1980 to 2020 and a more moderate increase is expected for the future. The most positive outcome is that the projections showed that Vibrio morbidity will remain relatively stable over the coming decades.
Responding to infrastructural damage in the aftermath of natural disasters at a national, regional, and local level poses a significant challenge. Damage to road networks, clean water supply, and sanitation infrastructures, as well as social amenities like schools and hospitals, exacerbates the circumstances. As safe water sources are destroyed or mixed with contaminated water during a disaster, the risk of a waterborne disease outbreak is elevated in those disaster-affected locations. A country such as Haiti, where a large quantity of the population is deprived of safe water and basic sanitation facilities, would suffer more in post-disaster scenarios. Early warning of waterborne diseases like cholera would be of great help for humanitarian aid, and the management of disease outbreak perspectives. The challenging task in disease forecasting is to identify the suitable variables that would better predict a potential outbreak. In this study, we developed five (5) models including a machine learning approach, to identify and determine the impact of the environmental and social variables that play a significant role in post-disaster cholera outbreaks. We implemented the model setup with cholera outbreak data in Haiti after the landfall of Hurricane Matthew in October 2016. Our results demonstrate that adding high-resolution data in combination with appropriate social and environmental variables is helpful for better cholera forecasting in a post-disaster scenario. In addition, using a machine learning approach in combination with existing statistical or mechanistic models provides important insights into the selection of variables and identification of cholera risk hotspots, which can address the shortcomings of existing approaches.
BACKGROUND: Climate change is expected to alter the global footprint of many infectious diseases, particularly vector-borne diseases such as malaria and dengue. Knowledge of the range and geographical context of expected climate change impacts on disease transmission and spread, combined with knowledge of effective adaptation strategies and responses, can help to identify gaps and best practices to mitigate future health impacts. To investigate the types of evidence for impacts of climate change on two major mosquito-borne diseases of global health importance, malaria and dengue, and to identify the range of relevant policy responses and adaptation strategies that have been devised, we performed a scoping review of published review literature. Three electronic databases (PubMed, Scopus and Epistemonikos) were systematically searched for relevant published reviews. Inclusion criteria were: reviews with a systematic search, from 2007 to 2020, in English or French, that addressed climate change impacts and/or adaptation strategies related to malaria and/or dengue. Data extracted included: characteristics of the article, type of review, disease(s) of focus, geographic focus, and nature of the evidence. The evidence was summarized to identify and compare regional evidence for climate change impacts and adaptation measures. RESULTS: A total of 32 reviews met the inclusion criteria. Evidence for the impacts of climate change (including climate variability) on dengue was greatest in the Southeast Asian region, while evidence for the impacts of climate change on malaria was greatest in the African region, particularly in highland areas. Few reviews explicitly addressed the implementation of adaptation strategies to address climate change-driven disease transmission, however suggested strategies included enhanced surveillance, early warning systems, predictive models and enhanced vector control. CONCLUSIONS: There is strong evidence for the impacts of climate change, including climate variability, on the transmission and future spread of malaria and dengue, two of the most globally important vector-borne diseases. Further efforts are needed to develop multi-sectoral climate change adaptation strategies to enhance the capacity and resilience of health systems and communities, especially in regions with predicted climatic suitability for future emergence and re-emergence of malaria and dengue. This scoping review may serve as a useful precursor to inform future systematic reviews of the primary literature.
BACKGROUND: Early warning systems (EWSs) are of increasing importance in the context of outbreak-prone diseases such as chikungunya, dengue, malaria, yellow fever, and Zika. A scoping review has been undertaken for all 5 diseases to summarize existing evidence of EWS tools in terms of their structural and statistical designs, feasibility of integration and implementation into national surveillance programs, and the users’ perspective of their applications. METHODS: Data were extracted from Cochrane Database of Systematic Reviews (CDSR), Google Scholar, Latin American and Caribbean Health Sciences Literature (LILACS), PubMed, Web of Science, and WHO Library Database (WHOLIS) databases until August 2019. Included were studies reporting on (a) experiences with existing EWS, including implemented tools; and (b) the development or implementation of EWS in a particular setting. No restrictions were applied regarding year of publication, language or geographical area. FINDINGS: Through the first screening, 11,710 documents for dengue, 2,757 for Zika, 2,706 for chikungunya, 24,611 for malaria, and 4,963 for yellow fever were identified. After applying the selection criteria, a total of 37 studies were included in this review. Key findings were the following: (1) a large number of studies showed the quality performance of their prediction models but except for dengue outbreaks, only few presented statistical prediction validity of EWS; (2) while entomological, epidemiological, and social media alarm indicators are potentially useful for outbreak warning, almost all studies focus primarily or exclusively on meteorological indicators, which tends to limit the prediction capacity; (3) no assessment of the integration of the EWS into a routine surveillance system could be found, and only few studies addressed the users’ perspective of the tool; (4) almost all EWS tools require highly skilled users with advanced statistics; and (5) spatial prediction remains a limitation with no tool currently able to map high transmission areas at small spatial level. CONCLUSIONS: In view of the escalating infectious diseases as global threats, gaps and challenges are significantly present within the EWS applications. While some advanced EWS showed high prediction abilities, the scarcity of tool assessments in terms of integration into existing national surveillance systems as well as of the feasibility of transforming model outputs into local vector control or action plans tends to limit in most cases the support of countries in controlling disease outbreaks.
Water-related diseases such as diarrhoeal diseases from viral, bacterial and parasitic organisms and Aedes-borne arboviral diseases are major global health problems. We believe that these two disease groups share common risk factors, namely inadequate household water management, poor sanitation and solid waste management. Where water provision is inadequate, water storage is essential. Aedes mosquitoes commonly breed in household water storage containers, which can hold water contaminated with enteric disease-causing organisms. Microbiological contamination of water between source and point-of-use is a major cause of reduced drinking-water quality. Inadequate sanitation and solid waste management increase not only risk of water contamination, but also the availability of mosquito larval habitats. In this article we discuss integrated interventions that interrupt mosquito breeding while also providing sanitary environments and clean water. Specific interventions include improving storage container design, placement and maintenance and scaling up access to piped water. Vector control can be integrated into sanitation projects that target sewers and drains to avoid accumulation of stagnant water. Better management of garbage and solid waste can reduce the availability of mosquito habitats while improving human living conditions. Our proposed integration of disease interventions is consistent with strategies promoted in several global health frameworks, such as the sustainable development goals, the global vector control response, behavioural change, and water, sanitation and hygiene initiatives. Future research should address how interventions targeting water, sanitation, hygiene and community waste disposal also benefit Aedes-borne disease control. The projected effects of climate change mean that integrated management and control strategies will become increasingly important.
Human health is threatened by antibiotic-resistant bacteria and their related infections, which cause thousands of human deaths every year worldwide. Surface waters are vulnerable to human activities and natural processes that facilitate the emergence and spread of antibiotic-resistant bacteria in the environment. This study evaluated the pathways and drivers of antimicrobial resistance (AR) in surface waters. We analyzed antibiotic resistance healthcare-associated infection (HAI) data reported to the CDC’s National Healthcare Safety Network to determine the number of antimicrobial-resistant pathogens and their isolates detected in healthcare facilities. Ten pathogens and their isolates associated with HAIs tested resistant to the selected antibiotics, indicating the role of healthcare facilities in antimicrobial resistance in the environment. The analyzed data and literature research revealed that healthcare facilities, wastewater, agricultural settings, food, and wildlife populations serve as the major vehicles for AR in surface waters. Antibiotic residues, heavy metals, natural processes, and climate change were identified as the drivers of antimicrobial resistance in the aquatic environment. Food and animal handlers have a higher risk of exposure to resistant pathogens through ingestion and direct contact compared with the general population. The AR threat to public health may grow as pathogens in aquatic systems adjust to antibiotic residues, contaminants, and climate change effects. The unnecessary use of antibiotics increases the risk of AR, and the public should be encouraged to practice antibiotic stewardship to decrease the risk.
The reality of climate change and biodiversity collapse is irrefutable in the 21st century, with urgent action required not only to conserve threatened species but also to protect human life and wellbeing. This existential threat forces us to recognise that our existence is completely dependent upon well-functioning ecosystems that sustain the diversity of life on our planet, including that required for human health. By synthesising data on the ecology, epidemiology and evolutionary biology of various pathogens, we are gaining a better understanding of factors that underlie disease emergence and spread. However, our knowledge remains rudimentary with limited insight into the complex feedback loops that underlie ecological stability, which are at risk of rapidly unravelling once certain tipping points are breached. In this paper, we consider the impact of climate change and biodiversity collapse on the ever-present risk of infectious disease emergence and spread. We review historical and contemporaneous infectious diseases that have been influenced by human environmental manipulation, including zoonoses and vector- and water-borne diseases, alongside an evaluation of the impact of migration, urbanisation and human density on transmissible diseases. The current lack of urgency in political commitment to address climate change warrants enhanced understanding and action from paediatricians – to ensure that we safeguard the health and wellbeing of children in our care today, as well as those of future generations.
Aedes aegypti and Aedes albopictus transmit diseases such as dengue, and are of major public health concern. Driven by climate change and global trade/travel both species have recently spread to new tropic/subtropic regions and Ae. albopictus also to temperate ecoregions. The capacity of both species to adapt to new environments depends on their ecophysiological plasticity, which is the width of functional niches where a species can survive. Mechanistic distribution models often neglect to incorporate ecophysiological plasticity especially in regards to overwintering capacity in cooler habitats. To portray the ecophysiological plasticity concerning overwintering capability, we conducted temperature experiments with multiple populations of both species originating from an altitudinal gradient in South Asia and tested as follows: the cold tolerance of eggs (-2 °C- 8 days and – 6 °C- 2 days) without and with an experimental winter onset (acclimation: 10 °C- 60 days), differences between a South Asian and a European Ae. albopictus population and the temperature response in life cycles (13 °C, 18 °C, 23 °C, 28 °C). Ecophysiological plasticity in overwintering capacity in Ae. aegypti is high in populations originating from low altitude and in Ae. albopictus populations from high altitude. Overall, ecophysiological plasticity is higher in Ae. albopictus compared to Ae. aegypti. In both species acclimation and in Ae. albopictus temperate continental origin had a huge positive effect on survival. Our results indicate that future mechanistic prediction models can include data on winter survivorship of both, tropic and subtropic Ae. aegypti, whereas for Ae. albopictus this depends on the respective temperate, tropical region the model is focusing on. Future research should address cold tolerance in multiple populations worldwide to evaluate the full potential of the ecophysiological plasticity in the two species. Furthermore, we found that Ae. aegypti can survive winter cold especially when acclimated and will probably further spread to colder ecoregions driven by climate change.
This review highlights two intersecting environmental phenomena that have significantly impacted the Tokyo Summer Olympic and Paralympic Games: infectious disease outbreaks and anthropogenic climate change. Following systematic searches of five databases and the gray literature, 15 studies were identified that addressed infectious disease and climate-related health risks associated with the Summer Games and similar sports mega-events. Over two decades, infectious disease surveillance at the Summer Games has identified low-level threats from vaccine-preventable illnesses and respiratory conditions. However, the COVID-19 pandemic and expansion of vector-borne diseases represent emerging and existential challenges for cities that host mass gathering sports competitions due to the absence of effective vaccines. Ongoing threats from heat injury among athletes and spectators have also been identified at international sports events from Asia to North America due to a confluence of rising Summer temperatures, urban heat island effects and venue crowding. Projections for the Tokyo Games and beyond suggest that heat injury risks are reaching a dangerous tipping point, which will necessitate relocation or mitigation with long-format and endurance events. Without systematic change to its format or staging location, the Summer Games have the potential to drive deleterious health outcomes for athletes, spectators and host communities.
Global climate change exposes workers to increased air temperature, polluted air, and ultraviolet radiation due to ozone depletion, increased extreme weather events, and evolving patterns of vector-borne diseases. These climate change hazards are causing acute and chronic health problems to workers. The occupational distribution of the population is the most vulnerable to the negative impacts of climate change worldwide. Climate change-related adverse health hazards to the general population is getting evident around the globe. A limited focus has been made on developing a relationship between climate change and related occupational health hazards. This policy paper aims to guide health officials and policymakers to develop a climate change mitigation policy for the occupational distribution of the population. Absolute magnitude determination of climate changerelated health risks is essential to developing projecting models and predicting future hazards and risks. These models will help us to estimate climate change and environmental exposure, susceptibility of the exposed population, and capacity of public health practice and services to reduce climate change impact. Adaptation policies in international, national, and local occupational settings are required to acclimatize the workers and mitigate climate change-related adverse effects.
BACKGROUND: Mosquito-borne diseases are expanding their range, and re-emerging in areas where they had subsided for decades. The extent to which climate change influences the transmission suitability and population at risk of mosquito-borne diseases across different altitudes and population densities has not been investigated. The aim of this study was to quantify the extent to which climate change will influence the length of the transmission season and estimate the population at risk of mosquito-borne diseases in the future, given different population densities across an altitudinal gradient. METHODS: Using a multi-model multi-scenario framework, we estimated changes in the length of the transmission season and global population at risk of malaria and dengue for different altitudes and population densities for the period 1951-99. We generated projections from six mosquito-borne disease models, driven by four global circulation models, using four representative concentration pathways, and three shared socioeconomic pathways. FINDINGS: We show that malaria suitability will increase by 1·6 additional months (mean 0·5, SE 0·03) in tropical highlands in the African region, the Eastern Mediterranean region, and the region of the Americas. Dengue suitability will increase in lowlands in the Western Pacific region and the Eastern Mediterranean region by 4·0 additional months (mean 1·7, SE 0·2). Increases in the climatic suitability of both diseases will be greater in rural areas than in urban areas. The epidemic belt for both diseases will expand towards temperate areas. The population at risk of both diseases might increase by up to 4·7 additional billion people by 2070 relative to 1970-99, particularly in lowlands and urban areas. INTERPRETATION: Rising global mean temperature will increase the climatic suitability of both diseases particularly in already endemic areas. The predicted expansion towards higher altitudes and temperate regions suggests that outbreaks can occur in areas where people might be immunologically naive and public health systems unprepared. The population at risk of malaria and dengue will be higher in densely populated urban areas in the WHO African region, South-East Asia region, and the region of the Americas, although we did not account for urban-heat island effects, which can further alter the risk of disease transmission. FUNDING: UK Space Agency, Royal Society, UK National Institute for Health Research, and Swedish Research Council.
The American Society for Reproductive Medicine compels centers providing reproductive medicine care to develop and implement an emergency preparedness plan in the event of a disaster. Reproductive care is vulnerable to disruptions in energy, transportation, and supply chains as well as may have potential destructive impacts on infrastructure. With the relentless progression of events related to climate change, centers can expect a growing number of such disruptive events and must prepare to deal with them. This article provides a case study of the impact of Hurricane Sandy on one center in New York City and proposes recommendations for future preparedness and mitigation.
Climate warming is occurring most rapidly in the Arctic, which is both a sentinel and a driver of further global change. Ecosystems and human societies are already affected by warming. Permafrost thaws and species are on the move, bringing pathogens and vectors to virgin areas. During a five-year project, the CLINF – a Nordic Center of Excellence, funded by the Nordic Council of Ministers, has worked with the One Health concept, integrating environmental data with human and animal disease data in predictive models and creating maps of dynamic processes affecting the spread of infectious diseases. It is shown that tularemia outbreaks can be predicted even at a regional level with a manageable level of uncertainty. To decrease uncertainty, rapid development of new and harmonised technologies and databases is needed from currently highly heterogeneous data sources. A major source of uncertainty for the future of contaminants and infectious diseases in the Arctic, however, is associated with which paths the majority of the globe chooses to follow in the future. Diplomacy is one of the most powerful tools Arctic nations have to influence these choices of other nations, supported by Arctic science and One Health approaches that recognise the interconnection between people, animals, plants and their shared environment at the local, regional, national and global levels as essential for achieving a sustainable development for both the Arctic and the globe.
Climate change affects ecological processes and interactions, including parasitism. Because parasites are natural components of ecological systems, as well as agents of outbreak and disease-induced mortality, it is important to summarize current knowledge of the sensitivity of parasites to climate and identify how to better predict their responses to it. This need is particularly great in marine systems, where the responses of parasites to climate variables are less well studied than those in other biomes. As examples of climate’s influence on parasitism increase, they enable generalizations of expected responses as well as insight into useful study approaches, such as thermal performance curves that compare the vital rates of hosts and parasites when exposed to several temperatures across a gradient. For parasites not killed by rising temperatures, some simple physiological rules, including the tendency of temperature to increase the metabolism of ectotherms and increase oxygen stress on hosts, suggest that parasites’ intensity and pathologies might increase. In addition to temperature, climate-induced changes in dissolved oxygen, ocean acidity, salinity, and host and parasite distributions also affect parasitism and disease, but these factors are much less studied. Finally, because parasites are constituents of ecological communities, we must consider indirect and secondary effects stemming from climate-induced changes in host-parasite interactions, which may not be evident if these interactions are studied in isolation.
Arthropod-borne viruses (Arboviruses) continue to generate significant health and economic burdens for people living in endemic regions. Of these viruses, some of the most important (e.g., dengue, Zika, chikungunya, and yellow fever virus), are transmitted mainly by Aedes mosquitoes. Over the years, viral infection control has targeted vector population reduction and inhibition of arboviral replication and transmission. This control includes the vector control methods which are classified into chemical, environmental, and biological methods. Some of these control methods may be largely experimental (both field and laboratory investigations) or widely practised. Perceptively, one of the biological methods of vector control, in particular, Wolbachia-based control, shows a promising control strategy for eradicating Aedes-borne arboviruses. This can either be through the artificial introduction of Wolbachia, a naturally present bacterium that impedes viral growth in mosquitoes into heterologous Aedes aegypti mosquito vectors (vectors that are not natural hosts of Wolbachia) thereby limiting arboviral transmission or via Aedes albopictus mosquitoes, which naturally harbour Wolbachia infection. These strategies are potentially undermined by the tendency of mosquitoes to lose Wolbachia infection in unfavourable weather conditions (e.g., high temperature) and the inhibitory competitive dynamics among co-circulating Wolbachia strains. The main objective of this review was to critically appraise published articles on vector control strategies and specifically highlight the use of Wolbachia-based control to suppress vector population growth or disrupt viral transmission. We retrieved studies on the control strategies for arboviral transmissions via arthropod vectors and discussed the use of Wolbachia control strategies for eradicating arboviral diseases to identify literature gaps that will be instrumental in developing models to estimate the impact of these control strategies and, in essence, the use of different Wolbachia strains and features.
Environmental factors play a crucial role in the population dynamics of arthropod endosymbionts, and therefore in the deployment of Wolbachia symbionts for the control of dengue arboviruses. The potential of Wolbachia to invade, persist, and block virus transmission depends in part on its intracellular density. Several recent studies have highlighted the importance of larval rearing temperature in modulating Wolbachia densities in adults, suggesting that elevated temperatures can severely impact some strains, while having little effect on others. The effect of a replicated tropical heat cycle on Wolbachia density and levels of virus blocking was assessed using Aedes aegypti lines carrying strains wMel and wAlbB, two Wolbachia strains currently used for dengue control. Impacts on intracellular density, maternal transmission fidelity, and dengue inhibition capacity were observed for wMel. In contrast, wAlbB-carrying Ae. aegypti maintained a relatively constant intracellular density at high temperatures and conserved its capacity to inhibit dengue. Following larval heat treatment, wMel showed a degree of density recovery in aging adults, although this was compromised by elevated air temperatures. IMPORTANCE In the past decades, dengue incidence has dramatically increased all over the world. An emerging dengue control strategy utilizes Aedes aegypti mosquitoes artificially transinfected with the bacterial symbiont Wolbachia, with the ultimate aim of replacing wild mosquito populations. However, the rearing temperature of mosquito larvae is known to impact on some Wolbachia strains. In this study, we compared the effects of a temperature cycle mimicking natural breeding sites in tropical climates on two Wolbachia strains, currently used for open field trials. When choosing the Wolbachia strain to be used in a dengue control program it is important to consider the effects of environmental temperatures on invasiveness and virus inhibition. These results underline the significance of understanding the impact of environmental factors on released mosquitoes, in order to ensure the most efficient strategy for dengue control.
The potential for adaptive evolution to enable species persistence under a changing climate is one of the most important questions for understanding impacts of future climate change. Climate adaptation may be particularly likely for short-lived ectotherms, including many pest, pathogen, and vector species. For these taxa, estimating climate adaptive potential is critical for accurate predictive modeling and public health preparedness. Here, we demonstrate how a simple theoretical framework used in conservation biology-evolutionary rescue models-can be used to investigate the potential for climate adaptation in these taxa, using mosquito thermal adaptation as a focal case. Synthesizing current evidence, we find that short mosquito generation times, high population growth rates, and strong temperature-imposed selection favor thermal adaptation. However, knowledge gaps about the extent of phenotypic and genotypic variation in thermal tolerance within mosquito populations, the environmental sensitivity of selection, and the role of phenotypic plasticity constrain our ability to make more precise estimates. We describe how common garden and selection experiments can be used to fill these data gaps. Lastly, we investigate the consequences of mosquito climate adaptation on disease transmission using Aedes aegypti-transmitted dengue virus in Northern Brazil as a case study. The approach outlined here can be applied to any disease vector or pest species and type of environmental change.
Wolbachia intracellular bacteria successfully reduce the transmissibility of arthropod-borne viruses (arboviruses) when introduced into virus-carrying vectors such as mosquitoes. Despite the progress made by introducing Wolbachia bacteria into the Aedes aegypti wild-type population to control arboviral infections, reports suggest that heat-induced loss-of-Wolbachia-infection as a result of climate change may reverse these gains. Novel, supplemental Wolbachia strains that are more resilient to increased temperatures may circumvent these concerns, and could potentially act synergistically with existing variants. In this article, we model the ecological dynamics among three distinct mosquito (sub)populations: a wild-type population free of any Wolbachia infection; an invading population infected with a particular Wolbachia strain; and a second invading population infected with a distinct Wolbachia strain from that of the first invader. We explore how the range of possible characteristics of each Wolbachia strain impacts mosquito prevalence. Further, we analyse the differential system governing the mosquito populations and the Wolbachia infection dynamics by computing the full set of basic and invasive reproduction numbers and use these to establish stability of identified equilibria. Our results show that releasing mosquitoes with two different strains of Wolbachia did not increase their prevalence, compared with a single-strain Wolbachia-infected mosquito introduction and only delayed Wolbachia dominance.
Evidence climate change is impacting ticks and tick-borne infections is generally lacking. This is primarily because, in most parts of the world, there are no long-term and replicated data on the distribution and abundance of tick populations, and the prevalence and incidence of tick-borne infections. Notable exceptions exist, as in Canada where the northeastern advance of Ixodes scapularis and Lyme borreliosis in the USA prompted the establishment of tick and associated disease surveillance. As a result, the past 30 years recorded the encroachment and spread of I. scapularis and Lyme borreliosis across much of Canada concomitant with a 2-3 degrees C increase in land surface temperature. A similar northerly advance of I. ricinus [and associated Lyme borreliosis and tick-borne encephalitis (TBE)] has been recorded in northern Europe together with expansion of this species’ range to higher altitudes in Central Europe and the Greater Alpine Region, again concomitant with rising temperatures. Changes in tick species composition are being recorded, with increases in more heat tolerant phenotypes (such as Rhipicephalus microplus in Africa), while exotic species, such as Haemaphysalis longicornis and Hyalomma marginatum, are becoming established in the USA and Southern Europe, respectively. In the next 50 years these trends are likely to continue, whereas, at the southern extremities of temperate species’ ranges, diseases such as Lyme borreliosis and TBE may become less prevalent. Where socioeconomic conditions link livestock with livelihoods, as in Pakistan and much of Africa, a One Health approach is needed to tackling ticks and tick-borne infections under the increasing challenges presented by climate change.
The effects of current and future global warming on the distribution and activity of the primary ixodid vectors of human babesiosis (caused by Babesia divergens, B. venatorum and B. microti) are discussed. There is clear evidence that the distributions of both Ixodes ricinus, the vector in Europe, and I. scapularis in North America have been impacted by the changing climate, with increasing temperatures resulting in the northwards expansion of tick populations and the occurrence of I. ricinus at higher altitudes. Ixodes persulcatus, which replaces I. ricinus in Eurasia and temperate Asia, is presumed to be the babesiosis vector in China and Japan, but this tick species has not yet been confirmed as the vector of either human or animal babesiosis. There is no definite evidence, as yet, of global warming having an effect on the occurrence of human babesiosis, but models suggest that it is only a matter of time before cases occur further north than they do at present.
The COVID-19 pandemic has shed light on the challenges we face as a global society in preventing and containing emerging and re-emerging pathogens. Multiple intersecting factors, including environmental changes, host immunological factors, and pathogen dynamics, are intimately connected to the emergence and re-emergence of communicable diseases. There is a large and expanding list of communicable diseases that can cause neurological damage, either through direct or indirect routes. Novel pathogens of neurotropic potential have been identified through advanced diagnostic techniques, including metagenomic next-generation sequencing, but there are also known pathogens which have expanded their geographic distribution to infect non-immune individuals. Factors including population growth, climate change, the increase in animal and human interface, and an increase in international travel and trade are contributing to the expansion of emerging and re-emerging pathogens. Challenges exist around antimicrobial misuse giving rise to antimicrobial-resistant infectious neurotropic organisms and increased susceptibility to infection related to the expanded use of immunomodulatory treatments. In this article, we will review key concepts around emerging and re-emerging pathogens and discuss factors associated with neurotropism and neuroinvasion. We highlight several neurotropic pathogens of interest, including West Nile virus (WNV), Zika Virus, Japanese Encephalitis Virus (JEV), and Tick-Borne Encephalitis Virus (TBEV). We emphasize neuroinfectious diseases which impact the central nervous system (CNS) and focus on flaviviruses, a group of vector-borne pathogens that have expanded globally in recent years and have proven capable of widespread outbreak.
Dengue virus (DENV) is the causative agent of dengue fever and severe dengue. Every year, millions of people are infected with this virus. There is no vaccine available for this disease. Dengue virus is present in four serologically varying strains, DENV 1, 2, 3, and 4, and each of these serotypes is further classified into various genotypes based on the geographic distribution and genetic variance. Mosquitoes play the role of vectors for this disease. Tropical countries and some temperate parts of the world witness outbreaks of dengue mainly during the monsoon (rainy) seasons. Several algorithms have been developed to predict the occurrence and prognosis of dengue disease. These algorithms are mainly based on epidemiological data, climate factors, and online search patterns in the infected area. Most of these algorithms are based on either machine learning or deep learning techniques. We summarize the different software tools available for predicting the outbreaks of dengue based on the aforementioned factors, briefly outline the methodology used in these algorithms, and provide a comprehensive list of programs available for the same in this article.
The leishmaniases are a group of four vector-borne neglected tropical diseases (NTDs) with 1.6 billion people in some 100 countries at risk. They occur in certain eco-epidemiological foci that reflect manipulation by human activities, such as migration, urbanization and deforestation, of which poverty, conflict and climate change are key drivers. Given their synergistic impacts, risk factors and the vulnerabilities of poor populations and the launch of a new 2030 roadmap for NTDs in the context of the global sustainability agenda, it is warranted to update the state of knowledge of the leishmaniases and their effects. Using existing literature, we review socioeconomic and psychosocial impacts of leishmaniasis within a framework of risk factors and vulnerabilities to help inform policy interventions. Studies show that poverty is an overarching primary risk factor. Low-income status fosters inadequate housing, malnutrition and lack of sanitation, which create and exacerbate complexities in access to care and treatment outcomes as well as education and awareness. The co-occurrence of the leishmaniases with malnutrition and HIV infection further complicate diagnosis and treatment, leading to poor diagnostic outcomes and therapeutic response. Even with free treatment, households may suffer catastrophic health expenditure from direct and indirect medical costs, which compounds existing financial strain in low-income communities for households and healthcare systems. The dermatological presentations of the leishmaniases may result in long-term severe disfigurement, leading to stigmatization, reduced quality of life, discrimination and mental health issues. A substantial amount of recent literature points to the vulnerability pathways and burden of leishmaniasis on women, in particular, who disproportionately suffer from these impacts. These emerging foci demonstrate a need for continued international efforts to address key risk factors and population vulnerabilities if leishmaniasis control, and ultimately elimination, is to be achieved by 2030.
The rapid spread of arboviral infections in recent years has continually established arthropod-borne encephalitis to be a pressing global health concern. Causing a wide range of clinical presentations ranging from asymptomatic infection to fulminant neurological disease, the hallmark features of arboviral infection are important to clinically recognise. Arboviral infections may cause severe neurological presentations such as meningoencephalitis, epilepsy, acute flaccid paralysis and stroke. While the pathogenesis of arboviral infections is still being investigated, shared neuroanatomical pathways among these viruses may give insight into future therapeutic targets. The shifting infection transmission patterns and evolving distribution of arboviral vectors are heavily influenced by global climate change and human environmental disruption, therefore it is of utmost importance to consider this potential aetiology when assessing patients with encephalitic presentations.
The steady and ongoing change in climatic patterns across the globe is triggering a cascade of climate-adaptive phenomena, both genetic and behavioral in parasites, and influencing the host-pathogen-transmission triangle. Parasite and vector traits are now heavily influenced due to increasing temperature that almost dissolved geospatial boundaries and impacted the basic reproductive number of parasites. As consequence, continents unknown to some parasites are experiencing altered distribution and abundance of new and emerging parasites that are developing into a newer epidemiological model. These are posing a burden to healthcare and higher disease prevalence. This calls for multidisciplinary actions focusing on One Health to improve and innovate in areas of detection, reporting, and medical countermeasures to combat the growing threat of parasite emergence owing to climate adaptations for better public health outcomes.
PURPOSE OF REVIEW: The COVID-19 pandemic has cast increased attention on emerging infections. Clinicians and public health experts should be aware of emerging infectious causes of encephalitis, mechanisms by which they are transmitted, and clinical manifestations of disease. RECENT FINDINGS: A number of arthropod-borne viral infections — transmitted chiefly by mosquitoes and ticks — have emerged in recent years to cause outbreaks of encephalitis. Examples include Powassan virus in North America, Chikungunya virus in Central and South America, and tick-borne encephalitis virus in Europe. Many of these viruses exhibit complex life cycles and can infect multiple host animals in addition to humans. Factors thought to influence emergence of these diseases, including changes in climate and land use, are also believed to underlie the emergence of the rickettsial bacterium Orientia tsutsugamushi, now recognized as a major causative agent of acute encephalitis syndrome in South Asia. In addition, the COVID-19 pandemic has highlighted the role of bats as carriers of viruses. Recent studies have begun to uncover mechanisms by which the immune systems of bats are poised to allow for viral tolerance. Several bat-borne infections, including Nipah virus and Ebola virus, have resulted in recent outbreaks of encephalitis. SUMMARY: Infectious causes of encephalitis continue to emerge worldwide, in part because of climate change and human impacts on the environment. Expansion of surveillance measures will be critical in rapid diagnosis and limiting of outbreaks in the future.
PURPOSE OF REVIEW: Following their use for medicinal purposes, volatile inhalational anaesthetic agents are expelled into the atmosphere where they contribute to anthropogenic climate change. We describe recent evidence examining the benefits and harms associated with their use. RECENT FINDINGS: The environmental harms associated with desflurane and nitrous oxide likely outweigh any purported clinical benefits. Life cycle analyses are beginning to address the many gaps in our understanding, and informing choices made on all aspects of anaesthetic care. There is, however, an urgent need to move beyond the debate about anaesthetic technique A vs. B and focus also on areas such as sustainable procurement, waste management, pharmacological stewardship and joined-up solutions. SUMMARY: There is now compelling evidence that anaesthetists, departments and hospitals should avoid desflurane completely, and limit nitrous oxide use to settings where there is no viable alternative, as their environmental harms outweigh any perceived clinical benefit. Life cycle analyses seem supportive of total intravenous and/or regional anaesthesia. There are many other areas where choices can be made by individual anaesthetists that contribute towards reducing the environmental burden of healthcare, such as prioritising the reduction of inappropriate resource use and over-treatment. However, this all requires joined up solutions where all parts of an organisation engage.
The complex nature of climate change-mediated multitrophic interaction is an underexplored area, but has the potential to dramatically shift transmission and distribution of many insects and their pathogens, placing some populations closer to the brink of extinction. However, for individual insect-pathogen interactions climate change will have complicated hard-to-anticipate impacts. Thus, both pathogen virulence and insect host immunity are intrinsically linked with generalized stress responses, and in both pathogen and host have extensive trade-offs with nutrition (e.g., host plant quality), growth and reproduction. Potentially alleviating or exasperating these impacts, some pathogens and hosts respond genetically and rapidly to environmental shifts. This review identifies many areas for future research including a particular need to identify how altered global warming interacts with other environmental changes and stressors, and how consistent these impacts are across pathogens and hosts. With that achieved we would be closer to producing an overarching framework to integrate knowledge on all environmental interplay and infectious disease events.
Species distribution models (SDMs) that use climate variables to make binary predictions are effective tools for niche prediction in current and future climate scenarios. In this study, a Hutchinson hypervolume is defined with temperature, humidity, air pressure, precipitation, and cloud cover climate vectors collected from the National Oceanic and Atmospheric Administration (NOAA) that were matched to mosquito presence and absence points extracted from NASA’s citizen science platform called GLOBE Observer and the National Ecological Observatory Network. An 86% accurate Random Forest model that operates on binary classification was created to predict mosquito threat. Given a location and date input, the model produces a threat level based on the number of decision trees that vote for a presence label. The feature importance chart and regression show a positive, linear correlation between humidity and mosquito threat and between temperature and mosquito threat below a threshold of 28 °C. In accordance with the statistical analysis and ecological wisdom, high threat clusters in warm, humid regions and low threat clusters in cold, dry regions were found. With the model running on the cloud and within ArcGIS Dashboard, accurate and granular real-time threat level predictions can be made at any latitude and longitude. A device leveraging Global Positioning System (GPS) smartphone technology and the Internet of Things (IoT) to collect and analyze data on the edge was developed. The data from the edge device along with its respective date and location collected are automatically inputted into the aforementioned Random Forest model to provide users with a real-time threat level prediction. This inexpensive hardware can be used in developing countries that are threatened by vector-borne diseases or in remote areas without cloud connectivity. Such devices can be linked with citizen science mosquito data platforms to build training datasets for machine learning based SDMs.
With advancements in big geospatial data and artificial intelligence, multi-source data and diverse data-driven methods have become common in dengue risk prediction. Understanding the current state of data and models in dengue risk prediction enables the implementation of efficient and accurate prediction in the future. Focusing on predictors, data sources, spatial and temporal scales, data-driven methods, and model evaluation, we performed a literature review based on 53 journal and conference papers published from 2018 to the present and concluded the following. (1) The predominant predictors include local climate conditions, historical dengue cases, vegetation indices, human mobility, population, internet search indices, social media indices, landscape, time index, and extreme weather events. (2) They are mainly derived from the official meteorological agency satellite-based datasets, public websites, department of health services and national electronic diseases surveillance systems, official statistics, and public transport datasets. (3) Country-level, province/state-level, city-level, district-level, and neighborhood-level are used as spatial scales, and the city-level scale received the most attention. The temporal scales include yearly, monthly, weekly, and daily, and both monthly and weekly are the most popular options. (4) Most studies define dengue risk forecasting as a regression task, and a few studies define it as a classification task. Data-driven methods can be categorized into single models, ensemble learning, and hybrid learning, with single models being further subdivided into time series, machine learning, and deep learning models. (5) Model evaluation concentrates primarily on the quantification of the difference/correlation between time-series observations and predicted values, the ability of models to determine whether a dengue outbreak occurs or not, and model uncertainty. Finally, we highlighted the importance of big geospatial data, data cloud computing, and other deep learning models in future dengue risk forecasting.
When a rare pathogen emerges to cause a pandemic, it is critical to understand its dynamics and the impact of mitigation measures. We use experimental data to parametrize a temperature-dependent model of Zika virus (ZIKV) transmission dynamics and analyse the effects of temperature variability and control-related parameters on the basic reproduction number (R(0)) and the final epidemic size of ZIKV. Sensitivity analyses show that these two metrics are largely driven by different parameters, with the exception of temperature, which is the dominant driver of epidemic dynamics in the models. Our R(0) estimate has a single optimum temperature (≈30°C), comparable to other published results (≈29°C). However, the final epidemic size is maximized across a wider temperature range, from 24 to 36°C. The models indicate that ZIKV is highly sensitive to seasonal temperature variation. For example, although the model predicts that ZIKV transmission cannot occur at a constant temperature below 23°C (≈ average annual temperature of Rio de Janeiro, Brazil), the model predicts substantial epidemics for areas with a mean temperature of 20°C if there is seasonal variation of 10°C (≈ average annual temperature of Tampa, Florida). This suggests that the geographical range of ZIKV is wider than indicated from static R(0) models, underscoring the importance of climate dynamics and variation in the context of broader climate change on emerging infectious diseases.
Arboviruses (arthropod-borne viruses) are expanding their geographic range, posing significant health threats to millions of people worldwide. This expansion is associated with efficient and suitable vector availability. Apart from the well-known Aedes aegypti and Ae. albopictus, other Aedes species may potentially promote the geographic spread of arboviruses because these viruses have similar vector requirements. Aedes japonicus, Ae. vexans and Ae. vittatus are a growing concern, given their potential and known vector competence for several arboviruses including dengue, chikungunya, and Zika viruses. In the present study, we developed detailed maps of their global potential distributions under both current and future (2050) climate conditions, using an ecological niche modeling approach (Maxent). Under present-day conditions, Ae. japonicus and Ae. vexans have suitable areas in the northeastern United States, across Europe and in southeastern China, whereas the tropical regions of South America, Africa and Asia are more suitable for Ae. vittatus. Future scenarios anticipated range changes for the three species, with each expected to expand into new areas that are currently not suitable. By 2050, Ae. japonicus will have a broader potential distribution across much of Europe, the United States, western Russia and central Asia. Aedes vexans may be able to expand its range, especially in Libya, Egypt and southern Australia. For Ae. vittatus, future projections indicated areas at risk in sub-Saharan Africa and the Middle East. As such, these species deserve as much attention as Ae. aegypti and Ae. albopictus when processing arboviruses risk assessments and our findings may help to better understand the potential distribution of each species.
BACKGROUND: Mosquito-borne diseases (e.g., transmitted by Aedes aegypti) affect almost 700 million people each year and result in the deaths of more than 1 million people annually. METHODS: We examined research undertaken during the period 1951-2020 on the effects of temperature and climate change on Ae. aegypti, and also considered research location and between-country collaborations. RESULTS: The frequency of publications on the effects of climate change on Ae. aegypti increased over the period examined, and this topic received more attention than the effects of temperature alone on this species. The USA, UK, Australia, Brazil, and Argentina were the dominant research hubs, while other countries fell behind with respect to number of scientific publications and/or collaborations. The occurrence of Ae. aegypti and number of related dengue cases in the latter are very high, and climate change scenarios predict changes in the range expansion and/or occurrence of this species in these countries. CONCLUSIONS: We conclude that some of the countries at risk of expanding Ae. aegypti populations have poor research networks that need to be strengthened. A number of mechanisms can be considered for the improvement of international collaboration, representativity and diversity, such as research networks, internationalization programs, and programs that enhance representativity. These types of collaboration are considered important to expand the relevant knowledge of these countries and for the development of management strategies in response to climate change scenarios.
BACKGROUND AND AIM: Vector-borne diseases (VBDs) constitute a global problem for humans and animals. Knowledge related to the spatial distribution of various species of vectors and their relationship with the environment where they develop is essential to understand the current risk of VBDs and for planning surveillance and control strategies in the face of future threats. This study aimed to identify models, variables, and factors that may influence the emergence and resurgence of VBDs and how these factors can affect spatial local and global distribution patterns. MATERIALS AND METHODS: A systematic review was designed based on identification, screening, selection, and inclusion described in the research protocols according to the preferred reporting items for systematic reviews and meta-analyses guide. A literature search was performed in PubMed, ScienceDirect, Scopus, and SciELO using the following search strategy: Article type Original research, Language: English, Publishing period: 2010-2020, Search terms: Spatial analysis, spatial models, VBDs, climate, ecologic, life cycle, climate variability, vector-borne, vector, zoonoses, species distribution model, and niche model used in different combinations with “AND” and “OR.” RESULTS: The complexity of the interactions between climate, biotic/abiotic variables, and non-climate factors vary considerably depending on the type of disease and the particular location. VBDs are among the most studied types of illnesses related to climate and environmental aspects due to their high disease burden, extended presence in tropical and subtropical areas, and high susceptibility to climate and environment variations. CONCLUSION: It is difficult to generalize our knowledge of VBDs from a geospatial point of view, mainly because every case is inherently independent in variable selection, geographic coverage, and temporal extension. It can be inferred from predictions that as global temperatures increase, so will the potential trend toward extreme events. Consequently, it will become a public health priority to determine the role of climate and environmental variations in the incidence of infectious diseases. Our analysis of the information, as conducted in this work, extends the review beyond individual cases to generate a series of relevant observations applicable to different models.
Ticks exist on all continents and carry more zoonotic pathogens than any other type of vector. Ticks spend most of their lives in the external environment away from the host and are thus expected to be affected by changes in climate. Most empirical and theoretical studies demonstrate or predict range shifts or increases in ticks and tick-borne diseases, but there can be a lot of heterogeneity in such predictions. Tick-borne disease systems are complex, and determining whether changes are due to climate change or other drivers can be difficult. Modeling studies can help tease apart and understand the roles of different drivers of change. Predictive models can also be invaluable in projecting changes according to different climate change scenarios. However, validating these models remains challenging, and estimating uncertainty in predictions is essential. Another focus for future research should be assessing the resilience of ticks and tick-borne pathogens to climate change.
Dengue, a mosquito-borne disease, poses a tremendous burden to human health with about 390 million annual dengue infections worldwide. The environmental temperature plays a major role in the mosquito life-cycle as well as the mosquito-human-mosquito dengue transmission cycle. While previous studies have provided useful insights into the understanding of dengue diseases, there is little emphasis put on the role of environmental temperature variation, especially diurnal variation, in the mosquito vector and dengue dynamics. In this study, we develop a mathematical model to investigate the impact of seasonal and diurnal temperature variations on the persistence of mosquito vector and dengue. Importantly, using a threshold dynamical system approach to our model, we formulate the mosquito reproduction number and the infection invasion threshold, which completely determine the global threshold dynamics of mosquito population and dengue transmission, respectively. Our model predicts that both seasonal and diurnal variations of the environmental temperature can be determinant factors for the persistence of mosquito vector and dengue. In general, our numerical estimates of the mosquito reproduction number and the infection invasion threshold show that places with higher diurnal or seasonal temperature variations have a tendency to suffer less from the burden of mosquito population and dengue epidemics. Our results provide novel insights into the theoretical understanding of the role of diurnal temperature, which can be beneficial for the control of mosquito vector and dengue spread.
Early warning system (EWS) for vector-borne diseases is incredibly complex due to numerous factors originating from human, environmental, vector and the disease itself. Dengue EWS aims to collect data that leads to prompt decision-making processes that trigger disease intervention strategies to minimize the impact on a specific population. Dengue EWS may have a similar structural design, functions, and analytical approaches but different performance and ability to predict outbreaks. Hence, this review aims to summarise and discuss the evidence of different EWSs, their performance, and their ability to predict dengue outbreaks. A systematic literature search was performed of four primary databases: Scopus, Web of Science, Ovid MEDLINE, and EBSCOhost. Eligible articles were evaluated using a checklist for assessing the quality of the studies. A total of 17 studies were included in this systematic review. All EWS models demonstrated reasonably good predictive abilities to predict dengue outbreaks. However, the accuracy of their predictions varied greatly depending on the model used and the data quality. The reported sensitivity ranged from 50 to 100%, while specificity was 74 to 94.7%. A range between 70 to 96.3% was reported for prediction model accuracy and 43 to 86% for PPV. Overall, meteorological alarm indicators (temperatures and rainfall) were the most frequently used and displayed the best performing indicator. Other potential alarm indicators are entomology (female mosquito infection rate), epidemiology, population and socioeconomic factors. EWS is an essential tool to support district health managers and national health planners to mitigate or prevent disease outbreaks. This systematic review highlights the benefits of integrating several epidemiological tools focusing on incorporating climatic, environmental, epidemiological and socioeconomic factors to create an early warning system. The early warning system relies heavily on the country surveillance system. The lack of timely and high-quality data is critical for developing an effective EWS.
Climate change is unexpected weather patterns that can create an alarming situation. Due to climate change, various sectors are affected, and one of the sectors is healthcare. As a result of climate change, the geographic range of several vector-borne human infectious diseases will expand. Currently, dengue is taking its toll, and climate change is one of the key reasons contributing to the intensification of dengue disease transmission. The most important climatic factors linked to dengue transmission are temperature, rainfall, and relative humidity. The present study carries out a systematic literature review on the surveillance system to predict dengue outbreaks based on Machine Learning modeling techniques. The systematic literature review discusses the methodology and objectives, the number of studies carried out in different regions and periods, the association between climatic factors and the increase in positive dengue cases. This study also includes a detailed investigation of meteorological data, the dengue positive patient data, and the pre-processing techniques used for data cleaning. Furthermore, correlation techniques in several studies to determine the relationship between dengue incidence and meteorological parameters and machine learning models for predictive analysis are discussed. In the future direction for creating a dengue surveillance system, several research challenges and limitations of current work are discussed.
BACKGROUND: Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. METHODOLOGY/PRINCIPAL FINDINGS: We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. CONCLUSIONS/SIGNIFICANCE: Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.
In this paper, we propose a new dynamical system model pertaining to Dengue transmission, and investigate its consequent morphology. We present and study various ramifications of our mathematical model for Dengue spread, encapsulated in a spatio-temporal differential system made of reaction-diffusion equations. Diffusion terms are incorporated into the said model by using specific derivations for infected mosquitoes, and infected humans, as well. Moreover, mechanisms for the nearest neighbor(s) infections are integrated into the model. Furthermore, using adaptive multigrid finite difference with decoupling and quasi-linearization techniques, we investigate two main factors for Dengue spatial propagation. We determine the effects of temperature variations, and the mobility of infectious agents, be they mosquitoes or humans. Finally, the proposed model-based analytico-numerical results are obtained, and rendered in graphical profiles, which show the major role the climate temperature and the mobility of infected humans have on the spread and speed of the disease. The consequent proposed model outcomes and health-based ramifications are then raised, discussed, and then validated.
Climate change impacts represent one of the most important ecological and medical issues during this century. Several fungal species will change their distribution through space and time as a response to climate changes. This will rearrange many fungal diseases throughout the world. One of the most important and very common fungi is the black mold Aspergillus niger. The COVID-19 pandemic reforms the way in which mycologists think about this fungus as an emerging healthy issue. Through this work, about one thousand records of Aspergillus niger were used to model its current and future global distribution using 19 bioclimatic variables under several climate change scenarios. Maximum entropy implemented in Maxent was chosen as the modeling tool, especially with its accuracy and reliability over the other modeling techniques. The annual mean temperature (bio 1) forms the most contributed climatological parameter to black mold distribution. The produced current distribution model came compatible with the real distribution of the species with a cosmopolitan range. The rise of temperature due to global warming will form a limitation to Aspergillus niger through several parts of its range. The generated maps of the future status of this fungus under two different RCPs for 2050 and 2070, indicate several parts that become free from black mold due to temperature limitations. The present results need more intensive future evaluation using data science and GIS, especially on a local scale including more ecological parameters other than climatological data.
BACKGROUND: Meningitis can cause devastating epidemics and is susceptible to climate change. It is unclear how temperature variability, an indicator of climate change, is associated with meningitis incidence. METHODS: We used global meningitis incidence data along with meteorological and demographic data over 1990-2019 to identify the association between temperature variability and meningitis. We also employed future (2020-2100) climate data to predict meningitis incidence under different emission levels (SSPs: Shared Socioeconomic Pathways). RESULTS: We found that the mean temperature variability increased by almost 3 folds in the past 30 years. The largest changes occurred in Australasia, Tropical Latin America, and Central Sub-Saharan Africa. With a logarithmic unit increase in temperature variability, the overall global meningitis risk increases by 4.8 %. Australasia, Central Sub-Saharan Africa, and High-income North America are the most at-risk regions. Higher statistical differences were identified in males, children, and the elderly population. Compared to high-emission (SSP585) scenario, we predicted a median reduction of 85.8 % in meningitis incidence globally under the low-emission (SSP126) climate change scenario by 2100. CONCLUSION: Our study provides evidence for temperature variability being in association with meningitis incidence, which suggests that global actions are urgently needed to address climate change and to prevent meningitis occurrence.
ABSTRACT: Throughout the past decades, climate change has been one of the most complex global issues. Characterized by worldwide alterations in weather patterns, along with a concomitant increase in the temperature of the Earth, climate change will undoubtedly have significant effects on food security and food safety. Climate change engenders climate variability: significant variations in weather variables and their frequency. Both climate variability and climate change are thought to threaten the safety of the food supply chain through different pathways. One such pathway is the ability to exacerbate foodborne diseases by influencing the occurrence, persistence, virulence and, in some cases, toxicity of certain groups of disease-causing microorganisms. Food safety can also be compromised by various chemical hazards, such as pesticides, mycotoxins, and heavy metals. With changes in weather patterns, such as lower rainfall, higher air temperature, and higher frequency of extreme weather events among others, this translates to emerging food safety concerns. These include the shortage of safe water for irrigation of agricultural produce, greater use of pesticides due to pest resistance, increased difficulty in achieving a well-controlled cold chain resulting in temperature abuse, or the occurrence of flash floods, which cause runoff of chemical contaminants in natural water courses. Together, these can result in foodborne infection, intoxication, antimicrobial resistance, and long-term bioaccumulation of chemicals and heavy metals in the human body. Furthermore, severe climate variability can result in extreme weather events and natural calamities, which directly or indirectly impair food safety. This review discusses the causes and impacts of climate change and variability on existing and emerging food safety risks and also considers mitigation and adaptation strategies to address the global warming and climate change problem.
Gambierdiscus is a dinoflagellate genus widely distributed throughout tropical and subtropical regions. Some members of this genus can produce a group of potent polycyclic polyether neurotoxins responsible for ciguatera fish poisoning (CFP), one of the most significant food-borne illnesses associated with fish consumption. Ciguatoxins and maitotoxins, the two major toxins produced by Gambierdiscus, act on voltage-gated channels and TRPA1 receptors, consequently leading to poisoning and even death in both humans and animals. Over the past few decades, the occurrence and geographic distribution of CFP have undergone a significant expansion due to intensive anthropogenic activities and global climate change, which results in more human illness, a greater public health impact, and larger economic losses. The global spread of CFP has led to Gambierdiscus and its toxins being considered an environmental and human health concern worldwide. In this review, we seek to provide an overview of recent advances in the field of Gambierdiscus and its associated toxins based on the existing literature combined with re-analyses of current data. The taxonomy, phylogenetics, geographic distribution, environmental regulation, toxin detection method, toxin biosynthesis, and pharmacology and toxicology of Gambierdiscus are summarized and discussed. We also highlight future perspectives on Gambierdiscus and its associated toxins.
The One Health concept that human, animal, plant, environmental, and ecosystem health are linked provides a framework for examining and addressing complex health challenges. This framework can be represented as a multi-dimensional matrix that can be used as a tool to identify upstream drivers of disease potential in a concise, systematic, and comprehensive way. The matrix can involve up to four dimensions depending on users’ needs. This paper describes and illustrates how the matrix tool might be used to facilitate systems thinking, enabling the development of effective and equitable public policies. The multidimensional One Health matrix tool will be used to examine, as an example, global human and animal fecal wastes. The fecal wastes are analyzed at the microbial and population levels over a timeframe of years. Political, social, and economic factors are part of the matrix and will be examined as well. The One Health matrix tool illustrates how foodborne illnesses, food insecurity, antimicrobial resistance, and climate change are inter-related. Understanding these inter-relationships is essential to develop the public policies needed to achieve many of the United Nations’ Sustainable Development Goals.
Bivalve shellfish consumption (ark shells, clams, cockles, and oysters) has increased over the last decades. Following this trend, infectious disease outbreaks associated with their consumption have been reported more frequently. Molluscs are a diverse group of organisms found wild and farmed. They are common on our tables, but unfortunately, despite their great taste, they can also pose a threat as a potential vector for numerous species of pathogenic microorganisms. Clams, in particular, might be filled with pathogens because of their filter-feeding diet. This specific way of feeding favors the accumulation of excessive amounts of pathogenic microorganisms like Vibrio spp., including Vibrio cholerae and V. parahaemolyticus, Pseudomonas aeruginosa, Escherichia coli, Arcobacter spp., and fecal coliforms, and intestinal enterococci. The problems of pathogen dissemination and disease outbreaks caused by exogenous bacteria in many geographical regions quickly became an unwanted effect of globalized food supply chains, global climate change, and natural pathogen transmission dynamics. Moreover, some pathogens like Shewanella spp., with high zoonotic potential, are spreading worldwide along with food transport. These bacteria, contained in food, are also responsible for the potential transmission of antibiotic-resistance genes to species belonging to the human microbiota. Finally, they end up in wastewater, thus colonizing new areas, which enables them to introduce new antibiotic-resistance genes (ARG) into the environment and extend the existing spectrum of ARGs already present in local biomes. Foodborne pathogens require modern methods of detection. Similarly, detecting ARGs is necessary to prevent resistance dissemination in new environments, thus preventing future outbreaks, which could threaten associated consumers and workers in the food processing industry.
Harmful algal blooms (HABs) are considered one of the main risks for marine ecosystems and human health worldwide. Climate change is projected to induce significant changes in species geographic distribution, and, in this sense, it is paramount to accurately predict how it will affect toxin-producing microalgae. In this context, the present study was intended to project the potential biogeographical changes in habitat suitability and occurrence distribution of three key amnesic shellfish toxin (AST)-producing diatom species (i.e., Pseudo-nitzschia australis, P. seriata, and P. fraudulenta) under four different climate change scenarios (i.e., RCP-2.6, 4.5, 6.0, and 8.5) up to 2050 and 2100. For this purpose, we applied species distribution models (SDMs) using four abiotic predictors (i.e., sea surface temperature, salinity, current velocity, and bathymetry) in a MaxEnt framework. Overall, considerable contraction and potential extirpation were projected for all species at lower latitudes together with projected poleward expansions into higher latitudes, mainly in the northern hemisphere. The present study aims to contribute to the knowledge on the impacts of climate change on the biogeography of toxin-producing microalgae species while at the same time advising the correct environmental management of coastal habitats and ecosystems.
Vibrio vulnificus is a zoonotic pathogen linked to aquaculture that is spreading due to climate change. The pathogen can be transmitted to humans and animals by ingestion of raw shellfish or seafood feed, respectively. The aim of this work was to design and test a new procedure to detect V. vulnificus hazardous to human and/or animal health in food/feed samples. For this purpose, we combined a pre-enrichment step with multiplex PCR using primers for the species and for human and animal virulence markers. In vitro assays with mixed DNA from different Vibrio species and Vibrio cultures showed that the new protocol was 100 % specific with a detection limit of 10 cfu/mL. The protocol was successfully validated in seafood using artificially contaminated live shrimp and proved useful also in pathogen isolation from animals and their ecosystem. In conclusion, this novel protocol could be applied in health risk studies associated with food/feed consumption, as well as in the routine identification and subtyping of V. vulnificus from environmental or clinical samples.
Protozoan parasites are single-celled eukaryotic organisms that cause significant human disease and pose a substantial health and socioeconomic burden worldwide. They are responsible for at least 1 million deaths annually. The treatment of such diseases is hindered by the ability of parasites to form latent cysts, develop drug resistance, or be transmitted by insect vectors. Additionally, these pathogens have developed complex mechanisms to alter host gene expression. The prevalence of these diseases is predicted to increase as climate change leads to the augmentation of ambient temperatures, insect ranges, and warm water reservoirs. Therefore, the discovery of novel treatments is necessary. Transcription factors lie at the junction of multiple signalling pathways in eukaryotes and aberrant transcription factor function contributes to the progression of numerous human diseases including cancer, diabetes, inflammatory disorders and cardiovascular disease. Transcription factors were previously thought to be undruggable. However, due to recent advances, transcription factors now represent appealing drug targets. It is conceivable that transcription factors, and the pathways they regulate, may also serve as targets for anti-parasitic drug design. Here, we review transcription factors and transcriptional modulators of protozoan parasites, and discuss how they may be useful in drug discovery. We also provide information on transcription factors that play a role in stage conversion of parasites, TATA box-binding proteins, and transcription factors and cofactors that participate with RNA polymerases I, II and III. We also highlight a significant gap in knowledge in that the transcription factors of some of parasites have been under-investigated. Understanding parasite transcriptional pathways and how parasites alter host gene expression will be essential in discovering innovative drug targets.
BACKGROUND: Numerous studies have quantified the associations between ambient temperature and enteric infections, particularly all-cause enteric infections. However, the temperature sensitivity of enteric infections might be pathogen dependent. Here, we sought to identify pathogen-specific associations between ambient temperature and enteric infections. METHODS: We did a systematic review and meta-analysis by searching PubMed, Web of Science, and Scopus for peer-reviewed research articles published from Jan 1, 2000, to Dec 31, 2019, and also hand searched reference lists of included articles and excluded reviews. We included studies that quantified the effects of ambient temperature increases on common pathogen-specific enteric infections in humans. We excluded studies that expressed ambient temperature as a categorical or diurnal range, or in a standardised format. Two authors screened the search results, one author extracted data from eligible studies, and four authors verified the data. We obtained the overall risks by pooling the relative risks of enteric infection by pathogen for each 1°C temperature rise using random-effects modelling and robust variance estimation for the correlated effect estimates. Between-study heterogeneity was measured using I(2), τ(2), and Q-statistic. Publication bias was determined using funnel plot asymmetry and the trim-and-fill method. Differences among pathogen-specific pooled estimates were determined using subgroup analysis of taxa-specific meta-analysis. The study protocol was not registered but followed the PRISMA guidelines. FINDINGS: We identified 2981 articles via database searches and 57 articles from scanning reference lists of excluded reviews and included articles, of which 40 were eligible for pathogen-specific meta-analyses. The overall increased risks of incidence per 1°C temperature rise, expressed as relative risks, were 1·05 (95% CI 1·04-1·07; I(2) 97%) for salmonellosis, 1·07 (1·04-1·10; I(2) 99%) for shigellosis, 1·02 (1·01-1·04; I(2) 98%) for campylobacteriosis, 1·05 (1·04-1·07; I(2) 36%) for cholera, 1·04 (1·01-1·07; I(2) 98%) for Escherichia coli enteritis, and 1·15 (1·07-1·24; I(2) 0%) for typhoid. Reduced risks per 1°C temperature increase were 0·96 (95% CI 0·90-1·02; I(2) 97%) for rotaviral enteritis and 0·89 (0·81-0·99; I(2) 96%) for noroviral enteritis. There was evidence of between-pathogen differences in risk for bacterial infections but not for viral infections. INTERPRETATION: Temperature sensitivity of enteric infections can vary according to the enteropathogen causing the infection, particularly for bacteria. Thus, we encourage a pathogen-specific health adaptation approach, such as vaccination, given the possibility of increasingly warm temperatures in the future. FUNDING: Japan Society for the Promotion of Science (Kakenhi) Grant-in-Aid for Scientific Research.
Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.
The Covid-19 pandemic is of zoonotic origin, and many other emerging infections of humans have their origin in an animal host population. We review the challenges involved in modelling the dynamics of wildlife-human interfaces governing infectious disease emergence and spread. We argue that we need a better understanding of the dynamic nature of such interfaces, the underpinning diversity of pathogens and host-pathogen association networks, and the scales and frequencies at which environmental conditions enable spillover and host shifting from animals to humans to occur. The major drivers of the emergence of zoonoses are anthropogenic, including the global change in climate and land use. These, and other ecological processes pose challenges that must be overcome to counterbalance pandemic risk. The development of more detailed and nuanced models will provide better tools for analysing and understanding infectious disease emergence and spread.
Climate change can have a complex impact that also influences human and animal health. For example, climate change alters the conditions for pathogens and vectors of zoonotic diseases. Signs of this are the increasing spread of the West Nile and Usutu viruses and the establishment of new vector species, such as specific mosquito and tick species, in Europe and other parts of the world. With these changes come new challenges for maintaining human and animal health. This paper reports on an analysis of the literature focused on a bibliometric analysis of the Scopus database and VOSviewer software for creating visualization maps which identifies the zoonotic health risks for humans and animals caused by climate change. The sources retained for the analysis totaled 428 and different thresholds (N) were established for each item varying from N 5 to 10. The main findings are as follows: First, published documents increased in 2009-2015 peaking in 2020. Second, the primary sources have changed since 2018, partly attributable to the increase in human health concerns due to human-to-human transmission. Third, the USA, the UK, Canada, Australia, Italy, and Germany perform most zoonosis research. For instance, sixty documents and only 17 countries analyzed for co-authorship analysis met the threshold led by the USA; the top four author keywords were “climate change”, “zoonosis”, “epidemiology”, and “one health;” the USA, the UK, Germany, and Spain led the link strength (inter-collaboration); the author keywords showed that 37 out of the 1023 keywords met the threshold, and the authors’ keyword’s largest node of the bibliometric map contains the following: infectious diseases, emerging diseases, disease ecology, one health, surveillance, transmission, and wildlife. Finally, zoonotic diseases, which were documented in the literature in the past, have evolved, especially during the years 2010-2015, as evidenced by the sharp augmentation of publications addressing ad-hoc events and peaking in 2020 with the COVID-19 outbreak.
Emerging infectious diseases (EIDs), especially those with zoonotic potential, are a growing threat to global health, economy, and safety. The influence of global warming and geoclimatic variations on zoonotic disease epidemiology is evident by alterations in the host, vector, and pathogen dynamics and their interactions. The objective of this article is to review the current literature on the observed impacts of climate change on zoonoses and discuss future trends. We evaluated several climate models to assess the projections of various zoonoses driven by the predicted climate variations. Many climate projections revealed potential geographical expansion and the severity of vector-borne, waterborne, foodborne, rodent-borne, and airborne zoonoses. However, there are still some knowledge gaps, and further research needs to be conducted to fully understand the magnitude and consequences of some of these changes. Certainly, by understanding the impact of climate change on zoonosis emergence and distribution, we could better plan for climate mitigation and climate adaptation strategies.
Species are shifting their distributions in response to climate change. This geographic reshuffling may result in novel co-occurrences among species, which could lead to unseen biotic interactions, including the exchange of parasites between previously isolated hosts. Identifying potential new host-parasite interactions would improve forecasting of disease emergence and inform proactive disease surveillance. However, accurate predictions of future cross-species disease transmission have been hampered by the lack of a generalized approach and data availability. Here, we propose a framework to predict novel host-parasite interactions based on a combination of niche modelling of future host distributions and parasite sharing models. Using the North American ungulates as a proof of concept, we show this approach has high cross-validation accuracy in over 85% of modelled parasites and find that more than 34% of the host-parasite associations forecasted by our models have already been recorded in the literature. We discuss potential sources of uncertainty and bias that may affect our results and similar forecasting approaches, and propose pathways to generate increasingly accurate predictions. Our results indicate that forecasting parasite sharing in response to shifts in host geographic distributions allow for the identification of regions and taxa most susceptible to emergent pathogens under climate change. This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.
Tropical diseases (TDs) are among the leading cause of mortality and fatality globally. The emergence and reemergence of TDs continue to challenge healthcare system. Several tropical diseases such as yellow fever, tuberculosis, cholera, Ebola, HIV, rotavirus, dengue, and malaria outbreaks have led to endemics and epidemics around the world, resulting in millions of deaths. The increase in climate change, migration and urbanization, overcrowding, and other factors continue to increase the spread of TDs. More cases of TDs are recorded as a result of substandard health care systems and lack of access to clean water and food. Early diagnosis of these diseases is crucial for treatment and control. Despite the advancement and development of numerous diagnosis assays, the healthcare system is still hindered by many challenges which include low sensitivity, specificity, the need of trained pathologists, the use of chemicals and a lack of point of care (POC) diagnostic. In order to address these issues, scientists have adopted the use of CRISPR/Cas systems which are gene editing technologies that mimic bacterial immune pathways. Recent advances in CRISPR-based biotechnology have significantly expanded the development of biomolecular sensors for diagnosing diseases and understanding cellular signaling pathways. The CRISPR/Cas strategy plays an excellent role in the field of biosensors. The latest developments are evolving with the specific use of CRISPR, which aims for a fast and accurate sensor system. Thus, the aim of this review is to provide concise knowledge on TDs associated with mosquitoes in terms of pathology and epidemiology as well as background knowledge on CRISPR in prokaryotes and eukaryotes. Moreover, the study overviews the application of the CRISPR/Cas system for detection of TDs associated with mosquitoes.
There are more than 3,000 mosquito species. Aedes aegypti, Ae. communis, and C. quinquefasciatus are, among others, three of the most important mosquito allergen sources in the tropics, western, and industrialized countries. Several individuals are sensitized to mosquito allergens, but the epidemiological data indicates that the frequency of sensitization markedly differs depending on the geographical region. Additionally, the geographical localization of mosquito species has been affected by global warming and some mosquito species have invaded areas where they were not previously found, at the same time as other species have been displaced. This phenomenon has repercussions in the pathogenesis and the accuracy of the diagnosis of mosquito allergy. Allergic individuals are sensitized to mosquito allergens from two origins: saliva and body allergens. Exposure to saliva allergens occurs during mosquito bite and induces cutaneous allergic reactions. Experimental and clinical data suggest that body allergens mediate different manifestations of allergic reactions such as asthma and rhinitis. The most studied mosquito species is Ae. aegypti, from which four and five allergens of the saliva and body, respectively, have been reported. Many characterized allergens are homologs to arthropod-derived allergens, which cause strong cross-reactivity at the humoral and cellular level. The generalized use of whole body Ae. communis or C. quinquefasciatus extracts complicates the diagnosis of mosquito allergy because they have low concentration of saliva allergens and may result in poor diagnosis of the affected population when other species are the primary sensitizer. This review article discusses the current knowledge about mosquito allergy, allergens, cross-reactivity, and proposals of component resolved approaches based on mixtures of purified recombinant allergens to replace saliva-based or whole-body extracts, in order to perform an accurate diagnosis of allergy induced by mosquito allergen exposure.
Purpose of Review To asses recent advances in our understanding of the epidemiology, clinical presentation, diagnosis, and treatment of infections caused by free-living amoebas Recent Findings The burden of disease by free-living amoebas is underestimated; global warming could increase incidence in future years. Early recognition of clinical syndromes may allow for prompt initiation of therapy and better disease outcome. Molecular tests allow for rapid identification of the amoeba. Treatment is based on successful clinical outcomes reported using repurposed drugs. The optimal regimen for each of the clinical syndromes is unknown. As global warming increases, clinicians will be challenged to diagnose and treat infections by free-living amoebas. Therefore, awareness of clinical syndromes, diagnostic tools, and therapeutic interventions is crucial.
The acceleration of climate change has been associated with an alarming increase in the prevalence and geographic range of tick-borne diseases (TBD), many of which have severe and long-lasting effects-particularly when treatment is delayed principally due to inadequate diagnostics and lack of physician suspicion. Moreover, there is a paucity of treatment options for many TBDs that are complicated by diagnostic limitations for correctly identifying the offending pathogens. This review will focus on the biology, disease pathology, and detection methodologies used for the Borreliaceae family which includes the Lyme disease agent Borreliella burgdorferi. Previous work revealed that Borreliaceae genomes differ from most bacteria in that they are composed of large numbers of replicons, both linear and circular, with the main chromosome being the linear with telomeric-like termini. While these findings are novel, additional gene-specific analyses of each class of these multiple replicons are needed to better understand their respective roles in metabolism and pathogenesis of these enigmatic spirochetes. Historically, such studies were challenging due to a dearth of both analytic tools and a sufficient number of high-fidelity genomes among the various taxa within this family as a whole to provide for discriminative and functional genomic studies. Recent advances in long-read whole-genome sequencing, comparative genomics, and machine-learning have provided the tools to better understand the fundamental biology and phylogeny of these genomically-complex pathogens while also providing the data for the development of improved diagnostics and therapeutics.
Background: The rapid circulation of arboviruses in the human population has been linked with changes in climatic, environmental, and socio-economic conditions. These changes are known to alter the transmission cycles of arboviruses involving the anthropophilic vectors and thus facilitate an extensive geographical distribution of medically important arboviral diseases, thereby posing a significant health threat. Using our current understanding and assessment of relevant literature, this review aimed to understand the underlying factors promoting the spread of arboviruses and how the three most renowned interdisciplinary and holistic approaches to health such as One Health, Eco-Health, and Planetary Health can be a panacea for control of arboviruses. Methods: A comprehensive structured search of relevant databases such as Medline, PubMed, WHO, Scopus, Science Direct, DOAJ, AJOL, and Google Scholar was conducted to identify recent articles on arboviruses and holistic approaches to health using the keywords including arboviral diseases, arbovirus vectors, arboviral infections, epidemiology of arboviruses, holistic approaches, One Health, Eco-Health, and Planetary Health. Results: Changes in climatic factors like temperature, humidity, and precipitation support the growth, breeding, and fecundity of arthropod vectors transmitting the arboviral diseases. Increased human migration and urbanization due to socio-economic factors play an important role in population increase leading to the rapid geographical distribution of arthropod vectors and transmission of arboviral diseases. Medical factors like misdiagnosis and misclassification also contribute to the spread of arboviruses. Conclusion: This review highlights two important findings: First, climatic, environmental, socio-economic, and medical factors influence the constant distributions of arthropod vectors. Second, either of the three holistic approaches or a combination of any two can be adopted on arboviral disease control. Our findings underline the need for holistic approaches as the best strategy to mitigating and controlling the emerging and reemerging arboviruses.
The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.
Coccidioidomycosis, colloquially known as Valley Fever, is an invasive dimorphic fungal infection caused by Coccidioides immitis and C. posadasii. The fungi are found in the arid desert soils of the southwestern US, as well as in parts of Mexico and Central and South America. Acquisition is typically via inhalation of arthroconidia which become airborne after both natural (e.g., earthquakes, dust storms, and fires) and human-related events (e.g., military maneuvers, recreational activities, agriculture, and construction). The incidence of infection in increasing likely a result of both climatic and populational changes. Further, the recognized geographic distribution of Coccidioides spp. is expanding, as cases are being diagnosed in new areas (e.g., eastern Washington, Oregon, and Utah). Most coccidioidal infections are asymptomatic (60%); however, approximately one-third develop a pulmonary illness which is a leading cause of community-acquired pneumonia in highly endemic areas. Uncommonly (0.5-2% of cases), the infection disseminates to extrapulmonary locations (e.g., skin, bones/joints, and the central nervous system), and is most commonly seen among persons with cellular immunodeficiencies (e.g., transplant recipients, HIV, and pregnancy) and non-Caucasian races (especially African Americans and Filipinos). The diagnosis of coccidioidomycosis requires astute clinical suspicion and laboratory findings, including positive serology, cultures, and/or histopathology results. Treatment is warranted among persons with pneumonia who have risk factors for complicated disease and among those with extrapulmonary disease. Novel antifungals with improved fungicidal activity and rapidity of action with fewer side effects and drug interactions are needed. Preventive strategies (e.g., education regarding the disease, dust avoidance, mask wearing, including among select groups, antifungal prophylaxis, and surveillance laboratory testing) are advised for residents and travelers to endemic areas. Currently, no preventive vaccine is available. Coccidioidomycosis has been recognized for over a century, and an expanding wealth of knowledge has been gained regarding this emerging infectious disease which will be reviewed here.
Human activities have significantly impacted the environment and are changing our climate in ways that will have major consequences for ourselves, and endanger animal, plant and microbial life on Earth. Rising global temperatures and pollution have been highlighted as potential drivers for increases in infectious diseases. Although infrequently highlighted, fungi are amongst the leading causes of infectious disease mortality, resulting in more than 1.5 million deaths every year. In this review we evaluate the evidence linking anthropomorphic impacts with changing epidemiology of fungal disease. We highlight how the geographic footprint of endemic mycosis has expanded, how populations susceptible to fungal infection and fungal allergy may increase and how climate change may select for pathogenic traits and indirectly contribute to the emergence of drug resistance.
A number of studies suggest that meteorological conditions are related to the risk of Legionnaires’ disease (LD) but the findings are not consistent. A systematic review was conducted to investigate the association of weather with sporadic LD and highlight the key meteorological conditions related to this outcome. PubMed, EMBASE, The Cochrane Library and OpenGrey were searched on 26-27 March 2020 without date, language or location restrictions. Key words included “legionellosis”, “legionnaires’ disease”, combined with “meteorological conditions”, “weather”, “temperature”, “humidity”, “rain”, “ultraviolet rays”, “wind speed”, etc. Studies were excluded if they did not examine the exposure of interest, the outcome of interest and their association or if they only reported LD outbreak cases. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and it was registered in PROSPERO (#CRD42020168869). There were 811 articles, of which 17 were included in the review. The studies investigated different meteorological variables and most of them examined the combined effect of several variables. The most commonly examined factors were precipitation and temperature, followed by relative humidity. The studies suggested that increased precipitation, temperature and relative humidity were positively associated with the incidence of LD. There was limited evidence that higher wind speed, pressure, visibility, UV radiation and longer sunshine duration were inversely linked with the occurrence of LD. A period of increased but not very high temperatures, followed by a period of increased precipitation, favour the occurrence of LD. Increased awareness of the association of temperature and precipitation and LD occurrence among clinicians and public health professionals can improve differential diagnosis for cases of sporadic community-acquired pneumonia and at the same time contribute to improving LD surveillance.
The World Health Organization has identified vaccine hesitancy as one of its top ten global health threats for 2019. Efforts are underway to define the factors responsible for reductions in vaccine confidence. However, as global measles cases accelerated beginning in 2018, it became evident that additional factors were promoting measles re-emergence, including war, political and socio-economic collapse, shifting poverty, and vulnerability to weather events and climate change. Accordingly, we propose a Global Vaccine Risk Index (VRI) to consider these variables as a more comprehensive means to identify vulnerable nations where we might expect measles and other vaccine-preventable diseases to emerge or re-emerge. In Sub-Saharan African and Middle Eastern nations, conflict and political instability predominated as the basis for high vaccine risk scores, whereas in Southeast Asian countries, the major reasons included climate variability, current levels of measles vaccination coverage, and economic and educational disparities. In Europe, low vaccine confidence and refugee movements predominated, while in the Americas, economic disparities and vaccine confidence were important. The VRI may serve as a useful indicator and predictor for international agencies committed to childhood immunizations and might find relevance for accelerating future COVID19 vaccination programs.
Non-technical summaryHumans have the tendency to damage the natural environment in many ways. Deforestation and conversion of forests for residential, industrial development, and expansion of agricultural crops, as well as the burning of fossil fuels, are some activities that disrupt natural ecosystems and wildlife and contribute to climate change. As a result, the life cycles of pathogens and intermediate hosts (insects, rodents, mammals) as well as biodiversity are affected. Through these activities, humans meet wild animals that transmit pathogens, resulting in their infection by zoonoses and causing epidemics-pandemics, the effects of which have as their final recipient himself and his activities. Technical summaryThis article aims to highlight the two-way relationship between those human activities and the occurrence of epidemics-pandemics. We will try to elaborate this two-way relationship, through the overview of the current pandemic (origin of SARS-CoV-2, modes of transmission, clinical picture of the disease of COVID-19, influence of weather and air pollution on prevalence and mortality, pandemic effects, and treatments). They are used as primary sources, scientific articles, literature, websites, and databases (Supplementary appendix) to analyze factors involved in the occurrence and transmission of zoonotic diseases in humans (Ebola, influenza, Lyme disease, dengue fever, cholera, AIDS/HIV, SARS-CoV, MERS-CoV). The present paper concluded that humanity today faces two major challenges: controlling the COVID-19 pandemic and minimizing the risk of a new global health crisis occurring in the future. The first can be achieved through equitable access to vaccines and treatments for all people. The second needs the global community to make a great change and start protecting the natural environment and its ecosystems through the adoption of prevention policies. Summary of social mediaTwo-way relationship between human activities and epidemics highlighted, through review of the COVID-19 pandemic.
BACKGROUND: Zoonotic diseases account for more than 70% of emerging infectious diseases (EIDs). Due to their increasing incidence and impact on global health and the economy, the emergence of zoonoses is a major public health challenge. Here, we use a biogeographic approach to predict future hotspots and determine the factors influencing disease emergence. We have focused on the following three viral disease groups of concern: Filoviridae, Coronaviridae, and Henipaviruses. METHODS: We modelled presence-absence data in spatially explicit binomial and zero-inflation binomial logistic regressions with and without autoregression. Presence data were extracted from published studies for the three EID groups. Various environmental and demographical rasters were used to explain the distribution of the EIDs. True Skill Statistic and deviance parameters were used to compare the accuracy of the different models. RESULTS: For each group of viruses, we were able to identify and map areas at high risk of disease emergence based on the spatial distribution of the disease reservoirs and hosts of the three viral groups. Common influencing factors of disease emergence were climatic covariates (minimum temperature and rainfall) and human-induced land modifications. CONCLUSIONS: Using topographical, climatic, and previous disease outbreak reports, we can identify and predict future high-risk areas for disease emergence and their specific underlying human and environmental drivers. We suggest that such a predictive approach to EIDs should be carefully considered in the development of active surveillance systems for pathogen emergence and epidemics at local and global scales.
COVID-19 has revealed how challenging it is to manage global, systemic and compounding crises. Like COVID-19, climate change impacts, and maladaptive responses to them, have potential to disrupt societies at multiple scales via networks of trade, finance, mobility and communication, and to impact hardest on the most vulnerable. However, these complex systems can also facilitate resilience if managed effectively. This review aims to distil lessons related to the transboundary management of systemic risks from the COVID-19 experience, to inform climate change policy and resilience building. Evidence from diverse fields is synthesised to illustrate the nature of systemic risks and our evolving understanding of resilience. We describe research methods that aim to capture systemic complexity to inform better management practices and increase resilience to crises. Finally, we recommend specific, practical actions for improving transboundary climate risk management and resilience building. These include mapping the direct, cross-border and cross-sectoral impacts of potential climate extremes, adopting adaptive risk management strategies that embrace heterogenous decision-making and uncertainty, and taking a broader approach to resilience which elevates human wellbeing, including societal and ecological resilience.
The Lancet Countdown is an international collaboration that independently monitors the health consequences of a changing climate. Publishing updated, new, and improved indicators each year, the Lancet Countdown represents the consensus of leading researchers from 43 academic institutions and UN agencies. The 44 indicators of this report expose an unabated rise in the health impacts of climate change and the current health consequences of the delayed and inconsistent response of countries around the globe—providing a clear imperative for accelerated action that puts the health of people and planet above all else.
Background: Recent studies indicated the possible relationship between climate change, environmental pollution, and Coronavirus Disease 2019 (COVID-19) pandemic. This study reviewed the effects of air pollution, climate parameters, and lockdown on the number of cases and deaths related to COVID-19. Methods: The present review was performed to determine the effects of weather and air pollution on the number of cases and deaths related to COVID-19 during the lockdown. Articles were collected by searching the existing online databases, such as PubMed, Science Direct, and Google Scholar, with no limitations on publication dates. Afterwards, this review focused on outdoor air pollution, including PM2.5, PM10, NO2, SO2, and O-3, and weather conditions affecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/COVID-19. Results: Most reviewed investigations in the present study showed that exposure to air pollutants, particularly PM2.5 and NO2, is positively related to COVID-19 patients and mortality. Moreover, these studies showed that air pollution could be essential in transmitting COVID-19. Local meteorology plays a vital role in coronavirus spread and mortality. Temperature and humidity variables are negatively correlated with virus transmission. The evidence demonstrated that air pollution could lead to COVID-19 transmission. These results support decision-makers in curbing potential new outbreaks. Conclusions: Overall, in environmental perspective-based COVID-19 studies, efforts should be accelerated regarding effective policies for reducing human emissions, bringing about air pollution and weather change. Therefore, using clean and renewable energy sources will increase public health and environmental quality by improving global air quality.
Internists are experts in complexity, and the COVID-19 pandemic is disclosing complex and unexpected interactions between communicable and non-communicable diseases, environmental factors, and socio-economic disparities. The medicine of complexity cannot be limited to facing comorbidities and to the clinical management of multifaceted diseases. Evidence indicates how climate change, pollution, demographic unbalance, and inequalities can affect the spreading and outcomes of COVID-19 in vulnerable communities. These elements cannot be neglected, and a wide view of public health aspects by a “one-health” approach is strongly and urgently recommended. According to World Health Organization, 35% of infectious diseases involving the lower respiratory tract depend on environmental factors, and infections from SARS-Cov-2 is not an exception. Furthermore, environmental pollution generates a large burden of non-communicable diseases and disabilities, increasing the individual vulnerability to COVID-19 and the chance for the resilience of large communities worldwide. In this field, the awareness of internists must increase, as privileged healthcare providers. They need to gain a comprehensive knowledge of elements characterizing COVID-19 as part of a syndemic. This is the case when pandemic events hit vulnerable populations suffering from the increasing burden of chronic diseases, disabilities, and social and economic inequalities. Mastering the interplay of such events requires a change in overall strategy, to adequately manage not only the SARS-CoV-2 infection but also the growing burden of non-communicable diseases by a “one health” approach. In this context, experts in internal medicine have the knowledge and skills to drive this change.
COVID-19 has caused 100s of millions of infections and millions of deaths worldwide, overwhelming health and economic capacities in many countries and at multiple scales. The immediacy and magnitude of this crisis has resulted in government officials, practitioners and applied scholars turning to reflexive learning exercises to generate insights for managing the reverberating effects of this disease as well as the next inevitable pandemic. We contribute to both tasks by assessing COVID-19 as a super wicked problem denoted by four features we originally formulated to describe the climate crisis: time is running out, no central authority, those causing the problem also want to solve it, and policies irrationally discount the future (Levin et al. in Playing it forward: path dependency, progressive incrementalism, and the super wicked problem of global climate change, 2007; Levin et al. in Playing it forward: Path dependency, progressive incrementalism, and the super wicked problem of global climate change, 2009; Levin et al. in Policy Sci 45(2):123-152, 2012). Doing so leads us to identify three overarching imperatives critical for pandemic management. First, similar to requirements to address the climate crisis, policy makers must establish and maintain durable policy objectives. Second, in contrast to climate, management responses must always allow for swift changes in policy settings and calibrations given rapid and evolving knowledge about a particular disease’s epidemiology. Third, analogous to, but with swifter effects than climate, wide-ranging global efforts, if well designed, will dramatically reduce domestic costs and resource requirements by curbing the spread of the disease and/or fostering relevant knowledge for managing containment and eradication. Accomplishing these tasks requires building the analytic capacity for engaging in reflexive anticipatory policy design exercises aimed at maintaining, or building, life-saving thermostatic institutions at the global and domestic levels.
This study investigated the spatio-temporal variations in the occurrence of COVID-19 (confirmed cases and deaths) in relation to climate fluctuations in 61 countries, scattered around the world, from December 31, 2019 to May 28, 2020. Logarithm transformation of the count variable (COVID-19 cases) was used in a multiple linear regression model to predict the potential effects of weather variables on the prevalence of the disease. The study revealed strong associations (-0.510 ≤ r ≤ -0.967; 0.519 ≤ r ≤ 0.999) between climatic variables and confirmed cases of COVID-19 in majority (68.85%) of the selected countries. It showed evidences of 1 to 7-day delays in the response of the infection to changes in weather pattern. Model simulations suggested that a unit fall in temperature and humidity could increase (0.04-18.70%) the infection in 19.67% and 16.39% of the countries, respectively, while a general reduction (-0.05 to 9.40%) in infection cases was projected in 14.75% countries with a unit drop in precipitation. In conclusion, the study suggests that effective public health interventions are crucial to containing the projected upsurge in COVID-19 cases during both cold and warm seasons in the southern and northern hemispheres.
Structural and genetic differences among various viruses play a significant factor in host infectivity and vulnerability to environmental stressors. Zoonoses of viruses require several recombinations and mutations in their genetic material and among several viruses allowing them to switch hosts and infect new species. Additionally, the host genetics play a significant role in successful viral transmission among various hosts. For example, human immunodeficiency virus (HIV), Ebola virus and influenza viruses. In efficient zoonotic events, selective stresses in the host milieu-interieur are critical during viral infection of the first human host. The genetic rearrangement of the virus and the selective environmental pressure of the host immune system dominate the emergence of new viral disease outbreaks.
Evidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM(10), PM(2.5)), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m(3) increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM(2.5) and PM(10) respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.
Why have some countries done significantly better than others in fighting the Covid-19 pandemic? Had some countries been better prepared than others? This paper attempts to shed light on these questions by examining the role of climate risk and culture in explaining the cross-country variation in the Covid-19 mortality, while controlling for other potential drivers. In our analysis, we consider climate risk, readiness to climate change and individualism as main indicators reflecting the climate and culture status of individual countries. Using data from 110 countries, we find that the greater the climate risk; the lower the readiness to climate change and the more individualistic the society, the higher the pandemic mortality rate. We also present a series of sensitivity checks and show that our findings are robust to different specifications, alternative definitions of the mortality rate; and different estimation methods. One policy implication arising from our results is that countries that were better prepared for the climate emergency were also better placed to fight the pandemic. Overall, countries in which individuals look after each other and the environment, creating sustainable societies, are better able to cope with climate and public health emergencies.
BACKGROUND: Climate change based on temperature, humidity and wind can improve many characteristics of the arthropod carrier life cycle, including survival, arthropod population, pathogen communication, and the spread of infectious agents from vectors. This study aimed to find association between content of disease followed climate change we demonstrate in humans. METHODS: All the articles from 2016 to 2021 associated with global climate change and the effect of vector-borne disease were selected form databases including PubMed and the Global Biodiversity information facility database. All the articles selected for this short review were English. RESULTS: Due to the high burden of infectious diseases and the growing evidence of the possible effects of climate change on the incidence of these diseases, these climate changes can potentially be involved with the COVID-19 epidemic. We highlighted the evidence of vector-borne diseases and the possible effects of climate change on these communicable diseases. CONCLUSION: Climate change, specifically in rising temperature system is one of the world’s greatest concerns already affected pathogen-vector and host relation. Lice parasitic, fleas, mites, ticks, and mosquitos are the prime public health importance in the transmission of virus to human hosts.
RNA viruses include respiratory viruses, such as coronaviruses and influenza viruses, as well as vector-borne viruses, like dengue and West Nile virus. RNA viruses like these encounter various environments when they copy themselves and spread from cell to cell or host to host. Ex vivo differences, such as geographical location and humidity, affect their stability and transmission, while in vivo differences, such as pH and host gene expression, impact viral receptor binding, viral replication, and the host immune response against the viral infection. A critical factor affecting RNA viruses both ex vivo and in vivo, and defining the outcome of viral infections and the direction of viral evolution, is temperature. In this minireview, we discuss the impact of temperature on viral replication, stability, transmission, and adaptation, as well as the host innate immune response. Improving our understanding of how RNA viruses function, survive, and spread at different temperatures will improve our models of viral replication and transmission risk analyses.
Climate change is causing weather conditions to abruptly change and is directly impacting the health of humans. Due to climate change, there is an upsurge in conditions suitable for infectious pathogens and their carriers to survive and multiply. Infections that were eliminated decades ago are regaining their grounds among humans. Climate change is increasing the possibility of new outbreaks for these vector-borne, airborne, or waterborne infections. While adverse impacts of these outbreaks are only subject to the predictions, nevertheless, it is certain that these outbreaks will affect health status, mortality status and economy at local and international levels. However, these threats may be minimized if national and international public health departments would be willing to implement research- and evidence-based advanced preparedness strategies. This scientific review aims to explore how climate change is facilitating the spread of vector-borne (tick-borne encephalitis, dengue, West Nile virus, leishmaniasis), airborne (by weather conditions like storms), and waterborne infectious diseases (due to floods and droughts) and is triggering new outbreaks among humans.
Background: Most respiratory viruses show pronounced seasonality, but for SARS-CoV-2, this still needs to be documented. Methods: We examined the disease progression of COVID-19 in 6,914 patients admitted to hospitals in Europe and China. In addition, we evaluated progress of disease symptoms in 37,187 individuals reporting symptoms into the COVID Symptom Study application. Findings: Meta-analysis of the mortality risk in seven European hospitals estimated odds ratios per 1-day increase in the admission date to be 0.981 (0.973-0.988, p < 0.001) and per increase in ambient temperature of 1°C to be 0.854 (0.773-0.944, p = 0.007). Statistically significant decreases of comparable magnitude in median hospital stay, probability of transfer to the intensive care unit, and need for mechanical ventilation were also observed in most, but not all hospitals. The analysis of individually reported symptoms of 37,187 individuals in the UK also showed the decrease in symptom duration and disease severity with time. Interpretation: Severity of COVID-19 in Europe decreased significantly between March and May and the seasonality of COVID-19 is the most likely explanation.
Previous studies on the connection between climate and plague were mostly conducted without considering the influence of large-scale atmospheric circulations and long-term historical observations. The current study seeks to reveal the sophisticated role of climatic control on plague by investigating the combined effect of North Atlantic Oscillation (NAO) and temperature on plague outbreaks in Europe from 1347 to 1760 CE. Moving correlation analysis is applied to explore the non-linear relationship between NAO and plague transmission over time. Also, we apply the cross-correlation function to identify the role of temperature in mediating the NAO-plague connection and the lead-lag relationship in between. Our statistical results show that the pathway from climate change to plague incidence is distinctive in its spatial, temporal, and non-linear patterns. The multi-decadal temperature change exerted a 15-22 years lagged impact on the NAO-plague correlation in different European regions. The NAO-plague correlation in Atlantic-Central Europe primarily remained positive, while the correlation in Mediterranean Europe switched between positive and negative alternately. The modulating effect of temperature over the NAO-plague correlation increases exponentially with the magnitude of the temperature anomaly, but the effect is negligible between 0.3 and -0.3 degrees C anomaly. Our findings show that a lagged influence from the temperature extremes dominantly controls the correlation between NAO and plague incidence. A forecast from our study suggests that large-scale plague outbreaks are unlikely to happen in Europe if NAO remains at its current positive phase during the earth’s future warming. (C) 2020 Elsevier B.V. All rights reserved.
PURPOSE: This report describes a rare autochthonous case of human D. repens infection in Austria. Dirofilariosis is a mosquito-borne parasitic infection that predominantly affects dogs. Human D. repens infections have primarily been reported in Mediterranean countries, but are emerging throughout Central and Northern Europe. METHODS: The worm was removed surgically and identified using PCR and DNA sequencing. The consensus sequences were compared against reference sequences of Dirofilaria repens from GenBank. RESULTS: The 56-year-old woman acquired the infection, which presented as a subcutaneous nodule, in Vienna, Austria. This is the second autochthonous case of human D. repens infection in Austria. CONCLUSION: The reasons for the emergence of D. repens and other parasitic infections in Central and Northern Europe are manifold, including climate change and globalization. This case demonstrates that with the growing number of D. repens infections, health care professionals must place further emphasis on emerging infectious diseases to ensure appropriate diagnostics and treatment in the future.
Background: Tickborne-encephalitis (TBE) is a potentially life-threating neurological disease that is mainly transmitted by ticks. The goal of the present study is to analyze the potential uniform environmental patterns of the identified TBEV microfoci in Germany. The results are used to calculate probabilities for the present distribution of TBEV microfoci in Germany based on a geostatistical model. Methods: We aim to consider the specification of environmental characteristics of locations of TBEV microfoci detected in Germany using open access epidemiological, geographical and climatological data sources. We use a two-step geostatistical approach, where in a first step, the characteristics of a broad set of environmental variables between the 56 TBEV microfoci and a control or comparator set of 3575 sampling points covering Germany are compared using Fisher’s Exact Test. In the second step, we select the most important variables, which are then used in a MaxEnt distribution model to calculate a high resolution (400 × 400 m) probability map for the presence of TBEV covering the entire area of Germany. Results: The findings from the MaxEnt prediction model indicate that multi annual actual evapotranspiration (27.0%) and multi annual hot days (22.5%) have the highest contribution to our model. These two variables are followed by four additional variables with a lower, but still important, explanatory influence: Land cover classes (19.6%), multi annual minimum air temperature (14.9%), multi annual sunshine duration (9.0%), and distance to coniferous and mixed forest border (7.0%). Conclusions: Our findings are based on defined TBEV microfoci with known histories of infection and the repeated confirmation of the virus in the last years, resulting in an in-depth high-resolution model/map of TBEV microfoci in Germany. Multi annual actual evapotranspiration (27%) and multi annual hot days (22.5%) have the most explanatory power in our model. The results may be used to tailor specific regional preventive measures and investigations.
Mosquitoes are the major vectors that can transmit many diseases agents to humans and animals. This study was conducted in Edirne central district between July 2017 and July 2018 to identify important mosquito vector species, to determine their seasonality and distribution pattern in general terms. Larvae, pupae, and adults were collected from areas assessed as being particularly suitable for medically important species of the genus Aedes Meigen, Culex Linnaeus, and Anopheles Meigen. In addition to the foci naturally found in the areas, ovitraps placed in suitable places for ovipositing were also used. As a result, a total of 3155 females and 353 males belonging to 11 species of 5 genera were obtained. Among these species, Anopheles sacharovi Favre (the primary vector of malaria in Turkey) and Culex pipiens s.l. Linnaeus (the primary vector of West Nile Fever) has been recognized as a public health threat to the province. Anopheles sacharovi was present at a very low population level, while Cx. pipiens s.l. was determined as the most common and numerous species in the study area. Known to have a high preference for warmer climate compared to members of the Anopheles maculipennis s.l. Meigen, An. sacharovi has the risk of increasing its population in the region with possible global warming in the future. The importance of this risk increases even more since rice production is widespread especially in Edirne and this species can use the paddy fields as an effective breeding place. While Aedes caspius Pallas was commonly encountered, Aedes albopictus Skuse was not found during the field observation and ovitrap controls.
Vector-borne diseases (VBDs), such as dengue, Zika, West Nile virus (WNV) and tick-borne encephalitis, account for substantial human morbidity worldwide and have expanded their range into temperate regions in recent decades. Climate change has been proposed as a likely driver of past and future expansion, however, the complex ecology of host and vector populations and their interactions with each other, environmental variables and land-use changes makes understanding the likely impacts of climate change on VBDs challenging. We present an environmentally driven, stage-structured, host-vector mathematical modelling framework to address this challenge. We apply our framework to predict the risk of WNV outbreaks in current and future UK climates. WNV is a mosquito-borne arbovirus which has expanded its range in mainland Europe in recent years. We predict that, while risks will remain low in the coming two to three decades, the risk of WNV outbreaks in the UK will increase with projected temperature rises and outbreaks appear plausible in the latter half of this century. This risk will increase substantially if increased temperatures lead to increases in the length of the mosquito biting season or if European strains show higher replication at lower temperatures than North American strains.
Climate change has influenced the transmission of a wide range of vector-borne diseases in Europe, which is a pressing public health challenge for the coming decades. Numerous theories have been developed in order to explain how tick-borne diseases are associated with climate change. These theories include higher proliferation rates, extended transmission season, changes in ecological balances, and climate-related migration of vectors, reservoir hosts, or human populations. Changes of the epidemiological pattern have potentially catastrophic consequences, resulting in increasing prevalence of tick-borne diseases. Thus, investigation of the relationship between climate change and tick-borne diseases is critical. In this regard, climate models that predict the ticks’ geographical distribution changes can be used as a predicting tool. The aim of this review is to provide the current evidence regarding the contribution of the climatic changes to Lyme borreliosis (LB) disease and tick-borne encephalitis (TBE) and to present how computational models will advance our understanding of the relationship between climate change and tick-borne diseases in Europe.
BACKGROUND: Understanding of the impacts of climatic variability on human health remains poor despite a possibly increasing burden of vector-borne diseases under global warming. Numerous socioeconomic variables make such studies challenging during the modern period while studies of climate-disease relationships in historical times are constrained by a lack of long datasets. Previous studies have identified the occurrence of malaria vectors, and their dependence on climate variables, during historical times in northern Europe. Yet, malaria in Sweden in relation to climate variables is understudied and relationships have never been rigorously statistically established. This study seeks to examine the relationship between malaria and climate fluctuations, and to characterise the spatio-temporal variations at parish level during severe malaria years in Sweden 1749-1859. METHODS: Symptom-based annual malaria case/death data were obtained from nationwide parish records and military hospital records in Stockholm. Pearson (r(p)) and Spearman’s rank (r(s)) correlation analyses were conducted to evaluate inter-annual relationship between malaria data and long meteorological series. The climate response to larger malaria events was further explored by Superposed Epoch Analysis, and through Geographic Information Systems analysis to map spatial variations of malaria deaths. RESULTS: The number of malaria deaths showed the most significant positive relationship with warm-season temperature of the preceding year. The strongest correlation was found between malaria deaths and the mean temperature of the preceding June-August (r(s) = 0.57, p < 0.01) during the 1756-1820 period. Only non-linear patterns can be found in response to precipitation variations. Most malaria hot-spots, during severe malaria years, concentrated in areas around big inland lakes and southern-most Sweden. CONCLUSIONS: Unusually warm and/or dry summers appear to have contributed to malaria epidemics due to both indoor winter transmission and the evidenced long incubation and relapse time of P. vivax, but the results also highlight the difficulties in modelling climate-malaria associations. The inter-annual spatial variation of malaria hot-spots further shows that malaria outbreaks were more pronounced in the southern-most region of Sweden in the first half of the nineteenth century compared to the second half of the eighteenth century.
BackgroundWaterborne disease outbreaks (WBDO) associated with tap water consumption are probably underestimated in France.AimIn order to improve their detection, Santé publique France launched a surveillance system in 2019, based on the periodical analysis of health insurance data for medicalised acute gastroenteritis (mAGE).MethodsSpatio-temporal cluster detection methods were applied to mAGE cases to prioritise clusters for further investigation. These investigations determined the plausibility that infection is of waterborne origin and the strength of association.ResultsBetween January 2010 and December 2019, 3,323 priority clusters were detected (53,878 excess mAGE cases). They involved 3,717 drinking water supply zones (WSZ), 15.4% of all French WSZ. One third of these WSZ (33.4%; n = 1,242 WSZ) were linked to repeated clusters. Moreover, our system detected 79% of WBDO voluntarily notified to health authorities.ConclusionEnvironmental investigations of detected clusters are necessary to determine the plausibility that infection is of waterborne origin. Consequently, they contribute to identifying which WSZ are linked to clusters and for which specific actions are needed to avoid future outbreaks. The surveillance system incorporates three priority elements: linking environmental investigations with water safety plan management, promoting the systematic use of rainfall data to assess waterborne origin, and focusing on repeat clusters. In the absence of an alternative clear hypothesis, the occurrence of a mAGE cluster in a territory completely matching a distribution zone indicates a high plausibility of water origin.
Despite the relative prosperity of Scandinavian countries, contamination of the drinking water supply with parasites has occurred on various occasions in the last few decades. These events have resulted in outbreaks of disease involving several thousand cases and/or the necessity for implementation of boil-water advisories. Against this background, in 2008, and again in 2019, the Norwegian Food Safety Authority requested a risk assessment from an independent scientific body regarding parasites in Norwegian drinking water. On each occasion, it was requested that specific questions were addressed. For the first assessment, data, both of general relevance and specific for Norway, were collected from appropriate sources, as available. Based on some of this information, a quantitative probability model was established and run to estimate the number of cases of waterborne cryptosporidiosis and giardiasis that may be expected in Norway, both in the general public and the immunocompromised, and under conditions where water treatment should be optimal, and also when water treatment efficacy may be compromised by weather conditions. For the second assessment, approximately a decade after the first, an update on the previous assessment was requested. Differences in information availability and other changes between the two assessments were described; although more data were available at the second assessment, considerable gaps still remained. For both assessments, data on the occurrence of these parasites in the Norwegian population, particularly those infected in Norway, were considered a challenge. However, due to changes in reporting requirements in 2020, the situation was improved for the second assessment. In addition, data were lacking for both assessments on whether animals or humans are most likely to contaminate water sources, and the species and genotypes of these parasites in Norwegian animals. It was also noted that some of the newer data on parasite numbers detected in water samples should be treated with caution. Due to this, further modelling was not conducted. The relevance of risk-based sampling rather than ad hoc sampling of water sources was also addressed. Despite the data gaps, this article provides an overview of the opportunities provided by conducting such assessments. In addition, some of the challenges encountered in attempting to estimate the risk posed from parasite contamination of water sources in Norway, particularly under predicted conditions of climate change, are described.
Complex, multihazard risks such as private groundwater contamination necessitate multiannual risk reduction actions including seasonal, weather-based hazard evaluations. In the Republic of Ireland (ROI), high rural reliance on unregulated private wells renders behavior promotion a vital instrument toward safeguarding household health from waterborne infection. However, to date, pathways between behavioral predictors remain unknown while latent constructs such as extreme weather event (EWE) risk perception and self-efficacy (perceived behavioral competency) have yet to be sufficiently explored. Accordingly, a nationwide survey of 560 Irish private well owners was conducted, with structural equation modeling (SEM) employed to identify underlying relationships determining key supply management behaviors. The pathway analysis (SEM) approach was used to model three binary outcomes: information seeking, post-EWE action, and well testing behavior. Upon development of optimal models, perceived self-efficacy emerged as a significant direct and/or indirect driver of all three behavior types-demonstrating the greatest indirect effect (beta = -0.057) on adoption of post-EWE actions and greatest direct (beta = 0.222) and total effect (beta = 0.245) on supply testing. Perceived self-efficacy inversely influenced EWE risk perception in all three models but positively influenced supply awareness (where present). Notably, the presence of a vulnerable (infant and/or elderly) household member negatively influenced adoption of post-EWE actions (beta = -0.131, p = 0.016). Results suggest that residential and age-related factors constitute key demographic variables influencing risk mitigation and are strongly mediated by cognitive variables-particularly self-efficacy. Study findings may help contextualize predictors of private water supply management, providing a basis for future risk-based water interventions.
Background Wastewater reuse represents a promising alternative source of water supply considering the water scarcity related to climate change. However, if not adequately treated, wastewater represents a source of microbiological health risk. The purpose of this work was to investigate the role of wastewater treatment on microbiological contamination by evaluating the possible risks associated with wastewater effluent reuse, taking into account new EU legislation (2020/741) on minimum requirements for water reuse. E. coli that produce Shiga toxins (STEC) and thermotolerant Campylobacter were monitored using an enrichment step associated with specific PCR, while Salmonella spp. and Legionella were detected with both cultural and molecular methods (PCR and q-PCR, respectively). Culture method was also used for the enumeration of different microbial indicators. The bacteria detection was compared in different wastewater plants with membrane bioreactor (MBR) system or with disinfection step with chlorine dioxide (ClO2). Moreover a comparison between molecular and culture methods was discussed. Results The results obtained showed good abatement performance for WWTPs equipped with MBR. The high concentrations of E. coli (range between 0.88 and 5.21 Log MPN/100 mL) and contamination by Salmonella spp. in effluent disinfected with ClO2 (17% of samples) showed the need to control the quality of this effluent. In addition, despite the absence of Legionella spp. with the culture method required by EU regulation, high concentrations of Legionella spp. (range between 2 and 7 log GU/L) and the presence of Leg. pneumophila with qPCR (15% of samples) highlight the need to carry out further investigations for reuse associated with aerosol formation (e.g. spray irrigation in agriculture). Conclusions The results obtained underline that the MBR technology can be suitable for wastewater reuse applications allowing to achieve the requirement proposed by the new European legislation. More attention should be given to wastewater reuse of effluents treated with ClO2. The use of the molecular methods for pathogens detection in wastewater could allow a more precautionary risks estimation associated with reuse. The overall results highlight that an evaluation of the effectiveness of the wastewater treatments is required for the prevention of a possible risk to public health.
Reuse of reclaimed wastewater for agricultural irrigation is an expanding practice worldwide. This practice needs to be monitored, partly because of pathogens that the water may contain after treatments. More particularly, sprinkler irrigation is known to generate aerosols which may lead to severe health risks to the population close to irrigated areas in case of the presence of Legionella bacteria in the water. A pilot experiment was conducted on two corn fields in South-Western France, irrigated with wastewater undergoing two different water treatments (ultra-filtration and UV). Water analyses have shown high levels of Legionella in the water even after a standard wastewater treatment plant (WWTP) cleaning process followed by the UV treatment (up to 10(6) GC per L in 2019). In this context, an updated general Bayesian network (GBN), using discrete and continuous random variables, in quantitative microbial risk assessment (QMRA) is proposed to monitor the risk of Legionella infection in the vicinity of the irrigated plots. The model’s originality is based on i) a graphical probabilistic model that describes the exposure pathway of Legionella from the WWTP to the population using observed and non-observed variables and ii) the model inference updating at each new available measurement. Different scenarios are simulated according to the exposure time of the persons, taking into account various distances from the emission source and a large dataset of climatic data. From the learning process included in the Bayesian principle, quantities of interest (contaminations before and after water treatments, inhaled dose, probabilities of infection) can be quantified with their uncertainty before and after the inclusion of each new data collected in situ. This approach gives a rigorous tool that allows monitoring the risks, facilitates discussions with reuse experts and progressively reduces uncertainty quantification through field data accumulation. For the two pilot treatments analyzed in this study, the median annual risk of Legionella infection did not exceed the US EPA annual infection benchmark of 10(-4) for any of the population at risk during the past few months of the pilot experiment (DALYs are estimated up to 10(-5)). The risk still bears watching with support from the method shown in this work.
Extreme weather events (EWEs) may significantly increase pathogenic contamination of private (unregulated) groundwater supplies. However, due to the paucity of protective guidance, private well users may be ill-equipped to undertake adaptive actions. With rising instances of waterborne illness documented in groundwater-dependent, developed regions such as the Republic of Ireland, a better understanding of well user risk perceptions pertaining to EWEs is required to establish appropriate educational interventions. To this end, the current study employed an online and physical questionnaire to identify current risk perceptions and correspondent predictors among Irish private well users concerning extreme weather. Respondents were elicited via purposive sampling, with 515 private well users elucidating perceived supply contamination risk in the wake of five EWEs between the years 2013-2018 including drought and pluvial flooding. A novel scoring protocol was devised to quantify overall risk perception (i.e. perceived likelihood, severity and consequences) of extreme weather impacts. Overall risk perception of EWEs was found to demonstrate a significant relationship with gender (p = 0.017) and event experience (p < 0.001), with female respondents and those reporting prior event experience exhibiting higher median risk perception scores. Risk perception was additionally mediated by perceived self-efficacy in undertaking supply maintenance (p = 0.001), as well users citing confidence in ability scored significantly lower than those citing no confidence. Two-step cluster analysis identified three distinct respondent subsets based on risk perception of EWEs (high, moderate and low perception), with female respondents and those with a third-level education significantly more likely to fall within the high perception cluster. Study findings affirm that certain demographic, experiential and cognitive factors exert a significant influence on private well user risk perceptions of EWE impacts and highlight potential focal points for future educational interventions seeking to reduce the risk of human infection associated with groundwater and extreme weather.
BackgroundVibrio spp. are aquatic bacteria that prefer warm seawater with moderate salinity. In humans, they can cause gastroenteritis, wound infections, and ear infections. During the summers of 2018 and 2019, unprecedented high sea surface temperatures were recorded in the German Baltic Sea.AimWe aimed to describe the clinical course and microbiological characteristics of Vibrio infections in Germany in 2018 and 2019.MethodsWe performed an observational retrospective multi-centre cohort study of patients diagnosed with domestically-acquired Vibrio infections in Germany in 2018 and 2019. Demographic, clinical, and microbiological data were assessed, and isolates were subjected to whole genome sequencing and antimicrobial susceptibility testing.ResultsOf the 63 patients with Vibrio infections, most contracted the virus between June and September, primarily in the Baltic Sea: 44 (70%) were male and the median age was 65 years (range: 2-93 years). Thirty-eight patients presented with wound infections, 16 with ear infections, six with gastroenteritis, two with pneumonia (after seawater aspiration) and one with primary septicaemia. The majority of infections were attributed to V. cholerae (non-O1/non-O139) (n = 30; 48%) or V. vulnificus (n = 22; 38%). Phylogenetic analyses of 12 available isolates showed clusters of three identical strains of V. vulnificus, which caused wound infections, suggesting that some clonal lines can spread across the Baltic Sea.ConclusionsDuring the summers of 2018 and 2019, severe heatwaves facilitated increased numbers of Vibrio infections in Germany. Since climate change is likely to favour the proliferation of these bacteria, a further increase in Vibrio-associated diseases is expected.
Climate change will lead to more extreme weather events in Europe. In Norway, little is known about how this will affect drinking water quality and population’s health due to waterborne diseases. The aim of our work was to generate new knowledge on the effect of extreme weather conditions and climate change on drinking water and waterborne disease. In this respect we studied the relationship between temperature, precipitation and runoff events, raw and treated water quality, and gastroenteritis consultations in Norway in 2006-2014 to anticipate the risk with changing climate conditions. The main findings are positive associations between extreme weather events and raw water quality, but only few with treated drinking water. Increase in maximum temperature was associated with an increase in risk of disease among all ages and 15-64 years olds for the whole year. Heavy rain and high runoff were associated with a decrease in risk of gastroenteritis for different age groups and time periods throughout the year. No evidence was found that increase in precipitation and runoff trigger increased gastroenteritis outbreaks. Large waterworks in Norway currently seem to manage extreme weather events in preventing waterborne disease. However, with more extreme weather in the future, this may change. Therefore, modelling future climate scenarios is necessary to assess the need for improved water treatment capacity in a future climate.
This paper provides a summary of the knowledge of drinking-water temperature increases and present daily, seasonal, and yearly temperature data of drinking-water distribution systems (DWDS). The increasing water temperatures lead to challenges in DWDS management, and we must assume a promotion of invertebrates as pipe inhabitants. Macro-, meio-, and microinvertebrates were found in nearly all DWDS. Data in relation to diversity and abundance clearly point out a high probability of mass development, and invertebrate monitoring must be the focus of any DWDS management. The water temperature of DWDS is increasing due to climate change effects, and as a consequence, the growth and reproduction of invertebrates is increasing. The seasonal development of a chironomid (Paratanytarus grimmii) and longtime development of water lice (Asellus aquaticus) are given. Due to increased water temperatures, a third generation of water lice per year has been observed, which is one reason for the observed mass development. This leads to an impact on drinking-water quality and an increased health risk, as invertebrates can serve as a host or vehicle for potential harmful microbes. More research is needed especially on (i) water temperature monitoring in drinking-water distribution systems, (ii) invertebrate development, and (iii) health risks.
BACKGROUND: Vibrio infections are becoming more frequent in the Baltic Sea region, which is caused by an increase in the sea surface temperature. Climate change creates the conditions for the emergence of new environmental niches that are beneficial for Vibrio spp., especially in the summer months. Vibrio vulnificus, which causes wound infections and septicaemia, represents a particularly dangerous species of Vibrio spp. There are numerous publications on the prevalence of V. vulnificus in various regions of the Baltic Sea, but there is a lack of such data for the Polish coast. This prompted us to conduct a pilot study into the prevalence of the bacteria in the Gulf of Gdansk. The study aimed to detect Vibrio spp. in the coastal waters and the wet sand at the beaches and bathing areas in the Gulf of Gdansk. MATERIALS AND METHODS: During the period from June 16th to September 23rd 2020, 112 samples of seawater and 105 samples of wet sand were collected at 16 locations along the coast of the Gulf of Gdansk and Hel peninsula. Isolation of Vibrio spp. was conducted by filtering method and the isolated bacteria was cultured on CHROM agar Vibrio and TCBS agar. Final genus identification was performed by the MALDI TOF technique. RESULTS: In the present study, 10 isolates of Vibrio spp. were obtained from seawater and wet sand samples collected in the Gulf of Gdansk and Hel peninsula coast. Three of the isolates were identified as V. vulnificus; the presence of the species was confirmed in the seawater samples which had been collected in Hel (1 isolate), Jastarnia (1 isolate), and Chalupy (1 isolate). One strain of Vibrio alginolyticus was isolated from the seawater sample collected in Hel. Moreover, identification was incomplete for 6 of the isolated strains, these were identified as Vibrio cholerae/mimicus These strains were collected in Jastarnia (1 isolate), Kuznica (1 isolate), Gdansk-Brzezno (1 isolate), Puck (2 isolates), Chalupy (1 isolate). CONCLUSIONS: Our preliminary research study confirmed the presence of potentially pathogenic V. vulnificus in the Gulf of Gdansk in the summer months. Therefore, further monitoring of the presence of Vibrio spp. in the Baltic coast area is necessary.
BACKGROUND: Leptospirosis is the most common zoonotic disease in Tuzla Canton. Objective: Determine the influence of environmental and precipitation factors on the incidence of leptospirosis. METHODS: A retrospective study included 80 patients with leptospirosis. Data on precipitation were obtained from the online database of Federal Hydrometeorological Institute of BiH. OpenStreetMap (OSM) was used for spatial analysis; patients were geolocated and put on a map. Statistical data processing included basic tests of descriptive statistics. RESULTS: In the period between 01.01.2014 and 31.12.2014, 80 patients with leptospirosis confirmed by clinical and serological testing were hospitalized in the Clinic for Infectious Diseases of the University Clinical Center Tuzla. Gender wise, out of 80 patients, 54 were male (67.5% of the total), and 26 were female (32.5%). More patients lived in the countryside: 64/80 (or 89%). The largest number of patients was engaged in agriculture and animal husbandry: 48/80 (or 60%), mostly cows 32/80 (40%), chickens 12/80 (15%), sheep 4/80 (5%) and pigs 3/80 (3.8%). Of the total number of patients, 50 (or 62.5%) had contact with domestic animals: dogs 10/80 (or 12.5%) and cats 5/80 (or 6.3%). Half of 53/80 (66.3%) patients had contact with flooded areas in the study period. The increase in leptospirosis diagnosed patients in the City of Srebrenik was statistically significant for 2014 (p<0.01). CONCLUSION: Leptospirosis in one of the neglected infectious diseases in our area, but the proven increase in the number of infected people after heavy rainfall obliges us to control the risks associated with this disease.
The frequency and severity of flooding events will increase over the coming decades due to global climate change. While close attention has typically been paid to infrastructural and environmental outcomes of flood events, the potential adverse human health consequences associated with post-event consumption from private groundwater sources have received minimal attention, leading to a poor understanding of private well users’ preparedness and the drivers of positive behavioural adoption. The current study sought to quantify the capacity of private well users to cope with flood-triggered contamination risks and identify the social psychological determinants of proactive attitudes in the Republic of Ireland, using a cross-sectional questionnaire incorporating two distinct models of health behaviour, the Health Belief Model and Risk-Attitude-Norms-Ability-Self Regulation model. Adoption of healthy behaviours prior to flooding was evaluated with respect to respondents’ risk exposure, risk experience and risk perception, in addition to systematic supply stewardship under normal conditions. Associations between adoption of protective behaviours and perception, experience and socio-demographic factors were evaluated through multinomial and multiple logistic regressions, while a multi-model inferential approach was employed with the predictors of health behaviour models. Findings suggest that floods are not considered likely to occur, nor were respondents worried about their occurrence, with 72.5% of respondents who reported previous flooding experience failing to adopt protective actions. Prior experience of well water contamination increased adoption of proactive attitudes when flooding occurred (+47%), with a failure to adopt healthy behaviours higher among rural non-agricultural residents (136%). Low levels of preparedness to deal with flood-related contamination risks are a side-effect of the general lack of appropriate well stewardship under normal conditions; just 10.1% of respondents adopted both water treatment and frequent testing, in concurrence with limited risk perception and poor awareness of the nexus between risk factors (e.g. floods, contamination sources) and groundwater quality. Perceived risk, personal norms and social norms were the best predictors of protective behaviour adoption and should be considered when developing future awareness campaigns.
During a 6-week period in November and December 2015, a series of Atlantic Storms swept across the Republic of Ireland (ROI) causing widespread pluvial and fluvial flooding. Flooding was particularly severe in the west and midlands, with rainfall up to 200% above normal in many regions, making it the wettest winter ever recorded. While the infrastructural damage and subsequent costs associated with flood events have, and continue to receive widespread attention, far less coverage is given to the associated adverse human health effects. This is particularly significant in the ROI, which is characterised by the highest crude incidence rates of verotoxigenic E. coli (VTEC) enteritis and cryptosporidiosis in Europe. Accordingly, weekly spatially-referenced infection incidence from July 2015 to June 2016 were employed in concurrence with weekly time-series of cumulative antecedent rainfall, surface water discharge and groundwater level, and high-resolution flood risk mapping. An ensemble of statistical and time-series analyses were used to quantify the influence of flood hydrometeorology on the incidence of confirmed infections. Seasonal decomposition (excluding seasonal patterns and long-term trends) identified a high residual infection peak during April 2016, with space-timing scanning used to identify the location, size and temporal extent of clustering. Excess cases of VTEC enteritis were geographically associated with the midlands, while cryptosporidiosis clusters were widespread. Generalised linear modelling of infection locations show that areas with a surface water body exhibited significantly higher incidence rates for both VTEC (OR: 1.225; p < 0.001) and cryptosporidiosis (OR: 1.363; p < 0.001). ARIMA models show a clear association between rainfall, surface water discharge, groundwater levels and infection incidence, with lagged associations from 16 to 20 weeks particularly strong, thus indicating a link between infection peaks (April 2016) and the flood event which began approximately 18 weeks earlier. All three hydrometeorological variables were associated with the increase in cryptosporidiosis during April 2016, while only surface water discharge was associated with VTEC enteritis. Study findings may be employed for improved risk communication, risk management and surveillance to safeguard public health after large hydrometeorological events.
Dermacentor reticulatus ticks are one of the most important vectors and reservoirs of tick-borne pathogens in Europe. Changes in the abundance and range of this species have been observed in the last decade and these ticks are collected in areas previously considered tick-free. This may be influenced by progressive climate change. Eastern Poland is an area where the local population of D. reticulatus is one of the most numerous among those described so far. At the same time, the region is characterized by a significant increase in the mean air temperature in recent years (by 1.81 °C in 2020) and a decrease in the average number of days with snow cover (by 64 days in 2020) and in the number of days with frost (by 20 days in 2020) on an annual basis compared to the long-term average. The aim of our research was to investigate the rhythms of seasonal activity and the population size of D. reticulatus in the era of progressive climate change. To this end, questing ticks were collected in 2017-2020. Next, the weather conditions in the years of observation were analyzed and compared with multi-year data covering 30 years preceding the study. The research results show that, in eastern Poland, there is a stable population of D. reticulatus with the peak of activity in spring or autumn (up to a maximum of 359 individuals within 30 min of collection) depending on the year of observation. Ticks of this species may also be active in winter months. The activity of D. reticulatus is influenced by a saturation deficit.
Human dirofilariasis is a vector-borne helminth disease caused by two species of Dirofilaria: D. repens and D. immitis. The vectors of the helminth are mosquitoes in the family Culicidae. The definitive hosts of Dirofilaria are dogs and, to a lesser extent, cats. Humans are accidental hosts. Dirofilariasis has been reported in the territory of Russia since 1915. Sporadic cases of the disease have been reported occasionally, but the number of cases showed a distinct increasing trend in the late 1980s-early 1990s, when the number of cases reached several hundred in the southern territories of Russia, with geographic coordinates of 43° N-45° N. A comparison of the timing of the global trend of climate warming during the 1990s with the temporal pattern of the incidence of dirofilariasis in the territory of Russia indicated a close association between the two phenomena. At present, the northern range of Dirofilaria includes latitudes higher than 58° in both the European and Asian parts of the country. The phenomenon of climate warming in the territory of Russia has shaped the contemporary epidemiology of the disease. The emerging public health problem of dirofilariasis in Russia warrants the establishment of a comprehensive epidemiological monitoring system.
The paper reports the assessment of the spatiotemporal trends of climatic changes favoring the spread of West Nile fever (WNF) in the southern part of the European Russia. Data from 58 meteorological stations (1997–2018) and ERA-Interim reanalysis data (1981–2018) were used. The degree-day method was employed to assess whether the climatic conditions favor the spread of the West Nile virus (WNV). As a result an increase in the sum of the effective temperatures (ETs) was demonstrated. No increase in the length of the efficient infectivity season was observed. A coincidence of the trends of ET sum growth and the increase in the average air temperature for the epidemic season was noted. This creates favorable conditions for virus development in mosquitoes, because virus circulation becomes more efficient with an increase in ET. The most favorable temperature conditions for WNV form in the Caspian Sea region and the Ciscaucasia, where WNV circulation conditions are further improved due to an increase in the total ET. conditions favoring WNV transmission form more rapidly in the central part of European Russia than in the Cis-Ural region, which may cause further spread of WNF in this region.
West Nile virus (WNV) is an arthropod-born pathogen, which is transmitted from wild birds through mosquitoes to humans and animals. At the end of the 20th century, the first West Nile fever (WNF) outbreaks among humans in urban environments in Eastern Europe and the United States were reported. The disease continued to spread to other parts of the continents. In Serbia, the largest number of WNV-infected people was recorded in 2018. This research used spatial statistics to identify clusters of WNV infection in humans and animals in South Banat County, Serbia. The occurrence of WNV infection and risk factors were analyzed using a negative binomial regression model. Our research indicated that climatic factors were the main determinant of WNV distribution and were predictors of endemicity. Precipitation and water levels of rivers had an important influence on mosquito abundance and affected the habitats of wild birds, which are important for maintaining the virus in nature. We found that the maximum temperature of the warmest part of the year and the annual temperature range; and hydrographic variables, e.g., the presence of rivers and water streams were the best environmental predictors of WNF outbreaks in South Banat County.
After mosquitoes, ticks are the most important vectors of infectious diseases. They play an important role in public health. In recent decades, we discovered new tick-borne diseases; additionally, those that are already known are spreading to new areas because of climate change. Slovenia is an endemic region for Lyme borreliosis and one of the countries with the highest incidence of this disease on a global scale. Thus, the spatial pattern of Slovenian Lyme borreliosis prevalence was modelled with 246 indicators and transformed into 24 uncorrelated predictor variables that were applied in geographically weighted regression and regression tree algorithms. The projected potential shifts in Lyme borreliosis foci by 2050 and 2070 were calculated according to the RCP8.5 climate scenario. These results were further applied to developing a Slovenian Lyme borreliosis infection risk map, which could be used as a preventive decision support system.
BACKGROUND: The emergence of tick-borne disease is increasing because of the effects of the temperature rise driven by global warming. In Turkey, 19 pathogens transmitted by ticks to humans and animals have been reported. Based on this, this study aimed to investigate tick-borne pathogens including Hepatozoon spp., Theileria spp., Babesia spp., Anaplasma spp., Borrelia spp., and Bartonella spp. in tick samples (n = 110) collected from different hosts (dogs, cats, cattle, goats, sheep, and turtles) by molecular methods. METHODS: To meet this objective, ticks were identified morphologically at the genus level by microscopy; after DNA isolation, each tick sample was identified at the species level using the molecular method. Involved pathogens were then investigated by PCR method. RESULTS: Seven different tick species were identified including Rhipicephalus sanguineus, R. turanicus, R. bursa, Hyalomma marginatum, H. anatolicum, H. aegyptium, and Haemaphysalis erinacei. Among the analyzed ticks, Hepatozoon spp., Theileria spp., Babesia spp., and Anaplasma spp. were detected at rates of 6.36%, 16.3%, 1.81%, and 6.36%, respectively while Borrelia spp. and Bartonella spp. were not detected. Hepatozoon spp. was detected in R. sanguineus ticks while Theileria spp., Babesia spp., and Anaplasma spp. were detected in R. turanicus and H. marginatum. According to the results of sequence analyses applied for pathogen positive samples, Hepatozoon canis, Theileria ovis, Babesia caballi, and Anaplasma ovis were identified. CONCLUSION: Theileria ovis and Anaplasma ovis were detected for the first time to our knowledge in H. marginatum and R. turanicus collected from Turkey, respectively. Also, B. caballi was detected for the first time to our knowledge in ticks in Turkey.
Lungworms in the genus Angiostrongylus cause disease in animals and humans. The spread of Angiostrongylus vasorum within Europe and the recent establishment of Angiostrongylus cantonensis increase the relevance of these species to veterinary and medical practitioners, and to researchers in parasitology, epidemiology, veterinary science and ecology. This review introduces the key members of the genus present in Europe and their impacts on health, and updates the current epidemiological situation. Expansion of A. vasorum from localized pockets to wide distribution across the continent has been confirmed by a rising prevalence in foxes and increasing reports of infection and disease in dogs, while the list of carnivore and mustelid definitive hosts continues to grow. The tropically distributed rat lungworm A. cantonensis, meanwhile, has been recorded on islands south of Europe, previously the Canary Islands, and now also the Balearic Islands, although so far with limited evidence of zoonotic disease. Other members of the genus, namely, A. chabaudi, A. daskalovi and A. dujardini, are native to Europe and mainly infect wildlife, with unknown consequences for populations, although spill-over can occur into domestic animals and those in zoological collections. The epidemiology of angiostrongylosis is complex, and further research is needed on parasite maintenance in sylvatic hosts, and on the roles of ecology, behaviour and genetics in disease emergence. Improved surveillance in animals and humans is also required to support risk assessments and management.
The Asian tiger mosquito (Aedes albopictus), a vector of dengue, Zika and other diseases, was introduced in Europe in the 1970s, where it is still widening its range. Spurred by public health concerns, several studies have delivered predictions of the current and future distribution of the species for this region, often with differing results. We provide the first joint analysis of these predictions, to identify consensus hotspots of high and low suitability, as well as areas with high uncertainty. The analysis focused on current and future climate conditions and was carried out for the whole of Europe and for 65 major urban areas. High consensus on current suitability was found for the northwest of the Iberian Peninsula, southern France, Italy and the coastline between the western Balkans and Greece. Most models also agree on a substantial future expansion of suitable areas into northern and eastern Europe. About 83% of urban areas are expected to become suitable in the future, in contrast with ~ 49% nowadays. Our findings show that previous research is congruent in identifying wide suitable areas for Aedes albopictus across Europe and in the need to effectively account for climate change in managing and preventing its future spread.
Understanding and predicting mosquito population dynamics is crucial for gaining insight into the abundance of arthropod disease vectors and for the design of effective vector control strategies. In this work, a climate-conditioned Markov chain (CMC) model was developed and applied for the first time to predict the dynamics of vectors of important medical diseases. Temporal changes in mosquito population profiles were generated to simulate the probabilities of a high population impact. The simulated transition probabilities of the mosquito populations achieved from the trained model are very near to the observed data transitions that have been used to parameterize and validate the model. Thus, the CMC model satisfactorily describes the temporal evolution of the mosquito population process. In general, our numerical results, when temperature is considered as the driver of change, indicate that it is more likely for the population system to move into a state of high population level when the former is a state of a lower population level than the opposite. Field data on frequencies of successive mosquito population levels, which were not used for the data inferred MC modeling, were assembled to obtain an empirical intensity transition matrix and the frequencies observed. Our findings match to a certain degree the empirical results in which the probabilities follow analogous patterns while no significant differences were observed between the transition matrices of the CMC model and the validation data (ChiSq = 14.58013, df = 24, p = 0.9324451). The proposed modeling approach is a valuable eco-epidemiological study. Moreover, compared to traditional Markov chains, the benefit of the current CMC model is that it takes into account the stochastic conditional properties of ecological-related climate variables. The current modeling approach could save costs and time in establishing vector eradication programs and mosquito surveillance programs.
We extend a previously developed epidemiological model for West Nile virus (WNV) infection in humans in Greece, employing laboratory-confirmed WNV cases and mosquito-specific characteristics of transmission, such as host selection and temperature-dependent transmission of the virus. Host selection was defined by bird host selection and human host selection, the latter accounting only for the fraction of humans that develop symptoms after the virus is acquired. To model the role of temperature on virus transmission, we considered five temperature intervals (≤ 19.25 °C; > 19.25 and < 21.75 °C; ≥ 21.75 and < 24.25 °C; ≥ 24.25 and < 26.75 °C; and > 26.75 °C). The capacity of the new model to fit human cases and the week of first case occurrence was compared with the original model and showed improved performance. The model was also used to infer further quantities of interest, such as the force of infection for different temperatures as well as mosquito and bird abundances. Our results indicate that the inclusion of mosquito-specific characteristics in epidemiological models of mosquito-borne diseases leads to improved modelling capacity.
Environmentally suitable habitats of Aedes albopictus (Ae. albopictus) in Europe are identified by several modeling studies. However, it is noticeable that even after decades of invasion process in Europe, the vector mosquito has not yet been established in all its environmentally suitable areas. Natural barriers and human-mediated transport play a role, but the potential of wind speed to explain Ae. albopictus’ absences and its inability to establish in its suitable areas are largely unknown. This study therefore evaluates the potential of wind speed as an explanatory parameter of the non-occurrence of Ae. albopictus. We developed a global ecological niche model with relevant environmental parameters including wind speed and projected it to current climatic conditions in Europe. Differences in average wind speed between areas of occurrence and non-occurrence of Ae. albopictus within its modeled suitable areas were tested for significance. A second global ecological niche model was trained with the same species records and environmental parameters, excluding windspeed parameters. Using multiple linear regression analyses and a test of average marginal effect, the effect of increasing wind speed on the average marginal effect of temperature and precipitation on the projected habitat suitability was estimated. We found that climatically suitable and monitored areas where Ae. albopictus is not established (3.12 ms-1 +/- 0.04 SD) have significantly higher wind speed than areas where the species is already established (2.54 ms-1 +/- 0.04 SD). Among temperature-related bioclimatic variables, the annual mean temperature was the most important variable contributing to the performance of both global models. Wind speed has a negative effect on the predicted habitat suitability of Ae. albopictus and reduces false-positive rates in model predictions. With increasing wind speed, the average marginal effect of annual mean temperatures decreases but that of the annual precipitation increases. Wind speed should be considered in future modeling efforts aimed at limiting the spread and dispersal of Ae. albopictus and in the implementation of surveillance and early warning systems. Local-scale data collected from fieldwork or laboratory experiments will help improve the state of the art on how wind speed influences the distribution, flight, and dispersal activity of the mosquito.
Dirofilariasis is a zoonosis caused by nematodes of the genus Dirofilaria.Dirofilaria immitis is cosmopolitan as regards its distribution in animals, being responsible for human pulmonary dirofilariasis in the New World. However, human infections by Dirofilaria immitis are exceptional in Europe, and the previously reported Italian cases of pulmonary dirofilariasis were due to Dirofilaria repens. We performed a systematic literature review of the Italian cases of human dirofilariasis due to Dirofilariaimmitis according to the PRISMA guidelines. We also report the first autochthonous case of human pulmonary dirofilariasis due to Dirofilariaimmitis, confirmed by polymerase chain reaction analysis. The patient was a 60-year-old man who lived in the Po river valley and had never traveled abroad; on histological examination, the 2-cm nodule found in his right upper lung was an infarct due to a parasitic thrombotic lesion. Only one other autochthonous (but conjunctival) case due to Dirofilariaimmitis (molecularly confirmed) was previously found in the same geographic area. Climatic changes, the increasing movements of animal reservoirs and vectors, and new competent carriers have expanded the geographic distribution of the Dirofilaria species, increasing the risk of human infections. Our report demonstrates that at least some pulmonary Italian cases of human dirofilariasis are due to Dirofilaria immitis, as in the New World.
The Asian tiger mosquito, Aedes albopictus, competent vector of several arboviruses, poses significant impact on human health worldwide. Although global warming is a driver of A . albopictus range expansion, few studies focused on its effects on homodynamicity (i.e. the ability to breed all-year-round), a key factor of vectorial capacity and a primary condition for an Aedes-borne disease to become endemic in temperate areas. Data from a 4-year monitoring network set in Central Italy and records from weather stations were used to assess winter adult activity and weekly minimum temperatures. Winter oviposition occurred in 38 localities with a seasonal mean photoperiod of 9.7 : 14.3 (L : D) h. Positive collections (87) occurred with an average minimum temperature of the two and three weeks before sampling of approximately 4°C. According to these evidences and considering the climate projections of three global climate models and three shared socio-economic pathways for the next three 20-year periods (from 2021 to 2080), the minimum temperature of January will increase enough to allow an all-year-round oviposition of A . albopictus in several areas of the Mediterranean Basin. Due to vector homodynamicity, Aedes-borne diseases could become endemic in Southern Europe by the end of the twenty-first century, worsening the burden on human health.
Increases in temperature and extreme weather events due to global warming can create an environment that is beneficial to mosquito populations, changing and possibly increasing the suitable geographical range for many vector-borne diseases. West Nile Virus (WNV) is a flavivirus, maintained in a mosquito-avian host cycle that is usually asymptomatic but can cause primarily flu-like symptoms in human and equid accidental hosts. In rare circumstances, serious disease and death are possible outcomes for both humans and horses. The main European vector of WNV is the Culex pipiens mosquito. This study examines the effect of environmental temperature on WNV establishment in Europe via Culex pipiens populations through use of a basic reproduction number ( R0 ) model. A metric of thermal suitability derived from R0 was developed by collating thermal responses of different Culex pipiens traits and combining them through use of a next-generation matrix. WNV establishment was determined to be possible between 14°C and 34.3°C, with the optimal temperature at 23.7°C. The suitability measure was plotted against monthly average temperatures in 2020 and the number of months with high suitability mapped across Europe. The average number of suitable months for each year from 2013 to 2019 was also calculated and validated with reported equine West Nile fever cases from 2013 to 2019. The widespread thermal suitability for WNV establishment highlights the importance of European surveillance for this disease and the need for increased research into mosquito and bird distribution.
West Nile virus is a widely spread arthropod-born virus, which has mosquitoes as vectors and birds as reservoirs. Humans, as dead-end hosts of the virus, may suffer West Nile Fever (WNF), which sometimes leads to death. In Europe, the first large-scale epidemic of WNF occurred in 1996 in Romania. Since then, human cases have increased in the continent, where the highest number of cases occurred in 2018. Using the location of WNF cases in 2017 and favorability models, we developed two risk models, one environmental and the other spatio-environmental, and tested their capacity to predict in 2018: 1) the location of WNF; 2) the intensity of the outbreaks (i.e. the number of confirmed human cases); and 3) the imminence of the cases (i.e. the Julian week in which the first case occurred). We found that climatic variables (the maximum temperature of the warmest month and the annual temperature range), human-related variables (rain-fed agriculture, the density of poultry and horses), and topo-hydrographic variables (the presence of rivers and altitude) were the best environmental predictors of WNF outbreaks in Europe. The spatio-environmental model was the most useful in predicting the location of WNF outbreaks, which suggests that a spatial structure, probably related to bird migration routes, has a role in the geographical pattern of WNF in Europe. Both the intensity of cases and their imminence were best predicted using the environmental model, suggesting that these features of the disease are linked to the environmental characteristics of the areas. We highlight the relevance of river basins in the propagation dynamics of the disease, as outbreaks started in the lower parts of the river basins, from where WNF spread towards the upper parts. Therefore, river basins should be considered as operational geographic units for the public health management of the disease.
West Nile Virus (WNV) has recently emerged as a major public health concern in Europe; its recent expansion also coincided with some remarkable socio-economic and environmental changes, including an economic crisis and some of the warmest temperatures on record. Here we empirically investigate the drivers of this phenomenon at a European wide scale by constructing and analyzing a unique spatial-temporal data-set, that includes data on climate, land-use, the economy, and government spending on environmental related sectors. Drivers and risk factors of WNV were identified by building a conceptual framework, and relationships were tested using a Generalized Additive Model (GAM), which could capture complex non-linear relationships and also account for spatial and temporal auto-correlation. Some of the key risk factors identified in our conceptual framework, such as a higher percentage of wetlands and arable land, climate factors (higher summer rainfall and higher summer temperatures) were positive predictors of WNV infections. Interestingly, winter temperatures of between 2 °C and 6 °C were among some of the strongest predictors of annual WNV infections; one possible explanation for this result is that successful overwintering of infected adult mosquitoes (likely Culex pipiens) is key to the intensity of outbreaks for a given year. Furthermore, lower surface water extent over the summer is also associated with more intense outbreaks, suggesting that drought, which is known to induce positive changes in WNV prevalence in mosquitoes, is also contributing to the upward trend in WNV cases in affected regions. Our indicators representing the economic crisis were also strong predictors of WNV infections, suggesting there is an association between austerity and cuts to key sectors, which could have benefited vector species and the virus during this crucial period. These results, taken in the context of recent winter warming due to climate change, and more frequent droughts, may offer an explanation of why the virus has become so prevalent in Europe.
Some of the climate-sensitive infections (CSIs) affecting humans are zoonotic vector-borne diseases, such as Lyme borreliosis (BOR) and tick-borne encephalitis (TBE), mostly linked to various species of ticks as vectors. Due to climate change, the geographical distribution of tick species, their hosts, and the prevalence of pathogens are likely to change. A recent increase in human incidences of these CSIs in the Nordic regions might indicate an expansion of the range of ticks and hosts, with vegetation changes acting as potential predictors linked to habitat suitability. In this paper, we study districts in Fennoscandia and Russia where incidences of BOR and TBE have steadily increased over the 1995-2015 period (defined as ‘Well Increasing districts’). This selection is taken as a proxy for increasing the prevalence of tick-borne pathogens due to increased habitat suitability for ticks and hosts, thus simplifying the multiple factors that explain incidence variations. This approach allows vegetation types and strengths of correlation specific to the WI districts to be differentiated and compared with associations found over all districts. Land cover types and their changes found to be associated with increasing human disease incidence are described, indicating zones with potential future higher risk of these diseases. Combining vegetation cover and climate variables in regression models shows the interplay of biotic and abiotic factors linked to CSI incidences and identifies some differences between BOR and TBE. Regression model projections up until 2070 under different climate scenarios depict possible CSI progressions within the studied area and are consistent with the observed changes over the past 20 years.
There is now considerable evidence that in Europe, babesiosis is an emerging infectious disease, with some of the causative species spreading as a consequence of the increasing range of their tick vector hosts. In this review, we summarize both the historic records and recent findings on the occurrence and incidence of babesiosis in 20 European countries located in southeastern Europe (Bosnia and Herzegovina, Croatia, and Serbia), central Europe (Austria, the Czech Republic, Germany, Hungary, Luxembourg, Poland, Slovakia, Slovenia, and Switzerland), and northern and northeastern Europe (Lithuania, Latvia, Estonia, Iceland, Denmark, Finland, Sweden, and Norway), identified in humans and selected species of domesticated animals (cats, dogs, horses, and cattle). Recorded cases of human babesiosis are still rare, but their number is expected to rise in the coming years. This is because of the widespread and longer seasonal activity of Ixodes ricinus as a result of climate change and because of the more extensive use of better molecular diagnostic methods. Bovine babesiosis has a re-emerging potential because of the likely loss of herd immunity, while canine babesiosis is rapidly expanding in central and northeastern Europe, its occurrence correlating with the rapid, successful expansion of the ornate dog tick (Dermacentor reticulatus) populations in Europe. Taken together, our analysis of the available reports shows clear evidence of an increasing annual incidence of babesiosis across Europe in both humans and animals that is changing in line with similar increases in the incidence of other tick-borne diseases. This situation is of major concern, and we recommend more extensive and frequent, standardized monitoring using a “One Health” approach.
Hyalomma marginatum is the main vector of Crimean-Congo haemorrhagic fever virus (CCHFV) and spotted fever rickettsiae in Europe. The distribution of H. marginatum is currently restricted to parts of southern Europe, northern Africa and Asia, and one of the drivers limiting distribution is climate, particularly temperature. As temperatures rise with climate change, parts of northern Europe currently considered too cold for H. marginatum to be able to survive may become suitable, including the United Kingdom (UK), presenting a potential public health concern. Here we use a series of modelling methodologies to understand whether mean air temperatures across the UK during 2000-2019 were sufficient for H. marginatum nymphs to moult into adult stages and be able to overwinter in the UK if they were introduced on migratory birds. We then used UK-specific climate projections (UKCP18) to determine whether predicted temperatures would be sufficient to allow survival in future. We found that spring temperatures in parts of the UK during 2000-2019 were warm enough for predicted moulting to occur, but in all years except 2006, temperatures during September to December were too cold for overwintering to occur. Our analysis of the projections data suggests that whilst temperatures in the UK during September to December will increase in future, they are likely to remain below the threshold required for H. marginatum populations to become established.
Global warming affects the aquatic ecosystems, accelerating pathogenic microorganisms’ and toxic microalgae’s growth and spread in marine habitats, and in bivalve molluscs. New parasite invasions are directly linked to oceanic warming. Consumption of pathogen-infected molluscs impacts human health at different rates, depending, inter alia, on the bacteria taxa. It is therefore necessary to monitor microbiological and chemical contamination of food. Many global cases of poisoning from bivalve consumption can be traced back to Mediterranean regions. This article aims to examine the marine bivalve’s infestation rate within the scope of climate change, as well as to evaluate the risk posed by climate change to bivalve welfare and public health. Biological and climatic data literature review was performed from international scientific sources, Greek authorities and State organizations. Focusing on Greek aquaculture and bivalve fisheries, high-risk index pathogenic parasites and microalgae were observed during summer months, particularly in Thermaikos Gulf. Considering the climate models that predict further temperature increases, it seems that marine organisms will be subjected in the long term to higher temperatures. Due to the positive linkage between temperature and microbial load, the marine areas most affected by this phenomenon are characterized as ‘high risk’ for consumer health.
The article, based on the reports of the Ministry of Health of Ukraine, presents the materials of the epidemiological surveillance of salmonellosis in 2011-2018. To assess the influence of factors on the epidemic process of salmonellosis, the demographic situation, income and living conditions of the population were studied; average monthly air temperature, relative humidity, precipitation; the quantitative and qualitative composition of the microbiocenosis of patients with signs of acute intestinal infection. It was found that in Ukraine the incidence of salmonellosis is high. Outbreaks of salmonellosis are recorded. S. enteritidis is most often isolated from the clinical material of patients, carriers and human objects (p <0.05). The risk groups for salmonellosis are children (p <0.05), as well as the rural population (p 7lt;0.05). The low level of sanitary and epidemiological control at the stages of production, transportation and sale of food products, water supply contributes to the spread of salmonellosis. Natural factors have a regulating effect on the intensity of the epidemic salmonella process: a strong direct relationship is established between the incidence and air temperature and precipitation (p <0.05). Salmonella enters into a competitive or synergistic relationship with other microorganisms in the intestinal biotope. Thus, the intensity of the epidemic process of salmonellosis can be influenced not only by external (natural and social), but also by internal factors.
Dinophysis is a genus of dinoflagellates with the potential to cause diarrhoeic Shellfish Poisoning (DSP) in humans. The lipophilic toxins produced by some species of Dinophysis spp. can accumulate within shellfish flesh even at low cell abundances, and this may result in the closure of a shellfish farm if toxins exceed the recommended upper limit. Over the period 2014 to 2020 inclusive there were several toxic events along the South West coast of U.K. related to Dinophysis spp. The Food Standards Agency (FSA) monitoring programme measure Dinophysis cell abundances and toxin concentration within shellfish flesh around the coasts of England and Wales, but there are few schemes routinely measuring the environmental parameters that may be important drivers for these Harmful Algal Blooms (HABs). This study uses retrospective data from the FSA monitoring at three sites on the south Cornwall coast as well as environmental data from some novel platforms such as coastal WaveRider buoys to investigate potential drivers and explore whether either blooms or toxic events at these sites can be predicted from environmental data. Wind direction was found to be important in determining whether a bloom develops at these sites, and low air temperature in June was associated with low toxicity in the shellfish flesh. Using real time data from local platforms may help shellfish farmers predict future toxic events and minimise financial loss.
Due to anthropogenic activities, associated with climate change, many freshwater ecosystems are expected to experience an increase in salinity. This phenomenon is predicted to favor the development and expansion of freshwater cyanobacteria towards brackish waters due to their transfer along the estuarine freshwater-marine continuum. Since freshwater cyanobacteria are known to produce toxins, this represents a serious threat for animal and human health. Saxitoxins (STXs) are classified among the most powerful cyanotoxins. It becomes thus critical to evaluate the capacity of cyanobacteria producing STXs to face variations in salinity and to better understand the physiological consequences of sodium chloride (NaCl) exposure, in particular on their toxicity. Laboratory experiments were conducted on three filamentous cyanobacteria species isolated from brackish (Dolichospermum sp.) and fresh waters (Aphanizomenon gracile and Cylindrospermopsis raciborskii) to determine how salinity variations affect their growth, photosynthetic activity, pigment composition, production of reactive oxygen species (ROS), synthesis of compatible solutes and STXs intracellular quotas. Salinity tolerance was found to be species-specific. Dolichospermum sp. was more resistant to salinity variations than A. gracile and C. raciborskii. NaCl variations reduced growth in all species. In A. gracile, carotenoids content was dose-dependently reduced by NaCl. By contrast, in C. raciborskii and Dolichospermum sp., variations in carotenoids content did not show obvious relationships with NaCl concentration. While in Dolichospermum sp. phycocyanin and phycoerythrin increased within the first 24 h exposure to NaCl, in both A. gracile and C. raciborskii, these pigments decreased proportionally to NaCl concentration. Low changes in salinity did not impact STXs production in A. gracile and C. raciborskii while higher increase in salinity could modify the toxin profile and content of C. raciborskii (intracellular STX decreased while dc-GTX2 increased). In estuaries, A. gracile and C. raciborskii would not be able to survive beyond the oligohaline area (i.e. salinity > 5). Conversely, in part due to its ability to accumulate compatible solutes, Dolichospermum sp. has the potential to face consequent salinity variations and to survive in the polyhaline area (at least up to salinity = 24).
Background Geopolitical and climate changes form the background of the current migration crisis. It has many faces. One of them are the tragic cases of poisoning of refugees due to eating wild forest mushrooms for socioeconomic reasons in the Western and Northern European countries. The most serious food poisonings in Europe, but not only, are caused by lamellar mushrooms, the most dangerous being Amanita phalloides. Its poisonous properties can be attributed to alpha-amanitin, an RNA polymerase II inhibitor. Unfortunately, as it is characterized by a delayed onset of symptoms, A. phalloides poisoning has a high risk of complications. Case presentation Our article presents a case of A. phalloides poisoning in a 28-year-old man, in which the responding medical emergency unit made errors in diagnosis and treatment. Since the correct diagnosis was made too late, the typical treatment of A. phalloides poisoning was ineffective. The patient suffered a life-threatening liver failure and needed liver transplant from a deceased donor. Conclusions Mushroom poisoning is a particularly important problem not only in countries with a mushroom picking tradition, but also-due to the inflow of refugees-in countries where mushroom poisoning was very rare until recently. In such cases it is crucial to quickly implement the correct procedure, as this can prevent the need for liver transplant or even death. This is a particularly important consideration for the first medical professionals to contact the patient, especially in cases where the patient reports mushrooms consumption and presents alarming symptoms of the gastrointestinal tract. Such situations cannot be underestimated and ignored.
Current and emerging veterinary public health (VPH) challenges raised by globalization, climate change, and industrialization of food production require the veterinarian’s role to evolve in parallel and veterinary education to adapt to reflect these changes. The European Food Hygiene catalog was developed to provide a list of topics relevant to Day One Competencies in VPH. A study was undertaken to ensure that the catalog and teaching practices were pertinent to the work of public health veterinarians. Relevant stakeholders were consulted using questionnaires and semi-structured interviews. A long questionnaire was distributed to 49 academics teaching VPH in European veterinary schools to review topics listed in the catalog. Eighteen responses were received (36.7%), representing 12 European countries. There was general agreement that most topics were appropriate for the undergraduate VPH curriculum. A short questionnaire was distributed to 348 European veterinarians working in the industry. Twenty-four questionnaires (6.7%) were received, representing eight European countries. Despite the low participation rate, topics needing greater emphasis in the undergraduate curriculum included Hazard Analysis Critical Control Points (HACCP), food microbiology, and audits. Seven semi-structured interviews with public health veterinarians working in the UK identified the need for curricular changes including greater practical experience and a shift from a focus on meat inspection to risk management. This may be partly achieved by replacing traditional lectures with authentic case-based scenarios. The study findings can be used to inform the future direction to VPH education for veterinary students across Europe.
BACKGROUND: Universal mass vaccination (UMV) against rotavirus has been implemented in many but not all European countries. This study investigated the impact of UMV on rotavirus incidence trends by comparing European countries with UMV: Belgium, England/Wales and Germany versus countries without UMV: Denmark and the Netherlands. METHODS: For this observational retrospective cohort study, time series data (2001-2016) on rotavirus detections, meteorological factors and population demographics were collected. For each country, several meteorological and population factors were investigated as possible predictors of rotavirus incidence. The final set of predictors were incorporated in negative binomial models accounting for seasonality and serial autocorrelation, and time-varying incidence rate ratios (IRR) were calculated for each age group and country separately. The overall vaccination impact two years after vaccine implementation was estimated by pooling the results using a random effects meta-analyses. Independent t-tests were used to compare annual epidemics in the pre-vaccination and post-vaccination era to explore any changes in the timing of rotavirus epidemics. RESULTS: The population size and several meteorological factors were predictors for the rotavirus epidemiology. Overall, we estimated a 42% (95%-CI 23;56%) reduction in rotavirus incidence attributable to UMV. Strongest reductions were observed for age-groups 0-, 1- and 2-years (IRR 0.47, 0.48 and 0.63, respectively). No herd effect induced by UMV in neighbouring countries was observed. In all UMV countries, the start and/or stop and corresponding peak of the rotavirus season was delayed by 4-7 weeks. CONCLUSIONS: The introduction of rotavirus UMV resulted in an overall reduction of 42% in rotavirus incidence in Western European countries two years after vaccine introduction and caused a change in seasonal pattern. No herd effect induced by UMV neighbouring countries was observed for Denmark and the Netherlands.
BACKGROUND: The aim of our study was to analyse the influence of air temperature and implemented veterinary measures on salmonellosis incidence in the Czech Republic (CZ). METHODS: We conducted a descriptive analysis of salmonellosis as reported to the Czech national surveillance system during 1998-2017 and evaluated the influence of applied veterinary measures (started in January 2008) on salmonellosis incidence by comparing two 9-year periods (1998-2006, 2009-2017). Using a generalized additive model, we analysed association between monthly mean air temperature and log-transformed salmonellosis incidence over the entire twenty-year period. RESULTS: A total of 410,533 salmonellosis cases were reported during the study period in the CZ. Annual mean incidences of salmonellosis were 313.0/100,000 inhabitants before and 99.0/100,000 inhabitants after implementation of the veterinary measures. The time course of incidence was non-linear, with a sharp decline during 2006-2010. Significant association was found between disease incidence and air temperature. On average, the data indicated that within a common temperature range every 1 °C rise in air temperature contributed to a significant 6.2% increase in salmonellosis cases. CONCLUSIONS: Significant non-linear effects of annual trend, within-year seasonality, and air temperature on the incidence of salmonellosis during 1998-2017 were found. Our study also demonstrates significant direct effect of preventive veterinary measures taken in poultry in reducing incidence of human salmonellosis in the CZ. The annual mean number of salmonellosis cases in the period after introducing the veterinary measures was only 32.5% of what it had been in the previous period.
This study aimed to estimate dynamic changes in seroprevalence of Toxoplasma gondii within the general population living in the northern part of the Republic of Serbia (Province of Vojvodina) during a 14-year period. The differences in prevalence of anti-toxoplasma antibodies were analyzed in correlation with age, gender, residential area (rural/urban) and meteorological factors. In this cohort retrospective study, 24,440 subjects between 1 and 88 years old were enrolled. To determine the presence of T. gondii-specific IgM and IgG antibodies in serum samples, commercially available ELISA kits were used (Euroimmun, Luebeck, Germany). During the study period, the overall T. gondii seroprevalence was 23.5%. The seroprevalence continuously decreased over time from 31.7% in 2008 to 20.4% in 2021 (0.81% per year, p < 0.001). Approximately 2% of patients had a serologic profile positive for both anti-Toxoplasma IgG and IgM antibodies. The seroprevalence was higher (28.87%) among men compared to women (24.28%), while urban residents (24.94%) had lower seroprevalence than the rural population (28.17%). A statistically significant negative correlation (r = -0.559) was found between serologic profile of patients positive for both T. gondii IgG and IgM antibodies and the annual mean air temperature. No significant association was observed between seropositivity to T. gondii infection and examined meteorological factors. These data could be useful to national and regional health authorities to create an optimal health policy to reduce rate of T. gondii infections.
Harmful algal blooms (HAB) are recurrent phenomena in northern Europe along the coasts of the Baltic Sea, Kattegat-Skagerrak, eastern North Sea, Norwegian Sea and the Barents Sea. These HABs have caused occasional massive losses for the aquaculture industry and have chronically affected socioeconomic interests in several ways. This status review gives an overview of historical HAB events and summarises reports to the Harmful Algae Event Database from 1986 to the end of year 2019 and observations made in long term monitoring programmes of potentially harmful phytoplankton and of phycotoxins in bivalve shellfish. Major HAB taxa causing fish mortalities in the region include blooms of the prymnesiophyte Chrysochromulina leadbeateri in northern Norway in 1991 and 2019, resulting in huge economic losses for fish farmers. A bloom of the prymesiophyte Prymnesium polylepis (syn. Chrysochromulina polylepis) in the Kattegat-Skagerrak in 1988 was ecosystem disruptive. Blooms of the prymnesiophyte Phaeocystis spp. have caused accumulations of foam on beaches in the southwestern North Sea and Wadden Sea coasts and shellfish mortality has been linked to their occurrence. Mortality of shellfish linked to HAB events has been observed in estuarine waters associated with influx of water from the southern North Sea. The first bloom of the dictyochophyte genus Pseudochattonella was observed in 1998, and since then such blooms have been observed in high cell densities in spring causing fish mortalities some years. Dinoflagellates, primarily Dinophysis spp., intermittently yield concentrations of Diarrhetic Shellfish Toxins (DST) in blue mussels, Mytilus edulis, above regulatory limits along the coasts of Norway, Denmark and the Swedish west coast. On average, DST levels in shellfish have decreased along the Swedish and Norwegian Skagerrak coasts since approximately 2006, coinciding with a decrease in the cell abundance of D. acuta. Among dinoflagellates, Alexandrium species are the major source of Paralytic Shellfish Toxins (PST) in the region. PST concentrations above regulatory levels were rare in the Skagerrak-Kattegat during the three decadal review period, but frequent and often abundant findings of Alexandrium resting cysts in surface sediments indicate a high potential risk for blooms. PST levels often above regulatory limits along the west coast of Norway are associated with A. catenella (ribotype Group 1) as the main toxin producer. Other Alexandrium species, such as A. ostenfeldii and A. minutum, are capable of producing PST among some populations but are usually not associated with PSP events in the region. The cell abundance of A. pseudogonyaulax, a producer of the ichthyotoxin goniodomin (GD), has increased in the Skagerrak-Kattegat since 2010, and may constitute an emerging threat. The dinoflagellate Azadinium spp. have been unequivocally linked to the presence of azaspiracid toxins (AZT) responsible for Azaspiracid Shellfish Poisoning (AZP) in northern Europe. These toxins were detected in bivalve shellfish at concentrations above regulatory limits for the first time in Norway in blue mussels in 2005 and in Sweden in blue mussels and oysters (Ostrea edulis and Crassostrea gigas) in 2018. Certain members of the diatom genus Pseudo-nitzschia produce the neurotoxin domoic acid and analogs known as Amnesic Shellfish Toxins (AST). Blooms of Pseudo-nitzschia were common in the North Sea and the Skagerrak-Kattegat, but levels of AST in bivalve shellfish were rarely above regulatory limits during the review period. Summer cyanobacteria blooms in the Baltic Sea are a concern mainly for tourism by causing massive fouling of bathing water and beaches. Some of the cyanobacteria produce toxins, e.g. Nodularia spumigena, producer of nodularin, which may be a human health problem and cause occasional dog mortalities. Coastal and shelf sea regions in northern Europe provide a key supply of seafood, socioeconomic well-being and ecosystem services. I
BACKGROUND: Visceral leishmaniasis (VL) is re-emerging in Armenia since 1999 with 167 cases recorded until 2019. The objectives of this study were (i) to determine for the first time the genetic diversity and population structure of the causative agent of VL in Armenia; (ii) to compare these genotypes with those from most endemic regions worldwide; (iii) to monitor the diversity of vectors in Armenia; (iv) to predict the distribution of the vectors and VL in time and space by ecological niche modeling. METHODOLOGY/PRINCIPAL FINDINGS: Human samples from different parts of Armenia previously identified by ITS-1-RFLP as L. infantum were studied by Multilocus Microsatellite Typing (MLMT). These data were combined with previously typed L. infantum strains from the main global endemic regions for population structure analysis. Within the 23 Armenian L. infantum strains 22 different genotypes were identified. The combined analysis revealed that all strains belong to the worldwide predominating MON1-population, however most closely related to a subpopulation from Southeastern Europe, Maghreb, Middle East and Central Asia. The three observed Armenian clusters grouped within this subpopulation with strains from Greece/Turkey, and from Central Asia, respectively. Ecological niche modeling based on VL cases and collected proven vectors (P. balcanicus, P. kandelakii) identified Yerevan and districts Lori, Tavush, Syunik, Armavir, Ararat bordering Georgia, Turkey, Iran and Azerbaijan as most suitable for the vectors and with the highest risk for VL transmission. Due to climate change the suitable habitat for VL transmission will expand in future all over Armenia. CONCLUSIONS: Genetic diversity and population structure of the causative agent of VL in Armenia were addressed for the first time. Further genotyping studies should be performed with samples from infected humans, animals and sand flies from all active foci including the neighboring countries to understand transmission cycles, re-emergence, spread, and epidemiology of VL in Armenia and the entire Transcaucasus enabling epidemiological monitoring.
Free-ranging wild ungulates are widespread in Austria, and act as hosts (i.e. feeding hosts) for ticks, including Ixodes ricinus, and as reservoir hosts for pathogens transmitted by I. ricinus. Due to climate change, the abundance of I. ricinus might be increasing, which could potentially lead to higher prevalences of tick-borne pathogens, such as Babesia spp. and Anaplasma phagocytophilum, some known for their zoonotic potential. Human babesiosis is classified as an emerging zoonosis, but sufficient data of these parasites in central Austria is lacking. In order to assess the abundance of vector-borne pathogens, blood of roe deer (Capreolus capreolus; n = 137), red deer (Cervus elaphus; n = 37), mouflons (Ovis gmelini; n = 2) and chamois (Rupicapra rupicapra; n = 1), was collected and tested for pathogen DNA in two different sampling sites in central Austria. DNA of tick-borne pathogens was detected in 15.5 % (n = 27) of these animals. Babesia capreoli (n = 22 in roe deer; n = 1 in mouflon), Babesia divergens (n = 1, in red deer), and Anaplasma phagocytophilum (n = 4, in roe deer) were detected. DNA sequencing of the 18S rRNA gene of two C. capreolus samples from Upper Austria featured another new genotype of Babesia, which differs in one nucleotide position to B. divergens and B. capreoli, and is intermediate between the main genotypes of B. capreoli and B. divergens within the partial gene sequence analyzed. This study thus confirms that B. capreoli, B. divergens, and A. phagocytophilum are present in free-ranging ungulates in central Austria. Further testing over a longer period is recommended in order to assess the impact of climate change on the prevalence of blood parasites in central Austria.
Recently, ticks of Hyalomma spp. have been found more often in areas previously lacking this tick species. Due to their important role as a vector of different diseases, such as Crimean-Congo-hemorrhagic fever (CCHF), the occurrence and potential spread of this tick species is of major concern. So far, eight Hyalomma sp. ticks were found between 2018 and 2021 in Austria. A serological investigation on antibodies against the CCHF virus in 897 cattle as indicator animals displayed no positive case. During observation of climatic factors, especially in the period from April to September, the year 2018 displayed an extraordinary event in terms of higher temperature and dryness. To estimate the risk for humans to come in contact with Hyalomma sp. in Austria, many parameters have to be considered, such as the resting place of birds, availability of large livestock hosts, climate, density of human population, etc.
In the context of complex public health challenges led by interdependent changes such as climate change, biodiversity loss, and resistance to treatment, it is important to mobilize methods that guide us to generate innovative interventions in a context of uncertainty and unknown. Here, we mobilized the concept-knowledge (CK) design theory to identify innovative, cross-sectoral, and cross-disciplinary research and design programs that address the challenges posed by tick-borne Lyme disease in France, which is of growing importance in the French public health and healthcare systems. Within the CK methodological framework, we developed an iterative approach based on literature analysis, expert interviews, analysis of active French research projects, and work with CK experts to contribute to design “an action plan against Lyme disease.” We produced a CK diagram that highlights innovative concepts that could be addressed in research projects. The outcome is discussed within four areas: (i) effectiveness; (ii) environmental sustainability in prevention actions; (iii) the promotion of constructive involvement of citizens in Lyme challenges; and (iv) the development of care protocols for chronic conditions with an unknown diagnosis. Altogether, our analysis questioned the health targets ranging from population to ecosystem, the citizen involvement, and the patient consideration. This means integrating social and ecological science, as well as the multidisciplinary medical patient journey, from the start. CK theory is a promising framework to assist public health professionals in designing programs for complex yet urgent contexts, where research and data collection are still not sufficient to provide clear guidance.
Ixodes ricinus ticks (Acari: Ixodidae) are the most important vector for Lyme borreliosis in Europe. As climate change might affect their distributions and activities, this study aimed to determine the effects of environmental factors, i.e., meteorological, bioclimatic, and habitat characteristics on host-seeking (questing) activity of I. ricinus nymphs, an important stage in disease transmissions, across diverse climatic types in France over 8 years. Questing activity was observed using a repeated removal sampling with a cloth-dragging technique in 11 sampling sites from 7 tick observatories from 2014 to 2021 at approximately 1-month intervals, involving 631 sampling campaigns. Three phenological patterns were observed, potentially following a climatic gradient. The mixed-effects negative binomial regression revealed that observed nymph counts were driven by different interval-average meteorological variables, including 1-month moving average temperature, previous 3-to-6-month moving average temperature, and 6-month moving average minimum relative humidity. The interaction effects indicated that the phenology in colder climates peaked differently from that of warmer climates. Also, land cover characteristics that support the highest baseline abundance were moderate forest fragmentation with transition borders with agricultural areas. Finally, our model could potentially be used to predict seasonal human-tick exposure risks in France that could contribute to mitigating Lyme borreliosis risk.
BACKGROUND: In the face of ongoing climate warming, vector-borne diseases are expected to increase in Europe, including tick-borne diseases (TBD). The most abundant tick-borne diseases in Germany are Tick-Borne Encephalitis (TBE) and Lyme Borreliosis (LB), with Ixodes ricinus as the main vector. METHODS: In this study, we display and compare the spatial and temporal patterns of reported cases of human TBE and LB in relation to some associated factors. The comparison may help with the interpretation of observed spatial and temporal patterns. RESULTS: The spatial patterns of reported TBE cases show a clear and consistent pattern over the years, with many cases in the south and only few and isolated cases in the north of Germany. The identification of spatial patterns of LB disease cases is more difficult due to the different reporting practices in the individual federal states. Temporal patterns strongly fluctuate between years, and are relatively synchronized between both diseases, suggesting common driving factors. Based on our results we found no evidence that weather conditions affect the prevalence of both diseases. Both diseases show a gender bias with LB bing more commonly diagnosed in females, contrary to TBE being more commonly diagnosed in males. CONCLUSION: For a further investigation of of the underlying driving factors and their interrelations, longer time series as well as standardised reporting and surveillance system would be required.
BACKGROUND: Tick-borne encephalitis (TBE) is the most important tick-borne viral disease in Eurasia and causes disease in humans and in a number of animals, among them dogs and horses. There is still no good correlation between tick numbers, weather conditions and human cases. There is the hypothesis that co-feeding due to simultaneous occurrence of larvae and nymphs may be a factor for the increased transmission of the virus in nature and for human disease. Based on long-term data from a natural TBEV focus, phylogenetic results and meteorological data we sought to challenge this hypothesis. METHODS: Ticks from an identified TBE natural focus were sampled monthly from 04/2009 to 12/2018. Ticks were identified and pooled. Pools were tested by RT-qPCR. Positive pools were confirmed by virus isolation and/or sequencing of additional genes (E gene, NS2 gene). Temperature data such as the decadal (10-day) mean daily maximum air temperature (DMDMAT) were obtained from a nearby weather station and statistical correlations between tick occurrence and minimal infection rates (MIR) were calculated. RESULTS: In the study period from 04/2009 to 12/2018 a total of 15,530 ticks (2,226 females, 2,268 males, 11,036 nymphs) were collected. The overall MIR in nymphs over the whole period was 77/15,530 (0.49%), ranging from 0.09% (2009) to 1.36% (2015). The overall MIR of female ticks was 0.76% (17/2,226 ticks), range 0.14% (2013) to 3.59% (2016). The overall MIR of males was 0.57% (13/2,268 ticks), range from 0.26% (2009) to 0.97% (2015). The number of nymphs was statistically associated with a later start of spring/vegetation period, indicated by the onset of forsythia flowering. CONCLUSION: There was no particular correlation between DMDMAT dynamics in spring and/or autumn and the MIR of nymphs or adult ticks detected. However, there was a positive correlation between the number of nymphs and the number of reported human TBE cases in the following months, but not in the following year. The hypothesis of the importance of co-feeding of larvae and nymphs for the maintenance of transmission cycle of TBEV in nature is not supported by our findings.
Ticks are hematophagous parasites that can transmit a variety of human pathogens, and their life cycle is dependent on several climatic factors for development and survival. We conducted a study in Piedmont and Aosta Valley, Italy, between 2009 and 2018. The study matched human sample serologies for Borrelia spp. with publicly available climatic and meteorological data. A total of 12,928 serological immunofluorescence assays (IFA) and Western blot (WB) tests were analysed. The median number of IFA and WB tests per year was 1236 (range 700-1997), with the highest demand in autumn 2018 (N = 289). In the study period, positive WB showed an increasing trend, peaking in 2018 for both IgM (N = 97) and IgG (N = 61). These results were consistent with a regional climatic variation trending towards an increase in both temperature and humidity. Our results suggest that coupling data from epidemiology and the environment, and the use of a one health approach, may provide a powerful tool in understanding disease transmission and strengthen collaboration between specialists in the era of climate instability.
INTRODUCTION: Lyme disease is the most common tick-borne disease, caused by spirochetes of the genus Borrelia, transmitted by ticks of the Ixodes genus. According to ECDC, Poland should be considered as an endemic area. The risk of Lyme disease incidence in-creases with tick habitats increase, which is a response to environmental factors and climate change. AIM OF THE STUDY: The aim of the study is to assess the epidemiological situation of Lyme disease in Poland in 2018 compared to the situation in previous years. MATERIAL AND METHODS: The epidemiological situation of Lyme disease in Poland was assessed on the basis of the data sent to NIPH-NIH by voivodeship sanitary-epidemiological stations and published in the bulletin ‘Infectious diseases and poisoning in Poland in 2018’ . RESULTS: In 2018; 20,150 Lyme disease cases was registered, 2,124 people were hospitalized. You can also see an increase in cases in the second and third quarter in favor of the fourth quarter. The epidemiological situation in Western European countries is similar to the situation in Poland. SUMMARY AND CONCLUSION: The inability to determine the clear trend of the epidemiological situation in Poland indicates the sensitivity of the surveillance system, but also the difficulty in new cases diagnosis. You can also see a decrease in the number of cases, which may be a sign of having the right tools or experience in the Lyme disease diagnosis.
INTRODUCTION: Lyme disease is caused by Borrelia spirochetes transmitted by ticks of the genus Ixodes. In Poland, Lyme disease is the most common tick-borne disease. The entire territory of Poland is recognized by ECDC as an endemic area of Lyme disease. Environmental factors and climate change are responsible for the increase in the number of tick habitats, which leads to an increased risk of Lyme disease. AIM OF THE STUDY: The aim of the study is to present the epidemiological situation of Lyme disease in Poland in 2019 compared to the previous year. MATERIAL AND METHODS: The analysis of the epidemiological situation of Lyme disease in Poland was based on data sent to NIPH NIH – NRI by voivodeship sanitary-epidemiological stations and published in the bulletin “Infectious diseases and poisoning in Poland in 2019.” RESULTS: In 2019, 20,630 cases of Lyme disease were registered, and 1,701 people were hospitalized. Compared to 2018, there was a shift in the incidence from the first and second quarter to the fourth quarter. The highest incidence of 107.7 / 100,000 population was recorded in the Podlaskie voivodeship, which has belonged to the voivodeships with the highest incidence in the country for many years. Despite an increase in the total number of cases by 2.4% compared to 2018, the percentage of hospitalized cases was lower than in the previous year. SUMMARY AND CONCLUSION: Difficulties in the diagnosis of Lyme disease make it impossible to define an unequivocal trend in the epidemiological situation in Poland. A slight increase in the incidence may result from the growing number of infected ticks and a better understanding of the problem of Lyme diagnosis by doctors.
Compared to soil or aquatic ecosystems, the atmosphere is still an underexplored environment for microbial diversity. In this study, we surveyed the composition, variability and sources of microbes (bacteria and fungi) in the near surface atmosphere of a highly populated area, spanning ~ 4,000 Km(2) around the city center of Madrid (Spain), in different seasonal periods along two years. We found a core of abundant bacterial genera robust across space and time, most of soil origin, while fungi were more sensitive to environmental conditions. Microbial communities showed clear seasonal patterns driven by variability of environmental factors, mainly temperature and accumulated rain, while local sources played a minor role. We also identified taxa in both groups characteristic of seasonal periods, but not of specific sampling sites or plant coverage. The present study suggests that the near surface atmosphere of urban environments contains an ecosystem stable across relatively large spatial and temporal scales, with a rather homogenous composition, modulated by climatic variations. As such, it contributes to our understanding of the long-term changes associated to the human exposome in the air of highly populated areas.
In the present study, we analysed how geographical distribution of the fungal pathogen Cryptococcus gattii VGI in Europe and Mediterranean area has evolved in the last four decades based on the climatic changes, and we tried to predict the scenario for the next decade. Niche modelling by Maxent analysis showed that recent climate changes have significantly affected the distribution of the fungus revealing a gradual expansion of the fundamental niche from 1980 to 2009 followed by an impressive increase in the last decade (2010-2019) during which the environmental surface suitable for the fungal survival was more than doubled. In the next decade, our model predicted an increase in the area of distribution of C. gattii VGI from the coasts of the Mediterranean basin towards the more internal sub-continental areas. On the basis of these predictions, an increase of cases of cryptococcosis due to C. gattii VGI is expected in the next decade and a constant monitoring of the epidemiology of this fungal pathogen represents a crucial strategy to detect the onset of future outbreaks.
User behaviour for natural ventilation is known to be strongly corelated to outdoor temperatures. In areas of moderate climate, this leads to an increased fresh air supply in summer, which reduces the exposure level towards airborne pathogens. Modelling of numerous random exposure situations in household, school and various settings, based on the long-term climate data from Berlin, showed that this effect is likely to contribute significantly to the overall seasonality of airborne diseases.
Respiratory syncytial virus (RSV) bronchiolitis is a leading cause of global child morbidity and mortality. Every year, seasonal RSV outbreaks put high pressure on paediatric intensive care units (PICUs) worldwide, including in the Netherlands, and this burden appears to be increasing. Weather conditions have a strong influence on RSV activity, and climate change has been proposed as a potential important determinant of future RSV-related health care utilisation. In this national study spanning a total of 13 years with 2161 PICU admissions for RSV bronchiolitis, we aimed (1) to identify meteorological variables that were associated with the number of PICU admissions for RSV bronchiolitis in the Netherlands and (2) to determine if longitudinal changes in these variables occurred over time as a possible explanation for the observed increase in PICU burden. Poisson regression modelling was used to identify weather variables (aggregated in months and weeks) that predicted PICU admissions, and linear regression analysis was used to assess changes in the weather over time. Maximum temperature and global radiation best predicted PICU admissions, with global radiation showing the most stable strength of effect in both month and week data. However, we did not observe a significant change in these weather variables over the 13-year time period. Based on our study, we could not identify changing weather conditions as a potential contributing factor to the increased RSV-related PICU burden in the Netherlands.
The cold season is usually accompanied by an increased incidence of respiratory infections and increased air pollution from combustion sources. As we are facing growing numbers of COVID-19 cases caused by the novel SARS-CoV-2 coronavirus, an understanding of the impact of air pollutants and meteorological variables on the incidence of respiratory infections is crucial. The incidence of influenza-like illness (ILI) can be used as a close proxy for the circulation of influenza viruses. Recently, SARS-CoV-2 has also been detected in patients with ILI. Using distributed lag nonlinear models, we analyzed the association between ILI, meteorological variables and particulate matter concentration in Bialystok, Poland, from 2013-2019. We found an exponential relationship between cumulative PM(2.5) pollution and the incidence of ILI, which remained significant after adjusting for air temperatures and a long-term trend. Pollution had the greatest effect during the same week, but the risk of ILI was increased for the four following weeks. The risk of ILI was also increased by low air temperatures, low absolute humidity, and high wind speed. Altogether, our results show that all measures implemented to decrease PM(2.5) concentrations would be beneficial to reduce the transmission of SARS-CoV-2 and other respiratory infections.
The global crises of climate change and of the COVID-19 pandemic are straining young peoples’ mental health and their mitigation behaviours. We surveyed German-speaking university students aged 18 to 30 years on their negative emotions regarding both crises repeatedly before and during the COVID-19 crisis. Different emotional patterns emerged for climate change and for COVID-19 with negative emotions regarding COVID-19 increasing during the pandemic. We were further able to differentiate between emotional responses associated with impaired wellbeing and those associated with mitigation efforts. Our findings emphasise the need to focus on a mixture of highly inactivating and activating emotions regarding COVID-19 as they are associated with both reduced wellbeing and mitigation behaviours. The findings broaden the understanding of how young adults react to the burden of two global crises and what role negative emotions play.
COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran’s [Formula: see text] and global Geary’s [Formula: see text] were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran’s scatter plot, where states’ dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics.
During the ongoing global COVID-19 pandemic disease, like several countries, Romania experienced a multiwaves pattern over more than two years. The spreading pattern of SARS-CoV-2 pathogens in the Bucharest, capital of Romania is a multi-factorial process involving among other factors outdoor environmental variables and viral inactivation. Through descriptive statistics and cross-correlation analysis applied to daily time series of observational and geospatial data, this study aims to evaluate the synergy of COVID-19 incidence and lethality with air pollution and radon under different climate conditions, which may exacerbate the coronavirus’ effect on human health. During the entire analyzed period 1 January 2020-21 December 2021, for each of the four COVID-19 waves were recorded different anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere, and favorable stability conditions during fall-early winter seasons for COVID-19 disease fast-spreading, mostly during the second, and the fourth waves. As the temporal pattern of airborne SARS-CoV-2 and its mutagen variants is affected by seasonal variability of the main air pollutants and climate parameters, this paper found: 1) the daily outdoor exposures to air pollutants (particulate matter PM2.5 and PM10, nitrogen dioxide-NO(2), sulfur dioxide-SO(2), carbon monoxide-CO) and radon – (222)Rn, are directly correlated with the daily COVID-19 incidence and mortality, and may contribute to the spread and the severity of the pandemic; 2) the daily ground ozone-O(3) levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance are anticorrelated with the daily new COVID-19 incidence and deaths, averageingful for spring-summer periods. Outdoor exposure to ambient air pollution associated with radon is a non-negligible driver of COVID-19 transmission in large metropolitan areas, and climate variables are risk factors in spreading the viral infection. The findings of this study provide useful information for public health authorities and decision-makers to develop future pandemic diseases strategies in high polluted metropolitan environments.
Issues continuously compete for attention in the news media and on social media. Climate change is one of the most urgent problems for society and (re)gained wide public attention in 2019 through the global climate strike protest movement. However, we hypothesize that the outbreak of the COVID-19 pandemic in early 2020 challenged the role of climate change as a routine issue. We use extensive news media and Twitter data to explore if and how the pandemic as a so-called killer issue has shifted public attention away from the issue of climate change in Switzerland. Results show that the climate debate fell victim to the impact of the COVID-19 pandemic in the news media and the Twitter-sphere. Given the vast dominance of the pandemic, there is a strong indication this finding applies similarly to various other issues. Additional hashtag co-occurrence analysis shows that some climate activists react to this development and try to connect the issue of climate change to the pandemic. We argue that suppression of climate change by the pandemic is a problem for its long-term resolution, as it seems to have turned climate change back into a struggling issue.
Excess mortality not directly related to the virus has been shown to have increased during the COVID-19 pandemic. However, changes in heat-related mortality during the pandemic have not been addressed in detail. Here, we performed an observational study crossing daily mortality data collected in Portugal (SICO/DGS) with high-resolution temperature series (ERA5/ECMWF), characterizing their relation in the pre-pandemic, and how it aggravated during 2020. The combined result of COVID-19 and extreme temperatures caused the largest annual mortality burden in recent decades (~ 12 000 excess deaths [~ 11% above baseline]). COVID-19 caused the largest fraction of excess mortality during March to May (62%) and from October onwards (85%). During summer, its direct impact was residual, and deaths not reported as COVID-19 dominated excess mortality (553 versus 3 968). A prolonged hot spell led mortality to the upper tertile, reaching its peak in mid-July (+ 45% deaths/day). The lethality ratio (+ 14 deaths per cumulated ºC) was higher than that observed in recent heatwaves. We used a statistical model to estimate expected deaths due to cold/heat, indicating an amplification of at least 50% in heat-related deaths during 2020 compared to pre-pandemic years. Our findings suggest mortality during 2020 has been indirectly amplified by the COVID-19 pandemic, due to the disruption of healthcare systems and fear of population in attending healthcare facilities (expressed in emergency room admissions decreases). While lockdown measures and healthcare systems reorganization prevented deaths directly related to the virus, a significant burden due to other causes represents a strong secondary impact. This was particularly relevant during summer hot spells, when the lethality ratio reached magnitudes not experienced since the 2003 heatwaves. This severe amplification of heat-related mortality during 2020 stresses the need to resume normal healthcare services and public health awareness.
High ambient temperatures pose a significant risk to health. This study investigates the heatwave mortality in the summer of 2020 during the SARS-CoV-2 coronavirus (COVID-19) pandemic and related countermeasures. The heatwaves in 2020 caused more deaths than have been reported since the Heatwave Plan for England was introduced in 2004. The total and cause-specific mortality in 2020 was compared to previous heatwave events in England. The findings will help inform summer preparedness and planning in future years as society learns to live with COVID-19. Heatwave excess mortality in 2020 was similar to deaths occurring at home, in hospitals, and in care homes in the 65+ years group, and was comparable to the increases in previous years (2016-2018). The third heatwave in 2020 caused significant mortality in the younger age group (0-64) which has not been observed in previous years. Significant excess mortality was observed for cardiovascular disease, respiratory disease, and Alzheimer’s and Dementia across all three heatwaves in persons aged 65+ years. There was no evidence that the heatwaves affected the proportional increase of people dying at home and not seeking heat-related health care. The most significant spike in daily mortality in August 2020 was associated with a period of high night-time temperatures. The results provide additional evidence that contextual factors are important for managing heatwave risks, particularly the importance of overheating in dwellings. The findings also suggest more action is also needed to address the vulnerability in the community and in health care settings during the acute response phase of a heatwave.
Hydrometeorological hazards comprise a wide range of events, mainly floods, storms, droughts, and temperature extremes. Floods account for the majority of the related disasters in both developed and developing countries. Flooding alters the natural balance of the environment and frequently establish a favorable habitat for pathogens and vectors to thrive. Diseases caused by pathogens that require vehicle transmission from host to host (waterborne) or a host/vector as part of their life cycle (vector-borne) are those most likely to be affected by flooding. Considering the most notable recent destructive floods events of July 2021 that affected several Central Europe countries, we conducted a systematic literature review in order to identify documented sporadic cases and outbreaks of infectious diseases in humans in Europe, where hydrometeorological hazards, mainly floods, were thought to have been involved. The occurrence of water-, rodent-, and vector-borne diseases in several European countries is highlighted, as flooding and the harsh post-flood conditions favor their emergence and transmission. In this context, strategies for prevention and management of infectious disease outbreaks in flood-prone and flood-affected areas are also proposed and comprise pre- and post-flood prevention measures, pre- and post-outbreak prevention measures, as well as mitigation actions when an infectious disease outbreak finally occurs. Emphasis is also placed on the collision of floods, flood-related infectious disease outbreaks, and the evolving COVID-19 pandemic, which may result in unprecedented multi-hazard conditions and requires a multi-hazard approach for the effective disaster management and risk reduction.
Background Previous studies investigating the relationship of influenza with acute myocardial infarction (AMI) have not distinguished between AMI types 1 and 2. Influenza and cold temperature can explain the increased incidence of AMI during winter but, because they are closely related in temperate regions, their relative contribution is unknown. Methods and Results The temporal relationship between incidence rates of AMI with demonstrated culprit plaque (type 1 AMI) from the regional primary angioplasty network and influenza, adjusted for ambient temperature, was studied in Madrid region (Spain) during 5 influenza seasons (from June 2013 to June 2018). A time-series analysis with quasi-Poisson regression models and distributed lag-nonlinear models was used. The incidence rate of type 1 AMI according to influenza vaccination status was also explored. A total of 8240 cases of confirmed type 1 AMI were recorded. The overall risk ratio (RR) of type 1 AMI during epidemic periods, adjusted for year, month, and temperature, was 1.23 (95% CI, 1.03-1.47). An increase of weekly influenza rate of 50 cases per 100 000 inhabitants resulted in an RR for type 1 AMI of 1.16 (95% CI, 1.09-1.23) during the same week, disappearing 1 week after. When adjusted for influenza, a decrease of 1ºC in the minimum temperature resulted in an increase of 2.5% type 1 AMI. Influenza vaccination was associated with a decreased risk of type 1 AMI in subjects aged 60 to 64 years (RR, 0.58; 95% CI, 0.47-0.71) and ≥65 years (RR, 0.53; 95% CI, 0.49-0.57). Conclusions Influenza and cold temperature were both independently associated with an increased risk of type 1 AMI, whereas vaccination was associated with a reduced risk among older patients.
Climate extremes affected cities and their populations during the last decades. Future climate projections indicate climate extremes will increasingly impact urban areas during the 21st century. Humidity related fluctuations and extremes directly underpin convective processes, as well as can influence human health conditions. Regional climate models are a powerful tool to understand regional-to-local climate change processes for cities and their surroundings. Convection-permitting regional climate models, operating on very high resolutions, indicate improved simulation of convective extremes, particularly on sub-daily timescales and in regions with complex terrain such as cities. This research aims to understand how crossing spatial resolutions from similar to 12.5 km to similar to 3 km grid size affect humidity extremes and related variables under future climate change for urban areas and its surroundings. Taking Berlin and its surroundings as the case study area, the research identifies two categories of unprecedented future extreme atmospheric humidity conditions happening under 1.5 degrees C and 2.0 degrees C mean warming based on statistical distributions, respectively near surface specific humidity >0.02 kg/kg and near surface relative humidity <30%. Two example cases for each future extreme condition are dynamically downscaled for a two months period from the 0.44 degrees horizontal resolution following a double-nesting approach: first to the 0.11 degrees (similar to 12.5 km) horizontal resolution with the regional climate model REMO and thereafter to the 0.0275 degrees (similar to 3 km) horizontal resolution with the non-hydrostatic version of REMO. The findings show that crossing spatial resolutions from similar to 12.5 km to similar to 3 km grid size affects humidity extremes and related variables under climate change. Generally, a stronger decrease in moisture (up to 0.0007-0.005 kg/kg SH and 10-20% RH) and an increase in temperature (1-2 degrees C) is found on the 0.0275 degrees compared to the 0.11 degrees horizontal resolution, which is more profound in Berlin than in the surroundings. The convection-permitting scale mitigates the specific humidity moist extreme and intensifies the relative humidity dry extreme in Berlin, posing challenges with respect to health for urban dwellers.
BACKGROUND: The comprehensive epidemiology and impact of climate on viral meningitis (VM) in Kazakhstan are unknown. We aimed to study the incidence, in-hospital mortality and influence of climatic indicators on VM from 2014 to 2019. METHODS: Nationwide electronic healthcare records were used to explore this study. ICD-10 codes of VM, demographics, and hospital outcomes were evaluated using descriptive statistics and survival analysis. RESULTS: During the 2014-2019 period, 10,251 patients with VM were admitted to the hospital. 51.35% of them were children, 57.85% were males, and 85.9% were from the urban population. Enteroviral meningitis was the main cause of VM in children. The incidence rate was 13 and 18 cases per 100,000 population in 2014 and 2019, respectively. Case fatality rate was higher in 2015 (2.3%) and 2017 (2.0%). The regression model showed 1°C increment in the daily average temperature might be associated with a 1.05-fold (95% CI 1.047-1.051) increase in the daily rate of VM cases, 1hPa increment in the average air pressure and 1% increment in the daily average humidity might contribute to a decrease in the daily rate of VM cases with IRRs of 0.997 (95% CI 0.995-0.998) and 0.982 (95% CI 0.981-0.983), respectively. In-hospital mortality was 35% higher in males compared to females. Patients residing in rural locations had a 2-fold higher risk of in-hospital death, compared to city residents. Elderly patients had a 14-fold higher risk of in-hospital mortality, compared to younger patients. CONCLUSION: This is the first study in Kazakhstan investigating the epidemiology and impact of climate on VM using nationwide healthcare data. There was a tendency to decrease the incidence with outbreaks every 5 years, and mortality rates were higher for Russians and other ethnicities compared to Kazakhs, for males compared to females, for elder patients compared to younger patients, and for patients living in rural areas compared to city residents. The climatic parameters and the days of delay indicated a moderate interaction with the VM cases.
Aim The distribution of Yersinia pestis, the pathogen that causes plague in humans, is reliant upon transmission between host species; however, the degree to which host species distributions dictate the distribution of Y. pestis, compared with limitations imposed by the environmental niche of Y. pestis per se, is debated. We test whether the present-day environmental niche of Y. pestis differs between its native range and an invaded range and whether biotic factors (host distributions) can explain observed discrepancies. Location North America and Central Asia. Major taxa studied Yersinia pestis. Methods We use environmental niche models to determine whether the current climatic niche of Y. pestis differs between its native range in Asia and its invaded range in North America. We then test whether the inclusion of information on the distribution of host species improves the ability of models to capture the North American niche. We use geographical null models to guard against spurious correlations arising from spatially autocorrelated occurrence points. Results The current climatic niche of Y. pestis differs between its native and invaded regions. The Asian niche overpredicted the distribution of Y. pestis across North America. Including biotic factors along with the native climatic niche increased niche overlap between the native and invaded models, and models containing only biotic factors performed better than the native climatic niche alone. Geographical null models confirmed that the increased niche overlap through inclusion of biotic factors did not, with a couple of exceptions, arise solely from spatially autocorrelated occurrences. Main conclusions The current climatic niche in Central Asia differs from the current climatic niche in North America. Inclusion of biotic factors improved the fit of models to the Y. pestis distribution data in its invaded region better than climate variables alone. This highlights the importance of host species when investigating zoonotic disease introductions and suggests that climatic variables alone are insufficient to predict disease distribution in novel environments.
Several countries have been affected by natural hazards during the COVID-19 pandemic. The combination of the pandemic and natural hazards has led to serious challenges that include financial losses and psychosocial stress. Additionally, this compound disaster affected evacuation decision making, where to evacuate, volunteer participation in mitigation and recovery, volunteer support acceptance, and interest in other hazard risks. This study investigated the impact of COVID-19 on disaster response and recovery from various types of hazards, with regard to preparedness, evacuation, volunteering, early recovery, awareness and knowledge of different types of hazards, and preparedness capacity development. This study targets hazards such as Cyclone Amphan in India, the Kumamoto flood in Japan, Typhoon Rolly in the Philippines, and the California wildfires in the U.S. This study made several recommendations, such as the fact that mental health support must be taken into consideration during COVID-19 recovery. It is necessary to improve the genral condition of evacuation centers in order to encourage people to act immediately. A pandemic situation necessitates a strong communication strategy and campaign with particular regard to the safety of evacuation centers, the necessity of a lockdown, and the duration required for it to reduce the psychological impact. Both national and local governments are expected to strengthen their disaster risk reduction (DRR) capacity, which calls for the multi-hazard management of disaster risk at all levels and across all sectors.
Under-five years old acute watery diarrhea (U5AWD) accounts for most diarrheal diseases’ burden, but little is known about the adjusted effect of meteorological and socioeconomic determinants. A dataset containing the seasonal numbers of U5AWD cases at the district level of Iran is collected through MOHME. Accordingly, the district-level standardized incidence ratio and Moran’s I values are calculated to detect the significant clusters of U5AWD over sixteen seasons from 2014 to 2018. Additionally, the author tested twelve Bayesian hierarchical models in order to determine which one was the most accurate at forecasting seasonal number of incidents. Iran features a number of U5AWD hotspots, particularly in the southeast. An extended spatiotemporal model with seasonally varying coefficients and space-time interaction outperformed other models, and so became the paper’s proposal in modeling U5AWD. Temperature demonstrated a global positive connection with seasonal U5AWD in districts (IRR: 1.0497; 95% CrI: 1.0254-1.0748), owing to its varying effects during the winter ((IRR: 1.0877; 95% CrI: 1.0408-1.1375) and fall (IRR: 1.0866; 95% CrI: 1.0405-1.1357) seasons. Also, elevation (IRR: 0.9997; 95% CrI: 0.9996-0.9998), piped drinking water (IRR: 0.9948; 95% CrI: 0.9933-0.9964), public sewerage network (IRR: 0.9965; 95% CrI: 0.9938-0.9992), years of schooling (IRR: 0.9649; 95% CrI: 0.944-0.9862), infrastructure-to-household size ratio (IRR: 0.9903; 95% CrI: 0.986-0.9946), wealth index (IRR: 0.9502; 95% CrI: 0.9231-0.9781), and urbanization (IRR: 0.9919; 95% CrI: 0.9893-0.9944) of districts were negatively associated with seasonal U5AWD incidence. Strategically, developing geoinformation alarm systems based on meteorological data might help predict U5AWD high-risk areas. The study also anticipates increased rates of U5AWD in districts with poor sanitation and socioeconomic level. Therefore, governments should take appropriate preventative actions in these sectors.
Understanding the geographical distribution in the association of temperature with childhood diarrhea can assist in formulating effective localized diarrhea prevention practices. This study aimed to identify the geographical variation in terms of temperature thresholds, lag effects, and attributable fraction (AF) in the effects of ambient temperature on Class C Other Infectious Diarrhea (OID) among children <5 years in Jiangsu Province, China. Daily data of OID cases and meteorological variables from 2015 to 2019 were collected. City-specific minimum morbidity temperature (MMT), increasing risk temperature (IRT), maximum risk temperature (MRT), maximum risk lag day (MRD), and lag day duration (LDD) were identified as risk indicators for the temperature-OID relationship using distributed lag non-linear models. The AF of OID incidence due to temperature was evaluated. Multivariable regression was also applied to explore the underlying modifiers of the AF. The geographical distributions of MMT, IRT, and MRT generally decreased with the latitude increment varying between 22.3-34.7 °C, -2.9-18.1 °C, and -6.8-23.2 °C. Considerable variation was shown in the AF ranging from 0.2 to 8.5%, and the AF significantly increased with latitude (95% confidence interval (CI): -3.458, -0.987) and economic status decrement (95% CI: -0.161, -0.019). Our study demonstrated between-city variations in the association of temperature with OID, which should be considered in the localized clinical and public health practices to decrease the incidence of childhood diarrhea.
Nowadays the World is facing a scarcity of safe drinking water and the water sector encounters great challenges. The impact of a growing population and the change of climate on water availability and quality; public health and environmental issues related to emerging pollutants are the major challenges that need to be addressed. In drinking water, there may be a chance of having water-related diseases and health issues due to the occurrence of some pathogens. In the present study, we synthesized nanosilica from rice husk and it was encapsulated with sodium alginate beads and tested its efficiency for removal of bacteria from drinking water. These beads are novel since it is fully bio-origin, biodegradable and cost-effective. The isolated nanosilica were characterized spectroscopically and morphologically (FT-IR, XRD, FESEM, and HRTEM). The synthesized beads were characterized by FT-IR, FESEM, and EDX and antibacterial analysis. Using the Petrifilm method and column disinfection experiment, different filler loadings were optimized and found that higher content (1.25 g) of nanosilica reduced bacterial contamination of drinking water. The alginate-nanosilica beads are cost-effective compared to alginate beads incorporated with other nanomaterials. The antibacterial evaluation verified superior antibacterial efficacy against E.coli. The prepared alginate-nanosilica beads can be used in the wastewater treatment industry, as an effective antibacterial agent.
This study explored the environmental determinants of different months on snail density measured in April at different types of snail habitats (marshlands, inner embankments, and hills) by considering spatial effects. Data were gathered from surveys on snails that were conducted in Hunan Province in April 2016, and information was collected on environmental variables. To investigate the environmental factors influencing snail density in various types of snail habitats, the ordinary least square model, spatial lag model, and spatial error model were all used. The environmental determinants for snail density showed different effects in the three types of snail habitats. In marshlands, snail density measured in April was associated positively with the normalized difference vegetation index (NDVI) and was associated negatively with flooding duration and annual hours of sunshine. Extreme temperatures correlated strongly to snail density measured in April (P < 0.05). In areas inside embankments, snail density measured in April increased with a decreased distance between snail habitat and the nearest river (P < 0.05). In hills, extreme heat, annual hours of sunshine, NDVI in September, and annual average land surface temperature (LST) were associated negatively with snail density measured in April, whereas index of moisture (IM) was associated positively with snail density measured in April (P < 0.05). The effects of LST and hours of sunshine on snail density measured in April varied with months of the year in the three different types of snail habitats (P < 0.05). Our study might provide a theoretical foundation for preventing snail transmission and subsequent spread of schistosomiasis.
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BACKGROUND: Dengue is an endemic vector-borne disease influenced by environmental factors such as landscape and climate. Previous studies separately assessed the effects of landscape and climate factors on mosquito occurrence and dengue incidence. However, both factors concurrently coexist in time and space and can interact, affecting mosquito development and dengue disease transmission. For example, eggs laid in a suitable environment can hatch after being submerged in rain water. It has been difficult for conventional statistical modeling approaches to demonstrate these combined influences due to mathematical constraints. OBJECTIVES: To investigate the combined influences of landscape and climate factors on mosquito occurrence and dengue incidence. METHODS: Entomological, epidemiological, and landscape data from the rainy season (July-December) were obtained from respective government agencies in Metropolitan Manila, Philippines, from 2012 to 2014. Temperature, precipitation and vegetation data were obtained through remote sensing. A random forest algorithm was used to select the landscape and climate variables. Afterward, using the identified key variables, a model-based (MOB) recursive partitioning was implemented to test the combined influences of landscape and climate factors on ovitrap index (vector mosquito occurrence) and dengue incidence. RESULTS: The MOB recursive partitioning for ovitrap index indicated a high sensitivity of vector mosquito occurrence on environmental conditions generated by a combination of high residential density areas with low precipitation. Moreover, the MOB recursive partitioning indicated high sensitivity of dengue incidence to the effects of precipitation in areas with high proportions of residential density and commercial areas. CONCLUSIONS: Dengue dynamics are not solely influenced by individual effects of either climate or landscape, but rather by their synergistic or combined effects. The presented findings have the potential to target vector surveillance in areas identified as suitable for mosquito occurrence under specific climatic conditions and may be relevant as part of urban planning strategies to control dengue.
Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.
Melioidosis is endemic in the remote Katherine region of northern Australia. In a population with high rates of chronic disease, social inequities, and extreme remoteness, the impact of melioidosis is exacerbated by severe weather events and disproportionately affects First Nations Australians. All culture-confirmed melioidosis cases in the Katherine region of the Australian Top End between 1989-2021 were included in the study, and the clinical features and epidemiology were described. The diversity of Burkholderia pseudomallei strains in the region was investigated using genomic sequencing. From 1989-2021 there were 128 patients with melioidosis in the Katherine region. 96/128 (75%) patients were First Nations Australians, 72/128 (56%) were from a very remote region, 68/128 (53%) had diabetes, 57/128 (44%) had a history of hazardous alcohol consumption, and 11/128 (9%) died from melioidosis. There were 9 melioidosis cases attributable to the flooding of the Katherine River in January 1998; 7/9 flood-associated cases had cutaneous melioidosis, five of whom recalled an inoculating event injury sustained wading through flood waters or cleaning up after the flood. The 126 first-episode clinical B. pseudomallei isolates that underwent genomic sequencing belonged to 107 different sequence types and were highly diverse, reflecting the vast geographic area of the study region. In conclusion, melioidosis in the Katherine region disproportionately affects First Nations Australians with risk factors and is exacerbated by severe weather events. Diabetes management, public health intervention for hazardous alcohol consumption, provision of housing to address homelessness, and patient education on melioidosis prevention in First Nations languages should be prioritised.
BACKGROUND: Although studies have provided the estimates of floods-diarrhoea associations, little is known about the lag effect, effect modification, and attributable risk. Based on Sichuan, China, an uneven socio-economic development province with plateau, basin, and mountain terrains spanning different climatic zones, we aimed to systematically examine the impacts of floods on diarrheal morbidity. METHODS: We retrieved information on daily diarrheal cases, floods, meteorological variables, and annual socio-economic characteristics for 21 cities in Sichuan from January 1, 2017 to December 31, 2019. We fitted time-series Poisson models to estimate the city-specific floods-diarrhoea relation over the lags of 0-14 days, and then pooled them using meta-analysis for cumulative and lag effects. We further employed meta-regression to explore potential effect modifiers and identify effect modification. We calculated the attributable diarrheal cases and fraction of attributable morbidity within the framework of the distributed lag model. RESULTS: Floods had a significant cumulative association with diarrhoea at the provincial level, but varied by regions and cities. The effects of the floods appeared on the second day after the floods and lasted for 5 days. Floods-diarrhoea relations were modified by three effect modifiers, with stronger flood effects on diarrhoea found in areas with higher air pressure, lower diurnal temperature range, or warmer temperature. Floods were responsible for advancing a fraction of diarrhoea, corresponding to 0.25% within the study period and 0.48% within the flood season. CONCLUSIONS: The impacts imposed by floods were mainly distributed within the first week. The floods-diarrhoea relations varied by geographic and climatic conditions. The diarrheal burden attributable to floods is currently low in Sichuan, but this figure could increase with the exposure more intensive and the effect modifiers more detrimental in the future. Our findings are expected to provide evidence for the formulation of temporal- and spatial-specific strategies to reduce potential risks of flood-related diarrhoea.
BACKGROUND: Schistosomiasis is a highly recurrent parasitic disease that affects a wide range of areas and a large number of people worldwide. In China, schistosomiasis has seriously affected the life and safety of the people and restricted the economic development. Schistosomiasis is mainly distributed along the Yangtze River and in southern China. Anhui Province is located in the Yangtze River Basin of China, with dense water system, frequent floods and widespread distribution of Oncomelania hupensis that is the only intermediate host of schistosomiasis, a large number of cattle, sheep and other livestock, which makes it difficult to control schistosomiasis. It is of great significance to monitor and analyze spatiotemporal risk of schistosomiasis in Anhui Province, China. We compared and analyzed the optimal spatiotemporal interpolation model based on the data of schistosomiasis in Anhui Province, China and the spatiotemporal pattern of schistosomiasis risk was analyzed. METHODS: In this study, the root-mean-square-error (RMSE) and absolute residual (AR) indicators were used to compare the accuracy of Bayesian maximum entropy (BME), spatiotemporal Kriging (STKriging) and geographical and temporal weighted regression (GTWR) models for predicting the spatiotemporal risk of schistosomiasis in Anhui Province, China. RESULTS: The results showed that (1) daytime land surface temperature, mean minimum temperature, normalized difference vegetation index, soil moisture, soil bulk density and urbanization were significant factors affecting the risk of schistosomiasis; (2) the spatiotemporal distribution trends of schistosomiasis predicted by the three methods were basically consistent with the actual trends, but the prediction accuracy of BME was higher than that of STKriging and GTWR, indicating that BME predicted the prevalence of schistosomiasis more accurately; and (3) schistosomiasis in Anhui Province had a spatial autocorrelation within 20 km and a temporal correlation within 10 years when applying the optimal model BME. CONCLUSIONS: This study suggests that BME exhibited the highest interpolation accuracy among the three spatiotemporal interpolation methods, which could enhance the risk prediction model of infectious diseases thereby providing scientific support for government decision making.
In Jambi province, Sumatra, Indonesia, flooding is a recurrent rainy season phenomenon. Historically considered manageable, recent political economic developments have changed this situation. Today, flooding is an environmental hazard and a threat to people’s livelihoods and health. Based on qualitative research and literature that has developed relational approaches to risk and water, we investigate past and present hydrosocial relations in Jambi province and reconstruct the changing meaning of flooding. We suggest that flooding as a hazard in Jambi was produced through the introduction of the plantation industry to the area and its prioritization of dry land for agm-industrial development. This development altered the materiality of water flows, reconfigured power relations and changed the socio-cultural dimensions of flooding. Together, these changes have led to a separation of flooding from its original social and geographic realm, producing new risks and vulnerabilities. This paper provides insights into the material and symbolic dimensions that influence how environmental processes come to be imagined, controlled and contested. It shows how tracing the socionatural production of hazards may help explain the increasingly systemic nature of risks and provide insights into the wider social meaning of environmental risks.
The epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to prevent Leptospira infections.
Bacterial pathogens in surface waters threaten human health. The health risk is especially high in developing countries where sanitation systems are often lacking or deficient. Considering twelve flash-flood events sampled from 2011 to 2015 at the outlet of a 60-ha tropical montane headwater catchment in Northern Lao PDR, and using Escherichia coli as a fecal indicator bacteria, our objective was to quantify the contributions of both surface runoff and sub-surface flow to the in-stream concentration of E. coli during flood events, by (1) investigating E. coli dynamics during flood events and among flood events and (2) designing and comparing simple statistical and mixing models to predict E. coli concentration in stream flow during flood events. We found that in-stream E. coli concentration is high regardless of the contributions of both surface runoff and sub-surface flow to the flood event. However, we measured the highest concentration of E. coli during the flood events that are predominantly driven by surface runoff. This indicates that surface runoff, and causatively soil surface erosion, are the primary drivers of in-stream E. coli contamination. This was further confirmed by the step-wise regression applied to instantaneous E. coli concentration measured in individual water samples collected during the flood events, and by the three models applied to each flood event (linear model, partial least square model, and mixing model). The three models showed that the percentage of surface runoff in stream flow was the best predictor of the flood event mean E. coli concentration. The mixing model yielded a Nash-Sutcliffe efficiency of 0.65 and showed that on average, 89% of the in-stream concentration of E. coli resulted from surface runoff, while the overall contribution of surface runoff to the stream flow was 41%. We also showed that stream flow turbidity and E. coli concentration were positively correlated, but that turbidity was not a strong predictor of E. coli concentration during flood events. These findings will help building adequate catchment-scale models to predict E. coli fate and transport, and mapping the related risk of fecal contamination in a global changing context.
In this study, the adsorption-elution method was modified to concentrate viral particles in water samples and investigate the contamination of groundwater with norovirus genogroup II (NoV GII), rotavirus A (RVA), and Pepper mild mottle virus (PMMoV). The mean recovery rate of a murine norovirus strain, which was inoculated into groundwater samples collected from a deep well, was the highest (39%) when the viral RNA was directly extracted from the membrane instead of eluting the adsorbed viral particles. This adsorption-direct extraction method was applied to groundwater samples (20 liters) collected from deep wells used for the public drinking water supply (n = 22) and private wells (n = 9). RVA (85 copies/liter) and NoV GII (35 copies/liter) were detected in water samples from a deep well and a private well, respectively. PMMoV was detected in 95% and 89% of water samples from deep wells and private wells, respectively, at concentrations of up to 990 copies/liter. The modified method was also used to extract bacterial DNA from the membrane (recovery rate of inoculated Escherichia coli K-12 was 22%). The Bacteroidales genetic markers specific to ruminants (BacR) and pigs (Pig2Bac) were detected in samples from a deep well and a private well, respectively. The modified virus concentration method has important implications for the management of microbiological safety in the groundwater supply. IMPORTANCE We investigated the presence of enteric viruses and bacterial genetic markers to determine fecal contamination in groundwater samples from deep wells used for the public drinking water supply and private wells in Japan. Groundwater is often subjected to chlorination; malfunctions in chlorine treatment result in waterborne disease outbreaks. The modified method successfully concentrated both viruses and bacteria in 20-liter groundwater samples. Norovirus genogroup II (GII), rotavirus A, Pepper mild mottle virus, and Bacteroidales genetic markers specific to ruminants and pigs were detected. Frequent flooding caused by increased incidences of extreme rainfall events promotes the infiltration of surface runoff containing livestock wastes and untreated wastewater into wells, possibly increasing groundwater contamination risk. The practical and efficient method developed in this study will enable waterworks and the environmental health departments of municipal/prefectural governments to monitor water quality. Additionally, the modified method will contribute to improving the microbiological safety of groundwater.
Schistosomiasis is a water-borne parasitic disease distributed worldwide, while schistosomiasis japonica localizes in the People’s Republic of China, the Philippines, and a few regions of Indonesia. Although significant achievements have been obtained in these endemic countries, great challenges still exist to reach the elimination of schistosomiasis japonica, as the occurrence of flooding can lead to several adverse consequences on the prevalence of schistosomiasis. This review summarizes the influence of flooding on the transmission of schistosomiasis japonica and interventions responding to the adverse impacts from the One Health perspective in human beings, animals, and the environment. For human and animals, behavioral changes and the damage of water conservancy and sanitary facilities will increase the intensity of water contact. For the environment, the density of Oncomelania snails significantly increases from the third year after flooding, and the snail habitats can be enlarged due to active and passive diffusion. With more water contact of human and other reservoir hosts, and larger snail habitats with higher density of living snails, the transmission risk of schistosomiasis increases under the influence of flooding. With the agenda set for global schistosomiasis elimination, interventions from the One Health perspective are put forward to respond to the impacts of increased flooding. For human beings, conducting health education to increase the consciousness of self-protection, preventive chemotherapy for high-risk populations, supply of safe water, early case finding, timely reporting, and treating cases will protect people from infection and prevent the outbreak of schistosomiasis. For animals, culling susceptible domestic animals, herding livestock in snail-free areas, treating livestock with infection or at high risk of infection, harmless treatment of animal feces to avoid water contamination, and monitoring the infection status of wild animals in flooding areas are important to cut off the transmission chain from the resources. For the environment, early warning of flooding, setting up warning signs and killing cercaria in risk areas during and post flooding, reconstructing damaged water conservancy facilities, developing hygiene and sanitary facilities, conducting snail surveys, using molluscicide, and predicting areas with high risk of schistosomiasis transmission after flooding all contribute to reducing the transmission risk of schistosomiasis. These strategies need the cooperation of the ministry of health, meteorological administration, water resources, agriculture, and forestry to achieve the goal of minimizing the impact of flooding on the transmission of schistosomiasis. In conclusion, flooding is one of the important factors affecting the transmission of schistosomiasis japonica. Multi-sectoral cooperation is needed to effectively prevent and control the adverse impacts of flooding on human beings, animals, and the environment.
BACKGROUND: Flooding is considered to be one of the most important factors contributing to the rebound of Oncomelania hupensis, a small tropical freshwater snail and the only intermediate host of Schistosoma japonicum, in endemic foci. The aim of this study was to assess the risk of intestinal schistosomiasis transmission impacted by flooding in the region around Poyang Lake using multi-source remote sensing images. METHODS: Normalized Difference Vegetation Index (NDVI) data collected by the Landsat 8 satellite were used as an ecological and geographical suitability indicator of O. hupensis habitats in the Poyang Lake region. The expansion of the water body due to flooding was estimated using dual-polarized threshold calculations based on dual-polarized synthetic aperture radar (SAR). The image data were captured from the Sentinel-1B satellite in May 2020 before the flood and in July 2020 during the flood. A spatial database of the distribution of snail habitats was created using the 2016 snail survey in Jiangxi Province. The potential spread of O. hupensis snails after the flood was predicted by an overlay analysis of the NDVI maps in the flood-affected areas around Poyang Lake. The risk of schistosomiasis transmission was classified based on O. hupensis snail density data and the related NDVI. RESULTS: The surface area of Poyang Lake was approximately 2207 km(2) in May 2020 before the flood and 4403 km(2) in July 2020 during the period of peak flooding; this was estimated to be a 99.5% expansion of the water body due to flooding. After the flood, potential snail habitats were predicted to be concentrated in areas neighboring existing habitats in the marshlands of Poyang Lake. The areas with high risk of schistosomiasis transmission were predicted to be mainly distributed in Yongxiu, Xinjian, Yugan and Poyang (District) along the shores of Poyang Lake. By comparing the predictive results and actual snail distribution, we estimated the predictive accuracy of the model to be 87%, which meant the 87% of actual snail distribution was correctly identified as snail habitats in the model predictions. CONCLUSIONS: Data on water body expansion due to flooding and environmental factors pertaining to snail breeding may be rapidly extracted from Landsat 8 and Sentinel-1B remote sensing images. Applying multi-source remote sensing data for the timely and effective assessment of potential schistosomiasis transmission risk caused by snail spread during flooding is feasible and will be of great significance for more precision control of schistosomiasis.
INTRODUCTION: Skin and soft tissue infections have the potential to affect every patient admitted to a surgical service. Changes to the microbiota colonizing wounds during natural disasters, such as the Townsville floods of 2019, could impact empiric antibiotic choice and need for return to theatre. METHODS: This retrospective observational cohort study reviews culture data and demographics for patients undergoing surgical debridement of infected wounds over a six-month period starting in November 2018 to May 2019 at the Townsville Hospital. RESULTS: Of the 408 patients requiring operative intervention, only 61 patients met the inclusion criteria. The groups were comparative in terms of age and gender, but a greater proportion of patients (40.5% versus 29.1%, P = 0.368) in the post-flood group were diabetic. Common skin commensals, such as Staphylococcus aureus, were the most common pathogen in both groups, however the post-flood group had a higher proportion of atypical organisms (14 versus 8 patients), and an increased need for repeated debridement for infection control (24 versus 14 patients). CONCLUSION: Wound swabs and tissue culture are imperative during surgical debridement and may guide the use of more broad-spectrum coverage following a significant flooding event.
Surveillance programs are essential for the prevention and control of mosquito-borne arboviruses that cause serious human and animal diseases. Viral metatranscriptomic sequencing can enhance surveillance by enabling untargeted, high-throughput arbovirus detection. We used metatranscriptomic sequencing to screen field-collected mosquitoes for arboviruses to better understand how metatranscriptomics can be utilised in routine surveillance. Following a significant flood event in 2016, more than 56,000 mosquitoes were collected over seven weeks from field traps set up in Victoria, Australia. The traps were split into samples of 1000 mosquitoes or less and sequenced on the Illumina HiSeq. Five arboviruses relevant to public health (Ross River virus, Sindbis virus, Trubanaman virus, Umatilla virus, and Wongorr virus) were detected a total of 33 times in the metatranscriptomic data, with 94% confirmed using reverse transcription quantitative PCR (RT-qPCR). Analysis of metatranscriptomic cytochrome oxidase I (COI) sequences enabled the detection of 12 mosquito and two biting midge species. Screening of the same traps by an established public health arbovirus surveillance program corroborated the metatranscriptomic arbovirus and mosquito species detections. Assembly of genome sequences from the metatranscriptomic data also led to the detection of 51 insect-specific viruses, both known and previously undescribed, and allowed phylogenetic comparison to past strains. We have demonstrated how metatranscriptomics can enhance surveillance by enabling untargeted arbovirus detection, providing genomic epidemiological data, and simultaneously identifying vector species from large, unsorted mosquito traps.
Freshwater reservoirs are a crucial source of urban drinking water worldwide; thus, long-term evaluations of critical water quality determinants are essential. We conducted this study in a large drinking water reservoir for 11 years (2010-2020). The variabilities of ambient nutrients and total suspended solids (TSS) throughout the seasonal monsoon-mediated flow regime influenced algal chlorophyll (Chl-a) levels. The study determined the role of the monsoon-mediated flow regime on reservoir water chemistry. The reservoir conditions were mesotrophic to eutrophic based on nitrogen (N) and phosphorus (P) concentrations. An occasional total coliform bacteria (TCB) count of 16,000 MPN per 100 mL was recorded in the reservoir, presenting a significant risk of waterborne diseases among children. A Mann-Kendall test identified a consistent increase in water temperature, conductivity, and chemical oxygen demand (COD) over the study period, limiting a sustainable water supply. The drought and flood regime mediated by the monsoon resulted in large heterogeneities in Chl-a, TCB, TSS, and nutrients (N, P), indicating its role as a key regulator of the ecological functioning of the reservoir. The ambient N:P ratio is a reliable predictor of sestonic Chl-a productivity, and the reservoir was P-limited. Total phosphorus (TP) had a strong negative correlation (R(2) = 0.59, p < 0.05) with the outflow from the dam, while both the TSS (R(2) = 0.50) and Chl-a (R(2) = 0.32, p < 0.05) had a strong positive correlation with the outflow. A seasonal trophic state index revealed oligo-mesotrophic conditions, indicating a limited risk of eutrophication and a positive outcome for long-term management. In conclusion, the Asian monsoon largely controlled the flood and drought conditions and manipulated the flow regime. Exceedingly intensive crop farming in the basin may lead to oligotrophic nutrient enrichment. Although the reservoir water quality was good, we strongly recommend stringent action to alleviate sewage, nutrient, and pollutant inflows to the reservoir.
BACKGROUND: Despite concerns regarding increasingly frequent and intense heat waves due to global warming, there is still a lack of information on the effects of extremely high temperatures on the adult abundance of mosquito species that are known to transmit vector-borne diseases. This study aimed to evaluate the effects of extremely high temperatures on the abundance of mosquitoes by analyzing time series data for temperature and mosquito abundance in Incheon Metropolitan City (IMC), Republic of Korea, for the period from 2015 to 2020. METHODS: A generalized linear model with Poisson distribution and overdispersion was used to model the nonlinear association between temperature and mosquito count for the whole study area and for its constituent urban and rural regions. The association parameters were pooled using multivariate meta-regression. The temperature-mosquito abundance curve was estimated from the pooled estimates, and the ambient temperature at which mosquito populations reached maximum abundance (TMA) was estimated using a Monte Carlo simulation method. To quantify the effect of extremely high temperatures on mosquito abundance, we estimated the mosquito abundance ratio (AR) at the 99th temperature percentile (AR(99th)) against the TMA. RESULTS: Culex pipiens was the most common mosquito species (51.7%) in the urban region of the IMC, while mosquitoes of the genus Aedes (Ochlerotatus) were the most common in the rural region (47.8%). Mosquito abundance reached a maximum at 23.5 °C for Cx. pipiens and 26.4 °C for Aedes vexans. Exposure to extremely high temperatures reduced the abundance of Cx. pipiens mosquitoes {AR(99th) 0.34 [95% confidence interval (CI) 0.21-0.54]} to a greater extent than that of Anopheles spp. [AR(99th) 0.64 (95% CI 0.40-1.03)]. When stratified by region, Ae. vexans and Ochlerotatus koreicus mosquitoes showed higher TMA and a smaller reduction in abundance at extreme heat in urban Incheon than in Ganghwa, suggesting that urban mosquitoes can thrive at extremely high temperatures as they adapt to urban thermal environments. CONCLUSIONS: We confirmed that the temperature-related abundance of the adult mosquitoes was species and location specific. Tailoring measures for mosquito prevention and control according to mosquito species and anticipated extreme temperature conditions would help to improve the effectiveness of mosquito-borne disease control programs.
BACKGROUND: Rigorous assessment of the effect of malaria control strategies on local malaria dynamics is a complex but vital step in informing future strategies to eliminate malaria. However, the interactions between climate forcing, mass drug administration, mosquito control and their effects on the incidence of malaria remain unclear. METHODS: Here, we analyze the effects of interventions on the transmission dynamics of malaria (Plasmodium vivax and Plasmodium falciparum) on Hainan Island, China, controlling for environmental factors. Mathematical models were fitted to epidemiological data, including confirmed cases and population-wide blood examinations, collected between 1995 and 2010, a period when malaria control interventions were rolled out with positive outcomes. RESULTS: Prior to the massive scale-up of interventions, malaria incidence shows both interannual variability and seasonality, as well as a strong correlation with climatic patterns linked to the El Nino Southern Oscillation. Based on our mechanistic model, we find that the reduction in malaria is likely due to the large scale rollout of insecticide-treated bed nets, which reduce the infections of P. vivax and P. falciparum malaria by 93.4% and 35.5%, respectively. Mass drug administration has a greater contribution in the control of P. falciparum (54.9%) than P. vivax (5.3%). In a comparison of interventions, indoor residual spraying makes a relatively minor contribution to malaria control (1.3%-9.6%). CONCLUSIONS: Although malaria transmission on Hainan Island has been exacerbated by El Nino Southern Oscillation, control methods have eliminated both P. falciparum and P. vivax malaria from this part of China.
BACKGROUND: In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases. METHODS: In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand. RESULTS: From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence. CONCLUSIONS: A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.
Environmental temperature is a key driver of malaria transmission dynamics. Using detailed temperature records from four sites: low elevation (1800), mid elevation (2200 m), and high elevation (2600-3200 m) in the western Himalaya, we model how temperature regulates parasite development rate (the inverse of the extrinsic incubation period, EIP) in the wild. Using a Briére parametrization of the EIP, combined with Bayesian parameter inference, we study the thermal limits of transmission for avian (Plasmodium relictum) and human Plasmodium parasites (P. vivax and P. falciparum) as well as for two malaria-like avian parasites, Haemoproteus and Leucocytozoon. We demonstrate that temperature conditions can substantially alter the incubation period of parasites at high elevation sites (2600-3200 m) leading to restricted parasite development or long transmission windows. The thermal limits (optimal temperature) for Plasmodium parasites were 15.62-34.92°C (30.04°C) for P. falciparum, 13.51-34.08°C (29.02°C) for P. vivax, 12.56-34.46°C (29.16°C) for P. relictum and for two malaria-like parasites, 12.01-29.48°C (25.16°C) for Haemoproteus spp. and 11.92-29.95°C (25.51°C) for Leucocytozoon spp. We then compare estimates of EIP based on measures of mean temperature versus hourly temperatures to show that EIP days vary in cold versus warm environments. We found that human Plasmodium parasites experience a limited transmission window at 2600 m. In contrast, for avian Plasmodium transmission was not possible between September and March at 2600 m. In addition, temperature conditions suitable for both Haemoproteus and Leucocytozoon transmission were obtained from June to August and in April, at 2600 m. Finally, we use temperature projections from a suite of climate models to predict that by 2040, high elevation sites (~2600 m) will have a temperature range conducive for malaria transmission, albeit with a limited transmission window. Our study highlights the importance of accounting for fine-scale thermal effects in the expansion of the range of the malaria parasite with global climate change.
Malaria elimination is a global priority, which India has also adopted as a target. Despite the malaria control efforts like long-lasting insecticidal nets distribution, rounds of indoor residual spray, the introduction of bi-valent rapid diagnostic tests and artemisinin combination therapy, malaria remained consistent in Dolonibasti sub-center of Orang block primary health center (BPHC) under the district Udalguri, Assam state followed by abrupt rise in cases in 2018. Therefore, we aimed to investigate the factors driving the malaria transmission in the outbreak area of Dolonibasti sub-center. Malaria epidemiological data (2008-2018) of Udalguri district and Orang BPHC was collected. The annual (2011-2018) and monthly (2013-2018) malaria and meteorological data of Dolonibasti sub-center was collected. An entomological survey, Knowledge, Attitude and Practices study among malaria cases (n = 120) from Dolonibasti was conducted. In 2018, 26.1 % (2136/ 8188) of the population of Dolonibasti were found to be malaria positive, of which 55% were adults (n = 1176). Majority of cases were from tea tribe populations (90%), either asymptomatic or with fever only, 67.5 % (81/120) had experienced malaria infection during past years. The outbreak was characterized by a strong increase in cases in June 2018, high proportion of slide falciparum rate of 26.1% (other years average, 15.8%) and high proportion of P. falciparum of 81.2 % (other years average, 84.3%). Anopheles minimus s.l. was the major vector with 28.6% positivity and high larval density in paddy fields/ drainage area. Annual relative humidity was associated with rise in malaria cases, annual parasite incidence (r(s) = 0.69, 90%CI; p = 0.06) and slide positivity rate (r(s) = 0.83, 95%CI; p = 0.01). Older people were less educated (r(s) = -0.66; p < 0.001), had lesser knowledge about malaria cause (r(s) = -0.42; χ(2)=21.80; p < 0.001) and prevention (r(s) = -0.18; p = 0.04). Malaria control practices were followed by those having knowledge about cause of malaria (r(s) = 0.36; χ(2) = 13.50; p < 0.001) and prevention (r(s) = 0.40; χ(2) = 17.71; p < 0.001). Altogether, 84.6% (44/52) of the respondents did not use protective measures. We described a sudden increase in malaria incidence in a rural, predominantly tea tribe population group with high illiteracy rate and ignorance on protective measures against malaria. More efforts that are concerted needed to educate the community about malaria control practices.
Already climate-related hazards are impacting sanitation systems in Indonesia and elsewhere, and climate models indicate these hazards are likely to increase in frequency and intensity. Without due attention, to maintain existing progress on Sustainable Development Goal 6’s target 6.2 and to increase it to meet ambitions for 2030 will be difficult. City governments need new forms of evidence to respond, as well as approaches to enable them to consider sufficient breadth of strategies to adapt effectively. This paper describes a co-production research process which engaged local governments in four cities in Indonesia experiencing different climate hazards. Local government engagement took place across three stages of (i) inception and design, (ii) participation as key informants and (iii) joint analysis and engagement on the findings. We adapted and simplified a risk prioritisation process based on current literature and employed a novel framework of a ‘climate resilient sanitation system’ to prompt articulation of current and proposed climate change adaptation response actions. In contrast to many current framings of climate resilience in sanitation that focus narrowly on technical responses, the results paint a rich picture of efforts needed by city governments across all domains, including planning, institutions, financing, infrastructure and management options, user awareness, water cycle management and monitoring and evaluation. Local government commitment and improved comprehension on the implications of climate change for sanitation service delivery were key outcomes arising from the co-production process. With strengthened policy and capacity building initiatives from national level, this foundation can be supported, and Indonesian city governments will be equipped to move forward with adaptation actions that protect on-going access to sanitation services, public health and the environment.
BACKGROUND: Intensification of land use threatens to increase the emergence and prevalence of zoonotic diseases, with an adverse impact on human wellbeing. Understanding how the interaction between agriculture, natural systems, climate and socioeconomic drivers influence zoonotic disease distribution is crucial to inform policy planning and management to limit the emergence of new infections. OBJECTIVES: Here we assess the relative contribution of environmental, climatic and socioeconomic factors influencing reported cryptosporidiosis across Australia from 2001 to 2018. METHODS: We apply a Bayesian spatio-temporal analysis using Integrated Nested Laplace Approximation (INLA). RESULTS: We find that area-level risk of reported disease are associated with the proportions of the population under 5 and over 65 years of age, socioeconomic disadvantage, annual rainfall anomaly, and the proportion of natural habitat remaining. This combination of multiple factors influencing cryptosporidiosis highlights the benefits of a sophisticated spatio-temporal statistical approach. Two key findings from our model include: an estimated 4.6% increase in the risk of reported cryptosporidiosis associated with 22.8% higher percentage of postal area covered with original habitat; and an estimated 1.8% increase in disease risk associated with a 77.99 mm increase in annual rainfall anomaly at the postal area level. DISCUSSION: These results provide novel insights regarding the predictive effects of extreme rainfall and the proportion of remaining natural habitat, which add unique explanatory power to the model alongside the variance associated with other predictive variables and spatiotemporal variation in reported disease. This demonstrates the importance of including perspectives from land and water management experts for policy making and public health responses to manage environmentally mediated diseases, including cryptosporidiosis.
BACKGROUND: Many studies have reported the interactive effects between relative humidity and temperature on infectious diseases. However, evidence regarding the combined effects of relative humidity and temperature on bacillary dysentery (BD) is limited, especially for large-scale studies. To address this research need, humidex was utilized as a comprehensive index of relative humidity and temperature. We aimed to estimate the effect of humidex on BD across mainland China, evaluate its heterogeneity, and identify potential effect modifiers. METHODS: Daily meteorological and BD surveillance data from 2014 to 2016 were obtained for 316 prefecture-level cities in mainland China. Humidex was calculated on the basis of relative humidity and temperature. A multicity, two-stage time series analysis was then performed. In the first stage, a common distributed lag non-linear model (DLNM) was established to obtain city-specific estimates. In the second stage, a multivariate meta-analysis was conducted to pool these estimates, assess the significance of heterogeneity, and explore potential effect modifiers. RESULTS: The pooled cumulative estimates showed that humidex could promote the transmission of BD. The exposure-response relationship was nearly linear, with a maximum cumulative relative risk (RR) of 1.45 [95% confidence interval (CI): 1.29-1.63] at a humidex value of 40.94. High humidex had an acute adverse effect on BD. The humidex-BD relationship could be modified by latitude, urbanization rate, the natural growth rate of population, and the number of primary school students per thousand persons. CONCLUSIONS: High humidex could increase the risk of BD incidence. Thus, it is suitable to incorporate humidex as a predictor into the early warning system of BD and to inform the general public in advance to be cautious when humidex is high. This is especially true for regions with higher latitude, higher urbanization rates, lower natural growth rates of population, and lower numbers of primary school students per thousand persons.
The construction of spatio-temporal models can be either descriptive or dynamic. In this study we aim to evaluate the differences in model fitting between a descriptive model and a dynamic model of the transmission for intestinal schistosomiasis caused by Schistosoma japonicum in Guichi, Anhui Province, China. The parasitological data at the village level from 1991 to 2014 were obtained by cross-sectional surveys. We used the fixed rank kriging (FRK) model, a descriptive model, and the integro-differential equation (IDE) model, a dynamic model, to explore the space-time changes of schistosomiasis japonica. In both models, the average daily precipitation and the normalized difference vegetation index are significantly positively associated with schistosomiasis japonica prevalence, while the distance to water bodies, the hours of daylight and the land surface temperature at daytime were significantly negatively associated. The overall root mean square prediction error of the IDE and FRK models was 0.0035 and 0.0054, respectively, and the correlation reflected by Pearson’s correlation coefficient between the predicted and observed values for the IDE model (0.71; p<0.01) was larger than that for the FRK model (0.53; p=0.02). The IDE model fits better in capturing the geographic variation of schistosomiasis japonica. Dynamic spatio-temporal models have the advantage of quantifying the process of disease transmission and may provide more accurate predictions.
Meteorological factors and the increase in extreme weather events are closely related to the incidence rate of infectious diarrhea. However, few studies have explored whether the impact of the same meteorological factors on the incidence rate of infectious diarrhea in different climate regions has changed and quantified these changes. In this study, the time series fixed-effect Poisson regression model guided by climate was used to quantify the relationships between the incidence rate of various types of infectious diarrhea and meteorological factors in different climate regions of China from 2004 to 2018, with a lag of 0-2 months. In addition, six social factors, including per capita Gross Domestic Product (GDP), population density, number of doctors per 1000 people, proportion of urbanized population, proportion of children aged 0-14 years old, and proportion of elderly over 65 years old, were included in the model for confounding control. Additionally, the intercept of each province in each model was analyzed by a meta-analysis. Four climate regions were considered in this study: tropical monsoon areas, subtropical monsoon areas, temperate areas and alpine plateau areas. The results indicate that the influence of meteorological factors and extreme weather in different climate regions on diverse infectious diarrhea types is distinct. In general, temperature was positively correlated with all infectious diarrhea cases (0.2 ≤ r ≤ 0.6, p < 0.05). After extreme rainfall, the incidence rate of dysentery in alpine plateau area in one month would be reduced by 18.7% (95% confidence interval (CI): -27.8--9.6%). Two months after the period of extreme sunshine duration happened, the incidence of dysentery in the alpine plateau area would increase by 21.9% (95% CI: 15.4-28.4%) in that month, and the incidence rate of typhoid and paratyphoid in the temperate region would increase by 17.2% (95% CI: 15.5-18.9%) in that month. The meta-analysis showed that there is no consistency between different provinces in the same climate region. Our study indicated that meteorological factors and extreme weather in different climate areas had different effects on various types of infectious diarrhea, particularly extreme rainfall and extreme sunshine duration, which will help the government develop disease-specific and location-specific interventions, especially after the occurrence of extreme weather.
Abnormal climate changes have resulted in over-precipitation in many regions. The occurrence and contamination levels of mycotoxins in crops and cereals have been elevated largely. From 2017 to 2019, we did investigation targeting 15 mycotoxins shown in the wheat samples collected from Shandong, a region suffering over-precipitation in China. We found that deoxynivalenol (DON) was the dominant mycotoxin contaminating wheats, with detection rates 304/340 in 2017 (89.41%), 303/330 in 2018 (91.82%), and 303/340 in 2019 (89.12%). The ranges of DON levels were <4 to 580 mu g/kg in 2017, <4 to 3070 mu g/kg in 2018, and <4 to 1540 mu g/kg in 2019. The exposure levels were highly correlated with local precipitation. Male exposure levels were generally higher than female's, with significant difference found in 2017 (1.89-fold, p = 0.023). Rural exposure levels were higher than that of cities but not statistically significant (1.41-fold, p = 0.13). Estimated daily intake (EDI) and margin of exposure (MoE) approaches revealed that 8 prefecture cities have probabilistically extra adverse health effects (vomiting or diarrhea) cases > 100 patients in 100,000 residents attributable to DON exposure. As a prominent wheat-growing area, Dezhou city reached similar to 300/100,000 extra cases while being considered as a major regional contributor to DON contamination. Our study suggests that more effort should be given to the prevention and control of DON contamination in major wheat-growing areas, particularly during heavy precipitation year. The mechanistic association between DON and chronic intestinal disorder/diseases should be further investigated.
BACKGROUND: Although previous studies have shown that meteorological factors such as temperature are related to the incidence of bacillary dysentery (BD), researches about the non-linear and interaction effect among meteorological variables remain limited. The objective of this study was to analyze the effects of temperature and other meteorological variables on BD in Beijing-Tianjin-Hebei region, which is a high-risk area for BD distribution. METHODS: Our study was based on the daily-scale data of BD cases and meteorological variables from 2014 to 2019, using generalized additive model (GAM) to explore the relationship between meteorological variables and BD cases and distributed lag non-linear model (DLNM) to analyze the lag and cumulative effects. The interaction effects and stratified analysis were developed by the GAM. RESULTS: A total of 147,001 cases were reported from 2014 to 2019. The relationship between temperature and BD was approximately liner above 0 °C, but the turning point of total temperature effect was 10 °C. Results of DLNM indicated that the effect of high temperature was significant on lag 5d and lag 6d, and the lag effect showed that each 5 °C rise caused a 3% [Relative risk (RR) = 1.03, 95% Confidence interval (CI): 1.02-1.05] increase in BD cases. The cumulative BD cases delayed by 7 days increased by 31% for each 5 °C rise in temperature above 10 °C (RR = 1.31, 95% CI: 1.30-1.33). The interaction effects and stratified analysis manifested that the incidence of BD was highest in hot and humid climates. CONCLUSIONS: This study suggests that temperature can significantly affect the incidence of BD, and its effect can be enhanced by humidity and precipitation, which means that the hot and humid environment positively increases the incidence of BD.
Bacterial pathogens in surface waters may threaten human health, especially in developing countries, where untreated surface water is often used for domestic needs. The objective of the long-term multiscale monitoring of Escherichia coli ([E. coli]) concentration in stream water, and that of associated variables (temperature ( T), electrical conductance (EC), dissolved oxygen concentration ([DO]) and saturation (DO%), pH (pH), oxidation-reduction potential (ORP), turbidity (Turb), and total suspended sediment concentration ([TSS])), was to identify the drivers of bacterial dissemination across tropical catchments. This data description paper presents three datasets (see “Data availability” section) collected at 31 sampling stations located within the Mekong River and its tributaries in Lao PDR (0.6-25 946 km(2)) from 2011 to 2021. The 1602 records have been used to describe the hydrological processes driving in-stream E. coli concentration during flood events, to understand the land-use impact on bacterial dissemination on small and large catchment scales, to relate stream water quality and diarrhea outbreaks, and to build numerical models. The database may be further used, e.g., to interpret new variables measured in the monitored catchments, or to map the health risk posed by fecal pathogens.
The spatial distribution of environmental conditions may influence the dynamics of vectorborne diseases like leptospirosis. This study aims to investigate the global and localised relationships between leptospirosis with selected environmental variables. The association between environmental variables and the spatial density of geocoded leptospirosis cases was determined using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR). A higher prevalence of leptospirosis was detected in areas with higher water vapour pressure (exp(â): 1.12; 95% CI: 1.02 – 1.25) and annual precipitation (exp(â): 1.15; 95% CI: 1.02 – 1.31), with lower precipitation in the driest month (exp(â): 0.85; 95% CI: 0.75 – 0.96) and the wettest quarter (exp(â): 0.88; 95% CI: 0.77 – 1.00). Water vapor pressure (WVP) varied the most in the hotspot regions with a standard deviation of 0.62 (LQ: 0.15; UQ; 0.99) while the least variation was observed in annual precipitation (ANNP) with a standard deviation of 0.14 (LQ: 0.11; UQ; 0.30). The reduction in AICc value from 519.73 to 443.49 indicates that the GWPR model is able to identify the spatially varying correlation between leptospirosis and selected environmental variables. The results of the localised relationships in this study could be used to formulate spatially targeted interventions. This would be particularly useful in localities with a strong environmental or socio-demographical determinants for the transmission of leptospirosis.
Epidemiological studies have quantified the association between ambient temperature and diarrhoea. However, to our knowledge, no study has quantified the temperature association for severe diarrhoea cases. In this study, we quantified the association between mean temperature and two severe diarrhoea outcomes, which were mortality and hospital admissions accompanied with dehydration and/or co-morbidities. Using a 12-year dataset of three urban districts of the National Capital Region, Philippines, we modelled the non-linear association between weekly temperatures and weekly severe diarrhoea cases using a two-stage time series analysis. We computed the relative risks at the 95th (30.4 °C) and 5th percentiles (25.8 °C) of temperatures using minimum risk temperatures (MRTs) as the reference to quantify the association with high- and low-temperatures, respectively. The shapes of the cumulative associations were generally J-shaped with greater associations towards high temperatures. Mortality risks were found to increase by 53.3% [95% confidence interval (CI): 29.4%; 81.7%)] at 95th percentile of weekly mean temperatures compared with the MRT (28.2 °C). Similarly, the risk of hospitalised severe diarrhoea increased by 27.1% (95% CI: 0.7%; 60.4%) at 95th percentile in mean weekly temperatures compared with the MRT (28.6 °C). With the increased risk of severe diarrhoea cases under high ambient temperature, there may be a need to strengthen primary healthcare services and sustain the improvements made in water, sanitation, and hygiene, particularly in poor communities.
Leptospirosis is a zoonotic bacterial disease that remains an important public health problem, especially in tropical developing countries. Many previous studies in Thailand have revealed the outbreak of human leptospirosis after heavy rainfall, but research determining its quantitative risks associated with rainfall, especially at the national level, remains limited. This study aims to examine the association between rainfall and human leptospirosis across 60 provinces of Thailand. A quasi-Poisson regression framework combined with the distributed lag non-linear model was used to estimate province-specific association between rainfall and human leptospirosis, adjusting for potential confounders. Province-specific estimates were then pooled to derive regional and national estimates using random-effect meta-analysis. The highest risk of leptospirosis associated with rainfall at national level was observed at the same month (lag 0). Using 0 cm/month of rainfall as a reference, the relative risks of leptospirosis associated with heavy (90th percentile), very heavy (95th percentile), and extremely heavy (99th percentile) rainfall at the national level were 1.0994 (95% CI 0.9747, 1.2401), 1.1428 (95% CI 1.0154, 1.2862), and 1.1848 (95% CI 1.0494, 1.3378), respectively. The highest risk of human leptospirosis associated with rainfall was observed in the northern and north-eastern regions. Specifically, the relative risks of leptospirosis associated with extremely heavy rainfall in northern and north-eastern regions were 1.2362 (95% CI 0.9110, 1.6775) and 1.2046 (95% CI 0.9728, 1.4918), respectively. Increasing rainfall was associated with increased risks of leptospirosis, especially in the northern and northeastern regions of Thailand. This finding could be used for precautionary warnings against heavy rainfall. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00477-022-02250-x.
Leptospirosis has been recognized as a major public health concern in Thailand following dramatic outbreaks. We analyzed human leptospirosis incidence between 2004 and 2014 in Mahasarakham province, Northeastern Thailand, in order to identify the agronomical and environmental factors likely to explain incidence at the level of 133 sub-districts and 1982 villages of the province. We performed general additive modeling (GAM) in order to take the spatial-temporal epidemiological dynamics into account. The results of GAM analyses showed that the average slope, population size, pig density, cow density and flood cover were significantly associated with leptospirosis occurrence in a district. Our results stress the importance of livestock favoring leptospirosis transmission to humans and suggest that prevention and control of leptospirosis need strong intersectoral collaboration between the public health, the livestock department and local communities. More specifically, such collaboration should integrate leptospirosis surveillance in both public and animal health for a better control of diseases in livestock while promoting public health prevention as encouraged by the One Health approach.
This study evaluated the experience of implementing water safety plans (WSPs) in Vietnam. WSPs were introduced in Vietnam by the World Health Organization (WHO) in collaboration with the Ministry of Construction in 2006 and have been a mandatory requirement for municipal water supplies since 2012. Using a mixed-methods approach, we collected data on the perceived benefits and challenges of WSP implemen-tation from 23 provincial water companies between August and November 2021. Potential public health benefits of improved water quality were a key motivation; 87% of the water utilities were also motivated by the risk of climate change and prepared response plans to climate-related extreme events as part of WSPs. A decrease in E. coli and an improvement in disinfectant residual in treated water were reported by 61 and 83% of the water supplies, respectively. Sixty-five percent of the water supplies also reported improved revenue and cost recovery. Key barriers to WSP implementation were a lack of WSP guidance suitable for the local context (87%) and insufficient funds for WSP implementation (43%). Our study highlights the need for improved support and capacity building along with locally suited guidance on WSP implementation and audit.
The degradation of coastal water quality from fecal pollution poses a health risk to visitors at recreational beaches. Fecal indicator bacteria (FIB) are a proxy for fecal pollution; however the accuracy of their representation of fecal pollution health risks at recreational beaches impacted by non-point sources is disputed due to non-human derivation. This study aimed to investigate the relationship between FIB and a range of culturable and molecular-based microbial source tracking (MST) markers and pathogenic bacteria, and physicochemical parameters and rainfall. Forty-two marine water samples were collected from seven sampling stations during six events at two tourist beaches in Thailand. Both beaches were contaminated with fecal pollution as evident from the GenBac3 marker at 88%-100% detection and up to 8.71 log(10) copies/100 mL. The human-specific MST marker human polyomaviruses JC and BK (HPyVs) at up to 4.33 log(10) copies/100 mL with 92%-94% positive detection indicated that human sewage was likely the main contamination source. CrAssphage showed lower frequencies and concentrations; its correlations with the FIB group (i.e., total coliforms, fecal coliforms, and enterococci) and GenBac3 diminished its use as a human-specific MST marker for coastal water. Human-specific culturable AIM06 and SR14 bacteriophages and general fecal indicator coliphages also showed less sensitivity than the human-specific molecular assays. The applicability of the GenBac3 endpoint PCR assay as a lower-cost prescreening step prior to the GenBac3 qPCR assay was supported by its 100% positive predictive value, but its limited negative predictive values required subsequent qPCR confirmation. Human enteric adenovirus and Vibrio cholerae were not found in any of the samples. The HPyVs related to Vibrio parahaemolyticus, Vibrio vulnificus, and 5-d rainfall records, all of which were more prevalent and concentrated during the wet season. More monitoring is therefore recommended during wet periods. Temporal differences but no spatial differences were observed, suggesting the need for a sentinel site at each beach for routine monitoring. The exceedance of FIB water quality standards did not indicate increased prevalence or concentrations of the HPyVs or Vibrio spp. pathogen group, so the utility of FIB as an indicator of health risks at tropical beaches maybe challenged. Accurate assessment of fecal pollution by incorporating MST markers could lead to developing a more effective water quality monitoring plan to better protect human health risks in tropical recreational beaches.
Drinking water on isolated islands includes treated rainwater, water shipped from the mainland, and desalinated seawater. However, marine transportation and desalination plants are vulnerable to emergencies, such as extreme weather. making self-sustaining stand-by water for emergency response essential. Rainwater is ideal for producing the stand-by water, and rainwater harvesting is sustainable and clean, and prolonged biostability can be ensured by managing biological and chemical parameters. The present study applied a stand-by drinking water purification system (primarily including nanofiltration and low-dose chlorination) to explore the feasibility of producing and storing cleaner drinking water from rainwater and the following conclusions were drawn. First, treatment of rainwaters ensures biosafety for seven days, which is longer than that for untreated rainwater; the proportion of opportunistic pathogens decreased from 23.40-7.77% after nanofiltration, and it was proposed that the microbial community converges after advanced water treatment. Second, chemical qualities were improved. Local resource coral sand prevents pH in rainwater from decreasing below 6.5, and treated rainwater had lower disinfection by-product potential and higher disinfection efficiency, allowing periodical rainwater recycling. Third, harvesting rainwater was extremely cost-effective, with an operation cost of 1.5-2.5 RMB/m(3). From biosafety, chemical safety, and economic cost perspectives, self-sustaining water from rainwater can contributes to the development of sustainable and cost-effective water supply systems on isolated islands. Mixing treated rainwater and desalinated seawater reasonably guarantees sufficiency and safety. (C) 2021 Elsevier B.V. All rights reserved.
The tropical climate of Thailand encourages very high mosquito densities in certain areas and is ideal for dengue transmission, especially in the southern region where the province Nakhon Si Thammarat is located. It has the longest dengue fever transmission duration that is affected by some important climate predictors, such as rainfall, number of rainy days, temperature and humidity. We aimed to explore the relationship between weather variables and dengue and to analyse transmission hotspots and coldspots at the district-level. Poisson probability distribution of the generalized linear model (GLM) was used to examine the association between the monthly weather variable data and the reported number of dengue cases from January 2002 to December 2018 and geographic information system (GIS) for dengue hotspot analysis. Results showed a significant association between the environmental variables and dengue incidence when comparing the seasons. Temperature, sea-level pressure and wind speed had the highest coefficients, i.e. β=0.17, β= -0.12 and β= -0.11 (P<0.001), respectively. The risk of dengue incidence occurring during the rainy season was almost twice as high as that during monsoon. Statistically significant spatial clusters of dengue cases were observed all through the province in different years. Nabon was identified as a hotspot, while Pak Phanang was a coldspot for dengue fever incidence, explained by the fact that the former is a rubber-plantation hub, while the agricultural plains of the latter lend themselves to the practice of pisciculture combined with rice farming. This information is imminently important for planning apt sustainable control measures for dengue epidemics.
BACKGROUND: Dengue fever (DF) represents a significant health burden in Vietnam, which is forecast to worsen under climate change. The development of an early-warning system for DF has been selected as a prioritised health adaptation measure to climate change in Vietnam. OBJECTIVE: This study aimed to develop an accurate DF prediction model in Vietnam using a wide range of meteorological factors as inputs to inform public health responses for outbreak prevention in the context of future climate change. METHODS: Convolutional neural network (CNN), Transformer, long short-term memory (LSTM), and attention-enhanced LSTM (LSTM-ATT) models were compared with traditional machine learning models on weather-based DF forecasting. Models were developed using lagged DF incidence and meteorological variables (measures of temperature, humidity, rainfall, evaporation, and sunshine hours) as inputs for 20 provinces throughout Vietnam. Data from 1997-2013 were used to train models, which were then evaluated using data from 2014-2016 by Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). RESULTS AND DISCUSSION: LSTM-ATT displayed the highest performance, scoring average places of 1.60 for RMSE-based ranking and 1.95 for MAE-based ranking. Notably, it was able to forecast DF incidence better than LSTM in 13 or 14 out of 20 provinces for MAE or RMSE, respectively. Moreover, LSTM-ATT was able to accurately predict DF incidence and outbreak months up to 3 months ahead, though performance dropped slightly compared to short-term forecasts. To the best of our knowledge, this is the first time deep learning methods have been employed for the prediction of both long- and short-term DF incidence and outbreaks in Vietnam using unique, rich meteorological features. CONCLUSION: This study demonstrates the usefulness of deep learning models for meteorological factor-based DF forecasting. LSTM-ATT should be further explored for mitigation strategies against DF and other climate-sensitive diseases in the coming years.
OBJECTIVE: The aim research was to analyze the association between temperature and humidity and the incidence of dengue fever in Manado Municipality. METHODS: The research design used analytical descriptive with a cross-sectional survey approach. Data were analyzed using the Spearman rank test. RESULT: The highest temperature was in August (28.7 °C), the highest humidity was January (88%), and the most DHF incidence was in January (409 cases). There is a significant association between temperature and the prevalence of DHF (p=0.000, r=-0.845). Humidity with the prevalence of DHF (p=0.000, r=0.873). CONCLUSION: It was found that two variables had a significant association between temperature and humidity on the prevalence of DHF in Manado Municipality based on observations of patterns of temperature and humidity characteristics every month during 2019.
BACKGROUND: Dengue fever (DF) is a mosquito-borne infectious disease that has threatened tropical and subtropical regions in recent decades. An early and targeted warning of a dengue epidemic is important for vector control. Current studies have primarily determined weather conditions to be the main factor for dengue forecasting, thereby neglecting that environmental suitability for mosquito breeding is also an important factor, especially in fine-grained intra-urban settings. Considering that street-view images are promising for depicting physical environments, this study proposes a framework for facilitating fine-grained intra-urban dengue forecasting by integrating the urban environments measured from street-view images. METHODS: The dengue epidemic that occurred in 167 townships of Guangzhou City, China, between 2015 and 2019 was taken as a study case. First, feature vectors of street-view images acquired inside each township were extracted by a pre-trained convolutional neural network, and then aggregated as an environmental feature vector of the township. Thus, townships with similar physical settings would exhibit similar environmental features. Second, the environmental feature vector is combined with commonly used features (e.g., temperature, rainfall, and past case count) as inputs to machine-learning models for weekly dengue forecasting. RESULTS: The performance of machine-learning forecasting models (i.e., MLP and SVM) integrated with and without environmental features were compared. This indicates that models integrating environmental features can identify high-risk urban units across the city more precisely than those using common features alone. In addition, the top 30% of high-risk townships predicted by our proposed methods can capture approximately 50-60% of dengue cases across the city. CONCLUSIONS: Incorporating local environments measured from street view images is effective in facilitating fine-grained intra-urban dengue forecasting, which is beneficial for conducting spatially precise dengue prevention and control.
In 2014 and 2015, Southern Taiwan experienced two unprecedented outbreaks, with more than 10,000 laboratory-confirmed dengue cases in each outbreak. The present study was aimed to investigate the influence of meteorological and spatial factors on dengue outbreaks in Southern Taiwan and was conducted in Kaohsiung City, which is the most affected area in Taiwan. The distributed lag nonlinear model was used to investigate the role of climatic factors in the 2014 and 2015 dengue outbreaks. Spatial statistics in the Geographic Information System was applied to study the relationship between the dengue spreading pattern and locations of traditional markets (human motility) in the 2015 dengue outbreak. Meteorological analysis results suggested that the relative risk of dengue fever increased when the weekly average temperature was more than 15°C at lagged weeks 5 to 18. Elevated relative risk of dengue was observed when the weekly average rainfall was more than 150 mm at lagged weeks 12 to 20. The spatial analysis revealed that approximately 83% of dengue cases were located in the 1000 m buffer zone of traditional market, with statistical significance. These findings support the influence of climatic factors and human motility on dengue outbreaks. Furthermore, the study analysis may help authorities to identify hotspots and decide the timing for implementation of dengue control programs.
Dengue fever is a mosquito-borne infection with a rising trend, expected to increase further with the rise in global temperature. The study aimed to use the environmental and dengue data 2015-2018 to examine the seasonal variation and establish a probabilistic model of environmental predictors of dengue using the generalized linear model (GLM). In Delhi, dengue cases started emerging in the monsoon season, peaked in the post-monsoon, and thereafter, declined in early winter. The annual trend of dengue cases declined, but the seasonal pattern remained alike (2015-18). The Spearman correlation coefficient of dengue was significantly high with the maximum and minimum temperature at 2 months lag, but it was negatively correlated with the difference of average minimum and maximum temperature at lag 1 and 2. The GLM estimated β coefficients of environmental predictors such as temperature difference, cumulative rainfall, relative humidity and maximum temperature were significant (p < 0.01) at different lag (0 to 2), and maximum temperature at lag 2 was having the highest effect (IRR 1.198). The increasing temperature of two previous months and cumulative rainfall are the best predictors of dengue incidence. The vector control should be implemented at least 2 months ahead of disease transmission (August-November).
The susceptibility of Asian tiger mosquitoes to DENV-2 in different seasons was observed in simulated field environments as a reference to design dengue fever control strategies in Guangzhou. The life table experiments of mosquitoes in four seasons were carried out in the field. The susceptibility of Ae. albopictus to dengue virus was observed in both environments in Guangzhou in summer and winter. Ae. albopictus was infected with dengue virus by oral feeding. On day 7 and 14 after infection, the viral load in the head, ovary, and midgut of the mosquito was detected using real-time fluorescent quantitative PCR. Immune-associated gene expression in infected mosquitoes was performed using quantitative real-time reverse transcriptase PCR. The hatching rate and pupation rate of Ae. albopictus larvae in different seasons differed significantly. The winter hatching rate of larvae was lower than that in summer, and the incubation time was longer than in summer. In the winter field environment, Ae. albopictus still underwent basic growth and development processes. Mosquitoes in the simulated field environment were more susceptible to DENV-2 than those in the simulated laboratory environment. In the midgut, viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 14.459, P = 0.01); ovarian viral RNA levels on day 7 in summer were higher than those on day 7 in winter (F = 8.656, P < 0.001), but there was no significant difference in the viral load at other time points (P > 0.05). Dicer-2 mRNA expression on day 7 in winter was 4.071 times than that on day 7 in summer: the viral load and Dicer-2 expression correlated moderately. Ae. albopictus could still develop and transmit dengue virus in winter in Guangzhou. Mosquitoes under simulated field conditions were more susceptible to DENV-2 than those under simulated laboratory conditions.
Dengue is of great public health concern regarding the number of people affected. In addition, climate change is associated with the recent spread of dengue fever. Effects of meteorological factors on dengue incidence from 2003 to 2019 in Bangkok city: a model for dengue prediction. Mathematical statistical applied were principal component analysis (PCA), Poisson regression model (PRM), Mann-Kendall (MK), and Sen’s slope. PRM considers dengue incidence as the dependent variable and climate variables as independent variables. Meteorological factors are maximum temperature (T-max), minimum temperature (T-min), relative humidity (RH), and rainfall. The rainy season showed a high significant probability of occurrence for new patients. Most trends were statistically significant at 1% for seasonal and annual dengue cases. Another finding was that for every 5-50% of RH variation, there was an average increase (73.33-24,369.19%) in the number of dengue cases. Therefore, RH was the best predictor for increasing dengue incidence in Bangkok. In addition, predictions for dengue incidence were evaluated. This study is a significant result to warn the government, providing valuable information for human health protection.
OBJECTIVES: This study aimed to forecast the morbidity and mortality of dengue fever using a time series analysis from 2006 to 2016. METHODS: Data were compiled from the Jeddah Dengue Fever Operations Room (RFOR) in a primary health care centre. A time series analysis was conducted for all confirmed cases of dengue fever between 2006 and 2016. RESULTS: The results showed a significant seasonal association, particularly from May to September, and a time-varying behaviour. Air temperature was significantly associated with the incidence of dengue fever (p < 0.001) but was not correlated with its mortality. Similarly, relative humidity was not significantly associated with the incidence of dengue fever (p = 0.237). CONCLUSION: The strong seasonal association of dengue fever during May to September and its relation to air temperature should be communicated to all stakeholders. This will help improve the control interventions of dengue fever during periods of anticipated high incidence.
Meteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological exposures and the incidence of dengue fever. We matched the dengue surveillance data with the meteorological and air pollution data collected from monitoring sites from 2015 through 2019 in Guangzhou area. We developed generalized additive models with Poisson distribution to regress the daily counts of dengue against four meteorological exposures, while controlling for pollution and normalized difference vegetation index to evaluate the risk ratio (RR) of dengue for each unit increase in different exposures. The interaction terms of meteorological exposures and air pollution were then included to assess the modification effect of different pollution on the associations. Daily dengue cases were nonlinearly associated with one-week cumulative temperature and precipitation, while not associated with humidity and wind speed. RRs were 1.07 (1.04, 1.11) and 0.95 (0.88, 1.03) for temperature below and above 27.1 degrees C, 0.97 (0.96, 0.98) and 1.05 (1.01, 1.08) for precipitation below and above 20.3 mm, respectively. For the modification effect, the RRs of low-temperature, wind speed on higher SO2 days and low-precipitation on both higher PM2.5 and SO2 days were greater compared to the low-pollution days with P (interaction) being 0.037, 0.030, 0.022 and 0.018. But the RRs of both high-temperature on higher SO2 days and high-precipitation on higher PM2.5 d were smaller with P (interaction) being 0.001 and 0.043. Air pollution could alter the meteorology-dengue associations. The impact of low-temperature, low-precipitation and wind speed on dengue occurrence tended to increase on days with high SO2 levels while the impact of high-temperature decreased. The impact of low-precipitation increased on high-PM2.5 d while the impact of high-precipitation decreased.
BACKGROUND: Dengue fever is a widespread viral disease and one of the world’s major pandemic vector-borne infections, causing serious hazard to humanity. The World Health Organisation (WHO) reported that the incidence of dengue fever has increased dramatically across the world in recent decades. WHO currently estimates an annual incidence of 50-100 million dengue infections worldwide. To date, no tested vaccine or treatment is available to stop or prevent dengue fever. Thus, the importance of predicting dengue outbreaks is significant. The current issue that should be addressed in dengue outbreak prediction is accuracy. A limited number of studies have conducted an in-depth analysis of climate factors in dengue outbreak prediction. METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia. RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks. CONCLUSIONS: This research explored the factors used in dengue outbreak prediction systems. The major contribution of this study is identifying new significant factors that contribute to dengue outbreak prediction. From the evaluation result, we obtained a significant improvement in the accuracy of a machine learning model for dengue outbreak prediction.
Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.
This study assessed the impact of weather factors, including novel predictors-pollutant standards index (PSI) and wind speed-on dengue incidence in Singapore between 2012 and 2019. Autoregressive integrated moving average (ARIMA) model was fitted to explore the autocorrelation in time series and quasi-Poisson model with a distributed lag non-linear term (DLNM) was set up to assess any non-linear association between climatic factors and dengue incidence. In DLNM, a PSI level of up to 111 was positively associated with dengue incidence; incidence reduced as PSI level increased to 160. A slight rainfall increase of up to 7 mm per week gave rise to higher dengue risk. On the contrary, heavier rainfall was protective against dengue. An increase in mean temperature under around 28.0 °C corresponded with increased dengue cases whereas the association became negative beyond 28.0 °C; the minimum temperature was significantly positively associated with dengue incidence at around 23-25 °C, and the relationship reversed when temperature exceed 27 °C. An overall positive association, albeit insignificant, was observed between maximum temperature and dengue incidence. Wind speed was associated with decreasing relative risk (RR). Beyond prevailing conclusions on temperature, this study observed that extremely poor air quality, high wind speed, minimum temperature 27 °C, and rainfall volume beyond 12 mm per week reduced the risk of dengue transmission in an urbanized tropical environment.
BACKGROUND: The mosquitoes Aedes aegypti and Ae. albopictus are the primary vectors of dengue virus, and their geographic distributions are predicted to expand further with economic development, and in response to climate change. We aimed to estimate the impact of future climate change on dengue transmission through the development of a Suitable Conditions Index (SCI), based on climatic variables known to support vectorial capacity. We calculated the SCI based on various climate change scenarios for six countries in the Asia-Pacific region (Australia, China, Indonesia, The Philippines, Thailand and Vietnam). METHODS: Monthly raster climate data (temperature and precipitation) were collected for the period January 2005 to December 2018 along with projected climate estimates for the years 2030, 2050 and 2070 using Representative Concentration Pathway (RCP) 4·5, 6·0 and 8·5 emissions scenarios. We defined suitable temperature ranges for dengue transmission of between 17·05-34·61 °C for Ae. aegypti and 15·84-31·51 °C for Ae. albopictus and then developed a historical and predicted SCI based on weather variability to measure the expected geographic limits of dengue vectorial capacity. Historical and projected SCI values were compared through difference maps for the six countries. FINDINGS: Comparing different emission scenarios across all countries, we found that most South East Asian countries showed either a stable pattern of high suitability, or a potential decline in suitability for both vectors from 2030 to 2070, with a declining pattern particularly evident for Ae. albopictus. Temperate areas of both China and Australia showed a less stable pattern, with both moderate increases and decreases in suitability for each vector in different regions between 2030 and 2070. INTERPRETATION: The SCI will be a useful index for forecasting potential dengue risk distributions in response to climate change, and independently of the effects of human activity. When considered alongside additional correlates of infection such as human population density and socioeconomic development indicators, the SCI could be used to develop an early warning system for dengue transmission.
The rapid spread of dengue fever (DF) infection has posed severe threats to global health. Environmental factors, such as weather conditions, are believed to regulate DF spread. While previous research reported inconsistent change of DF risk with varying weather conditions, few of them evaluated the impact of extreme weather conditions on DF infection risk. This study aims to examine the short-term associations between extreme temperatures, extreme rainfall, and DF infection risk in South and Southeast Asia. A total of 35 locations in Singapore, Malaysia, Sri Lanka, and Thailand were included, and weekly DF data, as well as the daily meteorological data from 2012 to 2020 were collected. A two-stage meta-analysis was used to estimate the overall effect of extreme weather conditions on the DF infection risk. Location-specific associations were obtained by the distributed lag nonlinear models. The DF infection risk appeared to increase within 1-3 weeks after extremely high temperature (e.g. lag week 2: RR = 1.074, 95 % CI: 1.022-1.129, p = 0.005). Compared with no rainfall, extreme rainfall was associated with a declined DF risk (RR = 0.748, 95 % CI: 0.620-0.903, p = 0.003), and most of the impact was across 0-3 weeks lag. In addition, the DF risk was found to be associated with more intensive extreme weathers (e.g. seven extreme rainfall days per week: RR = 0.338, 95 % CI: 0.120-0.947, p = 0.039). This study provides more evidence in support of the impact of extreme weather conditions on DF infection and suggests better preparation of DF control measures according to climate change.
BACKGROUND: Global incidence of dengue has surged rapidly over the past decade. Each year, an estimated 390 million infections occur worldwide, with Asia-Pacific countries bearing about three-quarters of the global dengue disease burden. Global warming may influence the pattern of dengue transmission. While previous studies have shown that extremely high temperatures can impede the development of the Aedes mosquito, the effect of such extreme heat over a sustained period, also known as heatwaves, has not been investigated in a tropical climate setting. AIM: We examined the short-term relationships between maximum ambient temperature and heatwaves and reported dengue infections in Singapore, via ecological time series analysis, using data from 2009 to 2018. METHODS: We studied the effect of two measures of extreme heat – (i) heatwaves and (ii) maximum ambient temperature. We used a negative binomial regression, coupled with a distributed lag nonlinear model, to examine the immediate and lagged associations of extreme temperature on dengue infections, on a weekly timescale. We adjusted for long-term trend, seasonality, rainfall and absolute humidity, public holidays and autocorrelation. RESULTS: We observed an overall inhibitive effect of heatwaves on the risk of dengue infections, and a parabolic relationship between maximum temperature and dengue infections. A 1 °C increase in maximum temperature from 31 °C was associated with a 13.1% (Relative Risk (RR): 0.868, 95% CI: 0.798, 0.946) reduction in the cumulative risk of dengue infections over six weeks. Weeks with 3 heatwave days were associated with a 28.3% (RR: 0.717, 95% CI: 0.608, 0.845) overall reduction compared to weeks with no heatwave days. Adopting different heatwaves specifications did not substantially alter our estimates. CONCLUSION: Extreme heat was associated with decreased dengue incidence. Findings from this study highlight the importance of understanding the temperature dependency of vector-borne diseases in resource planning for an anticipated climate change scenario.
BACKGROUND: Dengue dynamics result from the complex interactions between the virus, the host and the vector, all being under the influence of the environment. Several studies explored the link between weather and dengue dynamics and some investigated the impact of climate change on these dynamics. Most attempted to predict incidence rate at a country scale or assess the environmental suitability at a global or regional scale. Here, we propose a new approach which consists in modeling the risk of dengue outbreak at a local scale according to climate conditions and study the evolution of this risk taking climate change into account. We apply this approach in New Caledonia, where high quality data are available. METHODS: We used a statistical estimation of the effective reproduction number (R(t)) based on case counts to create a categorical target variable : epidemic week/non-epidemic week. A machine learning classifier has been trained using relevant climate indicators in order to estimate the probability for a week to be epidemic under current climate data and this probability was then estimated under climate change scenarios. RESULTS: Weekly probability of dengue outbreak was best predicted with the number of days when maximal temperature exceeded 30.8°C and the mean of daily precipitation over 80 and 60 days prior to the predicted week respectively. According to scenario RCP8.5, climate will allow dengue outbreak every year in New Caledonia if the epidemiological and entomological contexts remain the same. CONCLUSION: We identified locally relevant climatic factor driving dengue outbreaks in New Caledonia and assessed the inter-annual and seasonal risk of dengue outbreak under different climate change scenarios up to the year 2100. We introduced a new modeling approach to estimate the risk of dengue outbreak depending on climate conditions. This approach is easily reproducible in other countries provided that reliable epidemiological and climate data are available.
BACKGROUND: Studies have shown that tropical cyclones are associated with several infectious diseases, while very few evidence has demonstrated the relationship between tropical cyclones and dengue fever. This study aimed to examine the potential impact of tropical cyclones on dengue fever incidence in the Pearl River Delta, China. METHODS: Data on daily dengue fever incidence, occurrence of tropical cyclones and meteorological factors were collected between June and October, 2013-2018 from nine cities in the Pearl River Delta. Multicollinearity of meteorological variables was examined via Spearman correlation, variables with strong correlation (r>0.7) were not included in the model simultaneously. A time-stratified case-crossover design combined with conditional Poisson regression model was performed to evaluate the association between tropical cyclones and dengue fever incidence. Stratified analyses were performed by intensity grades of tropical cyclones (tropical storm and typhoon), sex (male and female) and age-groups (<18, 18-59, ≥60 years). RESULTS: During the study period, 20 tropical cyclones occurred and 47,784 dengue fever cases were reported. Tropical cyclones were associated with an increased risk of dengue fever in the Pearl River Delta region, with the largest relative risk of 1.62 with the 95% confidence interval (1.45-1.80) occurring on the lag 5 day. The strength of association was greater and lasted longer for typhoon than for tropical storm. There was no difference in effect estimates between males and females. However, individuals aged over 60 years were more vulnerable than others. CONCLUSIONS: Tropical cyclones are associated with increased risk of local dengue fever incidence in south China, with the elderly more vulnerable than other population subgroups. Health protective strategies should be developed to reduce the potential risk of dengue epidemic after tropical cyclones.
Climate change is a global challenge expected to affect water-related diseases (WRDs). The present systematic study tried to review literature examining the relationship between meteorological conditions and WRDs in developing countries located in Western Asia. We searched Scopus, PubMed and Embase for studies describing the relationship between WRDs and climate variables (ambient temperature, rainfall and humidity) plus extreme events, drought and flooding. A total of 27 articles met the inclusion criteria. The key findings presented a positive association between temperature and WRDs in most of the evaluated records. However, rainfall and humidity showed inconsistent relationships with WRDs. No evidence was found reporting the effect of climate variables on water-based or water-washed diseases. Yemen is the only country in the studied region that still has major issues controlling WRDs and might be at greater risk of climate change. It is recommended that future researches evaluate the delayed effects of environmental factors on WRDs and multidimensional interactions of climate variables on each other or on socioeconomic variables affecting WRDs. Increased health risks due to climate change add additional value to the investigations studying the proven adaptation strategies such as improvements in water, sanitation and hygiene (WaSH) and effective early warning systems.
Japanese encephalitis (JE) is an important mosquito-borne infectious disease in rural areas of Asia that is caused by Japanese encephalitis virus (JEV). Culex tritaeniorhynchus is the major vector of JEV, nevertheless there are other mosquitoes that may be able to transmit JEV. This study confirms that the midgut, head tissue, salivary glands, and reproductive tissue of Aedes albopictus, Armigeres subalbatus, and Culex quinquefasciatus are all able to be infected with JEV after a virus-containing blood meal was ingested by female mosquitoes. Even though the susceptibility to JEV of the different tissues varies, the virus-positive rate increased with the number of days after JEV infection. Moreover, once JEV escapes the midgut barrier, the oral transmission rates of JEV were 16%, 2%, and 21% for Ae. albopictus, Ar. subalbatus, and Cx. quinquefasciatus at 14 days after infection at 30 °C, respectively. There is no supporting evidence to suggest vertical transmission of JEV by the tested mosquitoes. Collectively, raising the temperature enhances JEV replication in the salivary gland of the three mosquito species, suggesting that global warming will enhance mosquito vector competence and that this is likely to lead to an increase in the probability of JEV transmission.
OBJECTIVE: Dengue fever is a global burden because of high cases number. Climate factors became determinant of the mosquito’s growth. This study aimed to analyze the relationship between climate factors (humidity, temperature, wind speed, rainfall) and dengue cases in Makassar during 2011-2017. METHODS: It was quantitative study located in Makassar. Data were analyzed by General Estimating Equation (GEE). Gee was used to showing the model of variables. This study used secondary data from Health District Office of Makassar to get Dengue Cases Data and Meteorological, Climatological, and Geophysical Agency of Makassar for monthly climate data. RESULTS: The result showed significant correlation between climate variables that have been researched which were temperature, humidity, rainfall, and wind speed to dengue fever cases. CONCLUSIONS: As conclusion, the humidity had strongest correlation to dengue fever cases. It also showed positive correlation, while others showed negative correlation
For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt’s forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R (2)) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.
Zika virus (ZIKV) recently reemerged in the Americas and rapidly expanded in global range. It is posing significant concerns of public health due to its link to birth defects and its complicated transmission routes. Southeast Asia is badly hit by ZIKV, but limited information was found on the transmission potential of ZIKV in the region. In this paper, we develop a new dynamic process-based mathematical model, which incorporates the interactions among humans (sexual transmissibility), and between human and mosquitoes (biting transmissibility), as well as the essential impacts of temperature. The model is first validated by fitting the 2016 ZIKV outbreak in Singapore via Markov chain Monte Carlo method. Based on that, we demonstrate the effects of temperature on mosquito ecology and ZIKV transmission, and further clarify the potential risk of ZIKV outbreak in Southeast Asian countries. The results show that (i) the estimated infection reproduction number [Formula: see text] in Singapore fell from 6.93 (in which the contribution of sexual transmission was 0.89) to 0.24 after the deployment of control strategies; (ii) the optimal temperature for the reproduction of ZIKV infections and adult mosquitoes are estimated to be [Formula: see text]C and [Formula: see text]C, respectively; and (iii) the [Formula: see text] in Southeast Asia could be between 3 and 7, with an inverted-U shape around the year. The large values of [Formula: see text] and the simulative patterns of ZIKV transmission in each country highlights the high risk of ZIKV attack in Southeast Asia.
BACKGROUND: Seasonal activity patterns of mosquitoes are essential as baseline knowledge to understand the transmission dynamics of vector-borne diseases. This study was conducted to evaluate the monthly dynamics of the mosquito populations and their relation to meteorological factors in Mazandaran Province, north of Iran. METHODS: Mosquito adults and larvae were collected from 16 counties of Mazandaran Province using different sampling techniques, once a month from May to December 2014. Index of Species Abundance (ISA) along with Standardized ISA (SISA) was used for assessing the most abundant species of mosquitoes based on the explanations of Robert and Hsi. Pearson’s correlation coefficient (R) was used to assess the relationships between the monthly population fluctuations and meteorological variables. RESULTS: Overall, 23750 mosquitoes belonging to four genera and nineteen species were collected and identified. The highest population density of mosquitoes was in July and the lowest in May. The ISA/SISA indices for Culex pipiens were both 1 for larvae and 1.25/0.973 for adults in total catch performed in human dwellings. For Cx. tritaeniorhynchus, the ISA/SISA were 1.68/0.938 in pit shelter method. A significant positive correlation was observed between population fluctuations of Cx. tritaeniorhynchus and mean temperature (R: 0.766, P< 0.027). CONCLUSION: The results indicated that the mosquitoes are more active in July, and Cx. pipiens and Cx. tritaeniorhynchus were the most abundant species. Considering the potential of these species as vectors of numerous pathogens, control programs can be planed based on their monthly activity pattern in the area.
BACKGROUND: Echinococcosis is a global enzootic disease influenced by different biological and environmental factors and causes a heavy financial burden on sick families and governments. Currently, government subsidies for the treatment of patients with echinococcosis are only a fixed number despite patients’ finical income or cost of treatment, and health authorities are demanded to supply an annual summary of only endemic data. The risk to people in urban areas or non-endemic is increasing with climate, landscape, and lifestyle changes. METHODS: We conducted retrospective descriptive research on inpatients with human echinococcosis (HE) in Lanzhou hospitals and analyzed the healthcare expenditure on inpatient treatment and examined the financial inequalities relating to different levels of gross domestic product. The livestock losses were also estimated by infection ratio. The occurrence records of Echinococcus spp. composed of hospitalized patients and dogs infected in the Gansu province were collected for Ecological niche modeling (ENM) to estimate the current suitable spatial distribution for the parasite in Gansu province. Then, we imported the resulting current niche model into future global Shared Socioeconomic Pathways scenarios for estimation of future suitable habitat areas. RESULTS: Between 2000 to 2020, 625 hospitalized HE patients (51% men and 49% women) were identified, and 48.32 ± 15.62 years old. The average cost of hospitalization expenses per case of HE in Gansu Province was ￥24,370.2 with an increasing trend during the study period and was negative with different counties’ corresponding gross domestic product (GDP). The trend of livestock losses was similar to the average cost of hospitalization expenses from 2015 to 2017. The three factors with the strongest correlation to echinococcosis infection probability were (1) global land cover (GLC, 56.6%), (2) annual precipitation (Bio12, 21.2%), and (3) mean temperature of the Wettest Quarter (Bio12, 8.5% of variations). We obtained a robust model that provides detail on the distribution of suitable areas for Echinococcus spp. including areas that have not been reported for the parasite. An increasing tendency was observed in the highly suitable areas of Echinococcus spp. indicating that environmental changes would affect the distributions. CONCLUSION: This study may help in the development of policies for at-risk populations in geographically defined areas and monitor improvements in HE control strategies by allowing targeted allocation of resources, including spatial analyses of expenditure and the identification of non-endemic areas or risk for these parasites, and a better comprehension of the role of the environment in clarifying the transmission dynamics of Echinococcus spp. Raising healthcare workers’ and travelers’ disease awareness and preventive health habits is an urgent agenda. Due to unpredictable future land cover types, prediction of the future with only climatic variables involved needs to be treated cautiously.
Ross River virus (RRV) infection is one of the emerging and prevalent arboviral diseases in Australia and the Pacific Islands. Although many studies have been conducted to establish the relationship between temperature and RRV infection, there has been no comprehensive review of the association so far. In this study, we performed a systematic review and meta-analysis to assess the effect of temperature on RRV transmission. We searched PubMed, Scopus, Embase, and Web of Science with additional lateral searches from references. The quality and strength of evidence from the included studies were evaluated following the Navigation Guide framework. We have qualitatively synthesized the evidence and conducted a meta-analysis to pool the relative risks (RRs) of RRV infection per 1 °C increase in temperature. Subgroup analyses were performed by climate zones, temperature metrics, and lag periods. A total of 17 studies met the inclusion criteria, of which six were included in the meta-analysis The meta-analysis revealed that the overall RR for the association between temperature and the risk of RRV infection was 1.09 (95% confidence interval (CI): 1.02, 1.17). Subgroup analyses by climate zones showed an increase in RRV infection per 1 °C increase in temperature in humid subtropical and cold semi-arid climate zones. The overall quality of evidence was “moderate” and we rated the strength of evidence to be “limited”, warranting additional evidence to reduce uncertainty. The results showed that the risk of RRV infection is positively associated with temperature. However, the risk varies across different climate zones, temperature metrics and lag periods. These findings indicate that future studies on the association between temperature and RRV infection should consider local and regional climate, socio-demographic, and environmental factors to explore vulnerability at local and regional levels.
Ross River virus (RRV) has recently been suggested to be a potential emerging infectious disease worldwide. RRV infection remains the most common human arboviral disease in Australia, with a yearly estimated economic cost of $4.3 billion. Infection in humans and horses can cause chronic, long-term debilitating arthritogenic illnesses. However, current knowledge of immunopathogenesis remains to be elucidated and is mainly inferred from a murine model that only partially resembles clinical signs and pathology in human and horses. The epidemiology of RRV transmission is complex and multifactorial and is further complicated by climate change, making predictive models difficult to design. Establishing an equine model for RRV may allow better characterization of RRV disease pathogenesis and immunology in humans and horses, and could potentially be used for other infectious diseases. While there are no approved therapeutics or registered vaccines to treat or prevent RRV infection, clinical trials of various potential drugs and vaccines are currently underway. In the future, the RRV disease dynamic is likely to shift into temperate areas of Australia with longer active months of infection. Here, we (1) review the current knowledge of RRV infection, epidemiology, diagnostics, and therapeutics in both humans and horses; (2) identify and discuss major research gaps that warrant further research.
The eastern paralysis tick, Ixodes holocyclus, is an ectoparasite of medical and veterinary importance in Australia. The feeding of I. holocyclus is associated with an ascending flaccid paralysis which kills many dogs and cats each year, with the development of mammalian meat allergy in some humans, and with the transmission of Rickettsia australis (Australian scrub typhus) to humans. Although I. holocyclus has been well studied, it is still not known exactly why this tick cannot establish outside of its present geographic distribution. Here, we aim to account for the presence as well as the absence of I. holocyclus in regions of Australia. We modelled the climatic requirements of I. holocyclus with two methods, CLIMEX, and a new envelope-model approach which we name the ‘climatic-range method’. These methods allowed us to account for 93% and 96% of the geographic distribution of I. holocyclus, respectively. Our analyses indicated that the geographic range of I. holocyclus may not only shift south towards Melbourne, but may also expand in the future, depending on which climate-change scenario comes to pass.
The southern paralysis tick, Ixodes cornuatus, is a tick of veterinary and medical importance in Australia. We use two methods, CLIMEX, and an envelope-model approach which we name the ‘climatic-range method’ to study the climatic requirements of I. cornuatus and thus to attempt to account for the geographic distribution of I. cornuatus. CLIMEX and our climatic-range method allowed us to account for 94% and 97% of the records of I. cornuatus respectively. We also studied the host preferences of I. cornuatus which we subsequently used in conjunction with our species distribution methods to account for the presence and the absences of I. cornuatus across Australia. Our findings indicate that the actual geographic distribution of I. cornuatus is smaller than the potential geographic range of this tick, and thus, that there are regions in Australia which may be suitable for I. cornuatus where this tick has not been recorded. Although our findings indicate that I. cornuatus might be able to persist in these currently unoccupied regions, our findings also indicate that the potential geographic range of I. cornuatus may shrink by 51 to 76% by 2090, depending on which climate change scenario comes to pass.
The identification of the key factors influencing dengue occurrence is critical for a successful response to the outbreak. It was interesting to consider possible differences in meteorological factors affecting dengue incidence during epidemic and non-epidemic periods. In this study, the overall correlation between weekly dengue incidence rates and meteorological variables were conducted in southern Taiwan (Tainan and Kaohsiung cities) from 2007 to 2017. The lagged-time Poisson regression analysis based on generalized estimating equation (GEE) was also performed. This study found that the best-fitting Poisson models with the smallest QICu values to characterize the relationships between dengue fever cases and meteorological factors in Tainan (QICu = −8.49 × 10−3) and Kaohsiung (−3116.30) for epidemic periods, respectively. During dengue epidemics, the maximum temperature with 2-month lag (β = 0.8400, p < 0.001) and minimum temperature with 5-month lag (0.3832, p < 0.001). During non-epidemic periods, the minimum temperature with 3-month lag (0.1737, p < 0.001) and mean temperature with 2-month lag (2.6743, p < 0.001) had a positive effect on dengue incidence in Tainan and Kaohsiung, respectively.
Hong Kong is an Asia-Pacific City with low incidence but periodic local outbreaks of dengue. A mixed-method assessment of the risk of expansion of dengue endemicity in such setting was conducted. Archived blood samples of healthy adult blood donors were tested for anti-dengue virus IgG at 2 time-points of 2014 and 2018/2019. Data on the monthly notified dengue cases, meteorological and vector (ovitrap index) variables were collected. The dengue virus (DENV) IgG seroprevalence of healthy adults in 2014 was 2.2% (95%C.I. = 1.8-2.8%, n = 3827) whereas that in 2018/2019 was 1.7% (95%C.I. = 1.2-2.3%, n = 2320). Serotyping on 42 sera in 2018/2019 showed that 22 (52.4%) were DENV-2. In 2002-2019, importation accounted for 95.3% of all reported cases. By wavelet analysis, local cases were in weak or no association with meteorological and vector variables. Without strong association between local cases and meteorological/vector variables, there was no evidence of increasing level of dengue infection in Hong Kong.
BACKGROUND: Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes. OBJECTIVES: We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk. METHODS: A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors. RESULTS: We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green space coverage and lower elevation. CONCLUSIONS: Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.
Transmission of dengue virus is a complex process with interactions between virus, mosquitoes and humans, influenced by multiple factors simultaneously. Studies have examined the impact of climate or socio-ecological factors on dengue, or only analyzed the individual effects of each single factor on dengue transmission. However, little research has addressed the interactive effects by multiple factors on dengue incidence. This study uses the geographical detector method to investigate the interactive effect of climate and socio-ecological factors on dengue incidence from two perspectives: over a long-time series and during outbreak periods; and surmised on the possibility of dengue outbreaks in the future. Results suggest that the temperature plays a dominant role in the long-time series of dengue transmission, while socio-ecological factors have great explanatory power for dengue outbreaks. The interactive effect of any two factors is greater than the impact of single factor on dengue transmission, and the interactions of pairs of climate and socio-ecological factors have more significant impact on dengue. Increasing temperature and surge in travel could cause dengue outbreaks in the future. Based on these results, three recommendations are offered regarding the prevention of dengue outbreaks: mitigating the urban heat island effect, adjusting the time and frequency of vector control intervention, and providing targeted health education to travelers at the border points. This study hopes to provide meaningful clues and a scientific basis for policymakers regarding effective interventions against dengue transmission, even during outbreaks.
As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011-2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever.
BACKGROUND: Dengue has become an increasing public health threat around the world, and climate conditions have been identified as important factors affecting the transmission of dengue, so this study was aimed to establish a prediction model of dengue epidemic by meteorological methods. METHODS: The dengue case information and meteorological data were collected from Guangdong Provincial Center for Disease Prevention and Control and Guangdong Meteorological Bureau, respectively. We used spatio-temporal analysis to characterize dengue epidemics. Spearman correlation analysis was used to analyze the correlation between lagged meteorological factors and dengue fever cases and determine the maximum lagged correlation coefficient of different meteorological factors. Then, Generalized Additive Models were used to analyze the non-linear influence of lagged meteorological factors on local dengue cases and to predict the number of local dengue cases under different weather conditions. RESULTS: We described the temporal and spatial distribution characteristics of dengue fever cases and found that sporadic single or a small number of imported cases had a very slight influence on the dengue epidemic around. We further created a forecast model based on the comprehensive consideration of influence of lagged 42-day meteorological factors on local dengue cases, and the results showed that the forecast model has a forecast effect of 98.8%, which was verified by the actual incidence of dengue from 2005 to 2016 in Guangzhou. CONCLUSION: A forecast model for dengue epidemic was established with good forecast effects and may have a potential application in global dengue endemic areas after modification according to local meteorological conditions. High attention should be paid on sites with concentrated patients for the control of a dengue epidemic.
BACKGROUND: In recent years, frequent outbreaks of dengue fever (DF) have become an increasingly serious public health issue in China, especially in the Pearl River Delta (PRD) with fast socioeconomic developments. Previous studies mainly focused on the historic DF epidemics, their influencing factors, and the prediction of DF risks. However, the future risks of this disease under both different socioeconomic development and representative concentration pathways (RCPs) scenarios remain little understood. METHODOLOGY AND PRINCIPAL FINDINGS: In this study, a spatial dataset of gross domestic product (GDP), population density, and land use and land coverage (LULC) in 2050 and 2070 was obtained by simulation based on the different shared socioeconomic pathways (SSPs), and the future climatic data derived from the RCP scenarios were integrated into the Maxent models for predicting the future DF risk in the PRD region. Among all the variables included in this study, socioeconomics factors made the dominant contribution (83% or so) during simulating the current spatial distribution of the DF epidemics in the PRD region. Moreover, the spatial distribution of future DF risk identified by the climatic and socioeconomic (C&S) variables models was more detailed than that of the climatic variables models. Along with global warming and socioeconomic development, the zones with DF high and moderate risk will continue to increase, and the population at high and moderate risk will reach a maximum of 48.47 million (i.e., 63.78% of the whole PRD) under the RCP 4.5/SSP2 in 2070. CONCLUSIONS: The increasing DF risk may be an inevitable public health threat in the PRD region with rapid socioeconomic developments and global warming in the future. Our results suggest that curbs in emissions and more sustainable socioeconomic growth targets offer hope for limiting the future impact of dengue, and effective prevention and control need to continue to be strengthened at the junction of Guangzhou-Foshan, north-central Zhongshan city, and central-western Dongguan city. Our study provides useful clues for relevant hygienic authorities making targeted adapting strategies for this disease.
The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman’s rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran’s I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.
Dengue fever is a leading cause of illness and death in the tropics and subtropics, and the disease has become a threat to many nonendemic countries where the competent vectors such as Aedes albopictus and Aedes aegypti are abundant. The dengue epidemic in Tokyo, 2014, poses the critical importance to accurately model and predict the outbreak risk of dengue fever in nonendemic regions. Using climatological datasets and traveler volumes in Japan, where dengue was not seen for 70 years by 2014, we investigated the outbreak risk of dengue in 47 prefectures, employing the temperature-dependent basic reproduction number and a branching process model. Our results show that the effective reproduction number varies largely by season and by prefecture, and, moreover, the probability of outbreak if an untraced case is imported varies greatly with the calendar time of importation and location of destination. Combining the seasonally varying outbreak risk with time-dependent traveler volume data, the unconditional outbreak risk was calculated, illustrating different outbreak risks between southern coastal areas and northern tourist cities. As the main finding, the large travel volume with nonnegligible risk of outbreak explains the reason why a summer outbreak in Tokyo, 2014, was observed. Prefectures at high risk of future outbreak would be Tokyo again, Kanagawa or Osaka, and highly populated prefectures with large number of travelers.
Dengue is a complex disease with an increasing number of infections worldwide. This study aimed to analyse spatiotemporal dengue outbreaks using geospatial techniques and examine the effects of the weather on dengue outbreaks in the Klang Valley area, Kuala Lumpur, Malaysia. Daily weather variables including rainfall, temperature (maximum and minimum) and wind speed were acquired together with the daily reported dengue cases data from 2001 to 2011 and converted into geospatial format to identify whether there was a specific pattern of the dengue outbreaks. The association between these variables and dengue outbreaks was assessed using Spearman’s correlation. The result showed that dengue outbreaks consistently occurred in the study area during a 11-year study period. And that the strongest outbreaks frequently occurred in two high-rise apartment buildings located in Kuala Lumpur City centre. The results also show significant negative correlations between maximum temperature and minimum temperature on dengue outbreaks around the study area as well as in the area of the high-rise apartment buildings in Kuala Lumpur City centre.
Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980’s, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
BACKGROUND: This study aimed to explore whether the transmission routes of severe fever with thrombocytopenia syndrome (SFTS) will be affected by tick density and meteorological factors, and to explore the factors that affect the transmission of SFTS. We used the transmission dynamics model to calculate the transmission rate coefficients of different transmission routes of SFTS, and used the generalized additive model to uncover how meteorological factors and tick density affect the spread of SFTS. METHODS: In this study, the time-varying infection rate coefficients of different transmission routes of SFTS in Jiangsu Province from 2017 to 2020 were calculated based on the previous multi-population multi-route dynamic model (MMDM) of SFTS. The changes in transmission routes were summarized by collecting questionnaires from 537 SFTS cases in 2018-2020 in Jiangsu Province. The incidence rate of SFTS and the infection rate coefficients of different transmission routes were dependent variables, and month, meteorological factors and tick density were independent variables to establish a generalized additive model (GAM). The optimal GAM was selected using the generalized cross-validation score (GCV), and the model was validated by the 2016 data of Zhejiang Province and 2020 data of Jiangsu Province. The validated GAMs were used to predict the incidence and infection rate coefficients of SFTS in Jiangsu province in 2021, and also to predict the effect of extreme weather on SFTS. RESULTS: The number and proportion of infections by different transmission routes for each year and found that tick-to-human and human-to-human infections decreased yearly, but infections through animal and environmental transmission were gradually increasing. MMDM fitted well with the three-year SFTS incidence data (P<0.05). The best intervention to reduce the incidence of SFTS is to reduce the effective exposure of the population to the surroundings. Based on correlation tests, tick density was positively correlated with air temperature, wind speed, and sunshine duration. The best GAM was a model with tick transmissibility to humans as the dependent variable, without considering lagged effects (GCV = 5.9247E-22, R2 = 96%). Reported incidence increased when sunshine duration was higher than 11 h per day and decreased when temperatures were too high (>28°C). Sunshine duration and temperature had the greatest effect on transmission from host animals to humans. The effect of extreme weather conditions on SFTS was short-term, but there was no effect on SFTS after high temperature and sunshine hours. CONCLUSIONS: Different factors affect the infection rate coefficients of different transmission routes. Sunshine duration, relative humidity, temperature and tick density are important factors affecting the occurrence of SFTS. Hurricanes reduce the incidence of SFTS in the short term, but have little effect in the long term. The most effective intervention to reduce the incidence of SFTS is to reduce population exposure to high-risk environments.
BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS), one kind of tick-borne acute infectious disease, is caused by a novel bunyavirus. The relationship between meteorological factors and infectious diseases is a hot topic of current research. Liaoning Province has reported a high incidence of SFTS in recent years. However, the epidemiological characteristics of SFTS and its relationship with meteorological factors in the province remain largely unexplored. METHODS: Data on reported SFTS cases were collected from 2011 to 2019. Epidemiological characteristics of SFTS were analyzed. Spearman’s correlation test and generalized linear models (GLM) were used to identify the relationship between meteorological factors and the number of SFTS cases. RESULTS: From 2011 to 2019, the incidence showed an overall upward trend in Liaoning Province, with the highest incidence in 2019 (0.35/100,000). The incidence was slightly higher in males (55.9%, 438/783), and there were more SFTS patients in the 60-69 age group (31.29%, 245/783). Dalian City and Dandong City had the largest number of cases of SFTS (87.99%, 689/783). The median duration from the date of illness onset to the date of diagnosis was 8 days [interquartile range (IQR): 4-13 days]. Spearman correlation analysis and GLM showed that the number of SFTS cases was positively correlated with monthly average rainfall (r(s) = 0.750, P < 0.001; β = 0.285, P < 0.001), monthly average relative humidity (r(s) = 0.683, P < 0.001; β = 0.096, P < 0.001), monthly average temperature (r(s) = 0.822, P < 0.001; β = 0.154, P < 0.001), and monthly average ground temperature (r(s) = 0.810, P < 0.001; β = 0.134, P < 0.001), while negatively correlated with monthly average air pressure (r(s) = -0.728, P < 0.001; β = -0.145, P < 0.001), and monthly average wind speed (r(s) = -0.272, P < 0.05; β = -1.048, P < 0.001). By comparing both correlation coefficients and regression coefficients between the number of SFTS cases (dependent variable) and meteorological factors (independent variables), no significant differences were observed when considering immediate cases and cases with lags of 1 to 5 weeks for dependent variables. Based on the forward and backward stepwise GLM regression, the monthly average air pressure, monthly average temperature, monthly average wind speed, and time sequence were selected as relevant influences on the number of SFTS cases. CONCLUSION: The annual incidence of SFTS increased year on year in Liaoning Province. Incidence of SFTS was affected by several meteorological factors, including monthly average air pressure, monthly average temperature, and monthly average wind speed.
Lyme borreliosis, recognized as one of the most important tick-borne diseases worldwide, has been increasing in incidence and spatial extent. Currently, there are few geographic studies about the distribution of Lyme borreliosis risk across China. Here we established a nationwide database that involved Borrelia burgdorferi sensu lato (B. burgdorferi) detected in humans, vectors, and animals in China. The eco-environmental factors that shaped the spatial pattern of B. burgdorferi were identified by using a two-stage boosted regression tree model and the model-predicted risks were mapped. During 1986-2020, a total of 2,584 human confirmed cases were reported in 25 provinces. Borrelia burgdorferi was detected from 35 tick species with the highest positive rates in Ixodes granulatus, Hyalomma asiaticum, Ixodes persulcatus, and Haemaphysalis concinna ranging 20.1%-24.0%. Thirteen factors including woodland, NDVI, rainfed cropland, and livestock density were determined as important drivers for the probability of B. burgdorferi occurrence based on the stage 1 model. The stage 2 model identified ten factors including temperature seasonality, NDVI, and grasslands that were the main determinants used to distinguish areas at high or low-medium risk of B. burgdorferi, interpreted as potential occurrence areas within the area projected by the stage 1 model. The projected high-risk areas were not only concentrated in high latitude areas, but also were distributed in middle and low latitude areas. These high-resolution evidence-based risk maps of B. burgdorferi was first created in China and can help as a guide to future surveillance and control and help inform disease burden and infection risk estimates.
Ticks are known as vectors of several pathogens causing various human and animal diseases including Lyme borreliosis, tick-borne encephalitis, and Crimean-Congo hemorrhagic fever. While China is known to have more than 100 tick species well distributed over the country, our knowledge on the likely distribution of ticks in the future remains very limited, which hinders the prevention and control of the risk of tick-borne diseases. In this study, we selected four representative tick species which have different regional distribution foci in mainland China. i.e., Dermacentor marginatus, Dermacentor silvarum, Haemaphysalis longicornis and Ixodes granulatus. We used the MaxEnt model to identify the key environmental factors of tick occurrence and map their potential distributions in 2050 under four combined climate and socioeconomic scenarios (i.e., SSP1-RCP2.6, SSP2-RCP4.5, SSP3-RCP7.0 and SSP5-RCP8.5). We found that the extent of the urban fabric, cropland and forest, temperature annual range and precipitation of the driest month were the main determinants of the potential distributions of the four tick species. Under the combined scenarios, with climate warming, the potential distributions of ticks shifted to further north in China. Due to a decrease in the extent of forest, the distribution probability of ticks declined in central and southern China. In contrast with previous findings on an estimated amplification of tick distribution probability under the extreme emission scenario (RCP8.5), our studies projected an overall reduction in the distribution probability under RCP8.5, owing to an expected effect of land use. Our results could provide new data to help identify the emerging risk areas, with amplifying suitability for tick occurrence, for the prevention and control of tick-borne zoonoses in mainland China. Future directions are suggested towards improved quantity and quality of the tick occurrence database, comprehensiveness of factors and integration of different modelling approaches, and capability to model pathogen spillover at the human-tick interface.
Climate is widely known as an important driver to transmit vector-borne diseases (VBD). However, evidence of the role of climate variability on VBD risk in Indonesia has not been adequately understood. We conducted a systematic literature review to collate and critically review studies on the relationship between climate variability and VBD in Indonesia. We searched articles on PubMed, Scopus, and Google Scholar databases that are published until December 2021. Studies that reported the relationship of climate and VBD, such as dengue, chikungunya, Zika, and malaria, were included. For the reporting, we followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A total of 66 out of 284 studies were reviewed. Fifty-two (78.8%) papers investigated dengue, 13 (19.7%) papers studied malaria, one (1.5%) paper discussed chikungunya, and no (0%) paper reported on Zika. The studies were predominantly conducted in western Indonesian cities. Most studies have examined the short-term effect of climate variability on the incidence of VBD at national, sub-national, and local levels. Rainfall (n = 60/66; 90.9%), mean temperature (T(mean)) (n = 50/66; 75.8%), and relative humidity (RH) (n = 50/66; 75.8%) were the common climatic factors employed in the studies. The effect of climate on the incidence of VBD was heterogenous across locations. Only a few studies have investigated the long-term effects of climate on the distribution and incidence of VBD. The paucity of high-quality epidemiological data and variation in methodology are two major issues that limit the generalizability of evidence. A unified framework is required for future research to assess the impacts of climate on VBD in Indonesia to provide reliable evidence for better policymaking.
BACKGROUND: Scrub typhus (ST) and murine typhus (MT) are common but poorly understood causes of fever in Laos. We examined the spatial and temporal distribution of ST and MT, with the intent of informing interventions to prevent and control both diseases. METHODOLOGY AND PRINCIPLE FINDINGS: This study included samples submitted from 2003 to 2017 to Mahosot Hospital, Vientiane, for ST and MT investigation. Serum samples were tested using IgM rapid diagnostic tests. Patient demographic data along with meteorological and environmental data from Laos were analysed. Approximately 17% of patients were positive for either ST (1,337/8,150 patients tested) or MT (1,283/7,552 patients tested). While both diseases occurred in inhabitants from Vientiane Capital, from the univariable analysis MT was positively and ST negatively associated with residence in Vientiane Capital. ST was highly seasonal, with cases two times more likely to occur during the wet season months of July-September compared to the dry season whilst MT peaked in the dry season. Multivariable regression analysis linked ST incidence to fluctuations in relative humidity whereas MT was linked to variation in temperature. Patients with ST infection were more likely to come from villages with higher levels of surface flooding and vegetation in the 16 days leading up to diagnosis. CONCLUSIONS: The data suggest that as cities expand, high risk areas for MT will also expand. With global heating and risks of attendant higher precipitation, these data suggest that the incidence and spatial distribution of both MT and ST will increase.
Plasmodium knowlesi is an emerging species for malaria in Malaysia, particularly in East Malaysia. This infection contributes to almost half of all malaria cases and deaths in Malaysia and poses a challenge in eradicating malaria. The aim of this study was to develop a predictive model for P. knowlesi susceptibility areas in Sabah, Malaysia, using geospatial data and artificial neural networks (ANNs). Weekly malaria cases from 2013 to 2014 were used to identify the malaria hotspot areas. The association of malaria cases with environmental factors (elevation, water bodies, and population density, and satellite images providing rainfall, land surface temperature, and normalized difference vegetation indices) were statistically determined. The significant environmental factors were used as input for the ANN analysis to predict malaria cases. Finally, the malaria susceptibility index and zones were mapped out. The results suggested integrating geospatial data and ANNs to predict malaria cases, with overall correlation coefficient of 0.70 and overall accuracy of 91.04%. From the malaria susceptibility index and zoning analyses, it was found that areas located along the Crocker Range of Sabah and the East part of Sabah were highly susceptible to P. knowlesi infections. Following this analysis, targetted entomological mapping and malaria control programs can be initiated.
Simple Summary The globalization of trade and travel, in combination with climate change, have resulted in the geographical expansion of mosquito-borne diseases. Moreover, over-reliance on chemical pesticides to control mosquitoes has resulted in resistance, which threatens the management of disease risk. We show, for the first time, that mosquito traps baited with human odors, in combination with controlling mosquito larvae in breeding sites, resulted in the near elimination of mosquito populations on two small islands, and the elimination of Aedes mosquitoes for 6+ months on a third island, in the Maldives. The levels of control achieved are comparable to current genetic control methods that are far more costly and impractical for implementation on small islands. The approach presented here poses the first alternative in decades to manage mosquito-borne disease risk on small (tropical) islands in an affordable and environmentally friendly manner. Globally, environmental impacts and insecticide resistance are forcing pest control organizations to adopt eco-friendly and insecticide-free alternatives to reduce the risk of mosquito-borne diseases, which affect millions of people, such as dengue, chikungunya or Zika virus. We used, for the first time, a combination of human odor-baited mosquito traps (at 6.0 traps/ha), oviposition traps (7.2 traps/ha) and larval source management (LSM) to practically eliminate populations of the Asian tiger mosquito Aedes albopictus (peak suppression 93.0% (95% CI 91.7-94.4)) and the Southern house mosquito Culex quinquefasciatus (peak suppression 98.3% (95% CI 97.0-99.5)) from a Maldivian island (size: 41.4 ha) within a year and thereafter observed a similar collapse of populations on a second island (size 49.0 ha; trap densities 4.1/ha and 8.2/ha for both trap types, respectively). On a third island (1.6 ha in size), we increased the human odor-baited trap density to 6.3/ha and then to 18.8/ha (combined with LSM but without oviposition traps), after which the Aedes mosquito population was eliminated within 2 months. Such suppression levels eliminate the risk of arboviral disease transmission for local communities and safeguard tourism, a vital economic resource for small island developing states. Terminating intense insecticide use (through fogging) benefits human and environmental health and restores insect biodiversity, coral reefs and marine life in these small and fragile island ecosystems. Moreover, trapping poses a convincing alternative to chemical control and reaches impact levels comparable to contemporary genetic control strategies. This can benefit numerous communities and provide livelihood options in small tropical islands around the world where mosquitoes pose both a nuisance and disease threat.
Japanese encephalitis (JE) is the major cause of viral encephalitis (VE) in most Asian-Pacific countries. In Vietnam, there is no nationwide surveillance system for JE due to lack of medical facilities and diagnoses. Culex tritaeniorhynchus, Culex vishnui, and Culex quinquefasciatus have been identified as the major JE vectors in Vietnam. The main objective of this study was to forecast a risk map of Culex mosquitoes in Hanoi, which is one of the most densely populated cities in Vietnam. A total of 10,775 female adult Culex mosquitoes were collected from 513 trapping locations. We collected temperature and precipitation information during the study period and its preceding month. In addition, the other predictor variables (e.g., normalized difference vegetation index [NDVI], land use/land cover and human population density), were collected for our analysis. The final model selected for estimating the Culex mosquito abundance included centered rainfall, quadratic term rainfall, rice cover ratio, forest cover ratio, and human population density variables. The estimated spatial distribution of Culex mosquito abundance ranged from 0 to more than 150 mosquitoes per 900m2. Our model estimated that 87% of the Hanoi area had an abundance of mosquitoes from 0 to 50, whereas approximately 1.2% of the area showed more than 100 mosquitoes, which was mostly in the rural/peri-urban districts. Our findings provide better insight into understanding the spatial distribution of Culex mosquitoes and its associated environmental risk factors. Such information can assist local clinicians and public health policymakers to identify potential areas of risk for JE virus. Risk maps can be an efficient way of raising public awareness about the virus and further preventive measures need to be considered in order to prevent outbreaks and onwards transmission of JE virus.
Murine typhus is a flea-borne zoonotic disease that has been recently reported on Reunion Island, an oceanic volcanic island located in the Indian Ocean. Five years of survey implemented by the regional public health services have highlighted a strong temporal and spatial structure of the disease in humans, with cases mainly reported during the humid season and restricted to the dry southern and western portions of the island. We explored the environmental component of this zoonosis in an attempt to decipher the drivers of disease transmission. To do so, we used data from a previously published study (599 small mammals and 175 Xenopsylla fleas from 29 sampling sites) in order to model the spatial distribution of rat fleas throughout the island. In addition, we carried out a longitudinal sampling of rats and their ectoparasites over a 12 months period in six study sites (564 rats and 496 Xenopsylla fleas) in order to model the temporal dynamics of flea infestation of rats. Generalized Linear Models and Support Vector Machine classifiers were developed to model the Xenopsylla Genus Flea Index (GFI) from climatic and environmental variables. Results showed that the spatial distribution and the temporal dynamics of fleas, estimated through the GFI variations, are both strongly controlled by abiotic factors: rainfall, temperature and land cover. The models allowed linking flea abundance trends with murine typhus incidence rates. Flea infestation in rats peaked at the end of the dry season, corresponding to hot and dry conditions, before dropping sharply. This peak of maximal flea abundance preceded the annual peak of human murine typhus cases by a few weeks. Altogether, presented data raise novel questions regarding the ecology of rat fleas while developed models contribute to the design of control measures adapted to each micro region of the island with the aim of lowering the incidence of flea-borne diseases.
The population dynamics of mosquitoes in temperate regions are not as well understood as those in tropical and subtropical regions, despite concerns that vector-borne diseases may be prevalent in future climates. Aedes albopictus, a vector mosquito in temperate regions, undergoes egg diapause while overwintering. To assess the prevalence of mosquito-borne diseases in the future, this study aimed to simulate and predict mosquito population dynamics under estimated future climatic conditions. In this study, we tailored the physiology-based climate-driven mosquito population (PCMP) model for temperate mosquitoes to incorporate egg diapauses for overwintering. We also investigated how the incorporation of the effect of rainfall on larval carrying capacity (into a model) changes the population dynamics of this species under future climate conditions. The PCMP model was constructed to simulate mosquito population dynamics, and the parameters of egg diapause and rainfall effects were estimated for each model to fit the observed data in Tokyo. We applied the global climate model data to the PCMP model and observed an increase in the mosquito population under future climate conditions. By applying the PCMP models (with or without the rainfall effect on the carrying capacity of the A. albopictus), our projections indicated that mosquito population dynamics in the future could experience changes in the patterns of their active season and population abundance. According to our results, the peak population number simulated using the highest CO2 emission scenario, while incorporating the rainfall effect on the carrying capacity, was approximately 1.35 times larger than that predicted using the model that did not consider the rainfall effect. This implies that the inclusion of rainfall effects on mosquito population dynamics has a major impact on the risk assessments of mosquito-borne diseases in the future.
Dengue is a viral borne disease with complex transmission dynamics. Disease outbreak can exert an increasing pressure on the health system with high mortality. Understanding and predicting the outbreaks of dengue transmission is vital in controlling the spread. Mathematical models have become important tool in predicting the dynamics of dengue. Due to the complexity of the disease, general time series models do not describe the impact of the external parameters. In this work, we propose a generalised linear regression model to understand the dynamics of the dengue disease and predict the future outbreaks. To moderate the model, cross-correlation between reported dengue cases and climatic factors were identified using Pearson cross-correlation formula. Then threshold value was defined based on reported data in order to identify minimum risk level for the states of dengue outbreaks. Further, obtained results were compared.
INTRODUCTION: Numerous studies on the mosquito life cycle and transmission efficacy were performed under constant temperatures. Mosquito in wild, however, is not exposed to constant temperature but is faced with temperature variation on a daily basis. METHODS: In the present study, the mosquito life cycle and Zika virus transmission efficiency were conducted at daily fluctuating temperatures and constant temperatures. Aedes albopictus was infected with the Zika virus orally. The oviposition and survival of the infected mosquitoes and hatching rate, the growth cycle of larvae at each stage, and the infection rate (IR) of the progeny mosquitoes were performed at two constant temperatures (23°C and 31°C) and a daily temperature range (DTR, 23-31°C). RESULTS: It showed that the biological parameters of mosquitoes under DTR conditions were significantly different from that under constant temperatures. Mosquitoes in DTR survived longer, laid more eggs (mean number: 36.5 vs. 24.2), and had a higher hatching rate (72.3% vs. 46.5%) but a lower pupation rate (37.9% vs. 81.1%) and emergence rate (72.7% vs. 91.7%) than that in the high-temperature group (constant 31°C). When compared to the low-temperature group (constant 23°C), larvae mosquitoes in DTR developed faster (median days: 9 vs. 23.5) and adult mosquitoes carried higher Zika viral RNA load (median log(10) RNA copies/μl: 5.28 vs. 3.86). However, the temperature or temperature pattern has no effect on transovarial transmission. DISCUSSION: Those results indicated that there are significant differences between mosquito development and reproductive cycles under fluctuating and constant temperature conditions, and fluctuating temperature is more favorable for mosquitos’ survival and reproduction. The data would support mapping and predicting the distribution of Aedes mosquitoes in the future and establishing an early warning system for Zika virus epidemics.
Cutaneous leishmaniasis (CL) is one of the most important health challenges in hyperendemic countries like Iran. Geospatial information systems-based studies have shown that factors, including land cover, altitude, slope temperature, rainfall and animal livestock, affect CL distribution in Kohgyloyeh and Boyerahmad province, southwestern Iran. However, the question of the influence of nomadic tribes, who travel with their goats and sheep, on CL is unanswered. We, therefore, investigated their role in CL epidemiology from 2008 to 2017 and compare them with geoclimatic factors. CL patient demographic data and their village/city addresses were retrieved from Provincial Health Center and mapped on the geographic information system (GIS) layer of the province’s political divisions. Nomadic travel routes (NTRs) with a 2 km buffer were generated and their effect on CL was investigated together with the interpolated layers of rainfall, temperatures, humidity, slope, elevation, land covers, by binary regression. CL was significantly more common in villages/cities in the 2 km NTR zone (p value < .001; OR = 1.96; 95% CI = 1.4-2.745). Geoclimatic factors, including slope, elevation, rainfall, temperatures, humidity and most of the landcovers, were not significantly different inside and outside the NTR. Areas of irrigated farm were the only effective landcover on CL (p value = .049; OR = 2.717; 95% CI = 1.003-7.361) within the NTR versus non-NTR. Living within NTRs almost doubled the risk of acquiring CL. Several factors for this include passage through areas of high sand fly activity, increased contact between sandflies and humans, sheep and goats, and feeding on their blood and faeces, and low availability of health facilities that should be more investigated and considered in the future control programs.
Trypanosomes are the hemoflagellate kinetoplastid protozoan parasites affecting a wide range of vertebrate hosts having insufficient host specificity. Climatic change, deforestation, globalization, trade agreements, close association and genetic selection in links with environmental, vector, reservoir and potential susceptible hosts’ parameters have led to emergence of atypical human trypanosomosis (a-HT). Poor recording of such neglected tropical disease, low awareness in health professions and farming community has approached a serious intimidation for mankind. Reports of animal Trypanosoma species are now gradually increasing in humans, and lack of any compiled literature has diluted the issue. In the present review, global reports of livestock and rodent trypanosomes reported from human beings are assembled and discrepancies with the available literature are discussed along with morphological features of Trypanosoma species. We have described 21 human cases from the published information. Majority of cases 10 (47%) are due to T. lewisi, followed by 5 (24%) cases of T. evansi, 4 (19%) cases of T. brucei and 1 (5%) case each of T. vivax and T. congolense. Indian subcontinent witnessed 13 cases of a-HT, of which 9 cases are reported from India, which includes 7 cases of T. lewisi and 2 cases of T. evansi. Apart from, a-HT case reports, epidemiological investigation and treatment aspects are also discussed. An attempt has been made to provide an overview of the current situation of atypical human trypanosomosis caused by salivarian animal Trypanosoma globally. The probable role of Trypanosoma lytic factors (TLF) present in normal human serum (NHS) in providing innate immunity against salivarian animal Trypanosoma species and the existing paradox in medical science after the finding on intact functional apolipoprotein L1 (ApoL1) in Vietnam T. evansi Type A case is also discussed to provide an update on all aspects of a-HT. Insufficient data and poor reporting in Asian and African countries are the major hurdle resulting in under-reporting of a-HT, which is a potential emerging threat. Therefore, concerted efforts must be directed to address attentiveness, preparedness and regular surveillance in suspected areas with training of field technicians, medical health professionals and veterinarians. Enhancing a one health approach is specifically important in case of trypanosomosis.
Zoonotic cutaneous leishmaniasis (ZCL) is an important vector-borne disease with an incidence of 15.8 cases per 100,000 people in Iran in 2019. Despite all efforts to control the disease, ZCL has expanded into new areas during the last decades. The aim of this study was to predict the best ecological niches for both vectors and reservoirs of ZCL under climate change scenarios in Iran. Several online scientific databases were searched. In this study, various scientific sources (Google Scholar, PubMed, SID, Ovid Medline, Web of Science, Irandoc, Magiran) were searched. The inclusion criteria for this study included all records with spatial information about vectors and reservoirs of ZCL which were published between 1980 and 2019. The bioclimatic data were downloaded from online databases. MaxEnt model was used to predict the ecological niches for each species under two climate change scenarios in two periods: the 2030s and 2050s. The results obtained from the model were analysed in ArcMap to find the vulnerability of different provinces for the establishment of ZCL foci. The area under the curve (AUC) for all models was >0.8, which suggests the models are able to make an accurate prediction. The distribution of all studied species in different climatic conditions showed changes. The variables affecting each of the studied species are introduced in the article. The predicted maps show that by 2050 there will be more suitable areas for the co-occurrence of vector and reservoir(s) of ZCL in Iran compared to the current climate condition and RCP2.6 scenario. An area in the northwest of Iran is predicted to have suitable environmental conditions for both vectors and reservoirs of ZCL, although the disease has not yet been reported in this area. These areas should be considered for field studies to confirm these results and to prevent the establishment of new ZCL foci in Iran.
Leishmania parasites can cause zoonotic cutaneous leishmaniasis (CL) by circulating between humans, rodents, and sandflies in Iran. In this study, published data were collected from scientific sources such as Web of Science, Scopus, PubMed, Springer, ResearchGate, Wiley Online, Ovid, Ebsco, Cochrane Library, Google scholar, and SID. Keywords searched in the articles, theses, and abstracts from 1983 to 2021 were cutaneous leishmaniasis, epidemiology, reservoir, vector, climatic factors, identification, and Iran. This review revealed that CL was prevalent in the west of Iran, while the center and south of Iran were also involved in recent years. The lack of facilities in suburban regions was an aggravating factor in the human community. Some parts of southern Iran were prominent foci of CL due the presence of potential rodent hosts in these regions. Rhombomys opimus, Meriones lybicus, and Tatera indica were well-documented species for hosting the Leishmania species in Iran. Moreover, R. opimus has been found with a coinfection of Leishmania major and L. turanica from the northeast and center of Iran. Mashhad, Kerman, Yazd, and sometimes Shiraz and Tehran foci were distinct areas for L. tropica. Molecular identifications using genomic diagnosis of kDNA and ITS1 fragments of the parasite indicated that there is heterogeneity in leishmaniasis in different parts of the country. Although cutaneous leishmaniasis has been a predicament for the health system, it is relatively under control in Iran.
INTRODUCTION: Cutaneous leishmaniasis (CL) is currently a health problem in several parts of Iran, particularly Kerman. This study was conducted to determine the incidence and trend of CL in Kerman during 2014-2020 and its forecast up to 2023. The effects of meteorological variables on incidence was also evaluated. MATERIALS AND METHODS: 4993 definite cases of CL recorded from January 2014 to December 2020 by the Vice-Chancellor for Health at Kerman University of Medical Sciences were entered. Meteorological variables were obtained from the national meteorological site. The time series SARIMA methods were used to evaluate the effects of meteorological variables on CL. RESULTS: Monthly rainfall at the lag 0 (β = -0.507, 95% confidence interval:-0.955,-0.058) and monthly sunny hours at the lag 0 (β = -0.214, 95% confidence interval:-0.308,-0.119) negatively associated with the incidence of CL. Based on the Akaike information criterion (AIC) the multivariable model (AIC = 613) was more suitable than univariable model (AIC = 690.66) to estimate the trend and forecast the incidence up to 36 months. CONCLUSION: The decreasing pattern of CL in Kerman province highlights the success of preventive, diagnostic and therapeutic interventions during the recent years. However, due to endemicity of disease, extension and continuation of such interventions especially before and during the time periods with higher incidence is essential.
Leishmaniasis is the most important parasitic infection in Iran. The aim of this study was to model the changing suitability patterns of Leishmania infantum, the causative agent of visceral leishmaniasis for the 21(st) century in the country. Temperature, precipitation, and aridity-nature distribution limiting bioclimatic variables were involved in the ecological modelling. The altitudinal trends were considered by using 100 m bars. In Iran, the topographical patterns strongly impact the changing patterns of the suitability of L. infantum due to climate change. In general, climate change will decrease the parasite’s suitability in the areas at low altitudes and increase in the middle and higher elevation regions. Increasing values are mainly predicted in the West, the decreasing suitability values in the East part of Iran. The altitudinal shifts and the reduced spatial distribution of L. infantum in the arid regions of East and Central Iran were modelled.
In this paper, a Chikungunya dynamical model with virus mutation and transovarial transmission is developed, which incorporates the effect of seasonal temperature changes on disease transmission through time-dependent parameters. Firstly, the threshold parameter (Rm) that determines the persistence and ex -0 tinction of mosquito populations is given, and then the disease reproduction number R-0 is defined. Sec-ondly, it is proved that if (R-0(m)) > 1 and R-0 < 1, the disease disappears; if (R-0(m)) > 1 and R-0 > 1, then 0 0 Chikungunya with mutants and non-mutants will persist simultaneously. Finally, a case study is carried out with the data in Kerala, India, where the virus mutation causes the outbreak of Chikungunya. Data on newly confirmed human cases in the state between 2007 and 2010 is fitted and the theoretical results obtained in the previous section are validated. In addition, the effects of seasonal temperature change, virus mutation and transovarial transmission on the prevalence of the disease are studied by numerical simulations from different aspects. 2020 MSC: 34K13; 37N25; 92D30.(C) 2022 Elsevier Ltd. All rights reserved.
As incidences of food poisoning, especially norovirus-induced diarrhea, are associated with climate change, there is a need for an approach that can be used to predict the risks of such illnesses with high accuracy. In this paper, we predict the winter norovirus incidence rate in Korea compared to that of other diarrhea-causing viruses using a model based on B-spline added to logistic regression to estimate the long-term pattern of illness. We also develop a risk index based on the estimated probability of occurrence. Our probabilistic analysis shows that the risk of norovirus-related food poisoning in winter will remain stable or increase in Korea based on various Representative Concentration Pathway (RCP) scenarios. Our approach can be used to obtain an overview of the changes occurring in regional and seasonal norovirus patterns that can help assist in making appropriate policy decisions.
In the Southeast Asian region, the Philippines and Malaysia are two of the most affected by Harmful Algal Blooms (HABs). Using long-term observations of HAB events, we determined if these are increasing in frequency and duration, and expanding across space in each country. Blooms of Paralytic Shellfish Toxin (PST)-producing species in the Philippines did increase in frequency and duration during the early to mid-1990s, but have stabilized since then. However, the number of sites affected by these blooms continue to expand though at a slower rate than in the 1990s. Furthermore, the type of HABs and causative species have diversified for both toxic blooms and fish kill events. In contrast, Malaysia showed no increasing trend in the frequency of toxic blooms over the past three decades since Pyrodinium bahamense was reported in 1976. However, similar to the Philippines, other PST producers such as Alexandrium minutum and Alexandrium tamiyavanichii have become a concern. No amnesic shellfish poisoning (ASP) has been confirmed in either Philippines or Malaysia thus far, while ciguatera fish poisoning cases are known from the Philippines and Malaysia but the causative organisms remain poorly studied. Since the 1990s and early 2000s, recognition of the distribution of other PST-producing species such as species of Alexandrium and Gymnodinium catenatum in Southeast Asia has grown, though there has been no significant expansion in the known distributions within the last decade. A major more recent problem in the two countries and for Southeast Asia in general are the frequent fish-killing algal blooms of various species such as Prorocentrum cordatum, Margalefidinium polykrikoides, Chattonella spp., and unarmored dinoflagellates (e.g., Karlodinium australe and Takayama sp.). These new sites affected and the increase in types of HABs and causative species could be attributed to various factors such as introduction through mariculture and eutrophication, and partly because of increased scientific awareness. These connections still need to be more concretely investigated. The link to the El Niño Southern Oscillation (ENSO) should also be better understood if we want to discern how climate change plays a role in these patterns of HAB occurrences.
This study aimed at, and developed, a climate-driven model for predicting the abundance of V. parahaemolyticus in oysters based on the local climatological and environmental conditions in Taiwan. The predictive model was constructed using the elastic net machine learning method, and the most influential predictors were evaluated using a permutation-based approach. The abundance of V. parahaemolyticus in oysters in different seasons, time horizons, and representative concentration pathways (RCPs) were predicted using the Elastic-net machine learning model. The results showed: (1) the variation in wind speed or gust wind speed, sea surface temperature, precipitation, and pH influenced the prediction of V. parahaemolyticus concentration in oysters, and (2) the level of V. parahaemolyticus in oysters in Taiwan was projected to be increased by 40-67% in the near future (2046-2065) and by 39-86% by the end of twentieth-century (2081-2100) if the global temperature continues to increase due to climate change. The findings in this study may be used as inputs for quantifying the V. parahaemolyticus infection risk from eating this seafood in Taiwan.
BACKGROUND: The incidence of Escherichia coli bloodstream infections (BSI) is high and increasing. We aimed to describe the effect of season and temperature on the incidence of E. coli BSI and antibiotic-resistant E. coli BSI and to determine differences by place of BSI onset. METHODS: All E. coli BSI in adult Israeli residents between January 1, 2018 and December 19, 2019 were included. We used the national database of mandatory BSI reports and outdoor temperature data. Monthly incidence and resistance were studied using multivariable negative binomial regressions with season (July-October vs. other) and temperature as covariates. RESULTS: We included 10,583 events, 9012 (85%) community onset (CO) and 1571 (15%) hospital onset (HO). For CO events, for each average monthly temperature increase of 5.5 °C, the monthly number of events increased by 6.2% (95% CI 1.6-11.1%, p = 0.008) and the monthly number of multidrug-resistant events increased by 4.9% (95% CI 0.3-9.7%, p = 0.04). The effect of season was not significant. For HO events, incidence of BSI and resistant BSI were not associated with temperature or season. CONCLUSION: Temperature increases the incidence of CO E. coli BSI and CO antibiotic-resistant E. coli BSI. Global warming threatens to increase the incidence of E. coli BSI.
Previous studies have explored the effect between ambient temperature and infectious diarrhea (ID) mostly using relative risk, which provides limited information in practical applications. Few studies have focused on the disease burden of ID caused by temperature, especially for different subgroups and cities in a multi-city setting. This study aims to estimate the effects and attributable risks of temperature on category C ID and explore potential modifiers among various cities in Guangdong. First, distributed lag non-linear models (DLNMs) were used to explore city-specific associations between daily mean temperature and category C ID from 2014 to 2016 in Guangdong and pooled by applying multivariate meta-analysis. Then, multivariate meta-regression was implemented to analyze the potential heterogeneity among various cities. Finally, we assessed the attributable burden of category C ID due to temperature, low (below the 5th percentile of temperature) and high temperature (above the 95th percentile of temperature) for each city and subgroup population. Compared with the 50th percentile of daily mean temperature, adverse effects on category C ID were found when the temperature was lower than 12.27 ℃ in Guangdong Province. Some city-specific factors (longitude, urbanization rate, population density, disposable income per capita, and the number of medical technicians and beds per thousand persons) could modify the relationship of temperature-category C ID. During the study period, there were 60,505 category C ID cases (17.14% of total cases) attributable to the exposure of temperature, with the attributable fraction (AF) of low temperature (4.23%, 95% empirical confidence interval (eCI): 1.79-5.71%) higher than high temperature (1.34%, 95% eCI: 0.86-1.64%). Males, people under 5 years, and workers appeared to be more vulnerable to temperature, with AFs of 29.40%, 19.25%, and 21.49%, respectively. The AF varied substantially at the city level, with the largest AF of low temperature occurring in Shaoguan (9.58%, 95% eCI: 8.36-10.09%), and that of high temperature occurring in Shenzhen (3.16%, 95% eCI: 2.70-3.51%). Low temperature was an important risk factor for category C ID in Guangdong Province, China. The exposure-response relationship could be modified by city-specific characteristics. Considering the whole population, the attributable risk of low temperature was much higher than that of high temperature, and males, people under 5 years, and workers were vulnerable populations.
Infectious diarrhea in China showed a significant pattern. Many researchers have tried to reveal the drivers, yet usually only meteorological factors were taken into consideration. Furthermore, the diarrheal data they analyzed were incomplete and the algorithms they exploited were inefficient of adapting realistic relationships. Here, we investigate the impacts of meteorological and social factors on the number of infectious diarrhea cases in China. A machine learning algorithm called the Random Forest is utilized. Our results demonstrate that nearly half of infectious diarrhea occurred among children under 5 years old. Generally speaking, increasing temperature or relative humidity leads to increased cases of infectious diarrhea in China. Nevertheless, people from different age groups or different regions own different sensitivities to meteorological factors. The weight of feces that are harmfully treated could be a possible reason for infectious diarrhea of the elderly as well as children under 5 years old. These findings indicate that infectious diarrhea prevention for children under 5 years old remains a primary task in China. Personalized prevention countermeasures ought to be provided to different age groups and different regions. It is essential to bring the weight of feces that are harmfully treated to the forefront when considering infectious diarrhea prevention.
No study has ever investigated how ambient temperature and PM(2.5) mediate rotavirus infection (RvI) in children. We used insurance claims data from Taiwan in 2006-2012 to evaluate the RvI characteristics in children aged ≤ 9. The RvI incidence rates were higher in colder months, reaching the highest in March (117.0/100 days), and then declining to the lowest in July (29.2/100 days). The age-sex-specific average incident cases were all higher in boys than in girls. Stratified analysis by temperature (<20, 20-24, and ≥25 °C) and PM(2.5) (<17.5, 17.5-31.4, 31.5-41.9, and ≥42.0 μg/m^3) showed that the highest incidence was 16.4/100 days at average temperatures of <20 °C and PM(2.5) of 31.5–41.9 μg/m^3, with Poisson regression analysis estimating an adjusted relative risk (aRR) of 1.26 (95% confidence interval (CI) = 1.11-1.43), compared to the incidence at the reference condition (<20 °C and PM2.5 < 17.5 μg/m^3). As the temperature increased, the incident RvI cases reduced to 4.84 cases/100 days (aRR = 0.40, 95% CI = 0.35-0.45) when it was >25 °C with PM(2.5) < 17.5 μg/m^3, or to 9.84/100 days (aRR = 0.81, 95% CI = 0.77-0.93) when it was >25 °C with PM2.5 > 42 μg/m^3). The seasonal RvI is associated with frequent indoor personal contact among children in the cold months. The association with PM(2.5) could be an alternative assessment due to temperature inversion.
Vibrio infection was rarely reported in Tasmania prior to 2016, when a multistate outbreak of Vibrio parahaemolyticus associated with Tasmanian oysters was identified and 11 people reported ill. Since then, sporadic foodborne cases have been identified following consumption of commercially- and recreationally-harvested oysters. The increases in both foodborne and non-foodborne Vibrio infections in Tasmania are likely associated with increased sea water temperatures. As oyster production increases and climate change raises the sea surface temperature of our coastline, Tasmania expects to see more vibriosis cases. Vibriosis due to oyster consumption has been reported in other Australian states, but the variability in notification requirements between jurisdictions makes case and outbreak detection difficult and potentially hampers any public health response to prevent further illness.
BACKGROUND: High atmospheric temperature has been associated with the occurrence of bacillary dysentery (BD). Recent studies have suggested that hot extremes may influence health outcomes, however, none have examined the association between hot extremes and BD risk, especially at the national level. OBJECTIVES: To assess the effect and attributable burden of hot extremes on BD cases and to identify populations at high risk of BD. METHODS: Daily incident BD data of 31 provincial capital cities from 2010 to 2018 were collected from the Chinese Center for Disease Control and Prevention, weather data was obtained from the fifth generation of the European Re-Analysis Dataset. Three types of hot extremes, including hot day, hot night, and hot day and night, were defined according to single or sequential occurrence of daytime hot and nighttime hot within 24 h. A two-stage analytical strategy combined with distributed lag non-linear models (DLNM) was used to evaluate city-specific associations and national pooled estimates. RESULTS: Hot extremes were significantly associated with the risk of BD on lagged 1-6 days. The overall cumulative relative risk (RR) was 1.136 [95% confidence interval (CI): 1.022, 1.263] for hot day, 1.181 (95% CI: 1.019, 1.369) for hot night, and 1.154 (95% CI: 1.038, 1.283) for hot day and night. Northern residents, females, and children younger than or equal to 14 years old were vulnerable under hot night, southern residents were vulnerable under hot day, and males were vulnerable under hot day and night. 1.854% (95% CI: 1.294%, 2.205%) of BD cases can be attributable to hot extremes, among which, hot night accounted for a large proportion. CONCLUSIONS: Hot extremes may significantly increase the incidence risk and disease burden of BD. Type-specific protective measures should be taken to reduce the risk of BD, especially in those we found to be particularly vulnerable.
BACKGROUND: Typhoid fever is endemic in some Pacific Island Countries including Fiji and Samoa yet genomic surveillance is not routine in such settings. Previous studies suggested imports of the global H58 clade of Salmonella enterica var Typhi (Salmonella Typhi) contribute to disease in these countries which, given the MDR potential of H58, does not auger well for treatment. The objective of the study was to define the genomic epidemiology of Salmonella Typhi in Fiji. METHODS: Genomic sequencing approaches were implemented to study the distribution of 255 Salmonella Typhi isolates from the Central Division of Fiji. We augmented epidemiological surveillance and Bayesian phylogenomic approaches with a multi-year typhoid case-control study to define geospatial patterns among typhoid cases. FINDINGS: Genomic analyses showed Salmonella Typhi from Fiji resolved into 2 non-H58 genotypes with isolates from the two dominant ethnic groups, the Indigenous (iTaukei) and non-iTaukei genetically indistinguishable. Low rates of international importation of clones was observed and overall, there were very low levels an antibiotic resistance within the endemic Fijian typhoid genotypes. Genomic epidemiological investigations were able to identify previously unlinked case clusters. Bayesian phylodynamic analyses suggested that genomic variation within the larger endemic Salmonella Typhi genotype expanded at discreet times, then contracted. INTERPRETATION: Cyclones and flooding drove ‘waves’ of typhoid outbreaks in Fiji which, through population aggregation, poor sanitation and water safety, and then mobility of the population, spread clones more widely. Minimal international importations of new typhoid clones suggest that targeted local intervention strategies may be useful in controlling endemic typhoid infection. These findings add to our understanding of typhoid transmission networks in an endemic island country with broad implications, particularly across Pacific Island Countries. FUNDING: This work was supported by the Coalition Against Typhoid through the Bill and Melinda Gates Foundation [grant number OPP1017518], the Victorian Government, the National Health and Medical Research Council Australia, the Australian Research Council, and the Fiji Ministry of Health and Medical Services.
BACKGROUND: Reported monthly scrub typhus (ST) cases in Thailand has an increase in the number of cases during 2009-2014. Humidity is a crucial climatic factor for the survival of chiggers, which is the disease vectors. The present study was to determine the role of humidity in ST occurrence in Thailand and its delayed effect. METHODS: We obtained the climate data from the Department of Meteorology, the disease data from Ministry of Public Health. Negative binomial regression combined with a distributed lag non-linear model (NB-DLNM) was employed to determine the non-linear effects of different types of humidity on the disease. This model controlled overdispersion and confounder, including seasonality, minimum temperature, and cumulative total rainwater. RESULTS: The occurrence of the disease in the 6-year period showed the number of cases gradually increased summer season (Mid-February – Mid-May) and then reached a plateau during the rainy season (Mid-May – Mid-October) and then steep fall after the cold season (Mid-October – Mid-February). The high level (at 70%) of minimum relative humidity (RHmin) was associated with a 33% (RR 1.33, 95% CI 1.13-1.57) significant increase in the number of the disease; a high level (at 14 g/m(3)) of minimum absolute humidity (AHmin) was associated with a 30% (RR 1.30, 95% CI 1.14-1.48); a high level (at 1.4 g/kg) of minimum specific humidity (SHmin) was associated with a 28% (RR 1.28, 95% CI 1.04-1.57). The significant effects of these types of humidity occurred within the past month. CONCLUSION: Humidity played a significant role in enhancing ST cases in Thailand, particularly at a high level and usually occurred within the past month. NB-DLNM had good controlled for the overdispersion and provided the precise estimated relative risk of non-linear associations. Results from this study contributed the evidence to support the Ministry of Public Health on warning system which might be useful for public health intervention and preparation in Thailand.
Scrub typhus is a zoonotic disease caused by the bacterium Orientia tsutsugamushi. In this study, the epidemiological characteristics of scrub typhus in Taiwan, including gender, age, seasonal variation, climate factors, and epidemic trends from 2010 to 2019 were investigated. Information about scrub typhus in Taiwan was extracted from annual summary data made publicly available on the internet by the Taiwan Centers for Disease Control. From 2010 to 2019, there were 4352 confirmed domestic and 22 imported cases of scrub typhus. The incidence of scrub typhus ranged from 1.39 to 2.30 per 100,000 from 2010-2019, and peaked in 2013 and 2015-2016. Disease incidence varied between genders, age groups, season, and residence (all p < 0.001) from 2010 to 2019. Risk factors were being male (odds ratio (OR) =1.358), age 40 to 64 (OR = 1.25), summer (OR = 1.96) or fall (OR = 1.82), and being in the Penghu islands (OR = 1.74) or eastern Taiwan (OR = 1.92). The occurrence of the disease varied with gender, age, and place of residence comparing four seasons (all p < 0.001). Weather, average temperature (°C) and rainfall were significantly correlated with confirmed cases. The number of confirmed cases increased by 3.279 for every 1 °C (p = 0.005) temperature rise, and 0.051 for every 1 mm rise in rainfall (p = 0.005). In addition, the total number of scrub typhus cases in different geographical regions of Taiwan was significantly different according to gender, age and season (all p < 0.001). In particular, Matsu islands residents aged 20-39 years (OR = 2.617) and residents of the Taipei area (OR = 3.408), northern Taiwan (OR = 2.268) and eastern Taiwan (OR = 2.027) were affected during the winter. Males and females in the 50-59 age group were at high risk. The total number of imported cases was highest among men, aged 20-39, during the summer months, and in Taipei or central Taiwan. The long-term trend of local cases of scrub typhus was predicted using the polynomial regression model, which predicted the month of most cases in a high-risk season according to the seasonal index (1.19 in June by the summer seasonal index, and 1.26 in October by the fall seasonal index). The information in this study will be useful for policy-makers and clinical experts for direct prevention and control of chigger mites with O. tsutsugamushi that cause severe illness and are an economic burden to the Taiwan medical system. These data can inform future surveillance and research efforts in Taiwan.
Vector-borne diseases have posed a heavy threat to public health, especially in the context of climate change. Currently, there is no comprehensive review of the impact of meteorological factors on all types of vector-borne diseases in China. Through a systematic review of literature between 2000 and 2021, this study summarizes the relationship between climate factors and vector-borne diseases and potential mechanisms of climate change affecting vector-borne diseases. It further examines the regional differences of climate impact. A total of 131 studies in both Chinese and English on 10 vector-borne diseases were included. The number of publications on mosquito-borne diseases is the largest and is increasing, while the number of studies on rodent-borne diseases has been decreasing in the past two decades. Temperature, precipitation, and humidity are the main parameters contributing to the transmission of vector-borne diseases. Both the association and mechanism show vast differences between northern and southern China resulting from nature and social factors. We recommend that more future research should focus on the effect of meteorological factors on mosquito-borne diseases in the era of climate change. Such information will be crucial in facilitating a multi-sectorial response to climate-sensitive diseases in China.
The geographic expansion of mosquitos is associated with a rising frequency of outbreaks of mosquito-borne diseases (MBD) worldwide. We collected occurrence locations and times of mosquito species, mosquito-borne arboviruses, and MBDs in the mainland of China in 1954-2020. We mapped the spatial distributions of mosquitoes and arboviruses at the county level, and we used machine learning algorithms to assess contributions of ecoclimatic, socioenvironmental, and biological factors to the spatial distributions of 26 predominant mosquito species and two MBDs associated with high disease burden. Altogether, 339 mosquito species and 35 arboviruses were mapped at the county level. Culex tritaeniorhynchus is found to harbor the highest variety of arboviruses (19 species), followed by Anopheles sinensis (11) and Culex pipiens quinquefasciatus (9). Temperature seasonality, annual precipitation, and mammalian richness were the three most important contributors to the spatial distributions of most of the 26 predominant mosquito species. The model-predicted suitable habitats are 60-664% larger in size than what have been observed, indicating the possibility of severe under-detection. The spatial distribution of major mosquito species in China is likely to be under-estimated by current field observations. More active surveillance is needed to investigate the mosquito species in specific areas where investigation is missing but model-predicted probability is high.
BACKGROUND: Anopheles philippinensis and Anopheles nivipes are morphologically similar and are considered to be effective vectors of malaria transmission in northeastern India. Environmental factors such as temperature and rainfall have a significant impact on the temporal and spatial distribution of disease vectors driven by future climate change. METHODS: In this study, we used the maximum entropy model to predict the potential global distribution of the two mosquito species in the near future and the trend of future distribution in China. Based on the contribution rate of environmental factors, we analyzed the main environmental factors affecting the distribution of the two mosquito species. We also constructed a disease vector risk assessment index system to calculate the comprehensive risk value of the invasive species. RESULTS: Precipitation has a significant effect on the distribution of potentially suitable areas for Anopheles philippinensis and Anopheles nivipes. The two mosquito species may spread in the suitable areas of China in the future. The results of the risk assessment index system showed that the two mosquito species belong to the moderate invasion risk level for China. CONCLUSIONS: China should improve the mosquito vector monitoring system, formulate scientific prevention and control strategies and strictly prevent foreign imports.
Scrub typhus is a climate-sensitive and life-threatening vector-borne disease that poses a growing public health threat. Although the climate-epidemic associations of many vector-borne diseases have been studied for decades, the impacts of climate on scrub typhus remain poorly understood, especially in the context of global warming. Here we incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate-driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). Our results demonstrate that temperature, precipitation, and relative humidity play key roles in driving the seasonal epidemic of scrub typhus in mainland China with a 2-month lag. Our findings show that the change of projected spatiotemporal dynamics of scrub typhus will be heterogeneous and will depend on specific combinations of regional climate conditions in future climate scenarios. Our results contribute to a better understanding of spatiotemporal dynamics of scrub typhus, which can help public health authorities refine their prevention and control measures to reduce the risks resulting from climate change.
Background: Scrub typhus (ST) is a climate-sensitive infectious disease. However, the nonlinear relationship between important meteorological factors and ST incidence is not clear. The present study identified the quantitative relationship between ST incidence and meteorological factors in southern China. Methods: The weekly number of ST cases and simultaneous meteorological variables in central Guangdong Province from 2006 to 2018 were obtained from the National Notifiable Infectious Disease Reporting Information System and the Meteorological Data Sharing Service System, respectively. A quasi-Poisson generalized additive model combined with a distributed lag nonlinear model (DLNM) was constructed to analyze the lag-exposure-response relationship between meteorological factors and the incidence of ST. Results: A total of 18,415 ST cases were reported in the study area. The estimated effects of meteorological factors on ST incidence were nonlinear and exhibited obvious lag characteristics. A J-shaped nonlinear association was identified between weekly mean temperature and ST incidence. A reversed U-shaped nonlinear association was noted between weekly mean relative humidity and ST incidence. The risk of ST incidence increased when the temperature ranged from 24 & DEG;C to 28 & DEG;C, the relative humidity was between 78% and 82%, or the precipitation was between 50 mm and 150 mm, using the medians as references. For high temperatures (75th percentile of temperature), the highest relative risk (RR) was 1.18 (95% CI: 1.10-1.27), with a lag effect that lasted 5 weeks. High relative humidity (75th percentile of relative humidity) and high precipitation (75th percentile of precipitation) could also increase the risk of ST. Conclusion: This study demonstrated the nonlinear relationship and the significant positive lag effects of temperature, relative humidity, and precipitation on the incidence of ST. Between particular thresholds, temperature, humidity, and levels of precipitation increased the risk of ST. These findings suggest that relevant government departments should address climate change and develop a meteorological conditions-depend strategy for ST prevention and control.
BACKGROUND: Scrub Typhus (ST) is a rickettsial disease caused by Orientia tsutsugamushi. The number of ST cases has been increasing in China during the past decades, which attracts great concerns of the public health. METHODS: We obtained monthly documented ST cases greater than 54 cases in 434 counties of China during 2012-2020. Spatiotemporal wavelet analysis was conducted to identify the ST clusters with similar pattern of the temporal variation and explore the association between ST variation and El Niño and La Niña events. Wavelet coherency analysis and partial wavelet coherency analysis was employed to further explore the co-effects of global and local climatic factors on ST. RESULTS: Wavelet cluster analysis detected seven clusters in China, three of which are mainly distributed in Eastern China, while the other four clusters are located in the Southern China. Among the seven clusters, summer and autumn-winter peak of ST are the two main outbreak periods; while stable and fluctuated periodic feature of ST series was found at 12-month and 4-(or 6-) month according to the wavelet power spectra. Similarly, the three-character bands were also found in the associations between ST and El Niño and La Niña events, among which the 12-month period band showed weakest climate-ST association and the other two bands owned stronger association, indicating that the global climate dynamics may have short-term effects on the ST variations. Meanwhile, 12-month period band with strong association was found between the four local climatic factors (precipitation, pressure, relative humidity and temperature) and the ST variations. Further, partial wavelet coherency analysis suggested that global climatic dynamics dominate annual ST variations, while local climatic factors dominate the small periods. CONCLUSION: The ST variations are not directly attributable to the change in large-scale climate. The existence of these plausible climatic determinants stimulates the interests for more insights into the epidemiology of ST, which is important for devising prevention and early warning strategies.
Scrub typhus, caused by Orientia tsutsugamushi, is a serious public health problem in the Asia-Pacific region, threatening the health of more than one billion people. China is one of the countries with the most serious disease burden of scrub typhus. Previous epidemiological evidence indicated that meteorological factors may affect the incidence of scrub typhus, but there was limited evidence for the correlation between local natural environment factors dominated by meteorological factors and scrub typhus. This study aimed to evaluate the correlation between monthly scrub typhus incidence and meteorological factors in areas with high scrub typhus prevalence using a distributed lag non-linear model (DLNM). The monthly data on scrub typhus cases in ten provinces from 2006 to 2018 and meteorological parameters were obtained from the Public Health Science Data Center and the National Meteorological Data Sharing Center. The results of the single-variable and multiple-variable models showed a non-linear relationship between incidence and meteorological factors of mean temperature (Tmean), rainfall (RF), sunshine hours (SH), and relative humidity (RH). Taking the median of meteorological factors as the reference value, the relative risks (RRs) of monthly Tmean at 0°C, RH at 46%, and RF at 800 mm were most significant, with RRs of 2.28 (95% CI: 0.95-5.43), 1.71 (95% CI: 1.39-2.09), and 3.33 (95% CI: 1.89-5.86). In conclusion, relatively high temperature, high humidity, and favorable rainfall were associated with an increased risk of scrub typhus.
Thirty-nine years ago, scrub typhus (ST), a disease, was not among the China’s notifiable diseases. However, ST has reemerged to become a growing public health issue in the southwest part of China. The major factors contributing to an increased incidence and prevalence of this disease include rapid globalization, urbanization, expansion of humans into previously uninhabited areas, and climate change. The clinical manifestation of ST also consists of high fever, headache, weakness, myalgia, rash, and an eschar. In severe cases, complications (e.g. multi-organ failure, jaundice, acute renal failure, pneumonitis, myocarditis, and even death) can occur. The diagnosis of ST is mainly based on serological identification by indirect immunofluorescence assay and other molecular methods. Furthermore, several groups of antibiotics (e.g. tetracycline, chloramphenicol, macrolides, and rifampicin) are currently effective in treating this disease. This fact suggests the need for robust early diagnostic techniques, increased surveillance, and prompt treatment, and develop future vaccine.
BACKGROUND: The impacts of non-pharmaceutical interventions (NPIs) and vaccine boosters on the transmission of the largest outbreak of COVID-19 (the fifth wave) in Hong Kong have not been reported. The outbreak, dominated by the Omicron BA.2 subvariant, began to spread substantially after the Spring Festival in February, 2022, when the temperature varied greatly (e.g. a cold surge event). Tightening social distancing measures did not succeed in containing the outbreak until later with the use of rapid antigen tests (RAT) and increased vaccination rates. Temperature has been previously found to have significant impact on the transmissibility. Understanding how the public health interventions influence the number of infections in this outbreak provide important insights on prevention and control of COVID-19 during different seasons. METHODS: We developed a transmission model incorporating stratified immunity with vaccine-induced antibody responses and the daily changes in population mobility, vaccination and weather factors (i.e. temperature and relative humidity). We fitted the model to the daily reported cases detected by either PCR or RAT between 1 February and 31 March using Bayesian statistics, and quantified the effects of individual NPIs, vaccination and weather factors on transmission dynamics. RESULTS: Model predicted that, with the vaccine uptake, social distancing reduced the cumulative incidence (CI) from 58.2% to 44.5% on average. The use of RAT further reduced the CI to 39.0%. Without vaccine boosters in these two months, the CI increased to 49.1%. While public health interventions are important in reducing the total infections, the outbreak was temporarily driven by the cold surge. If the coldest two days (8.5 °C and 8.8 °C) in February were replaced by the average temperature in that month (15.2 °C), the CI would reduce from 39.0% to 28.2%. CONCLUSION: Preventing and preparing for the transmission of COVID-19 considering the change in temperature appears to be a cost-effective preventive strategy to lead people to return to normal life.
Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration-response analyses were performed. An increase of each interquartile range in PM(2.5), PM(10), SO(2), NO(2), O(3), and CO at lag4 corresponded to 1.40 (1.37-1.43), 1.35 (1.32-1.37), 1.01 (1.00-1.02), 1.08 (1.07-1.10), 1.28 (1.27-1.29), and 1.26 (1.24-1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97-0.98), 0.96 (0.96-0.97), and 0.94 (0.92-0.95), respectively. The estimates of PM(2.5), PM(10), NO(2), and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration-response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM(2.5), PM(10), NO(2), may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.
Allergic diseases are increasing globally. Air pollution, climate change, and reduced biodiversity are major threats to human health with detrimental effects on chronic noncommunicable diseases. Outdoor and indoor air pollution and climate change are increasing. Asia has experienced rapid economic growth, a deteriorating environment, and an increase in allergic diseases to epidemic proportions. Air pollutant levels in Asian countries are substantially higher than in developed countries. Moreover, industrial, traffic-related, and household biomass combustion and indoor pollutants from chemicals and tobacco are major sources of air pollutants. We highlight the major components of pollutants and their impacts on respiratory allergies.
Objective To examine countries’engagement with the health impacts of climate change in their formal statements to intergovernmental organizations, and the factors driving engagement. Methods We obtained the texts of countries’annual statements in United Nations (UN) general debates from 2000 to 2019 and their nationally determined contributions at the Paris Agreement in 2016. To measure countries’ engagement, we used a keyword-in-context text search with relevant search terms to count the total number of references to the relationship of health to climate change. We used a machine learning model (random forest predictions) to identify the most important country-level predictors of engagement. The predictors included political and economic factors, health outcomes, climate change-related variables and membership of political negotiating groups in the UN. Findings For both UN general debate statements and nationally determined contributions, low-and middle-income countries discussed the health impacts of climate change much more than did high-income countries. The most important predictors of engagement were health outcomes (infant mortality, maternal deaths, life expectancy), countries’ income levels (gross domestic product per capita), and fossil fuel consumption. Membership of political negotiating groups (such as the Group of 77 and Small Island Developing States) was a less important predictor. Conclusion Our analysis indicated a higher engagement in countries that carry the heaviest climate-related health burdens, but lack necessary resources to address the impacts of climate change. These countries are shouldering responsibility for reminding the global community of the implications of climate change for people’s health. Climate change is taking an increasing toll on people’s health. The increase in heatwaves, drought, floods and other climate hazards is increasing the risk of climate-related illness and death as well as reversing gains made in reducing food insecurity and global hunger.1,2 Air pollution, primarily driven by fossil fuel emissions, is the major environmental risk factor for premature death and has impacts on child health and survival.3-5 Highlighting these human impacts is seen as a way of accelerating climate action
Worldwide, over half of the global population is living in urban areas. The metropolitan areas are highly populated and environmentally non-green regions on the planet. In green space regions, plants, grass, and green vegetation prevent soil erosion, absorb air pollutants, provide fresh and clean air, and minimize the burden of diseases. Presently, the entire world is facing a turmoil situation due to the COVID-19 pandemic. This study investigates the effect of the green space environment on air pollutants particulate matter PM2.5, PM10, carbon monoxide (CO), ozone (O(3)), incidence and mortality of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) in environmentally highly green and less-green countries. We randomly selected 17 countries based on the Environmental Performance Index (EPI) data. The 60% of the EPI score is based on seven categories: biodiversity and habitat, ecosystem, fisheries, climate change, pollution emissions, agriculture, and water resources. However, 40% of the score is based on four categories: air quality, sanitation and drinking water, heavy metals, and waste management. The air pollutants and SARS-CoV-2 cases and deaths were recorded from 25 January 2020, to 11 July 2021. The air pollutants PM2.5, PM10, CO, and O(3) were recorded from the metrological websites, Air Quality Index-AQI, 2021. The COVID-19 daily cases and deaths were obtained from the World Health Organization. The result reveals that air pollutants mean values for PM2.5 110.73 ± 1.09 vs. 31.35 ± 0.29; PM10 80.43 ± 1.11 vs. 17.78 ± 0.15; CO 7.92 ± 0.14 vs. 2.35 ± 0.03 were significantly decreased (p < 0.0001) in environmentally highly green space countries compared to less-green countries. Moreover, SARS-CoV-2 cases 15,713.61 ± 702.42 vs. 3445.59 ± 108.09; and deaths 297.56 ± 11.27 vs. 72.54 ± 2.61 were also significantly decreased in highly green countries compared to less-green countries. The green environment positively impacts human wellbeing. The policymakers must implement policies to keep the living areas, surroundings, towns, and cities clean and green to minimize air pollution and combat the present pandemic of COVID-19.
The COVID-19 pandemic continues to expand, while the relationship between weather conditions and the spread of the virus remains largely debatable. In this paper, we attempt to examine this question by employing a flexible econometric model coupled with fine-scaled hourly temperature variations and a rich set of covariates for 291 cities in the Chinese mainland. More importantly, we combine the baseline estimates with climate-change projections from 21 global climate models to understand the pandemic in different scenarios. We found a significant negative relationship between temperatures and caseload. A one-hour increase in temperatures from 25 degrees C to 28 degrees C tends to reduce daily cases by 15.1%, relative to such an increase from -2 degrees C to 1 degrees C. Our results also suggest an inverted U-shaped nonlinear relationship between relative humidity and confirmed cases. Despite the negative effects of heat, we found that rising temperatures induced by climate change are unlikely to contain a hypothesized pandemic in the future. In contrast, cases would tend to increase by 10.9% from 2040 to 2059 with a representative concentration pathway (RCP) of 4.5 and by 7.5% at an RCP of 8.5, relative to 2020, though reductions of 1.8% and 18.9% were projected for 2080-2099 for the same RCPs, respectively. These findings raise concerns that the pandemic could worsen under the climate-change framework.
Australia’s summer bushfires of 2020-2021 were catastrophic, negatively impacting people, and the natural environment. This climate change-related event exacerbated the influence of the COVID-19 pandemic on public health. Young people are a priority population whose health and livelihoods are significantly impacted by these events. At the same time, young people are active agents for climate action. This exploratory mixed-method study draws on descriptive analyses of survey data (n = 46) and thematic analyses of interview data (n = 6) which demonstrated that some young people, whilst concerned about existential and real impacts of climate change, use contact with nature to cope and as motivation for taking climate actions.
BACKGROUND: From November 2019 to January 2020, eastern Australia experienced the worst bushfires in recorded history. Two months later, Sydney and surrounds were placed into lockdown for six weeks due to the COVID-19 pandemic, followed by ongoing restrictions. Many pregnant women at this time were exposed to both the bushfires and COVID-19 restrictions. OBJECTIVE: To assess the impact of exposure to bushfires and pandemic restrictions on perinatal outcomes. METHODS: The study included 60 054 pregnant women who gave birth between November 2017 and December 2020 in South Sydney. Exposure cohorts were based on conception and birthing dates: 1) bushfire late pregnancy, born before lockdown; 2) bushfires in early-mid pregnancy, born during lockdown or soon after; 3) conceived during bushfires, lockdown in second trimester; 4) conceived after bushfires, pregnancy during restrictions. Exposure cohorts were compared with pregnancies in the matching periods in the two years prior. Associations between exposure cohorts and gestational diabetes, preeclampsia, hypertension, stillbirth, mode of birth, birthweight, preterm birth and small for gestational age were assessed using generalised estimating equations, adjusting for covariates. RESULTS: A decrease in low birth weight was observed for cohort 1 (aOR 0.81, 95%CI 0.69, 0.95). Conversely, cohort 2 showed an increase in low birth weight, and increases in prelabour rupture of membranes, and caesarean sections (aOR 1.18, 95%CI 1.03, 1.37; aOR 1.21, 95%CI 1.07, 1.37; aOR 1.10 (1.02, 1.18) respectively). Cohort 3 showed an increase in unplanned caesarean sections and high birth weight babies (aOR 1.15, 95%CI 1.04, 1.27 and aOR 1.16, 95%CI 1.02, 1.31 respectively), and a decrease in gestational diabetes mellitus was observed for both cohorts 3 and 4. CONCLUSION: Pregnancies exposed to both severe climate events and pandemic disruptions appear to have increased risk of adverse perinatal outcomes beyond only experiencing one event, but further research is needed.
Henan Province in Central China was hit by unprecedented, rain-triggered floods in July 2021 and experienced a recurrence of the COVID-19 pandemic. The current study aims to identify the latent profiles of psychological status and acceptance of change among Henan residents who have been cumulatively exposed to these floods and the COVID-19 pandemic. A total of 977 participants were recruited. Latent profile analysis (LPA) was used to explore underlying patterns of psychological status (i.e., perceived risk of the COVID-19 pandemic, post-traumatic stress symptoms, anxiety and rumination) and acceptance of change. The predictors were evaluated with multinomial logistic regression. LPA identified four patterns of psychological status and acceptance of change: high distress/high acceptance (5.1%), moderate distress/moderate acceptance (20.1%), mild distress/mild acceptance (45.5%), and resilience (29.3%). The additive impact of the floods and COVID-19 pandemic and negative emotion during the floods were the risk factors, while flood coping efficacy, trust, and a closer psychological distance change were the protective factors. The present study therefore provides novel evidence on psychological status after both a natural disaster and a major public health event. The cumulative effects of the floods and the COVID-19 pandemic may have heightened the risk of post-disaster maladaptation. A complex relationship between psychological outcomes and acceptance of change was also found. The findings of this study thus provide a foundation for both disaster management and psychological assistance for particular groups.
Since the outbreak of COVID-19, wearing masks, vaccinations, and maintaining a safe distance has become social behaviors advocated by the government and widely adopted by the public. At the same time, unpredictable natural disaster risks brought by extreme climate change compound difficulties during epidemics and cause systemic risks that influence the existing pattern of epidemic prevention. Therefore, it is necessary to explore the effect of natural disaster risk caused by climate change on the response to outbreaks in the context of the COVID-19 epidemic. This study will focus on individual-level epidemic prevention behaviors, taking as an example the significant risk of severe destructive flooding caused by heavy rains in Henan, China, on July 20, 2021, which claimed 398 lives, to explore the effect of floods on the preventive behaviors of residents in the hardest hit areas against COVID-19. Through the multi-stage stratified random sampling of the affected residents in Zhengzhou, Xinxiang, Hebi, Luoyang, Anyang, and other cities in Henan Province, 2,744 affected people were surveyed via questionnaires. Through the linear regression model and moderating effect analysis, the study found that after floods, the individual’s flood risk perception and response behaviors significantly correlated with the individual’s prevention behaviors against COVID-19. Specifically, both flood risk perception and response behaviors strengthened the individual’s prevention behaviors. Furthermore, the study also found that community risk preparation behavior and social capital can moderate the above relationship to a certain extent. The research can guide risk communication under the compound risk scenario and prevent risky public behavior under the consistent presence of COVID-19 in the community.
OBJECTIVE: This research seeks to identify climate-sensitive infectious diseases of concern with a present and future likelihood of increased occurrence in the geographically vulnerable Torres Strait Islands, Australia. The objective is to contribute evidence to the need for adequate climate change responses. METHODS: Case data of infectious diseases with proven, potential and speculative climate sensitivity were compiled. RESULTS: Five climate-sensitive diseases in the Torres Strait and Cape York region were identified as of concern: tuberculosis, dengue, Ross River virus, melioidosis and nontuberculous mycobacterial infection. The region constitutes 0.52% of Queensland’s population but has a disproportionately high proportion of the state’s cases: 20.4% of melioidosis, 2.4% of tuberculosis and 2.1% of dengue. CONCLUSIONS: The Indigenous Torres Strait Islander peoples intend to remain living on their traditional country long-term, yet climate change brings risks of both direct and indirect human health impacts. Implications for public health: Climate-sensitive infections pose a disproportionate burden and ongoing risk to Torres Strait Islander peoples. Addressing the causes of climate change is the responsibility of various agencies in parallel with direct action to minimise or prevent infections. All efforts should privilege Torres Strait Islander peoples’ voices to self-determine response actions.
BACKGROUND: The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016. METHODS: A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test. RESULTS: Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB. CONCLUSION: Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.
The aim of this study was to explore the effect of temperature on tuberculosis (TB) incidence using the distributed lag non-linear model (DLNM) from 2017 to 2021 in Kashgar city, the region with higher TB incidence than national levels, and assist public health prevention and control measures. From January 2017 to December 2021, a total of 8730 cases of TB were reported, with the higher incidence of male than that of female. When temperature was below 1 °C, it was significantly correlated with TB incidence compared to the median observed temperature (15 °C) at lag 7, 14, and 21, and lower temperatures showed larger RR (relative risk) values. High temperature produced a protective effect on TB transmission, and higher temperature from 16 to 31 °C has lower RR. In discussion stratified by gender, the maximum RRs were achieved for both male group and female group at - 15 °C with lag 21, reporting 4.28 and 2.02, respectively. At high temperature (higher than 20 °C), the RR value of developing TB for female group was significantly larger than 1. In discussion stratified by age, the maximum RRs were achieved for all age groups (≤ 35, 36-64, ≥ 65) at - 15 °C with lag 21, reporting 3.20, 2.07, and 3.45, respectively. When the temperature was higher than 20 °C, the RR of the 36-64-year-old group and the ≥ 65-year-old group was significantly larger than 1 at lag 21, while significantly smaller than 1 for cumulative RR at lag 21, reporting 0.11, 95% confidence interval (CI) (0.01, 0.83) and 0.06, 95% CI (0.01, 0.44), respectively. In conclusion, low temperature, especially in extreme level, acts as a high-risk factor inducing TB transmission in Kashgar city. Males exhibit a significantly higher RR of developing TB at low temperature than female, as well as the elderly group in contrast to the young or middle-aged groups. High temperature has a protective effect on TB transmission in the total population, but female and middle-aged and elderly groups are also required to be alert to the delayed RR induced by it.
BACKGROUND: The association between the incidence of hand, foot, and mouth disease (HFMD) and ambient temperature has been well documented. Although the severity of symptoms is an important indicator of disease burden and varies significantly across cases, it usually was ignored in previous studies, potentially leading to biased estimates of the health impact of temperature. METHODS: We estimated the disability-adjusted life year (DALY) by considering the severity of symptoms for each HFMD case reported during 2010-2012 in Guangdong and used distributed lag-nonlinear models to estimate the association between the daily average temperature and daily DALY of HFMD cases at the city-level. We investigated the potential effect modifiers on the pathway between temperature and DALY and pooled city-specific estimates to a provincial association using a meta-regression. The overall impact of temperature was further evaluated by estimates of DALYs that could be attributed to HFMD. RESULTS: The overall cumulative effect of daily mean temperature on the DALY of HFMD showed an inverse-U shape, with the maximum effect estimated to be β = 0.0331 (95%CI: 0.0199-0.0463) DALY at 23.8°C. Overall, a total of 6.432 (95%CI: 3.942-8.885) DALYs (attributable fraction = 2.721%, 95%CI: 1.660-3.759%) could be attributed to temperature exposure. All the demographic subgroups had a similar trend as the main analysis, while the magnitude of the peak of the temperature impact tended to be higher among the males, those aged ≥3yrs or from the Pear-River Delta region. Additionally, the impact of temperature on DALY elevated significantly with the increasing population density, per capita GDP, and per capita green space in parks. CONCLUSIONS: Temperature exposure was associated with increased burden of HFMD nonlinearly, with certain groups such as boys and those from areas with greater population density being more vulnerable.
The burden of disease caused by ambient high temperature has become a public health concern, but the associations between high temperature and hand, foot, and mouth disease (HFMD) remain indistinct. We used distributed lag non-linear model (DLNM) to estimate the burden of disease attribute to high temperature, adjusting for long-term trend and weather confounders. Total 18,167,455 cases were reported in 31 Chinese provinces, the incidence of HFMD showed a gradually increasing trend from 2008 to 2017 in China. Minimum morbidity temperature (MMT) was mainly concentrated at 17 to 23 °C in ≤ 5 years old group, 18 to 25 °C in 6 ~ 10 years old group and 19 to 27 °C in > 10 years old group. The greatest relative risk (RR) in age group ≤ 5 years old was 2.06 (95% CI: 1.85 ~ 2.30) in Heilongjiang, and the lowest RR was 1.02 (95% CI: 1.00 ~ 1.05) in Guangdong; the greatest RR in age group 6 ~ 10 years old was 2.24 (95% CI: 1.72 ~ 2.91) in Guizhou, and the lowest RR was 1.01 (95% CI: 0.97 ~ 1.12) in Tianjin; the greatest RR in the age group > 10 years old was 2.53 (95% CI: 1.66 ~ 3.87) in Heilongjiang, and the lowest RR was 1.02 (95% CI: 0.71 ~ 1.46) in Henan. We found the positive association between high temperature and HFMD in China.
BACKGROUND: Hand, foot and mouth disease (HFMD) is one of the common intestinal infectious diseases worldwide and has caused huge economic and disease burdens in many countries. The average annual incidence rate of HFMD was 11.66% in Shaanxi during the time span from 2009 to 2018. There are distinct differences within Shaanxi, as it is a special region that crosses three temperature zones. Hence, in this study, a spatiotemporal analysis of Shaanxi was performed to reveal the characteristics of the distribution of HFMD and to explore the meteorological determinants of HFMD. METHODS: The county-level and municipal data from Shaanxi Province from 2009 to 2018 were applied to research the spatiotemporal characteristics of HFMD and its meteorological determinants. Time series and spatial autocorrelation analyses were applied to assess the spatiotemporal characteristics of HFMD. This study used spatial econometric panel models to explore the relationship between HFMD and meteorological factors based on the data of 107 counties and 10 municipalities. RESULTS: The incidence rate of HFMD displayed no variable trend throughout the whole research period. A high incidence rate of HFMD was observed from June to September, corresponding to a time when the climate is characterized by heavy rain, high temperature, and high humidity. The high-incidence areas were mainly located in the central region in Shaanxi, whereas the low-incidence spots were mainly found in Northern Shaanxi. Regarding the meteorological factors analysed in this study, in general, the incidence rate of HFMD in specific regions was positively associated with the rainfall, temperature and humidity. CONCLUSION: These results could be applied by the government and the general public to take effective measures to prevent disease. Region-targeted policies could be enacted and implemented in the future according to specific situations in different areas and the relevant meteorological determinants. Additionally, meteorological conditions normally extend to a wide-ranging region; thus, cooperation among surrounding regions is necessary.
Salmonellosis is a climate-sensitive gastroenteritis with over 92 million cases and over 50,000 deaths a year globally. Australia has high rates of salmonellosis compared with other industrialised nations. This study used a negative binomial time-series regression model to investigate the association between Australian salmonellosis notifications and monthly climate variables including El Niño Southern Oscillation (ENSO) and mean temperature anomaly from 1991 to 2019. Between 1991 and 2019 in Australia there were 275,753 salmonellosis notifications and the median annual rate for salmonellosis was 40.1 per 100,000 population. Salmonellosis notifications exhibited strong seasonality, reaching a peak in summer and a minimum in winter. There was an estimated increase of 3.4 % in salmonellosis cases nationally per 1 °C increase in monthly mean temperature anomaly (incidence rate ratio [IRR] of 1.034, 95 % confidence interval [CI]: 1.009, 1.059). Similar associations between salmonellosis and mean temperature anomaly were found for some states. Mean temperature anomaly exhibited an upward trend of 0.9 °C over the period 1991 to 2019. Additionally, a positive association was found between salmonellosis in Australia and ENSO whereby El Niño periods were associated with 7.9 % more salmonellosis cases compared to neutral periods (IRR 1.079, 95 % CI: 1.019, 1.143). A similar ENSO association was detected in the two eastern states of New South Wales and Queensland. This study suggests public health preventative measures to reduce salmonellosis could be enhanced in some regions during El Niño as well as during times of increased temperatures.
Weather can impact infectious disease transmission, particularly for heat-sensitive pathogens, such as Salmonella. We conducted an ecological time-series analysis to estimate short-term associations between nonoutbreak-related notifications of Salmonella and weather conditions-temperature and rainfall-in Melbourne, Australia from 2000 to 2019. Distributed lag nonlinear models were created to analyze weather-salmonellosis associations and potential lag times on a weekly time scale, controlling for seasonality and long-term trends. Warmer temperatures were associated with increased risk of notification. Effects were temporally lagged, with the highest associations observed for warm temperatures 2-6 (greatest at 4) weeks before notification. The overall estimated relative risk of salmonellosis increased twofold at 33°C compared to the average weekly temperature (20.35°C) for the 8-week period preceding the disease notification. For Salmonella Typhimurium alone, this occurred at temperatures over 32°C. There were no statistically significant associations with rainfall and notification rates in any of the analyses performed. This study demonstrates the short-term influences of warm temperatures on Salmonella infections in Melbourne over a 20-year period. Salmonelloses are already the second most notified gastrointestinal diseases in Victoria, and these findings suggest that notifications may increase with increasing temperatures. This evidence contributes to previous findings that indicate concerns for public health with continued warm weather.
Bacterial dysentery (BD) brings a major disease burden to developing countries. Exploring the influence of temperature and its interaction with other meteorological factors on BD is significant for the prevention and early warning of BD in the context of climate change. Daily BD cases and meteorological data from 2008 to 2018 were collected in all nine prefecture-level cities in Jilin Province. A one-stage province-level model and a two-stage city-specific multivariate meta-pooled level distributed lag non-linear model were established to explore the correlation between temperature and BD, then the weather-stratified generalised additive model was used to test the interaction. During the study period, a total of 26 971 cases of BD were developed. The one-stage and two-stage cumulative dose-response ‘J’ curves overlapped, and results showed a positive correlation between temperature and BD with a 1-6 days lag effect. Age group ⩾5 years was found to be more sensitive to the effects. Moreover, there was a significant interaction between temperature, humidity and precipitation (P = 0.004, 0.002, respectively) on BD under high temperature (>0 °C), reminding residents and policymakers to pay attention to the prevention of BD in situations with both high temperature and humidity, high temperature and precipitation during the temperate monsoon climate.
ABSTRACT: Norovirus food poisoning outbreaks in Korea (South) appeared in the 2000s and have been increasing since then. We aimed to investigate the epidemiological features of norovirus food poisoning outbreaks in Korea from 2002 to 2017, on the basis of official food poisoning statistics and publically reliable reports, and to find any associations with climate factors. Norovirus was the most common cause of food poisoning among known causative substances in Korea during the study period. More than one-third of the outbreaks occurred in group meal service facilities, including school lunch programs. A few of these facilities used groundwater contaminated with noroviruses to wash or cook food, which contributed to outbreaks. Norovirus occurrences showed strong seasonality: cold and relatively dry winter air may help norovirus to flourish. Both norovirus genotypes GI and GII that are infectious to humans were detected, with GII becoming more prevalent than GI. According to our correlation analysis in connection with climate factors, average temperatures, the highest and lowest temperatures, precipitation, the number of rain days, and humidity showed a significant negative correlation with a monthly norovirus occurrence (P < 0.05). The lowest temperature and average temperature had higher coefficients of correlation, -0.377 and -0.376, respectively. The norovirus outbreaks in Korea showed complex etiological characteristics, although more prevailed in wintertime, and are now a major public health problem. The use of groundwater in group meal service settings is a public health issue, as well as a norovirus concern; therefore, groundwater used in food service facilities and businesses should be treated for safety.
Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.
OBJECTIVES: Evidence of the relationship between climate variability, air pollution and human parainfluenza virus (HPIV) infections has been inconsistent. We assessed this in a paediatric population from a highly urbanized tropical city-state. METHODS: We analysed all reports of HPIV infections in children <5 years old obtained from a major specialist women and children's hospital in Singapore. Assuming a negative binomial distribution and using multivariable fractional polynomial modelling, we examined the relations between climate variability, air quality and the risk of HPIV infections, adjusting for time-varying confounders. RESULTS: We identified 6393 laboratory-confirmed HPIV infections from 2009 to 2019. Every 1 °C decline in temperature was associated with a 5.8% increase (RR: 0.943, 95% Confidence Interval [95% CI]: 0.903-0.984) in HPIV infection risk 6 days later. Every 10% decrease in relative humidity was associated with a 15.8% cumulative increase in HPIV risk over the next 6 days (cumulative RR: 0.842, 95% CI: 0.771-0.919). Rainfall was positively associated with HPIV risk 2 days later (RR: 1.021, 95% CI: 1.000-1.043). A within-year seasonal rise of HPIV was driven by HPIV-3 and HPIV-1 and preceded by a seasonal decline in temperature. Gender was an effect modifier of the climate-HPIV relationship. Air quality was not associated with HPIV risk. CONCLUSIONS: This study demonstrates a close association between HPIV infection risk and tropical climate variability. The climate dependence and seasonal predictability of HPIV can inform the timing of community campaigns aimed at reducing infection risk and the development of hospital resources and climate adaption plans.
BACKGROUND: Acute respiratory infections (ARIs) are among the most common human illnesses globally. Previous studies that examined the associations between climate variability and ARIs or ARI pathogens have reported inconsistent findings. Few studies have been conducted in Southeast Asia to date, and the impact of climatic factors are not well-understood. This study aimed to investigate the short-term associations between climate variability and ARIs in Singapore. METHODS: We obtained reports of ARIs from all government primary healthcare services from 2005 to 2019 and analysed their dependence on mean ambient temperature, minimum temperature and maximum temperature using the distributed lag non-linear framework. Separate negative binomial regression models were used to estimate the association between each temperature (mean, minimum, maximum temperature) and ARIs, adjusted for seasonality and long-term trend, rainfall, relative humidity, public holidays and autocorrelations. For temperature variables and relative humidity we reported cumulative relative risks (RRs) at 10th and 90th percentiles compared to the reference value (centered at their medians) with corresponding 95% confidence intervals (CIs). For rainfall we reported RRs at 50th and 90th percentiles compared to 0 mm with corresponding 95% CIs. RESULTS: Statistically significant inverse S-curve shaped associations were observed between all three temperature variables (mean, minimum, maximum) and ARIs. A decrease of 1.1 °C from the median value of 27.8 °C to 26.7 °C (10th percentile) in the mean temperature was associated with a 6% increase (RR: 1.06, 95% CI: 1.03 to 1.09) in ARIs. ARIs also increased at 23.9 °C (10th percentile) compared to 24.9 °C of minimum temperature (RR: 1.11, 95% CI: 1.07 to 1.16). The effect of maximum temperature for the same comparison (30.5 °C vs 31.7 °C) was non-significant (RR: 1.02, 95% CI: 0.99 to 1.05). An increase in ambient temperature to 28.9 °C (90th percentile) was associated with an 18% decrease (RR: 0.82, 95% CI: 0.80 to 0.83) in ARIs. Similarly, ARIs decreased with the same increase to 90th percentile in minimum (RR: 0.84, 95% CI: 0.80 to 0.87) and maximum (RR: 0.89, 95% CI: 0.86 to 0.93) temperatures. Rainfall was inversely associated with ARIs and displayed similar shape in all three temperature models. Relative humidity, on the other hand, exhibited a U-shaped relationship with ARIs. CONCLUSION: Our findings suggest that lower temperatures increase the risk of ARIs. Anticipated extreme weather events that reduce ambient temperature can be used to inform increased healthcare resource allocation for ARIs.
The unfavorable effects of global climate change, which are mostly the result of human activities, have had a particularly negative effect on human health and the planet’s ecosystems. This study attempted to determine the seasonality and association of air pollution, in addition to climate conditions, with two respiratory infections, influenza and pneumonia, in Chiang Mai, Thailand, which has been considered the most polluted city on Earth during the hot season. We used a seasonal-trend decomposition procedure based on loess regression (STL) and a seasonal cycle subseries (SCS) plot to determine the seasonality of the two diseases. In addition, multivariable negative binomial regression (NBR) models were used to assess the association between the diseases and environmental variables (temperature, precipitation, relative humidity, PM(2.5), and PM(10)). The data revealed that influenza had a clear seasonal pattern during the cold months of January and February, whereas the incidence of pneumonia showed a weak seasonal pattern. In terms of forecasting, the preceding month’s PM(2.5) and temperature (lag1) had a significant association with influenza incidence, while the previous month’s temperature and relative humidity influenced pneumonia. Using air pollutants as an indication of respiratory disease, our models indicated that PM(2.5) lag1 was correlated with the incidence of influenza, but not pneumonia. However, there was a linear association between PM(10) and both diseases. This research will help in allocating clinical and public health resources in response to potential environmental changes and forecasting the future dynamics of influenza and pneumonia in the region due to air pollution.
The relationship between meteorological factors and the risk of hand, foot, and mouth disease (HFMD) has been well documented. However, researchers have failed to consider the complex interactive relationships among meteorological factors. The weekly number of HFMD cases along with meteorological factors were collected between 2009 to 2017 in four cities in Guangdong Province. We used Bayesian kernel machine regression to investigate the nonlinear and interactive relationship between meteorological factors, such as temperature and humidity, on the risk of HFMD. Multivariate meta-analysis was used to pool the city-specific effect estimates and identify factors underlying the inter-city heterogeneity. The risk ratios (RRs) for each percentile increase in temperature from the 50th percentile value, while humidity was at its 10th, 50th, and 90th percentile values, were 1.621(95%CI: 1.226, 2.141), 2.638(2.169, 3.208), and 3.734(2.908, 4.792), respectively (Q= 19.132, P (interaction)< 0.001). In contrast, the RRs for each percentile increase in humidity from its 50th percentile, while holding temperature at its 10th, 50th, and 90th percentile values, were 0.901(95%CI: 0.592, 1.369), 2.026(1.679, 2.448), and 0.884(0.632, 1.238), respectively (Q= 24.876, P (interaction) < 0.001). Increased wind speed and sunshine duration were also observed to strengthen the impact of other meteorological factors. Furthermore, we found increased gross domestic product per capita and per capital area of parks and green land in city tended to significantly strengthen the interactive effects of humidity on other meteorological factors including sunshine duration (P = 0.013 and 0.042), rainfall (P = 0.017 and 0.035), temperature (P = 0.021 and 0.031), win speed (P = 0.011 and 0.045), and pressure (P = 0.013 and 0.042). Our study contributed further understanding of complex interactions between meteorological factors on the risk of HFMD. Our findings provide epidemiological evidence for meteorological interactions on HFMD, which may provide knowledge for future research on the health effects of meteorological factors.
BACKGROUND: Hand, foot, and mouth disease (HFMD) is a febrile rash infection caused by enteroviruses, spreading mainly via the respiratory tract and close contact. In the past two decades, HFMD has been prevalent mainly in Asia, including China and South Korea, causing a huge disease burden and putting the lives and health of children at risk. Therefore, a further study of the factors influencing HFMD incidences has far-reaching implications. In existing studies, the environmental factors affecting such incidences are mainly divided into two categories: meteorological and air. Among these studies, the former are the majority of studies on HFMD. Some scholars have studied both factors at the same, but the number is not large and the findings are quite different. METHODS: We collect monthly cases of HFMD in children, meteorological factors and atmospheric pollution in Urumqi from 2014 to 2020. Trend plots are used to understand the approximate trends between meteorological factors, atmospheric pollution and the number of HFMD cases. The association between meteorological factors, atmospheric pollution and the incidence of HFMD in the Urumqi region of northwest China is then investigated using multiple regression models. RESULTS: A total of 16,168 cases in children are included in this study. According to trend plots, the incidence of HFMD shows a clear seasonal pattern, with O(3) (ug/m(3)) and temperature (°C) showing approximately the same trend as the number of HFMD cases, while AQI, PM(2.5) (ug/m(3)), PM(10) (ug/m(3)) and NO(2) (ug/m(3)) all show approximately opposite trends to the number of HFMD cases. Based on multiple regression results, O(3) (P = 0.001) and average station pressure (P = 0.037) are significantly and negatively associated with HFMD incidences, while SO(2) (P = 0.102), average dew point temperature (P = 0.072), hail (P = 0.077), and thunder (P = 0.14) have weak significant relationships with them.
Hand, foot, and mouth disease (HFMD) is a global infectious disease severely threatening children’s health. It has been recognized that climate factors play an important role in the transmission of HFMD. In this paper, the bootstrap test in the geographically weighted regression (GWR) literature is extended to geographically and temporally weighted regression (GTWR) models for identifying homogeneous explanatory variables and spatiotemporally heterogeneous ones. The resulting mixed GTWR model is then used to investigate spatiotemporal effect of climate factors on the HFMD incidence in Inner Mongolia, China, a provincial autonomous region with extensive area and different climatic conditions. The results demonstrate that the effect of relative humidity is global over space and time, while that of air temperature, air pressure and wind speed varies spatiotemporally. The extended bootstrap test provides a solid statistical basis for model selection. The findings from the study may provide not only a deep understanding of spatiotemporal variation characteristics of the climatic effect on the HFMD incidence, but also some useful evidences for taking measures of the disease prevention and control at the county level in different seasons.
Seasonal cycles of AHF are causally attributed to the seasonal pattern of respiratory tract infections. However, this assumption has never been formally validated. We aimed to determine whether the increase in winter admissions for acute heart failure (AHF) can be explained by seasonal infectious diseases. We studied 12,147 patients admitted for AHF over a period of 11 years (2005-2015). Detailed virology and bacteriology data were collected on each patient. Meteorological information including daily temperature and relative humidity was obtained for the same period. The peak-to-low ratio, indicating the intensity of seasonality, was calculated using negative binomial regression-derived incidence rate ratios (IRR). AHF admissions occurred with a striking annual periodicity, peaking in winter (December-February) and were lowest in summer (June-August), with a seasonal amplitude (January vs. August) of 2.00 ([95% CI 1.79-2.24]. Occurrence of confirmed influenza infections was low (1.59%). Clinical diagnoses of respiratory infections, confirmed influenza infections, and influenza-like infections also followed a strong seasonal pattern (P < 0.0001; Peak/low ratio 2.42 [95% CI 1.394-3.03]). However, after exclusion of all respiratory infections, the seasonal variation in AHF remained robust (Peak/low ratio January vs. August, 1.81 [95% CI 1.60-2.05]; P < 0.0001). There was a strong inverse association between AHF admissions and average monthly temperature (IRR 0.95 per 1℃ increase; 95% CI 0.94 to 0.96). In conclusion, these is a dominant seasonal modulation of AHF admissions which is only partly explained by the incidence of winter respiratory infections. Environmental factors modify the susceptibility of heart failure patients to decompensation.
Despite high incidence of acute kidney injury (AKI) among patients hospitalised for influenza, no previous work has attempted to analyse and quantify the association between the two. Herein, we made use of Hong Kong’s surveillance data to evaluate the time-varying relationship between seasonal influenza and risk of AKI with adjustment for potential environmental covariates. Generalized additive model was used in conjunction with distributed-lag non-linear model to estimate the association of interest with daily AKI admissions as outcome and daily influenza admissions as predictor, while controlling for environmental variables (i.e. temperature, relative humidity, total rainfall, nitrogen dioxide, and ozone). Results suggested a positive association between risk of AKI admission and number of influenza hospitalisation cases, with relative risk reaching 1.12 (95% confidence interval, 1.10-1.15) at the 95th percentile. Using median as reference, an almost U-shaped association between risk of AKI admission and temperature was observed; the risk increased significantly when the temperature was low. While ozone was not shown to be a risk factor for AKI, moderate-to-high levels of nitrogen dioxide (50-95th percentile) were significantly associated with increased risk of AKI admission. This study mentioned the possibility that AKI hospitalisations are subject to environmental influences and offered support for a positive association between seasonal influenza and AKI occurrence in Hong Kong. Authorities are urged to extend the influenza vaccination program to individuals with pre-existing renal conditions to safeguard the health of the vulnerable. Given that adverse health effects are evident at current ambient levels of nitrogen dioxide, the government is recommended to adopt clean-air policies at the earliest opportunity to protect the health of the community.
Many studies have shown that the relationship between ambient temperature, relative humidity and mumps has been highlighted. However, these studies showed inconsistent results. Therefore, the goal of our study is to conduct a meta-analysis to clarify this relationship and to quantify the size of these effects as well as the potential factors. Systematic literature researches on PubMed, Embase.com, Web of Science Core Collection, Cochrane library, Chinese BioMedical Literature Database (CBM) and China National Knowledge Infrastructure (CNKI) were performed up to February 7, 2022 for articles analyzing the relationships between ambient temperature, relative humidity and incidence of mumps. Eligibility assessment and data extraction were conducted independently by two researchers, and meta-analysis was performed to synthesize these data. We also assessed sources of heterogeneity by study region, regional climate, study population. Finally, a total of 14 studies were screened out from 1154 records and identified to estimate the relationship between ambient temperature, relative humidity and incidence of mumps. It was found that per 1 °C increase and decrease in the ambient temperature were significantly associated with increased incidence of mumps with RR of 1.0191 (95% CI: 1.0129-1.0252, I(2) = 92.0%, Egger’s test P = 0.001, N = 13) for per 1 °C increase and 1.0244 (95% CI: 1.0130-1.0359, I(2) = 86.6%, Egger’s test P = 0.077, N = 9) for per 1 °C decrease. As to relative humidity, only high effect of relative humidity was slightly significant (for per 1 unit increase with RR of 1.0088 (95% CI: 1.0027-1.0150), I(2) = 72.6%, Egger’s test P = 0.159, N = 9). Subgroup analysis showed that regional climate with temperate areas may have a higher risk of incidence of mumps than areas with subtropical climate in cold effect of ambient temperature and low effect of relative humidity. In addition, meta-regression analysis showed that regional climate may affect the association between incidence of mumps and cold effect of ambient temperature. Our results suggest ambient temperature could affect the incidence of mumps significantly, of which both hot and cold effect of ambient temperature may increase the incidence of mumps. Further studies are still needed to clarify the relationship between the incidence of mumps and ambient temperature outside of east Asia, and many other meteorological factors. These results of ambient temperature are important for establishing preventive measures on mumps, especially in temperate areas. The policy-makers should pay more attention to ambient temperature changes and take protective measures in advance.
Mumps is an acute and common childhood disease caused by paramyxovirus. It has been reported that the occurrence of mumps is influenced by seasonality. However, the role of meteorological variables in the incidence of mumps remains unclear. The purpose of this study was to explore the relationship between meteorological factors and the incidence of mumps infection. Poisson regression analysis was used to study the relationship between weather variability and the incidence of mumps in Taiwan. Between 2012 and 2018, 5459 cases of mumps cases were reported to the Centers for Disease Control, Taiwan (Taiwan CDC). The occurrence of mumps virus infections revealed significant seasonality in the spring and summer seasons in Taiwan. The incidence of mumps virus infections began to increase at temperatures of 15°C and started to decline if the temperature was higher than 29°C (r2 = 0.387, P = .008). Similarly, the number of mumps cases began to increase at a relative humidity of 65% to 69% (r2 = 0.838, P < .029). The number of mumps cases was positively associated with temperature and relative humidity during the period preceding the infection. This study showed that the occurrence of mumps is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as early warning signals and indicate the need to strengthen the intervention and prevention of mumps.
OBJECTIVE: In this study, we aimed to investigate the clinical epidemiology of lower respiratory tract infections with different respiratory syncytial virus (RSV) subtypes in hospitalized children in Suzhou and their correlation with climatic and environmental factors. METHOD: In this retrospective cross-sectional study, we collected nasopharyngeal secretion samples from children hospitalized with acute lower respiratory tract infection. We collected the clinical data of children with RSV infection, and compared and analyzed their epidemiological characteristics. RESULTS: RSV-B was the dominant strain in 2016. In 2018, RSV-A was the dominant strain. The positive detection rate of RSV-A was negatively correlated with monthly mean temperature, monthly mean wind speed, total monthly rainfall, and O(3) concentration and positively correlated with PM2.5, PM10, and NO(2), SO(2), and CO concentrations. The positive detection rate of RSV-B was negatively correlated with monthly average temperature, monthly total rainfall, monthly sunshine duration, and O(3) concentration and positively correlated with CO concentration. CONCLUSIONS: RSV-A was the main subtype detected in this study. The positive detection rate of RSV-A was related to temperature, wind speed, rainfall, PM2.5. PM10, and NO(2), SO(2), CO, and O(3) concentrations. The positive detection rate of RSV-B was related to temperature, rainfall, sunshine time, and O(3) concentration.
BACKGROUND: While influenza infections and environmental factors have been documented as potential drivers of tuberculosis, no investigations have simultaneously examined their impact on tuberculosis at a population level. This study thereby made use of Hong Kong’s surveillance data over 22 years to elucidate the temporal association between environmental influences, influenza infections, and tuberculosis activity. METHODS: Weekly total numbers of hospital admissions due to tuberculosis, meteorological data, and outdoor air pollutant concentrations in Hong Kong during 1998-2019 were obtained. All-type influenza-like illness positive (ILI+) rate and type-specific ILI+ rates were used as proxies for influenza activity. Quasi-Poisson generalized additive models together with distributed lag non-linear models were used to assess the association of interest. RESULTS: A total of 164,116 hospital admissions due to tuberculosis were notified in public settings over a period of 22 years. The cumulative adjusted relative risk (ARR) of hospital admission due to tuberculosis was 1.07 (95% CI, 1.00-1.14) when the mean ambient temperature increased from 15.1 °C (the 5th percentile) to 24.5 °C (median). Short-term exposure to air pollutants was not found to be statistically significantly related to tuberculosis hospitalization. Accounting for the environmental covariates in the analysis, the cumulative ARR of tuberculosis admission was elevated to 1.05 (95% CI, 1.01-1.08) when the rate of ILI+ total increased from zero to 19.9 per 1000 consultations, the 95th percentile. CONCLUSION: Our findings demonstrated that increased influenza activity and higher temperature were related to a higher risk of tuberculosis admissions. Stepping up the promotion of influenza vaccination, especially before the summer season, may lower the risk of tuberculosis infection/reactivation for vulnerable groups (e.g. elderly born before the launch of Bacillus Calmette-Guérin vaccination programme).
Background: Existing evidence suggests that mumps epidemics, a global public health issue, are associated with meteorological factors and air pollutants at the population scale. However, the interaction effect of meteorological factors and air pollutants on mumps remains underexplored.Methods: Daily cases of mumps, meteorological factors, and air pollutants were collected in Ningxia, China, from 2015 to 2019. First, a distributed lag nonlinear model (DLNM) was employed to assess the confounding-adjusted relationship between meteorological factors, ambient air pollutants, and mumps incidences. According to the results of DLNM, stratification in both air pollutants and meteorological factors was adopted to further explore the interaction effect of particulate matter less than or equal to 2.5 mu m in aerodynamic diameter (PM2.5) and ground-level ozone (O-3) with temperature and relative humidity (RH).Results: We reported significant individual associations between mumps incidences and environmental factors, including temperature, relative humidity, PM2.5, and O-3. Evident multiplicate and additive interactions between meteorological factors and PM2.5 were found with interaction relative risk (IRR) of 1.14 (95%CI: 1.01, 1.29) and relative excess risk due to interaction (RERI) of 0.17 (95%CI: 0.02, 0.32) for a moderate level of temperature at 12 degrees C, and IRR of 1.37 (95%CI: 1.14, 1.66), RERI of 0.36 (95%CI: 0.11, 0.60) for a high level of temperature at 20 degrees C, respectively. These results indicated that PM2.5 and temperature have a significant synergistic effect on the cases of mumps, while no interaction between relative humidity and PM2.5 is observed. Regarding O-3 and meteorological factors (temperature = 12 degrees C, 20 degrees C), IRR and RERI were 1.33 (95%CI: 1.17, 1.52) and 0.30 (95%CI: 0.16, 0.45), 1.91 (95%CI: 1.46, 2.49) and 0.69 (95%CI: 0.32, 1.07), respectively. And IRR of 1.17 (95%CI: 1.06, 1.29), RERI of 0.13 (95%CI: 0.04, 0.21) for a middle level of relative humidity at 48%.Conclusion: Our findings indicated that meteorological factors and air pollutants imposed a significantly lagged and nonlinear effect on the incidence of mumps. The interaction between low temperature and O-3 showed antagonistic effects, while temperature (medium and high) with PM2.5 and O-3 presented synergistic effects. For relative humidity, the interaction with O-3 is synergistic. These results provide scientific evidence to relevant health authorities for the precise disease control and prevention of mumps in arid and semi-arid areas.
Big data can be used to correlate diseases and climatic factors. The prevalence of influenza (flu) virus, accounting for a large proportion of respiratory infections, suggests that the effect of climate variables according to seasonal dynamics of influenza virus infections should be investigated. Here, trends in flu virus detection were analyzed using data from 9,010 tests performed between January 2012 and December 2018 at Dankook University Hospital, Cheonan, Korea. We compared the detection of the flu virus in Cheonan area and its association with climate change. The flu virus detection rate was 9.9% (894/9,010), and the detection rate was higher for flu virus A (FLUAV; 6.9%) than for flu virus B (FLUBV; 3.0%). Both FLUAV and FLUBV infections are considered an epidemic each year. We identified 43.1% (n = 385) and 35.0% (n = 313) infections in children aged < 10 years and adults aged > 60 years, respectively. The combination of these age groups encompassed 78.1% (n = 698/894) of the total data. Flu virus infections correlated with air temperature, relative humidity, vapor pressure, atmospheric pressure, particulate matter, and wind chill temperature (P < 0.001). However, the daily temperature range did not significantly correlate with the flu detection results. This is the first study to identify the relationship between long-term flu virus infection with temperature in the temperate region of Cheonan.
BACKGROUND: Sentinel physician surveillance in communities has played an important role in detecting early signs of epidemics. The traditional approach is to let the primary care physician voluntarily and actively report diseases to the health department on a weekly basis. However, this is labor-intensive work, and the spatio-temporal resolution of the surveillance data is not precise at all. In this study, we built up a clinic-based enhanced sentinel surveillance system named “Sentinel plus” which was designed for sentinel clinics and community hospitals to monitor 23 kinds of syndromic groups in Taipei City, Taiwan. The definitions of those syndromic groups were based on ICD-10 diagnoses from physicians. METHODS: Daily ICD-10 counts of two syndromic groups including ILI and EV-like syndromes in Taipei City were extracted from Sentinel plus. A negative binomial regression model was used to couple with lag structure functions to examine the short-term association between ICD counts and meteorological variables. After fitting the negative binomial regression model, residuals were further rescaled to Pearson residuals. We then monitored these daily standardized Pearson residuals for any aberrations from July 2018 to October 2019. RESULTS: The results showed that daily average temperature was significantly negatively associated with numbers of ILI syndromes. The ozone and PM2.5 concentrations were significantly positively associated with ILI syndromes. In addition, daily minimum temperature, and the ozone and PM2.5 concentrations were significantly negatively associated with the EV-like syndromes. The aberrational signals detected from clinics for ILI and EV-like syndromes were earlier than the epidemic period based on outpatient surveillance defined by the Taiwan CDC. CONCLUSIONS: This system not only provides warning signals to the local health department for managing the risks but also reminds medical practitioners to be vigilant toward susceptible patients. The near real-time surveillance can help decision makers evaluate their policy on a timely basis.
The bacteria (including pathogenic bacteria) attached to road deposited sediments (RDS) may interrelate with the microbe in the atmosphere, soil and water through resuspension and wash-off, and is of great significance to human and ecological health. However, the characteristics of bacterial communities with different time scale on RDS were unknown to dates. Climate change prolonged the dry days between rain events in many areas, making the varied trend of bacterial communities might be more significant in short term. This study revealed the characteristics of bacterial communities on RDS in urban and suburban areas through seasonal and daily scale. The correlations between other factors (land use, particle size, and chemical components) and the bacterial communities were also analyzed. It was found that the season showed a higher association with the bacterial community diversity than land use and particle size in urban areas. The bacterial community diversity increased substantially throughout the short-term study period (41 days) and the variation of dominant bacteria could be fitted by quadratic function in suburbs. In addition, urbanization notably increased the bacterial community diversity, while the potential pathogenic bacteria were more abundant in the suburban areas, coarse RDS (>75 μm), and in spring. The chemical components on RDS showed special correlations with the relative abundance of dominant bacteria. The research findings would fill the knowledge gap on RDS bacterial communities and be helpful for the future research on the assembly process of bacterial communities.
This study proposed a novel ensemble analysis strategy to improve hand, foot and mouth disease (HFMD) prediction by integrating environmental data. The approach began by establishing a vector autoregressive model (VAR). Then, a dynamic Bayesian networks (DBN) model was used for variable selection of environmental factors. Finally, a VAR model with constraints (CVAR) was established for predicting the incidence of HFMD in Chengdu city from 2011 to 2017. DBN showed that temperature was related to HFMD at lags 1 and 2. Humidity, wind speed, sunshine, PM10, SO2 and NO2 were related to HFMD at lag 2. Compared with the autoregressive integrated moving average model with external variables (ARIMAX), the CVAR model had a higher coefficient of determination (R2, average difference: + 2.11%; t = 6.2051, P = 0.0003 < 0.05), a lower root mean-squared error (-24.88%; t = -5.2898, P = 0.0007 < 0.05) and a lower mean absolute percentage error (-16.69%; t = -4.3647, P = 0.0024 < 0.05). The accuracy of predicting the time-series shape was 88.16% for the CVAR model and 86.41% for ARIMAX. The CVAR model performed better in terms of variable selection, model interpretation and prediction. Therefore, it could be used by health authorities to identify potential HFMD outbreaks and develop disease control measures.
BACKGROUND: Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. METHODS: The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. RESULTS: Overall, a 10 μg/m(3) increment of O(3), PM(2.5), PM(10) and NO(2) could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7-17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0-6 years and 18-64 years were more sensitive to air pollution. CONCLUSION: Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.
Airborne bacteria have an important role in atmospheric processes and human health. However, there is still little information on the transmission and distribution of bacteria via the airborne route. To characterize the impact of foggy, haze, haze-fog (HF) and dust days on the concentration and viability of bacteria in atmospheric aerosols, size-segregated bioaerosol samples were collected in the Qingdao coastal region from March 2018 to February 2019. The total airborne microbes and viable/non-viable bacteria in the bioaerosol samples were measured using an epifluorescence microscope after staining with DAPI (4′, 6-diamidino-2-phenylindole) and a LIVE/DEAD® BacLight Bacterial Viability Kit. The average concentrations of total airborne microbes on haze and dust days were 6.75 × 10(5) and 1.03 × 10(6) cells/m(3), respectively, which increased by a factor of 1.3 and 2.5 (on average), respectively, relative to those on sunny days. The concentrations of non-viable bacteria on haze and dust days increased by a factor of 1.2 and 3.6 (on average), respectively, relative to those on sunny days. In contrast, the concentrations of viable bacteria on foggy and HF days were 7.13 × 10(3) and 5.74 × 10(3) cells/m(3), decreases of 38% and 50%, respectively, compared with those on sunny days. Foggy, haze, dust and HF days had a significant effect on the trend of the seasonal variation in the total airborne microbes and non-viable bacteria. Bacterial viability was 20.8% on sunny days and significantly higher than the 14.1% on foggy days, 11.2% on haze days, 8.6% during the HF phenomenon and 6.1% on dust days, indicating that special weather is harmful to some bacterial species. Correlation analysis showed that the factors that influenced the bacterial concentration and viability depended on different weather conditions. The main influential factors were temperature, NO(2) and SO(2) concentrations on haze days, and temperature, particulate matter (PM(2.5)) and NO(2) concentrations on foggy days. The median size of particles containing viable bacteria was 1.94 μm on sunny days and decreased to 1.88 μm and 1.74 μm on foggy and haze days, respectively, but increased to 2.18 μm and 2.37 μm on dust and HF days, respectively.
BACKGROUND: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. METHODS: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). RESULTS: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15-35 mm, and MAAP showed a positive correlation in the range of 925-945 hPa. HFMD incidence increased with MAT in the range of 15-23 °C, and the effective value of MAWS was in the range of 1.3-1.7 m/s, which was positively correlated with incidences of HFMD. CONCLUSIONS: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.
Hand, foot, and mouth disease (HFMD) remains a serious health threat to young children. Urumqi is one of the most severely affected cities in northwestern China. This study aims to identify the spatiotemporal distribution characteristics of HFMD, and explore the relationships between driving factors and HFMD in Urumqi, Xinjiang. METHODS: HFMD surveillance data from 2014 to 2018 were obtained from the China Center for Disease Control and Prevention. The center of gravity and geographical detector model were used to analyze the spatiotemporal distribution characteristics of HFMD and identify the association between these characteristics and socioeconomic and meteorological factors. RESULTS: A total of 10,725 HFMD cases were reported in Urumqi during the study period. Spatially, the morbidity number of HFMD differed regionally and the density was higher in urban districts than in rural districts. Overall, the development of HFMD in Urumqi expanded toward the southeast. Temporally, we observed that the risk of HFMD peaked from June to July. Furthermore, socioeconomic and meteorological factors, including population density, road density, GDP, temperature and precipitation were significantly associated with the occurrence of HFMD. CONCLUSIONS: HFMD cases occurred in spatiotemporal clusters. Our findings showed strong associations between HFMD and socioeconomic and meteorological factors. We comprehensively considered the spatiotemporal distribution characteristics and influencing factors of HFMD, and proposed some intervention strategies that may assist in predicting the morbidity number of HFMD.
The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.
Hand, foot and mouth (HFM) disease is a common childhood illness. The paper aims to capture the spatiotemporal characters, and investigate the influence factors of the HFM epidemic in 15 regions of Xinjiang province from 2008 to 2017, China. Descriptive statistical analysis shows that the children aged 0-5 years have a higher HFM incidence, mostly boys. The male-female ratio is 1.5:1. Through the scanning method, we obtain the first cluster high-risk areas. The cluster time is usually from May to August every year. A spatiotemporal model is proposed to analyze the impact of meteorological factors on HFM disease. Comparing with the spatial model, the model is more effective in terms of R2, AIC, deviation, and mean-square error. Among meteorological factors, the number of HFM cases generally increases with the intensity of rainfall. As the temperature increases, there are more HFM patients. Some regions are mostly influenced by wind speed. Further, another spatiotemporal model is introduced to investigate the relationship between HFM disease and socioeconomic factors. The results show that socioeconomic factors have significant influence on the disease. In most areas, the risk of HFM disease tends to rise with the increase of the gross domestic product, the ratios of urban population and tertiary industry. The incidence is closely related to the number of beds and population density in some regions. The higher the ratio of primary school, the lower the number of HFM cases. Based on the above analysis, it is the key measure to prevent and control the spread of the HFM epidemic in high-risk areas, and influence factors should not be ignored.
Previous studies have reported that temperature is the main meteorological factor associated with influenza activity. This study used generalized additive models (GAMs) to explore the relationship between temperature and influenza activity in China. From the national perspective, the average temperature (AT) had an approximately negative linear correlation with the incidence of influenza, as well as a positive rate of influenza H1N1 virus (A/H1N1). Every degree that the monthly AT rose, the influenza cases decreased by 2.49% (95%CI: 1.24%-3.72%). The risk of influenza cases reached a peak at -5.35 °C with RRs of 2.14 (95%CI: 1.38-3.33) and the monthly AT in the range of -5.35 °C to 18.31 °C had significant effects on the incidence of influenza. Every degree that the weekly AT rose, the positive rate of A/H1N1 decreased by 5.28% (95%CI: 0.35%-9.96%). The risk of A/H1N1 reached a peak at -3.14 °C with RRs of 4.88 (95%CI: 1.01-23.75) and the weekly AT in the range of -3.14 °C to 17.25 °C had significant effects on the incidence of influenza. Our study found that AT is negatively associated with influenza activity, especially for A/H1N1. These findings indicate that temperature could be integrated into the current influenza surveillance system to develop early warning systems to better predict and prepare for the risks of influenza.
BACKGROUND: The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. METHODS: Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. RESULTS: Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. CONCLUSIONS: Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.
BACKGROUND: Influenza is a seasonal infectious disease, and meteorological parameters critically influence the incidence of influenza. However, the meteorological parameters linked to influenza occurrence in semi-arid areas are not studied in detail. This study aimed to clarify the impact of meteorological parameters on influenza incidence during 2010-2019 in Lanzhou. The results are expected to facilitate the optimization of influenza-related public health policies by the local healthcare departments. METHODS: Descriptive data related to influenza incidence and meteorology during 2010-2019 in Lanzhou were analyzed. The exposure-response relationship between the risk of influenza occurrence and meteorological parameters was explored according to the distributed lag no-linear model (DLNM) with Poisson distribution. The response surface model and stratified model were used to estimate the interactive effect between relative humidity (RH) and other meteorological parameters on influenza incidence. RESULTS: A total of 6701 cases of influenza were reported during 2010-2019. DLNM results showed that the risk of influenza would gradually increase as the weekly mean average ambient temperature (AT), RH, and absolute humidity (AH) decrease at lag 3 weeks when they were lower than 12.16°C, 51.38%, and 5.24 g/m(3), respectively. The low Tem (at 5th percentile, P(5)) had the greatest effect on influenza incidence; the greatest estimated relative risk (RR) was 4.54 (95%CI: 3.19-6.46) at cumulative lag 2 weeks. The largest estimates of RRs for low RH (P(5)) and AH (P(5)) were 4.81 (95%CI: 3.82-6.05) and 4.17 (95%CI: 3.30-5.28) at cumulative lag 3 weeks, respectively. An increase in AT by 1°C led to an estimates of percent change (95%CI) of 3.12% (-4.75% to -1.46%) decrease in the weekly influenza case counts in a low RH environment. In addition, RH showed significant interaction with AT and AP on influenza incidence but not with wind speed. CONCLUSION: This study indicated that low AT, low humidity (RH and AH), and high air pressure (AP) increased the risk of influenza. Moreover, the interactive effect of low RH with low AT and high AP can aggravate the incidence of influenza.
Under the variant climate conditions in the transitional regions between tropics and subtropics, the impacts of climate factors on influenza subtypes have rarely been evaluated. With the available influenza A (Flu-A) and influenza B (Flu-B) outbreak data in Shenzhen, China, which is an excellent example of a transitional marine climate, the associations of multiple climate variables with these outbreaks were explored in this study. Daily laboratory-confirmed influenza virus and climate data were collected from 2009 to 2015. Potential impacts of daily mean/maximum/minimum temperatures (T/T(max)/T(min)), relative humidity (RH), wind velocity (V), and diurnal temperature range (DTR) were analyzed using the distributed lag nonlinear model (DLNM) and generalized additive model (GAM). Under its local climate partitions, Flu-A mainly prevailed in summer months (May to June), and a second peak appeared in early winter (December to January). Flu-B outbreaks usually occurred in transitional seasons, especially in autumn. Although low temperature caused an instant increase in both Flu-A and Flu-B risks, its effect could persist for up to 10 days for Flu-B and peak at 17 C (relative risk (RR) = 14.16, 95% CI: 7.46-26.88). For both subtypes, moderate-high temperature (28 C) had a significant but delayed effect on influenza, especially for Flu-A (RR = 26.20, 95% CI: 13.22-51.20). The Flu-A virus was sensitive to RH higher than 76%, while higher Flu-B risks were observed at both low (< 65%) and high (> 83%) humidity. Flu-A was active for a short term after exposure to large DTR (e.g., DTR = 10 C, RR = 12.45, 95% CI: 6.50-23.87), whereas Flu-B mainly circulated under stable temperatures. Although the overall wind speed in Shenzhen was low, moderate wind (2-3 m/s) was found to favor the outbreaks of both subtypes. This study revealed the thresholds of various climatic variables promoting influenza outbreaks, as well as the distinctions between the flu subtypes. These data can be helpful in predicting seasonal influenza outbreaks and minimizing the impacts, based on integrated forecast systems coupled with short-term climate models.
BACKGROUND: Influenza epidemics occur during winter in temperate zones, but have less regular seasonality in the subtropics and tropics. Here we quantified the role of environmental drivers of influenza seasonality in temperate and subtropical China. METHODS: We used weekly surveillance data on influenza virus activity in mainland China and Hong Kong from 2005 through 2016. We estimated the transmissibility via the instantaneous reproduction number (R(t)), a real-time measure of transmissibility, and examined its relationship with different climactic drivers and allowed for the timing of school holidays and the decline in susceptibility in the population as an epidemic progressed. We developed a multivariable regression model for R(t) to quantify the contribution of various potential environmental drivers of transmission. FINDINGS: We found that absolute humidity is a potential driver of influenza seasonality and had a U-shaped association with transmissibility and hence can predict the pattern of influenza virus transmission across different climate zones. Absolute humidity was able to explain up to 15% of the variance in R(t), and was a stronger predictor of R(t) across the latitudes. Other climatic drivers including mean daily temperature explained up to 13% of variance in R(t) and limited to the locations where the indoor measures of these factors have better indicators of outdoor measures. The non-climatic driver, holiday-related school closures could explain up to 7% of variance in R(t). INTERPRETATION: A U-shaped association of absolute humidity with influenza transmissibility was able to predict seasonal patterns of influenza virus epidemics in temperate and subtropical locations.
OBJECTIVES: From 2013 to 2017, the avian influenza A (H7N9) virus frequently infected people in China, which seriously affected the public health of society. This study aimed to analyze the spatial characteristics of human infection with the H7N9 virus in China and assess the risk areas of the epidemic. METHODS: Using kernel density estimation, standard deviation ellipse analysis, spatial and temporal scanning cluster analysis, and Pearson correlation analysis, the spatial characteristics and possible risk factors of the epidemic were studied. Meteorological factors, time (month), and environmental factors were combined to establish an epidemic risk assessment proxy model to assess the risk range of an epidemic. RESULTS: The epidemic situation was significantly correlated with atmospheric pressure, temperature, and daily precipitation (P < 0.05), and there were six temporal and spatial clusters. The fitting accuracy of the epidemic risk assessment agent-based model for lower-risk, low-risk, medium-risk, and high-risk was 0.795, 0.672, 0.853, 0.825, respectively. CONCLUSIONS: This H7N9 epidemic was found to have more outbreaks in winter and spring. It gradually spread to the inland areas of China. This model reflects the risk areas of human infection with the H7N9 virus.
(1) Background: To explore whether meteorological factors have an impact on the prevalence of mumps, and to make a short−term prediction of the case number of mumps in Chongqing. (2) Methods: K−means clustering algorithm was used to divide the monthly mumps cases of each year into the high and low case number clusters, and Student t−test was applied for difference analysis. The cross−correlation function (CCF) was used to evaluate the correlation between the meteorological factors and mumps, and an ARIMAX model was constructed by additionally incorporating meteorological factors as exogenous variables in the ARIMA model, and a short−term prediction was conducted for mumps in Chongqing, evaluated by MAE, RMSE. (3) Results: All the meteorological factors were significantly different (p < 0.05), except for the relative humidity between the high and low case number clusters. The CCF and ARIMAX model showed that monthly precipitation, temperature, relative humidity and wind velocity were associated with mumps, and there were significant lag effects. The ARIMAX model could accurately predict mumps in the short term, and the prediction errors (MAE, RMSE) were lower than those of the ARIMA model. (4) Conclusions: Meteorological factors can affect the occurrence of mumps, and the ARIMAX model can effectively predict the incidence trend of mumps in Chongqing, which can provide an early warning for relevant departments.
BACKGROUND: Small-scale studies have identified temperature and other meteorological factors as risk factors for human health. However, only a few have quantified the specific impact of meteorological factors on mumps. A quantitative examination of the exposure-response relationship between meteorological factors and mumps is needed to provide new insights for multi-city analysis. METHODS: The daily recorded number of mumps cases and meteorological data in 17 cities of Shandong Province from 2009 to 2017 were collected. A two-stage model was built to explore the relationship between meteorological factors and mumps. RESULTS: A total of 104,685 cases of mumps were recorded from 2009 to 2017. After controlling for seasonality and long-term trends, the effect of low temperature on mumps was significant at the provincial level, with a cumulative RR of 1.035 (95%CI: 1.002-1.069) with a 1-day lagged effect. The proportion of primary and middle school students was determined as an effect modifier, which had a significant impact on mumps (Stat = 8.374, p = 0.039). There was heterogeneity in the combined effect of temperature on mumps (Q = 95.447, p = 0.000), and its size was I(2) = 49.7%. CONCLUSIONS: We have identified a non-linear relationship between mumps and temperature in Shandong Province. In particular, low temperatures could bring more cases of mumps, with certain lagged effects. More public health measures should be taken to reduce the risks when temperatures are low, especially for cities with a high proportion of primary and secondary school students.
Varicella (chickenpox) is a serious public health problem in China, with the most reported cases among childhood vaccine-preventable infectious diseases, and its reported incidence has increased over 20-fold since 2005. Few previous studies have explored the association of multiple meteorological factors with varicella and considered the potential confounding effects of air pollutants. It is the first study to investigate and analyze the effects of multiple meteorological factors on varicella incidence, controlling for the confounding effects of various air pollutants. Daily meteorological and air pollution data and varicella cases were collected from January 1, 2015, to December 31, 2020, in Lu’an, Eastern China. A combination of the quasi-Poisson generalized additive model (GAM) and distributed lag nonlinear model (DLNM) was used to evaluate the meteorological factor-lag-varicella relationship, and the risk of varicella in extreme meteorological conditions. The maximum single-day lag effects of varicella were 1.288 (95%CI, 1.201-1.381, lag 16 day), 1.475 (95%CI, 1.152-1.889, lag 0 day), 1.307 (95%CI, 1.196-1.427, lag 16 day), 1.271 (95%CI, 0.981-1.647, lag 4 day), and 1.266 (95%CI, 1.162-1.378, lag 21 day), when mean temperature, diurnal temperature range (DTR), mean air pressure, wind speed, and sunshine hours were -5.8°C, 13.5°C, 1035.5 hPa, 6 m/s, and 0 h, respectively. At the maximum lag period, the overall effects of mean temperature and pressure on varicella showed W-shaped curves, peaked at 17.5°C (RR=2.085, 95%CI: 1.480-2.937) and 1035.5 hPa (RR=5.481, 95%CI: 1.813-16.577), while DTR showed an M-shaped curve and peaked at 4.4°C (RR=6.131, 95%CI: 1.120-33.570). Sunshine hours were positively correlated with varicella cases at the lag of 0-8 days and 0-9 days when sunshine duration exceeded 10 h. Furthermore, the lag effects of extreme meteorological factors on varicella cases were statistically significant, except for the extremely high wind speed. We found that mean temperature, mean air pressure, DTR, and sunshine hours had significant nonlinear effects on varicella incidence, which may be important predictors of varicella early warning.
OBJECTIVE: To analyze the spatiotemporal dynamic distribution and detect the related meteorological factors of scarlet fever from an ecological perspective, which could provide scientific information for effective prevention and control of this disease. METHODS: The data on scarlet fever cases in mainland China were downloaded from the Data Center of the China Public Health Science, while monthly meteorological data were extracted from the official website of the National Bureau of Statistics. Global Moran’s I, local Getis-Ord G(i)(⁎) hotspot statistics, and Kulldorff’s retrospective space-time scan statistical analysis were used to detect the spatial and spatiotemporal clusters of scarlet fever across all settings. A spatial panel data model was conducted to estimate the impact of meteorological factors on scarlet fever incidence. RESULTS: Scarlet fever in China had obvious spatial, temporal, and spatiotemporal clustering, high-incidence spatial clusters were located mainly in the north and northeast of China. Nine spatiotemporal clusters were identified. A spatial lag fixed effects panel data model was the best fit for regression analysis. After adjusting for spatial individual effects and spatial autocorrelation (ρ = 0.5623), scarlet fever incidence was positively associated with a one-month lag of average temperature, precipitation, and total sunshine hours (all P-values < 0.05). Each 10 °C, 2 cm, and 10 h increase in temperature, precipitation, and sunshine hours, respectively, was associated with a 6.41% increment and 1.04% and 1.41% decrement in scarlet fever incidence, respectively. CONCLUSION: The incidence of scarlet fever in China showed an upward trend in recent years. It had obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in the north and northeast of China. Areas with high temperature and with low precipitation and sunshine hours tended to have a higher scarlet fever incidence, and we should pay more attention to prevention and control in these places.
The relationship between air temperature and the hospital admission of adult patients with community-acquired pneumonia (CAP) was analyzed. The hospitalization data pertaining to adult CAP patients (age ≥ 18 years) in two tertiary comprehensive hospitals in Baotou, Inner Mongolia Autonomous Region, China from 2014 to 2018 and meteorological data there in the corresponding period were collected. The exposure-response relationship between the daily average temperature and the hospital admission of adult CAP patients was quantified by using a distributed lag non-linear model. A total of 4466 cases of adult patients with CAP were admitted. After eliminating some confounding factors such as relative humidity, wind speed, air pressure, long-term trend, and seasonal trend, a lower temperature was found to be associated with a higher risk of adult CAP. Compared to 21 °C, lower temperature range of 4 to -12 °C was associated with a greater number of CAP hospitalizations among those aged ≥ 65 years, and the highest relative risk (RR) was 2.80 (95% CI 1.15-6.80) at a temperature of - 10 °C. For those < 65 years, lower temperature was not related to CAP hospitalizations. Cumulative lag RRs of low temperature with CAP hospitalizations indicate that the risk associated with colder temperatures appeared at a lag of 0-7 days. For those ≥ 65 years, the cumulative RR of CAP hospitalizations over lagging days 0-5 was 1.89 (95% CI 1.01-3. 56). In brief, the lower temperature had age-specific effects on CAP hospitalizations in Baotou, China, especially among those aged ≥ 65 years.
BACKGROUND: Most existing studies have only investigated the delayed effect of meteorological factors on pulmonary tuberculosis (PTB). However, the effect of extreme climate and the interaction between meteorological factors on PTB has been rarely investigated. METHODS: Newly diagonsed PTB cases and meteorological factors in Urumqi in each week between 2013 and 2019 were collected. The lag-exposure-response relationship between meteorological factors and PTB was analyzed using the distributed lag non-linear model (DLNM). The generalized additive model (GAM) was used to visualize the interaction between meteorological factors. Stratified analysis was used to explore the impact of meteorological factors on PTB in different stratification and RERI, AP and SI were used to quantitatively evaluate the interaction between meteorological factors. RESULTS: A total of 16,793 newly diagnosed PTB cases were documented in Urumqi, China from 2013 to 2019. The median (interquartile range) temperature, relative humidity, wind speed, and PTB cases were measured as 11.3°C (-5.0-20.5), 57.7% (50.7-64.2), 4.1m/s (3.4-4.7), and 47 (37-56), respectively. The effects of temperature, relative humidity and wind speed on PTB were non-linear, which were found with the “N”-shaped, “L”-shaped, “N”-shaped distribution, respectively. With the median meteorological factor as a reference, extreme low temperature was found to have a protective effect on PTB. However, extreme high temperature, extreme high relative humidity, and extreme high wind speed were found to increase the risk of PTB and peaked at 31.8°C, 83.2%, and 7.6 m/s respectively. According to the existing monitoring data, no obvious interaction between meteorological factors was found, but low temperature and low humidity (RR = 1.149, 95%CI: 1.003-1.315), low temperature and low wind speed (RR = 1.273, 95%CI: 1.146-1.415) were more likely to cause the high incidence of PTB. CONCLUSION: Temperature, relative humidity and wind speed were found to play vital roles in PTB incidence with delayed and non-linear effects. Extreme high temperature, extreme high relative humidity, and extreme high wind speed could increase the risk of PTB. Moreover, low temperature and low humidity, low temperature and low wind speed may increase the incidence of PTB.
In the present paper, an association between the growth rate of PTB and the environmental impacting elements in the pearl river delta region and the closed industry related cities in China is studied. We summarized the characteristics of different industry characteristics in this region by three echelons of urban agglomerations conducted by K-means clustering model on the time series of their monthly AQI data. To determine the impact of environmental factors on the increase of PTB, the SMLR in GLM has been applied. We then measured the seasonal effect and suggest the spring to be the leading season which keep the highest possibility of the incidence of PTB. Besides giving the analysis by fixed meteorological factors, we presented a sensitive analysis with a variation of precipitation. The Genetic algorithms (GAs) is used to determine the “tolerant” interval and as the results, the width of “tolerant” almost keep a declining trend as the precipitation increasing except when the precipitation comes the interval [68,74]. In addition, with the precipitation increasing higher than 64 mm, the “tolerant” for the AQI values from the first and the second echelon both trend to decline, and a lenient environmental policy currently may easily cause a rapid development of PTB growth rate.
BACKGROUND: Most studies on associations between meteorological factors and tuberculosis (TB) were conducted in a single city, used different lag times, or merely explored the qualitative associations between meteorological factors and TB. Thus, we performed a multicenter study to quantitatively evaluate the effects of meteorological factors on the risk of pulmonary tuberculosis (PTB). METHODS: We collected data on newly diagnosed PTB cases in 13 study sites in Jiangsu Province between January 1, 2014, and December 31, 2019. Data on meteorological factors, air pollutants, and socioeconomic factors at these sites during the same period were also collected. We applied the generalized additive mixed model to estimate the associations between meteorological factors and PTB. RESULTS: There were 20,472 newly diagnosed PTB cases reported in the 13 study sites between 2014 and 2019. The median (interquartile range) weekly average temperature, weekly average wind speed, and weekly average relative humidity of these sites were 17.3 °C (8.0-24.1), 2.2 m/s (1.8-2.7), and 75.1% (67.1-82.0), respectively. In the single-meteorological-factor models, for a unit increase in weekly average temperature, weekly average wind speed, and weekly average relative humidity, the risk of PTB decreased by 0.9% [lag 0-13 weeks, 95% confidence interval (CI): -1.5, -0.4], increased by 56.2% (lag 0-16 weeks, 95% CI: 32.6, 84.0) when average wind speed was <3 m/s, and decreased by 28.1% (lag 0-14 weeks, 95% CI: -39.2, -14.9) when average relative humidity was ≥72%, respectively. Moreover, the associations remained significant in the multi-meteorological-factor models. CONCLUSIONS: Average temperature and average relative humidity (≥72%) are negatively associated with the risk of PTB. In contrast, average wind speed (<3 m/s) is positively related to the risk of PTB, suggesting that an environment with low temperature, relatively high wind speed, and low relative humidity is conducive to the transmission of PTB.
The autoregressive integrated moving average with exogenous regressors (ARIMAX) modeling studies of pulmonary tuberculosis (PTB) are still rare. This study aims to explore whether incorporating air pollution and meteorological factors can improve the performance of a time series model in predicting PTB. We collected the monthly incidence of PTB, records of six air pollutants and six meteorological factors in Ningbo of China from January 2015 to December 2019. Then, we constructed the ARIMA, univariate ARIMAX, and multivariate ARIMAX models. The ARIMAX model incorporated ambient factors, while the ARIMA model did not. After prewhitening, the cross-correlation analysis showed that PTB incidence was related to air pollution and meteorological factors with a lag effect. Air pollution and meteorological factors also had a correlation. We found that the multivariate ARIMAX model incorporating both the ozone with 0-month lag and the atmospheric pressure with 11-month lag had the best performance for predicting the incidence of PTB in 2019, with the lowest fitted mean absolute percentage error (MAPE) of 2.9097% and test MAPE of 9.2643%. However, ARIMAX has limited improvement in prediction accuracy compared with the ARIMA model. Our study also suggests the role of protecting the environment and reducing pollutants in controlling PTB and other infectious diseases.
Few studies have examined the effects of inbound overseas travelers and meteorological conditions on the shift in human respiratory syncytial virus (HRSV) season in Japan. This study aims to test whether the number of inbound overseas travelers and meteorological conditions are associated with the onset week of HRSV epidemic season. The estimation of onset week for 46 prefectures (except for Okinawa prefecture) in Japan for 4-year period (2014-2017) was obtained from previous papers based on the national surveillance data. We obtained data on the yearly number of inbound overseas travelers and meteorological (yearly mean temperature and relative humidity) conditions from Japan National Tourism Organization (JNTO) and Japan Meteorological Agency (JMA), respectively. Multi-level mixed-effects linear regression analysis showed that every 1 person (per 100,000 population) increase in number of overall inbound overseas travelers led to an earlier onset week of HRSV epidemic season in the year by 0.02 week (coefficient -0.02; P<0.01). Higher mean temperature and higher relative humidity were also found to contribute to an earlier onset week by 0.30 week (coefficient -0.30; P<0.05) and 0.18 week (coefficient -0.18; P<0.01), respectively. Additionally, models that included the number of travelers from individual countries (Taiwan, South Korea, and China) except Australia showed that both the number of travelers from each country and meteorological conditions contributed to an earlier onset week. Our analysis showed the earlier onset week of HRSV epidemic season in Japan is associated with increased number of inbound overseas travelers, higher mean temperature, and relative humidity. The impact of international travelers on seasonality of HRSV can be further extended to investigations on the changes of various respiratory infectious diseases especially after the coronavirus disease 2019 (COVID-19) pandemic.
We investigated the association between climate variables and pulmonary tuberculosis (PTB) incidence in Brunei-Muara district, Brunei Darussalam. Weekly PTB case counts and climate variables from January 2001 to December 2018 were analysed using distributed lag non-linear model framework. After adjusting for long-term trend and seasonality, we observed positive but delayed relationship between PTB incidence and minimum temperature, with significant adjusted relative risk (adj.RR) at 25.1 °C (95th percentile) when compared to the median, from lag 30 onwards (adj.RR = 1.17 [95% Confidence Interval (95% CI): 1.01, 1.36]), suggesting effect of minimum temperature on PTB incidence after 30 weeks. Similar results were observed from a sub-analysis on smear-positive PTB case counts from lag 29 onwards (adj.RR = 1.21 [95% CI: 1.01, 1.45]), along with positive and delayed association with total rainfall at 160.7 mm (95th percentile) when compared to the median, from lag 42 onwards (adj.RR = 1.23 [95% CI: 1.01, 1.49]). Our findings reveal evidence of delayed effects of climate on PTB incidence in Brunei, but with varying degrees of magnitude, direction and timing. Though explainable by environmental and social factors, further studies on the relative contribution of recent (through primary human-to-human transmission) and remote (through reactivation of latent TB) TB infection in equatorial settings is warranted.
OBJECTIVES: The aim of this study was to explore whether indoor or outdoor relative humidity (RH) modulates the influenza epidemic transmission in temperate and subtropical climates. METHODS: In this study, the daily temperature and RH in 1558 households from March 2017 to January 2019 in five cities across both temperate and subtropical regions in China were collected. City-level outdoor temperature and RH from 2013 to 2019 were collected from the weather stations. We first estimated the effective reproduction number (R(t)) of influenza and then used time-series analyses to explore the relationship between indoor/outdoor RH/absolute humidity and the R(t) of influenza. Furthermore, we expanded the measured 1-year indoor temperature and the RH data into 5 years and used the same method to examine the relationship between indoor/outdoor RH and the R(t) of influenza. RESULTS: Indoor RH displayed a seasonal pattern, with highs during the summer months and lows during the winter months, whereas outdoor RH fluctuated with no consistent pattern in subtropical regions. The R(t) of influenza followed a U-shaped relationship with indoor RH in both temperate and subtropical regions, whereas a U-shaped relationship was not observed between outdoor RH and R(t). In addition, indoor RH may be a better indicator for R(t) of influenza than indoor absolute humidity. CONCLUSION: The findings indicated that indoor RH may be the driver of influenza seasonality in both temperate and subtropical locations in China.
The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010 similar to 2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM > RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.
OBJECTIVES: Hand, foot, and mouth disease (HFMD) is a viral infectious disease that poses a substantial threat in the Asia-Pacific region. It is widely reported that meteorological factors are associated with HFMD. However, the relationships between air pollutants and HFMD are still controversial. In addition, the interactive effects between meteorological factors and air pollutants on HFMD remain unknown. To fill this research gap, we conducted a time-series study. DESIGN: A time-series study. SETTING AND PARTICIPANTS: Daily cases of HFMD as well as meteorological and air pollution data were collected in Chengdu from 2011 to 2017. A total of 184 610 HFMD cases under the age of 15 were included in our study. OUTCOME MEASURES: Distributed lag nonlinear models were used to investigate the relationships between HFMD and environmental factors, including mean temperature, relative humidity, SO(2), NO(2), and PM(10). Then, the relative excess risk due to interaction (RERI) and the proportion attributable to interaction were calculated to quantitatively evaluate the interactions between meteorological factors and air pollutants on HFMD. Bivariate response surface models were used to visually display the interactive effects. RESULTS: The cumulative exposure-response curves of SO(2) and NO(2) were inverted ‘V’-shaped and ‘M’-shaped, respectively, and the risk of HFMD gradually decreased with increasing PM(10) concentrations. We found that there were synergistic interactions between mean temperature and SO(2), relative humidity and SO(2), as well as relative humidity and PM(10) on HFMD, with individual RERIs of 0.334 (95% CI 0.119 to 0.548), 0.428 (95% CI 0.214 to 0.642) and 0.501 (95% CI 0.262 to 0.741), respectively, indicating that the effects of SO(2) and PM(10) on HFMD were stronger under high temperature (>17.3°C) or high humidity (>80.0%) conditions. CONCLUSIONS: There were interactive effects between meteorological factors and air pollutants on HFMD. Our findings could provide guidance for targeted and timely preventive and control measures for HFMD.
Despite a conspicuous exacerbation of asthma among patients hospitalized due to influenza infection, no study has attempted previously to elucidate the relationship between environmental factors, influenza activity, and asthma simultaneously in adults. In this study, we examined this relationship using population-based hospitalization records over 22 years. Daily numbers of hospitalizations due to asthma in adults of 41 public hospitals in Hong Kong during 1998-2019 were obtained. The data were matched with meteorological records and air pollutant concentrations. We used type-specific and all-type influenza-like illness plus (ILI+) rates as proxies for seasonal influenza activity. Quasi-Poisson generalized additive models together with distributed-lag non-linear models were used to examine the association. A total of 212,075 hospitalization episodes due to asthma were reported over 22 years. The cumulative adjusted relative risk (ARR) of asthma hospitalizations reached 1.15 (95 % confidence interval [CI], 1.12-1.18) when the ILI+ total rate increased from zero to 20.01 per 1000 consultations. Compared with the median temperature, a significantly increased risk of asthma hospitalization (cumulative ARR = 1.10, 95 % CI, 1.05-1.15) was observed at the 5(th) percentile of temperature (i.e., 14.6 °C). Of the air pollutants, oxidant gas was significantly associated with asthma, but only at its extreme level of concentrations. In conclusion, cold conditions and influenza activities are risk factors to asthma exacerbation in adult population. Influenza-related asthma exacerbation that appeared to be more common in the warm and hot season, is likely to be attributable to influenza A/H3N2. The heavy influence of both determinants on asthma activity implies that climate change may complicate the asthma burden.
Varicella is a rising public health issue. Several studies have tried to quantify the relationships between meteorological factors and varicella incidence but with inconsistent results. We aim to investigate the impact of temperature and relative humidity on varicella, and to further explore the effect modification of these relationships. In this study, the data of varicella and meteorological factors from 2011 to 2019 in 21 cities of Guangdong Province, China were collected. Distributed lag nonlinear models (DLNM) were constructed to explore the relationship between meteorological factors (temperature and relative humidity) and varicella in each city, controlling in school terms, holidays, seasonality, long-term trends, and day of week. Multivariate meta-analysis was applied to pool the city-specific estimations. And the meta-regression was used to explore the effect modification for the spatial heterogeneity of city-specific meteorological factors and social factors (such as disposable income per capita, vaccination coverage, and so on) on varicella. The results indicated that the relationship between temperature and varicella in 21 cities appeared nonlinear with an inverted S-shaped. The relative risk peaked at 20.8 ℃ (RR = 1.42, 95% CI: 1.22, 1.65). The relative humidity-varicella relationship was approximately L-shaped, with a peaking risk at 69.5% relative humidity (RR = 1.25, 95% CI: 1.04, 1.50). The spatial heterogeneity of temperature-varicella relationships may be caused by income or varicella vaccination coverage. And varicella vaccination coverage may contribute to the spatial heterogeneity of the relative humidity-varicella relationship. The findings can help us deepen the understanding of the meteorological factors-varicella association and provide evidence for developing prevention strategy for varicella epidemic.
Hand, foot, and mouth disease (HFMD) poses a great disease burden in China. However, there are few studies on the relationship between temperature variability (TV) and HFMD. Moreover, whether air pollutions have modified effects on this relationship is still unknown. Therefore, this study aims to explore the modified effects of air pollutants on TV-HFMD association in Zibo City, China. Daily data of HFMD cases, meteorological factors, and air pollutants from 2015 to 2019 were collected for Zibo City. TV was estimated by calculating standard deviation of minimum and maximum temperatures over the exposure days. We used generalized additive model to estimate the association between TV and HFMD. The modified effects of air pollutants were assessed by comparing the estimated TV-HFMD associations between different air stratums. We found that TV increased the risk of HFMD. The effect was strongest at TV03 (4 days of exposure), when the incidence of HFMD increased by 3.6% [95% CI: 1.3-5.9%] for every 1℃ increases in TV. Males, children aged 0-4 years, were more sensitive to TV. We found that sulfur dioxide (SO(2)) enhanced TV’s effects on all considered exposure days, while ozone (O(3)) reduced TV’s effects on some exposure days in whole concerned population. However, we did not detect significant effect modification by particulate matter less than 10 microns in aerodynamic diameter (PM(10)). These findings are of significance in developing policies and public health practices to reduce the risks of HFMD by integrating changes in temperatures and air pollutants.
Respiratory infectious diseases (RID) are the major form of infectious diseases in China, and are highly susceptible to climatic conditions. Current research mainly focuses on the impact of weather on RID, but there is a lack of research on the effect of El Niño-Southern Oscillation (ENSO) on RID. Therefore, this paper uses the system generalized method of moments (SYS-GMM) and the data of 31 provinces in China from 2007 to 2018 to construct a dynamic panel model to empirically test the causality between ENSO and RID morbidity. Moreover, this paper considers the moderating effects of per capita disposable income and average years of education on this causality. The results show that ENSO can positively and significantly impact RID morbidity, which is 5.842% higher during El Niño years than normal years. In addition, per capita disposable income and average years of education can effectively weaken the relationship between ENSO and RID morbidity. Thus, this paper is of great significance for improving the RID early climate warning system in China and effectively controlling the spread of RID.
BACKGROUND: Acute lower respiratory infections (ALRIs) caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses, especially respiratory syncytial virus (RSV) and influenza virus. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. METHODS: We analysed monthly virus data on patients hospitalised with ALRI from 81 sentinel hospitals in 22 provinces in mainland China from Jan 1, 2009, to Sept 30, 2013. We considered seven common respiratory viruses: RSV, influenza virus, human parainfluenza virus, adenovirus, human metapneumovirus, human bocavirus, and human coronavirus. Meteorological data of the same period were used to analyse relationships between virus seasonality and seven meteorological factors according to region (southern vs northern China). The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs, on the respiratory viruses. FINDINGS: 28 369 hospitalised patients with ALRI were tested, 10 387 (36·6%) of whom were positive for at least one virus, including RSV (4091 [32·0%] patients), influenza virus (2665 [20·8%]), human parainfluenza virus (2185 [17·1%]), adenovirus (1478 [11·6%]), human bocavirus (1120 [8·8%]), human coronavirus (637 [5·0%]), and human metapneumovirus (615 [4·8%]). RSV and influenza virus had annual peaks in the north and biannual peaks in the south. Human parainfluenza virus and human bocavirus had higher positive rates in the spring-summer months. Human metapneumovirus had an annual peak in winter-spring, especially in the north. Adenovirus and human coronavirus exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly. INTERPRETATION: Spatiotemporal heterogeneity was detected for most viruses in this study, and interactions between pairs of meteorological factors were found to enhance their influence on virus variation. These findings might be helpful to guide government planning, such as public health interventions, infection control practice, and timing of passive immunoprophylaxis, and might facilitate the development of future vaccine strategies. FUNDING: National Natural Science Foundation of China, the Ministry of Science and Technology of China, and the Technology Major Project of China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.
BACKGROUND: This study reports a systematic review of association between meteorological parameters and hand, foot and mouth disease (HFMD) in mainland China. METHODS: Using predefined study eligibility criteria, three electronic databases (PubMed, Web of Science, and Embase) were searched for relevant articles. Using a combination of search terms, including “Hand foot and mouth disease,” “HFMD,” “Meteorological,” “Climate,” and “China,” After removal of duplicates, our initial search generated 2435 studies published from 1990 to December 31, 2019. From this cohort 51 full-text articles were reviewed for eligibility assessment. The meta-analysis was devised in accordance with the published guidelines of the Cochrane Collaboration and Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). Effect sizes, heterogeneity estimates and publication bias were computed using R software and Review Manager Software. RESULTS: The meta-analysis of 18 eligible studies showed that the meteorological parameters played an important role in the prevalence of HFMD. Lower air pressure may be the main risk factor for the incidence of HFMD in Chinese mainland, and three meteorological parameters (mean temperature, rainfall and relative humidity) have a significant association with the incidence of HFMD in subtropical regions. CONCLUSION: Lower air pressure might be the main risk factor for the incidence of HFMD in Chinese mainland. The influence of meteorological parameters on the prevalence of HFMD is mainly through changing virus viability in aerosols, which may be different in different climate regions. In an environment with low air pressure, wearing a mask that filters the aerosol outdoors may help prevent HFMD infection.
(1) Background: Assignment of pathogens to the correct genus, species, and type is vital for controlling infectious epidemics. However, the role of different enteroviruses during hand, foot, and mouth disease (HFMD) epidemics and the major contributing factors remain unknown. (2) Methods: HFMD cases from 2016 to 2018 in Guangzhou, China were collected. The relationship between HFMD cases and genotype frequency, as well as the association between genotype frequency and climate factors, were studied using general linear models. We transformed the genotype frequency to the isometric log-ratio (ILR) components included in the model. Additionally, vaccination rates were adjusted in the climate-driven models. (3) Results: We observed seasonal trends in HFMD cases, genotype frequency, and climate factors. The model regressing case numbers on genotype frequency revealed negative associations with both the ILRs of CAV16 (RR = 0.725, p < 0.001) and EV71 (RR = 0.421, p < 0.001). The model regressing genotype frequency on driven factors showed that the trends for EV71 proportions were inversely related to vaccination rate (%, β = -0.152, p = 0.098) and temperature (°C, β = -0.065, p = 0.004). Additionally, the trends for CVA16 proportions were inversely related to vaccination rate (%, β = -0.461, p = 0.004) and temperature (°C, β = -0.068, p = 0.031). The overall trends for genotype frequency showed that EV71 decreased significantly, while the trends for CVA16 increased annually. (4) Conclusions: Our findings suggest a potential pathway for climate factors, genotype frequency, and HFMD cases. Our study is practical and useful for targeted prevention and control, and provides environmental-based evidence.
Background: As an emerging infectious disease, the prevention and control of hand, foot, and mouth disease (HFMD) poses a significant challenge to the development of public health in China. In this study, we aimed to explore the mechanism of the seasonal transmission characteristics of HFMD and to reveal the correlation and potential path between key meteorological factors and the transmissibility of HFMD. Methods: Combined with daily meteorological data such as average temperature, average relative humidity, average wind velocity, amount of precipitation, average air pressure, evaporation capacity, and sunshine duration, a database of HFMD incidence and meteorological factors was established. Spearman rank correlation was used to calculate the correlation between the various meteorological factors and the incidence of HFMD. The effective reproduction number (R (eff) ) of HFMD was used as an intermediate variable to further quantify the dynamic relationship between the average temperature and R (eff) . Results: A total of 43,659 cases of HFMD were reported in Xiamen from 2014 to 2018. There was a significantly positive correlation between the average temperature and the incidence of HFMD (r = 0.596, p < 0.001), and a significantly negative correlation between the average air pressure and the incidence of HFMD (r = -0.511, p < 0.001). There was no correlation between the average wind velocity (r = 0.045, p > 0.05) or amount of precipitation (r = 0.043, p > 0.05) and incidence. There was a temperature threshold for HFMD’s transmissibility. Owing to the seasonal transmission characteristics of HFMD in Xiamen, the temperature threshold of HFMD’s transmissibility was 13.4-18.4°C and 14.5-29.3°C in spring and summer and in autumn and winter, respectively. Conclusions: HFMD’s transmissibility may be affected by the average temperature; the temperature threshold range of transmissibility in autumn and winter is slightly wider than that in spring and summer. Based on our findings, we suggest that the relevant epidemic prevention departments should pay close attention to temperature changes in Xiamen to formulate timely prevention strategies before the arrival of the high-risk period.
This study introduces a new approach for the investigation of infections after an accidental ingestion of contaminated floodwater. The concept of Expected Annual Probability of Infection (EAPI) is introduced and implemented in an infection risk-model approach, by combining a Quantitative Microbial Risk Assessment (QMRA) with the four steps in flood risk assessment. Two groups and exposure paths are considered: adults wading in floodwater and small children swimming/playing in floodwater. The study area is located in Ghana, West Africa. Even though Ghana is one of the most urbanized countries in Africa it has significant problems with water resources management and public health. While cholera is classified as endemic in Accra, the natural and human-made characteristics of the capital makes it prone to flooding. The results of the EAPI approach show that on one hand the concentration of pathogens in floodwater, and thus the risk of infection, decreases with the increase of the flood magnitude. On the other hand, larger floods can spread the pathogens further from the point source, threatening populations previously not identified as at risk by small-scale floods. The concept of EAPI is demonstrated for cholera but it can be extended to other waterborne diseases and also different pathways of exposure, requiring minimal adaptations. For future applications, better estimation of EAPI key components and improvement points are discussed and recommendations given for all the assessment steps.
The coronavirus disease 2019 (COVID-19) pandemic caused a crisis worldwide, due to both its public health impact and socio-economic consequences. Mental health was consistently affected by the pandemic, with the emergence of newly diagnosed psychiatric disorders and the exacerbation of pre-existing ones. Urban areas were particularly affected by the virus spread. In this review, we analyze how the urban environment may influence mental health during the COVID-19 pandemic, considering two factors that profoundly characterize urbanization: air pollution and migration. Air pollution serves as a possibly risk factor for higher viral spread and infection severity in the context of urban areas and it has also been demonstrated to play a role in the development of serious mental illnesses and their relapses. The urban environment also represents a complex social context where minorities such as migrants may live in poor hygienic conditions and lack access to adequate mental health care. A global rethinking of the urban environment is thus required to reduce the impact of these factors on mental health. This should include actions aimed at reducing air pollution and combating climate change, promoting at the same time a more inclusive society in a sustainable development perspective.
Global health threats including epidemics and climate change, know no political borders and require regional collaboration if they are to be dealt with effectively. This paper starts with a review of the COVID-19 outbreak in Israel, Palestine and Jordan, in the context of the regional health systems, demography and politics. We suggest that Israel and Palestine function as one epidemiological unit, due to extensive border crossing of inhabitants and tourists, resulting in cross-border infections and potential for outbreaks’ transmission. Indeed, there is a correlation between the numbers of confirmed cases with a 2-3 weeks lag. In contrast, Jordan has the ability to seal its borders and better contain the spread of the virus. We then discuss comparative public health aspects in relation to the management of COVID-19 and long term adaptation to climate change. We suggest that lessons from the current crisis can inform regional adaptation to climate change. There is an urgent need for better health surveillance, data sharing across borders, and more resilient health systems that are prepared and equipped for emergencies. Another essential and currently missing prerequisite is close cooperation within and across countries amidst political conflict, in order to protect the public health of all inhabitants of the region.
BACKGROUND: Globally, there is increasing scientific evidence of critical links between the oceans and human health, with research into issues such as pollution, harmful algal blooms and nutritional contributions. However, Oceans and Human Health (OHH) remains an emerging discipline. As such these links are poorly recognized in policy efforts such as the Sustainable Development Goals, with OHH not included in either marine (SDG14) or health (SDG3) goals. This is arguably short-sighted given recent development strategies such as the EU Blue Growth Agenda. OBJECTIVES: In this systematic map we aim to build on recent efforts to enhance OHH in Europe by setting a baseline of existing evidence, asking: What links have been researched between marine environments and the positive and negative impacts to human health and wellbeing? METHODS: We searched eight bibliographic databases and queried 57 organizations identified through stakeholder consultation. Results include primary research and systematic reviews which were screened double blind against pre-defined inclusion criteria as per a published protocol. Studies were limited to Europe, US, Australia, New Zealand and Canada. Data was extracted according to a stakeholder-defined code book. A narrative synthesis explores the current evidence for relationships between marine exposures and human health outcomes, trends in knowledge gaps and change over time in the OHH research landscape. The resulting database is available on the website of the Seas, Oceans and Public Health in Europe website (https://sophie2020.eu/). RESULTS: A total of 1,542 unique articles were included in the database, including those examined within 56 systematic reviews. Research was dominated by a US focus representing 50.1% of articles. A high number of articles were found to link: marine biotechnology and cardiovascular or immune conditions, consumption of seafood and cardiovascular health, chemical pollution and neurological conditions, microbial pollution and gastrointestinal or respiratory health, and oil industry occupations with mental health. A lack of evidence relates to direct impacts of plastic pollution and work within a number of industries identified as relevant by stakeholders. Research over time is dominated by marine biotechnology, though this is narrow in focus. Pollution, food and disease/injury research follow similar trajectories. Wellbeing and climate change have emerged more recently as key topics but lag behind other categories in volume of evidence. CONCLUSIONS: The evidence base for OHH of relevance to European policy is growing but remains patchy and poorly co-ordinated. Considerable scope for future evidence synthesis exists to better inform policy-makers, though reviews need to better incorporate complex exposures. Priorities for future research include: proactive assessments of chemical pollutants, measurable impacts arising from climate change, effects of emerging marine industries, and regional and global assessments for OHH interactions. Understanding of synergistic effects across multiple exposures and outcomes using systems approaches is recommended to guide policies within the Blue Growth Strategy. Co-ordination of research across Europe and dedicated centres of research would be effective first steps.
Infectious diarrhea (ID) is an intestinal infectious disease including cholera, typhoid and paratyphoid fever, bacterial and amebic dysentery, and other infectious diarrhea. There are many studies that have explored the relationship between ambient temperature and the spread of infectious diarrhea, but the results are inconsistent. It is necessary to systematically evaluate the impact of temperature on the incidence of ID. This study was based on the PRISMA statement to report this systematic review. We conducted literature searches from CNKI, VIP databases, CBM, PubMed, Web of Science, Cochrane Library, and other databases. The number registered in PROSPERO is CRD42021225472. After searching a total of 4915 articles in the database and references, 27 studies were included. The number of people involved exceeded 7.07 million. The overall result demonstrated when the temperature rises, the risk of infectious diarrhea increases significantly (RR(cumulative)=1.42, 95%CI: 1.07-1.88, RR(single-day)=1.08, 95%CI: 1.03-1.14). Subgroup analysis found the effect of temperature on the bacillary dysentery group (RR(cumulative)=1.85, 95%CI: 1.48-2.30) and unclassified diarrhea groups (RR(cumulative)=1.18, 95%CI: 0.59-2.34). The result of the single-day effect subgroup analysis was similar to the result of the cumulative effect. And the sensitivity analysis proved that the results were robust. This systematic review and meta-analysis support that temperature will increase the risk of ID, which is helpful for ID prediction and early warning in the future.
Purpose This article examines US official and public responses to the COVID-19 pandemic for insights into future policy and pubic responses to global climate change. Design/methodology/approach This article compares two contemporary global threats to human health and well-being: the COVID-19 pandemic and climate change. We identify several similarities and differences between the two environmental phenomena and explore their implications for public and policy responses to future climate-related disasters and disruptions. Findings Our review of research on environmental and public health crises reveals that though these two crises appear quite distinct, some useful comparisons can be made. We analyze several features of the pandemic for their implications for possible future responses to global climate change: elasticity of public responses to crises; recognition of environmental, health, racial, and social injustice; demand for effective governance; and resilience of the natural world. Originality/value This paper examines public and policy responses to the coronavirus pandemic for their implications for mitigating and adapting to future climate crises.
The COVID-19 pandemic has brought profound social, political, economic, and environmental challenges to the world. The virus may have emerged from wildlife reservoirs linked to environmental disruption, was transmitted to humans via the wildlife trade, and its spread was facilitated by economic globalization. The pandemic arrived at a time when wildfires, high temperatures, floods, and storms amplified human suffering. These challenges call for a powerful response to COVID-19 that addresses social and economic development, climate change, and biodiversity together, offering an opportunity to bring transformational change to the structure and functioning of the global economy. This biodefense can include a “One Health” approach in all relevant sectors; a greener approach to agriculture that minimizes greenhouse gas emissions and leads to healthier diets; sustainable forms of energy; more effective international environmental agreements; post-COVID development that is equitable and sustainable; and nature-compatible international trade. Restoring and enhancing protected areas as part of devoting 50% of the planet’s land to environmentally sound management that conserves biodiversity would also support adaptation to climate change and limit human contact with zoonotic pathogens. The essential links between human health and well-being, biodiversity, and climate change could inspire a new generation of innovators to provide green solutions to enable humans to live in a healthy balance with nature leading to a long-term resilient future.
Gender is a critical factor in how people respond to, and recover from major disruptions such as natural disasters or disease outbreaks. Climate-related disasters are known to pose-gender specific problems that disproportionately affect more women than men. Similarly, the COVID-19 pandemic’s impacts along gender lines are enormous, with women being the worst-affected. Existing studies have drawn connections between COVID-19 and climate change, with most arguing that responses to the pandemic provide an opportunity to tackle climate change through emission reduction strategies as part of recovery efforts. We introduce a new dimension to this connection by demonstrating that though different phenomena, COVID-19 and climate change are not so dissimilar in terms of their gendered socioeconomic impacts. Through a systematic review of the available literature, we establish a nexus between these impacts, and examine how the gender responses to COVID-19 can be leveraged to address gender-related climate impacts. We find that social protection, labor market, economic, and violence against women measures adopted in response to the pandemic provide a good opportunity to address the gender impacts of climate change as well. However, current COVID-19 gender responses do not incorporate the interconnections between the gender impacts of the pandemic and climate change. Adopting a nexus approach could help to leverage COVID-19 responses to address the gendered socioeconomic impacts of both crises.
Outbreaks of the novel coronavirus disease (severe acute respiratory syndrome coronavirus 2: SARS-CoV-2) (coronavirus disease 2019; COVID-19) remind us once again of the mechanisms of zoonotic outbreaks. Climate change and the expansion of agricultural lands and infrastructures due to population growth will ultimately reduce or eliminate wildlife and avian habitats and increase opportunities for wildlife and birds to come into contact with livestock and humans. Consequently, infectious pathogens are transmitted from wildlife and birds to livestock and humans, promoting zoonotic diseases. In addition, the spread of diseases has been associated with air pollution and social inequities, such as racial discrimination, gender inequality, and racial, economic, and educational disparities. The COVID-19 pandemic is a fresh reminder of the significance of excessive greenhouse gas excretion and air pollution, highlighting social inequities and distortions. This provides us with an opportunity to reflect on the appropriateness of our trajectory. Therefore, this review glances through the COVID-19 pandemic and discusses our future.
Numerous studies have linked outdoor levels of PM2.5, PM10, NO2, O-3, SO2, and other air pollutants to significantly higher rates of Covid 19 morbidity and mortality, although the rate in which specific concentrations of pollutants increase Covid 19 morbidity and mortality varies widely by specific country and study. As little as a 1-mu g/m(3) increase in outdoor PM2.5 is estimated to increase rates of Covid 19 by as much as 0.22 to 8%. Two California studies have strongly linked heavy wildfire burning periods with significantly higher outdoor levels of PM2.5 and CO as well as significantly higher rates of Covid 19 cases and deaths. Active smoking has also been strongly linked significantly increased risk of Covid 19 severity and death. Other exposures possibly related to greater risk of Covid 19 morbidity and mortality include incense, pesticides, heavy metals, dust/sand, toxic waste sites, and volcanic emissions. The exact mechanisms in which air pollutants increase Covid 19 infections are not fully understood, but are probably related to pollutant-related oxidation and inflammation of the lungs and other tissues and to the pollutant-driven alternation of the angiotensin-converting enzyme 2 in respiratory and other cells.
Climate change impacts the vulnerability of drinking water sources to contamination and water shortages. This review highlights key risk factors along the impact chain of climate change on water supply security, from precipitation and runoff to surface water quality and availability at drinking water intakes. How climate impacts water quantity (hydrology) and quality (fate, transport and loads of contaminants, via soils, forests, and urban water infrastructure) is examined across the scientific literature. An emphasis is placed on high-latitude regions, where the kinetics and intensity of projected changes are high. The province of Quebec, Canada, is used as a study area that covers diverse land and climate conditions, with extended relevance at a broader scale globally. This review aims at guiding researchers and water managers in considering the climate-related evolution of a range of threats when assessing the vulnerability of drinking water systems. It highlights how climate change increases the seasonal risks of water supply insecurity in a northern region, thereby increasing socioeconomic and public health risks. Accounting for multiple feedback effects is a major cause of uncertainty in assessing future risks in drinking water supplies. Under deep uncertainty, a paradigm change in assessing climate impacts on water supplies is needed.
The coincidence of floods and coronavirus disease 2019 (COVID-19) is a genuine multihazard problem. Since the beginning of 2020, many regions around the World have been experiencing this double hazard of serious flooding and the pandemic. There have been 70 countries with flood events occurring after detection of the country’s first COVID-19 case and hundreds of thousands of people have been evacuated. The main objective of this article is to assess challenges that arise from complex intersections between the threat multipliers and to provide guidance on how to address them effectively. We consider the limitations of our knowledge including “unknown unknowns.” During emergency evacuation, practicing social distancing can be very difficult. However, people are going to take action to respond to rising waters, even if it means breaking quarantine. This is an emergency manager’s nightmare scenario: two potentially serious emergencies happening at once. During this unprecedented year (2020), we are experiencing one of the most challenging flood seasons we have seen in a while. Practical examples of issues and guides for managing floods and COVID-19 are presented. We feel that a new approach is needed in dealing with multiple hazards. Our main messages are: a resilience approach is needed whether in response to floods or a pandemic; preparation is vital, in addition to defense; the responsible actors must be prepared with actions plans and command structure, while the general population must be involved in the discussions so that they are aware of the risk and the reasons for the actions they must take. This article is categorized under:Engineering Water > Methods.
Converging data would indicate the existence of possible relationships between climate change, environmental pollution and epidemics/pandemics, such as the current one due to SARS-CoV-2 virus. Each of these phenomena has been supposed to provoke detrimental effects on mental health. Therefore, the purpose of this paper was to review the available scientific literature on these variables in order to suggest and comment on their eventual synergistic effects on mental health. The available literature report that climate change, air pollution and COVID-19 pandemic might influence mental health, with disturbances ranging from mild negative emotional responses to full-blown psychiatric conditions, specifically, anxiety and depression, stress/trauma-related disorders, and substance abuse. The most vulnerable groups include elderly, children, women, people with pre-existing health problems especially mental illnesses, subjects taking some types of medication including psychotropic drugs, individuals with low socio-economic status, and immigrants. It is evident that COVID-19 pandemic uncovers all the fragility and weakness of our ecosystem, and inability to protect ourselves from pollutants. Again, it underlines our faults and neglect towards disasters deriving from climate change or pollution, or the consequences of human activities irrespective of natural habitats and constantly increasing the probability of spillover of viruses from animals to humans. In conclusion, the psychological/psychiatric consequences of COVID-19 pandemic, that currently seem unavoidable, represent a sharp cue of our misconception and indifference towards the links between our behaviour and their influence on the “health” of our planet and of ourselves. It is time to move towards a deeper understanding of these relationships, not only for our survival, but for the maintenance of that balance among man, animals and environment at the basis of life in earth, otherwise there will be no future.
PURPOSE OF REVIEW: This paper reviews the empirical literature on exposures to disaster or terrorism and their impacts on the health and well-being of children with disabilities and their families since the last published update in 2017. We also review the literature on studies examining the mental health and functioning of children with disabilities during the COVID-19 pandemic. RECENT FINDINGS: Few studies have examined the effects of disaster or terrorism on children with disabilities. Research shows that children with disabilities and their families have higher levels of disaster exposure, lower levels of disaster preparedness, and less recovery support due to longstanding discriminatory practices. Similarly, many reports of the COVID-19 pandemic have documented its negative and disproportionate impacts on children with disabilities and their families. In the setting of climate change, environmental disasters are expected to increase in frequency and severity. Future studies identifying mitigating factors to disasters, including COVID-19; increasing preparedness on an individual, community, and global level; and evaluating post-disaster trauma-informed treatment practices are imperative to support the health and well-being of children with disabilities and their families.
Climate change is driven primarily by humanity’s use of fossil fuels and the resultant greenhouse gases from their combustion. The effects of climate change on human health are myriad and becomingly increasingly severe as the pace of climate change accelerates. One relatively underreported intersection between health and climate change is that of infections, particularly antibiotic-resistant infections. In this perspective review, the aspects of climate change that have already, will, and could possibly impact the proliferation and dissemination of antibiotic resistance are discussed.
Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal-oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction.
The new severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) pandemic was first recognized at the end of 2019 and has caused one of the most serious global public health crises in the last years. In this paper, we review current literature on the effect of weather (temperature, humidity, precipitation, wind, etc.) and climate (temperature as an essential climate variable, solar radiation in the ultraviolet, sunshine duration) variables on SARS-CoV-2 and discuss their impact to the COVID-19 pandemic; the review also refers to respective effect of urban parameters and air pollution. Most studies suggest that a negative correlation exists between ambient temperature and humidity on the one hand and the number of COVID-19 cases on the other, while there have been studies which support the absence of any correlation or even a positive one. The urban environment and specifically the air ventilation rate, as well as air pollution, can probably affect, also, the transmission dynamics and the case fatality rate of COVID-19. Due to the inherent limitations in previously published studies, it remains unclear if the magnitude of the effect of temperature or humidity on COVID-19 is confounded by the public health measures implemented widely during the first pandemic wave. The effect of weather and climate variables, as suggested previously for other viruses, cannot be excluded, however, under the conditions of the first pandemic wave, it might be difficult to be uncovered. The increase in the number of cases observed during summertime in the Northern hemisphere, and especially in countries with high average ambient temperatures, demonstrates that weather and climate variables, in the absence of public health interventions, cannot mitigate the resurgence of COVID-19 outbreaks.
Corona virus is highly uncertain and complex in space and time. Atmospheric parameters such as type of pollutants and local weather play an important role in COVID-19 cases and mortality. Many studies were carried out to understand the impact of weather on spread and severity of COVID-19 and vice-versa. A review study is conducted to understand the impact of weather and atmospheric pollution on morbidity and mortality. Studies show that aerosols containing corona virus generated by sneezes and coughs are major route for spread of virus. Viability and virulence of SARS-CoV-2 stuck on the surface of particulate matter is not yet confirmed. Studies found that an increase in particulate matter concentration causes more COVID-19 cases and mortality. Gaseous pollutant and COVID-19 cases are positively correlated. Local meteorology plays crucial role in the spread of corona virus and thus mortality. Decline in number of cases with rising temperature observed. Few studies also find that lowest and highest temperatures were related to lesser number of cases. Similarly humidity shows negative or no relationship with COVID-19 cases. Rainfall was not related whilst wind-speed plays positive role in spread of COVID-19. Solar radiation threats survival of virus, areas with lower solar radiation showed high exposure rate. Air quality tremendously improved during lockdown. A significant reduction in PM10, PM2.5, BC, NOx, SO(2), CO and VOCs concentration were observed. Lockdown had a healing effect on ozone; significant increase in its concentration was observed. Aerosols Optical Depths were found to decrease up to 50%.
This paper highlights the important leadership role of the public health sector, working with other governmental sectors and nongovernmental entities, to advance environmental public health in Latin America and the Caribbean toward the achievement of 2030 Sustainable Development Goal 3: Health and Well-Being. The most pressing current and future environmental public health threats are discussed, followed by a brief review of major historical and current international and regional efforts to address these concerns. The paper concludes with a discussion of three major components of a regional environmental public health agenda that responsible parties can undertake to make significant progress toward ensuring the health and well-being of all people throughout Latin America and the Caribbean.
The association between floods and the risk of dysentery remain controversial. Therefore, we performed a meta-analysis to clarify this relationship. A literature search was performed in PubMed, Web of science, and Embase for relevant articles published up to November 2019. Random-effects model was used to pool relative risks with 95% confidence intervals. The sensitivity analysis was carried out to evaluate the stability of the results. Publication bias was estimated using Egger’s test. Eleven studies from 10 articles evaluated the association between floods and the risk of dysentery in China. The pooled RR (95% CI) of dysentery for the flooded time versus non-flooded period was 1.48 (95% CI: 1.14-1.91). Significant association was found in subgroup analysis stratified by dysentery styles [dysentery: 1.61 (95% CI: 1.34-1.93) and bacillary dysentery: 1.46 (95% CI: 1.06-2.01)]. The pooled RR (95%CI) of sensitivity analysis for dysentery was 1.26 (95% CI: 1.05-1.52). No significant publication bias was found in our meta-analysis. This meta-analysis confirms that floods have significantly increased the risk of dysentery in China. Our findings will provide more evidence to reduce negative health outcomes of floods in China.
Cholera is a severe diarrhoeal disease affecting vulnerable communities. A long-term solution to cholera transmission is improved access to and uptake of water, sanitation and hygiene (WASH). Climate change threatens WASH. A systematic review and meta-analysis determined five overarching WASH factors incorporating 17 specific WASH factors associated with cholera transmission, focussing upon community cases. Eight WASH factors showed lower odds and six showed higher odds for cholera transmission. These results were combined with findings in the climate change and WASH literature, to propose a health impact pathway illustrating potential routes through which climate change dynamics (e.g. drought, flooding) impact on WASH and cholera transmission. A causal process diagram visualising links between climate change dynamics, WASH factors, and cholera transmission was developed. Climate change dynamics can potentially affect multiple WASH factors (e.g. drought-induced reductions in handwashing and rainwater use). Multiple climate change dynamics can influence WASH factors (e.g. flooding and sea-level rise affect piped water usage). The influence of climate change dynamics on WASH factors can be negative or positive for cholera transmission (e.g. drought could increase pathogen desiccation but reduce rainwater harvesting). Identifying risk pathways helps policymakers focus on cholera risk mitigation, now and in the future.
SARS-CoV-2 virus infections in humans were first reported in December 2019, the boreal winter. The resulting COVID-19 pandemic was declared by the WHO in March 2020. By July 2020, COVID-19 was present in 213 countries and territories, with over 12 million confirmed cases and over half a million attributed deaths. Knowledge of other viral respiratory diseases suggests that the transmission of SARS-CoV-2 could be modulated by seasonally varying environmental factors such as temperature and humidity. Many studies on the environmental sensitivity of COVID-19 are appearing online, and some have been published in peer-reviewed journals. Initially, these studies raised the hypothesis that climatic conditions would subdue the viral transmission rate in places entering the boreal summer, and that southern hemisphere countries would experience enhanced disease spread. For the latter, the COVID-19 peak would coincide with the peak of the influenza season, increasing misdiagnosis and placing an additional burden on health systems. In this review, we assess the evidence that environmental drivers are a significant factor in the trajectory of the COVID-19 pandemic, globally and regionally. We critically assessed 42 peer-reviewed and 80 preprint publications that met qualifying criteria. Since the disease has been prevalent for only half a year in the northern, and one-quarter of a year in the southern hemisphere, datasets capturing a full seasonal cycle in one locality are not yet available. Analyses based on space-for-time substitutions, i.e., using data from climatically distinct locations as a surrogate for seasonal progression, have been inconclusive. The reported studies present a strong northern bias. Socio-economic conditions peculiar to the ‘Global South’ have been omitted as confounding variables, thereby weakening evidence of environmental signals. We explore why research to date has failed to show convincing evidence for environmental modulation of COVID-19, and discuss directions for future research. We conclude that the evidence thus far suggests a weak modulation effect, currently overwhelmed by the scale and rate of the spread of COVID-19. Seasonally modulated transmission, if it exists, will be more evident in 2021 and subsequent years.
Almost half of the Brazilian population has no access to sewage collection and treatment. Untreated effluents discharged in waters of reservoirs for human supply favor the flowering of cyanobacteria – and these microorganisms produce toxins, such as saxitoxin, which is a very potent neurotoxin present in reservoirs in the Northeast region. A recent study confirmed that chronic ingestion of neurotoxin-infected water associated with Zika virus infection could lead to a microcephaly-like outcome in pregnant mice. Cyanobacteria benefit from hot weather and organic matter in water, a condition that has been intensified by climate change, according to our previous studies. Considering the new findings, we emphasize that zika arbovirus is widespread and worsened when associated with climate change, especially in middle- or low-income countries with low levels of sanitation coverage.
INTRODUCTION: We are witnessing an alarming increase in the burden and range of mosquito-borne arboviral diseases. The transmission dynamics of arboviral diseases is highly sensitive to climate and weather and is further affected by non-climatic factors such as human mobility, urbanization, and disease control. As evidence also suggests, climate-driven changes in species interactions may trigger evolutionary responses in both vectors and pathogens with important consequences for disease transmission patterns. AREAS COVERED: Focusing on dengue and chikungunya, we review the current knowledge and challenges in our understanding of disease risk in a rapidly changing climate. We identify the most critical research gaps that limit the predictive skill of arbovirus risk models and the development of early warning systems, and conclude by highlighting the potentially important research directions to stimulate progress in this field. EXPERT OPINION: Future studies that aim to predict the risk of arboviral diseases need to consider the interactions between climate modes at different timescales, the effects of the many non-climatic drivers, as well as the potential for climate-driven adaptation and evolution in vectors and pathogens. An important outcome of such studies would be an enhanced ability to promulgate early warning information, initiate adequate response, and enhance preparedness capacity.
Water ecosystems can be rather sensitive to evolving or sudden changes in weather parameters. These changes can result in alterations in the natural habitat of pathogens, vectors, and human hosts, as well as in the transmission dynamics and geographic distribution of infectious agents. However, the interaction between climate change and infectious disease is rather complicated and not deeply understood. In this narrative review, we discuss climate-driven changes in the epidemiology of Vibrio species-associated diseases with an emphasis on cholera. Changes in environmental parameters do shape the epidemiology of Vibrio cholerae. Outbreaks of cholera cause significant disease burden, especially in developing countries. Improved sanitation systems, access to clean water, educational strategies, and vaccination campaigns can help control vibriosis. In addition, real-time assessment of climatic parameters with remote-sensing technologies in combination with robust surveillance systems could help detect environmental changes in high-risk areas and result in early public health interventions that can mitigate potential outbreaks.
BACKGROUND: Globally, dengue, Zika virus, and chikungunya are important viral mosquito-borne diseases that infect millions of people annually. Their geographic range includes not only tropical areas but also sub-tropical and temperate zones such as Japan and Italy. The relative severity of these arboviral disease outbreaks can vary depending on the setting. In this study we explore variation in the epidemiologic potential of outbreaks amongst these climatic zones and arboviruses in order to elucidate potential reasons behind such differences. METHODOLOGY: We reviewed the peer-reviewed literature (PubMed) to obtain basic reproduction number (R(0)) estimates for dengue, Zika virus, and chikungunya from tropical, sub-tropical and temperate regions. We also computed R(0) estimates for temperate and sub-tropical climate zones, based on the outbreak curves in the initial outbreak phase. Lastly we compared these estimates across climate zones, defined by latitude. RESULTS: Of 2115 studies, we reviewed the full text of 128 studies and included 65 studies in our analysis. Our results suggest that the R(0) of an arboviral outbreak depends on climate zone, with lower R(0) estimates, on average, in temperate zones (R(0) = 2.03) compared to tropical (R(0) = 3.44) and sub-tropical zones (R(0) = 10.29). The variation in R(0) was considerable, ranging from 0.16 to 65. The largest R(0) was for dengue (65) and was estimated by the Ross-Macdonald model in the tropical zone, whereas the smallest R(0) (0.16) was for Zika virus and was estimated statistically from an outbreak curve in the sub-tropical zone. CONCLUSIONS: The results indicate climate zone to be an important determinant of the basic reproduction number, R(0), for dengue, Zika virus, and chikungunya. The role of other factors as determinants of R(0), such as methods, environmental and social conditions, and disease control, should be further investigated. The results suggest that R(0) may increase in temperate regions in response to global warming, and highlight the increasing need for strengthening preparedness and control activities.
BACKGROUND: Malaria is one of the most life-threatening vector-borne diseases globally. Recent autochthonous cases registered in several European countries have raised awareness regarding the threat of malaria reintroduction to Europe. An increasing number of imported malaria cases today occur due to international travel and migrant flows from malaria-endemic countries. The cumulative factors of the presence of competent vectors, favourable climatic conditions and evidence of increasing temperatures might lead to the re-emergence of malaria in countries where the infection was previously eliminated. METHODS: We performed a systematic literature review following PRISMA guidelines. We searched for original articles focusing on rising temperature and the receptivity to malaria transmission in Europe. We evaluated the quality of the selected studies using a standardised tool. RESULTS: The search resulted in 1’999 articles of possible relevance and after screening we included 10 original research papers in the quantitative analysis for the systematic review. With further increasing temperatures studies predicted a northward spread of the occurrence of Anopheles mosquitoes and an extension of seasonality, enabling malaria transmission for annual periods up to 6 months in the years 2051-2080. Highest vector stability and receptivity were predicted in Southern and South-Eastern European areas. Anopheles atroparvus, the main potential malaria vector in Europe, might play an important role under changing conditions favouring malaria transmission. CONCLUSION: The receptivity of Europe for malaria transmission will increase as a result of rising temperature unless socioeconomic factors remain favourable and appropriate public health measures are implemented. Our systematic review serves as an evidence base for future preventive measures.
Respiratory viruses, including coronaviruses, are known to have a high incidence of infection during winter, especially in temperate regions. Dry and cold conditions during winter are the major drivers for increased respiratory tract infections as they increase virus stability and transmission and weaken the host immune system. The novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emerged in China in December 2020 and swiftly spread across the globe causing substantial health and economic burdens. Several countries are battling with the second wave of the virus after a devastating first wave of spread, while some are still in the midst of their first wave. It remains unclear whether SARS-CoV-2 will eventually become seasonal or will continue to circulate year-round. In an attempt to address this question, we review the current knowledge regarding the seasonality of respiratory viruses including coronaviruses and the viral and host factors that govern their seasonal pattern. Moreover, we discuss the properties of SARS-CoV-2 and the potential impact of meteorological factors on its spread.
BACKGROUND: Dengue is a mosquito-borne viral disease and its transmission is closely linked to climate. We aimed to review available information on the projection of dengue in the future under climate change scenarios. METHODS: Using five databases (PubMed, ProQuest, ScienceDirect, Scopus and Web of Science), a systematic review was conducted to retrieve all articles from database inception to 30th June 2019 which projected the future of dengue under climate change scenarios. In this review, “the future of dengue” refers to disease burden of dengue, epidemic potential of dengue cases, geographical distribution of dengue cases, and population exposed to climatically suitable areas of dengue. RESULTS: Sixteen studies fulfilled the inclusion criteria, and five of them projected a global dengue future. Most studies reported an increase in disease burden, a wider spatial distribution of dengue cases or more people exposed to climatically suitable areas of dengue as climate change proceeds. The years 1961-1990 and 2050 were the most commonly used baseline and projection periods, respectively. Multiple climate change scenarios introduced by the Intergovernmental Panel on Climate Change (IPCC), including B1, A1B, and A2, as well as Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, RCP6.0 and RCP8.5, were most widely employed. Instead of projecting the future number of dengue cases, there is a growing consensus on using “population exposed to climatically suitable areas for dengue” or “epidemic potential of dengue cases” as the outcome variable. Future studies exploring non-climatic drivers which determine the presence/absence of dengue vectors, and identifying the pivotal factors triggering the transmission of dengue in those climatically suitable areas would help yield a more accurate projection for dengue in the future. CONCLUSIONS: Projecting the future of dengue requires a systematic consideration of assumptions and uncertainties, which will facilitate the development of tailored climate change adaptation strategies to manage dengue.
The world is currently shadowed by the pandemic of COVID-19. Confirmed cases and the death toll has reached more than 12 million and more than 550,000 respectively as of 10 July 2020. In the unsettling pandemic of COVID-19, the whole Earth has been on an unprecedented lockdown. Social distancing among people, interrupted international and domestic air traffic and suspended industrial productions and economic activities have various far-reaching and undetermined implications on air quality and the climate system. Improvement in air quality has been reported in many cities during lockdown, while the death rate of COVID-19 has been found to be higher in more polluted cities. The relationship between the spread of the SARS-CoV-2 virus and air quality is under investigation. In addition, the battle against COVID-19 could bring short-lived and long-lasting and positive and negative impacts to the warming climate. The impacts on the climate system and the role of the climate in modulating the COVID-19 pandemic are the foci of scientific inquiry. The intertwined relationship among environment, climate change and public health is exemplified in the pandemic of COVID-19. Further investigation of the relationship is imperative in the Anthropocene, in particular, in enhancing disaster preparedness. This short article intends to give an up-to-date glimpse of the pandemic from air quality and climate perspectives and calls for a follow-up discussion.
The co-occurrence of the 2020 Atlantic hurricane season and the ongoing coronavirus disease 2019 (COVID-19) pandemic creates complex dilemmas for protecting populations from these intersecting threats. Climate change is likely contributing to stronger, wetter, slower-moving, and more dangerous hurricanes. Climate-driven hazards underscore the imperative for timely warning, evacuation, and sheltering of storm-threatened populations – proven life-saving protective measures that gather evacuees together inside durable, enclosed spaces when a hurricane approaches. Meanwhile, the rapid acquisition of scientific knowledge regarding how COVID-19 spreads has guided mass anti-contagion strategies, including lockdowns, sheltering at home, physical distancing, donning personal protective equipment, conscientious handwashing, and hygiene practices. These life-saving strategies, credited with preventing millions of COVID-19 cases, separate and move people apart. Enforcement coupled with fear of contracting COVID-19 have motivated high levels of adherence to these stringent regulations. How will populations react when warned to shelter from an oncoming Atlantic hurricane while COVID-19 is actively circulating in the community? Emergency managers, health care providers, and public health preparedness professionals must create viable solutions to confront these potential scenarios: elevated rates of hurricane-related injury and mortality among persons who refuse to evacuate due to fear of COVID-19, and the resurgence of COVID-19 cases among hurricane evacuees who shelter together.
Oncomelania hupensis quadrasi is the snail intermediate host of Schistosoma japonicum in the Philippines. It was discovered by Dr. Marcos Tubangui in 1932 more than two decades after the discovery of the disease in the country in 1906. This review, the first for O. h. quadrasi, presents past and present works on the taxonomy, biology, ecology, control, possible paleogeographic origin of the snail intermediate host and future in research, control and surveillance of the snail. Extensive references are made of other subspecies of O. hupensis such as the subspecies in China for which majority of the advances has been accomplished. Contrasting views on whether the snail is to be considered an independent species of Oncomelania or as one of several subspecies of Oncomelania hupensis are presented. Snail control methods such as chemical methods using synthetic and botanical molluscicides, environmental manipulation and biological control are reviewed. Use of technologies such as Remote Sensing, Geographical Information System and landscape genetics is stressed for snail surveillance. Control and prevention efforts in the Philippines have consistently focused on mass drug administration which has proved inadequate in elimination of the disease. An integrated approach that includes snail control, environmental sanitation and health education has been proposed. Population movement such as migration for employment and economic opportunities and ecotourism and global climate change resulting in heavy rains and flooding challenge the gains of control and elimination efforts. Concern for possible migration of snails to non-endemic areas is expressed given the various changes both natural and mostly man-made favoring habitat expansion.
This article compares two concurrent global crises: the decades-long climate change crisis and the months-long COVID-19 pandemic. These have many similarities. We draw attention to seven parallels and implications. Three of these feature change: business as usual is not acceptable; timeliness in relation to tipping points is critical; and communities can adapt to change with support. Two other points highlight the importance of data: decisions about policy, planning and management need to be based on evidence; and preparation needs to be based on expert advice, warnings, and long-term strategies. Two additional comments involve institutions and relationships: integrated multi-level governance is most effective to deal with global crises; and a sense of a shared burden on humanity globally is essential. We learn that adaptation can take place without having all the facts but accepting the trends, timing is critical, and political will is vital.
Leptospirosis is a zoonotic and waterborne disease worldwide. It is a neglected, reemerging disease of global public health importance with respect to morbidity and mortality both in humans and animals. Due to negligence, rapid, unplanned urbanization, and poor sanitation, leptospirosis emerges as a leading cause of acute febrile illness in many of the developing countries. Every individual has a risk of getting infected as domestic and wild animals carry leptospires; the at-risk population varies from the healthcare professionals, animal caretakers, farmers and agricultural workers, fishermen, rodent catchers, water sports people, National Disaster Response Force (NDRF) personnel, people who volunteer rescue operations in flood-affected areas, sanitary workers, sewage workers, etc. The clinical manifestations of leptospirosis range from flu-like illness to acute kidney failure (AKF), pneumonia, jaundice, pulmonary hemorrhages, etc. But many rare and uncommon clinical manifestations are being reported worldwide. This review will cover all possible updates in leptospirosis from occurrence, transmission, rare clinical manifestations, diagnosis, treatment, and prophylactic measures that are currently available, their advantages and the future perspectives, elaborately. There are less or very few reviews on leptospirosis in recent years. Thus, this work will serve as background knowledge for the current understanding of leptospirosis for researchers. This will provide a detailed analysis of leptospirosis and also help in finding research gaps and areas to focus on regarding future research perspectives.
This article explores the development of public health infrastructure in George Town, Penang, before the 1930s. It argues that the extreme weather of the tropical climate led to a unique set of health challenges for George Town’s administrators, as the town grew from a small British base to a multi-cultural and thriving port. Weather and public health were (and still are) integrally connected, although the framing of this relationship has undergone significant shifts in thinking and appearance over time. One lens into this association is the situation and expression of these elements within municipal structures. During the nineteenth century, government departments were fewer and shared roles and responsibilities. The Medical Department, for example, observed the weather. making connections between rain. drought and the incidence of disease. Engineers asked critical questions about mortality rates from disease after floods. As ideas about climate and health developed and changed, the shift became evident in the style, concerns and proliferation of governmental departments. This article thus considers the different ways in which weather, public health, and town planning were understood, managed and enacted by the Straits Settlements’ administration until the 1930s. It will start by exploring the situation facing the settlement’s inhabitants, in terms of specific climate and health challenges. It will then consider how these challenges were understood and addressed, why and by whom, and how these elements were repositioned over the period in question.
Exposure to harmful algal blooms (HABs) can lead to well recognised acute patterns of illness in humans. The objective of this scoping review was to use an established methodology and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting framework to map the evidence for associations between marine HABs and observed both acute and chronic human health effects. A systematic and reproducible search of publications from 1985 until May 2019 was conducted using diverse electronic databases. Following de-duplication, 5301 records were identified, of which 380 were included in the final qualitative synthesis. The majority of studies (220; 57.9%) related to Ciguatera Poisoning. Anecdotal and case reports made up the vast majority of study types (242; 63.7%), whereas there were fewer formal epidemiological studies (35; 9.2%). Only four studies related to chronic exposure to HABs. A low proportion of studies reported the use of human specimens for confirmation of the cause of illness (32; 8.4%). This study highlighted gaps in the evidence base including a lack of formal surveillance and epidemiological studies, limited use of toxin measurements in human samples, and a scarcity of studies of chronic exposure. Future research and policy should provide a baseline understanding of the burden of human disease to inform the evaluation of the current and future impacts of climate change and HABs on human health.
BACKGROUND: Pollution – unwanted waste released to air, water, and land by human activity – is the largest environmental cause of disease in the world today. It is responsible for an estimated nine million premature deaths per year, enormous economic losses, erosion of human capital, and degradation of ecosystems. Ocean pollution is an important, but insufficiently recognized and inadequately controlled component of global pollution. It poses serious threats to human health and well-being. The nature and magnitude of these impacts are only beginning to be understood. GOALS: (1) Broadly examine the known and potential impacts of ocean pollution on human health. (2) Inform policy makers, government leaders, international organizations, civil society, and the global public of these threats. (3) Propose priorities for interventions to control and prevent pollution of the seas and safeguard human health. METHODS: Topic-focused reviews that examine the effects of ocean pollution on human health, identify gaps in knowledge, project future trends, and offer evidence-based guidance for effective intervention. ENVIRONMENTAL FINDINGS: Pollution of the oceans is widespread, worsening, and in most countries poorly controlled. It is a complex mixture of toxic metals, plastics, manufactured chemicals, petroleum, urban and industrial wastes, pesticides, fertilizers, pharmaceutical chemicals, agricultural runoff, and sewage. More than 80% arises from land-based sources. It reaches the oceans through rivers, runoff, atmospheric deposition and direct discharges. It is often heaviest near the coasts and most highly concentrated along the coasts of low- and middle-income countries. Plastic is a rapidly increasing and highly visible component of ocean pollution, and an estimated 10 million metric tons of plastic waste enter the seas each year. Mercury is the metal pollutant of greatest concern in the oceans; it is released from two main sources – coal combustion and small-scale gold mining. Global spread of industrialized agriculture with increasing use of chemical fertilizer leads to extension of Harmful Algal Blooms (HABs) to previously unaffected regions. Chemical pollutants are ubiquitous and contaminate seas and marine organisms from the high Arctic to the abyssal depths. ECOSYSTEM FINDINGS: Ocean pollution has multiple negative impacts on marine ecosystems, and these impacts are exacerbated by global climate change. Petroleum-based pollutants reduce photosynthesis in marine microorganisms that generate oxygen. Increasing absorption of carbon dioxide into the seas causes ocean acidification, which destroys coral reefs, impairs shellfish development, dissolves calcium-containing microorganisms at the base of the marine food web, and increases the toxicity of some pollutants. Plastic pollution threatens marine mammals, fish, and seabirds and accumulates in large mid-ocean gyres. It breaks down into microplastic and nanoplastic particles containing multiple manufactured chemicals that can enter the tissues of marine organisms, including species consumed by humans. Industrial releases, runoff, and sewage increase frequency and severity of HABs, bacterial pollution, and anti-microbial resistance. Pollution and sea surface warming are triggering poleward migration of dangerous pathogens such as the Vibrio species. Industrial discharges, pharmaceutical wastes, pesticides, and sewage contribute to global declines in fish stocks. HUMAN HEALTH FINDINGS: Methylmercury and PCBs are the ocean pollutants whose human health effects are best understood. Exposures of infants in utero to these pollutants through maternal consumption of contaminated seafood can damage developing brains, reduce IQ and increase children’s risks for autism, ADHD and learning disorders. Adult exposures to methylmercury increase risks for cardiovascular disease and dementia. Manufactured chemicals – phthalates, bisphenol A, flame retardants, and perfluorinated chemicals, many of them
BACKGROUND: Outbreaks of infectious diseases are the major concern after flooding. Flood makes people displacement which would be more complicated with inadequate sanitation. Settling in crowded shelters in absence of clean water and inaccessibility to health care services makes people more vulnerable to get infection. This review aimed to discuss about potential undesirable outcomes of flooding occurred in 2019 in Iran. METHODS: A comprehensive search was carried out in databases including PubMed, Google scholar, Scopus, Science Direct, Iran medex, Magiran and SID (Scientific information database) from 2000 to 2019. All original descriptive articles on flood were concerned. Related articles on flood disturbance were considered. Also, publication of red cross society was considered as only reliable reference in evaluation of consequences of flood occurred in 2019 in Iran. RESULTS: Flooding in Iran, was started in March 2019 and lasted to April 2019. Flood affected 31 provinces and 140 rivers burst their banks, and southwestern Iran being hit most severely. According the reports of international federation of red cross society, 3800 cities and villages were affected by the floods with 65,000 destroyed houses and 114,000 houses partially damaged. Also 70 hospitals or health care centers with 1200 schools were damaged along with many infrastructures including 159 main roads and 700 bridges. CONCLUSIONS: Considering 365,000 displaced persons and estimation of mentioned damages, it was one of the greatest natural disaster during the last 20 years. Various risk factors in favor of infectious diseases such as overcrowding, disruption of sewage disposal, poor standards of hygiene, poor nutrition, negligible sanitation and human contact among refugees provide suitable conditions for increased incidence of infectious diseases after flooding and also cause epidemics.More attention is needed to provide hygienic situation for people after natural disasters including flood.
Sea level rise and the anthropogenic warming of the world’s oceans is not only an environmental tragedy, but these changes also result in a significant threat to public health. Along with coastal flooding and the encroachment of saltwater farther inland comes an increased risk of human interaction with pathogenic Vibrio species, such as Vibrio cholerae, V. vulnificus and V. parahaemolyticus. This minireview examines the current literature for updates on the climatic changes and practices that impact the location and duration of the presence of Vibrio spp., as well as the infection routes, trends and virulence factors of these highly successful pathogens. Finally, an overview of current treatments and methods for the mitigation of both oral and cutaneous exposures are presented.
Children’s bodies are in dynamic stages of development that make them more susceptible to harm from exposure to environmental agents. Children’s physical, physiological and behavioral traits can lead to increased exposure to toxic chemicals or pathogens. In addition, the social determinants of health interact with this exposure and create an increasing risk for further disparities among children. In Indonesia, the fourth most populated country in the world, children are under threat of exposure to contaminated water, air, food and soil, which can cause gastrointestinal and respiratory diseases, birth defects and neurodevelopmental disorders. A safe and balanced nutrition is still an unmet need for too many children. At the same time, the prevalence of obesity and the risk of later development of metabolic diseases, including diabetes and cardiovascular diseases, are increasing as a consequence of both unhealthy diets and inadequate physical activity. The risks of potential long-term toxicity, including carcinogenic, neurotoxic, immunotoxic, genotoxic, endocrine-disrupting and allergenic effects of many chemicals, are also close to their lives. This paper provides an overview of common disease risks in Indonesian children, including: acute hepatitis A, diarrheal diseases, dengue and malaria due to lack of water supply and sanitation, vectors, and parasites; asthma, bronchopneumonia, chronic obstructive pulmonary disease (COPD) and acute respiratory infections (ARIs) due to air pollution and climate change; some chronic diseases caused by toxic and hazardous waste; and direct or indirect consequences due to the occurrence of disasters and health emergencies.
The humanity is currently facing the COVID-19 pandemic challenge, the largest global health emergency after the Second World War. During summer months, many countries in the northern hemisphere will also have to counteract an imminent seasonal phenomenon, the management of extreme heat events. The novelty this year concerns that the world population will have to deal with a new situation that foresees the application of specific measures, including adjunctive personal protective equipment (i.e. facemasks and gloves), in order to reduce the potential transmission of the SARS-CoV-2 virus. These measures should help to decrease the risk of the infection transmission but will also represent an aggravating factor to counteract the heat effects on the population health both at occupational and environmental level. The use of a specific heat health warning system with personalized information based on individual, behavioural and environmental characteristics represents a necessary strategy to help a fast adaptation of the population at a time where the priority is to live avoiding SARS-CoV-2 infection.
Although the number of cholera infection decreased universally, climate change can potentially affect both incidence and prevalence rates of disease in endemic regions. There is considerable consistent evidence, explaining the associations between cholera and climatic variables. However, it is essentially required to compare and interpret these relationships globally. The aim of the present study was to carry out a systematic review in order to identify and appraise the literature concerning the relationship between nonanthropogenic climatic variabilities such as extreme weather- and ocean-related variables and cholera infection rates. The systematic literature review of studies was conducted by using determined search terms via four major electronic databases (PubMed, Web of Science, Embase, and Scopus) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. This search focused on published articles in English-language up to December 31, 2018. A total of 43 full-text studies that met our criteria have been identified and included in our analysis. The reviewed studies demonstrated that cholera incidence is highly attributed to climatic variables, especially rainfall, temperature, sea surface temperature (SST) and El Niño Southern Oscillation (ENSO). The association between cholera incidence and climatic variables has been investigated by a variety of data analysis methodologies, most commonly time series analysis, generalized linear model (GLM), regression analysis, and spatial/GIS. The results of this study assist the policy-makers who provide the efforts for planning and prevention actions in the face of changing global climatic variables.
OBJECTIVES: We systematically reviewed the published studies on the relationship between dengue fever and meteorological factors and applied a meta-analysis to explore the effects of ambient temperature and precipitation on dengue fever. METHODS: We completed the literature search by the end of September 1st, 2019 using databases including Science Direct, PubMed, Web of Science, and Google Scholar. We extracted relative risks (RRs) in selected studies and converted all effect estimates to the RRs per 1 °C increase in temperature and 10 mm increase in precipitation, and combined all standardized RRs together using random-effect meta-analysis. RESULTS: Our results show that dengue fever was significantly associated with both temperature and precipitation. Our subgroup analyses suggested that the effect of temperature on dengue fever was most pronounced in high-income subtropical areas. The pooled RR of dengue fever associated with the maximum temperature was much lower than the overall effect. CONCLUSIONS: Temperature and precipitation are important risk factors for dengue fever. Future studies should focus on factors that can distort the effects of temperature and precipitation.
Climate variability is heavily impacting human health all around the globe, in particular, on residents of developing countries. Impacts on surface water and groundwater resources and water-related illnesses are increasing, especially under changing climate scenarios such as diversity in rainfall patterns, increasing temperature, flash floods, severe droughts, heatwaves and heavy precipitation. Emerging water-related diseases such as dengue fever and chikungunya are reappearing and impacting on the life of the deprived; as such, the provision of safe water and health care is in great demand in developing countries to combat the spread of infectious diseases. Government, academia and private water bodies are conducting water quality surveys and providing health care facilities, but there is still a need to improve the present strategies concerning water treatment and management, as well as governance. In this review paper, climate change pattern and risks associated with water-related diseases in developing countries, with particular focus on Pakistan, and novel methods for controlling both waterborne and water-related diseases are discussed. This study is important for public health care, particularly in developing countries, for policy makers, and researchers working in the area of climate change, water quality and risk assessment.
Naegleria fowleri is a free-living protozoan that resides in soil and freshwater. Human intranasal amoebae exposure through water or potentially dust particles can culminate in primary amoebic meningoencephalitis, which generally causes death. While many questions remain regarding pathogenesis, the microbial ecology of N. fowleri is even less understood. This review outlines current knowledge of the environmental abiotic and biotic factors that affect the distribution and abundance of N. fowleri. Although the impacts of some abiotic factors remain poorly investigated or inconclusive, N. fowleri appears to have a wide pH range, low salinity tolerance and thermophilic preference. From what is known about biotic factors, the amoebae preferentially feed upon bacteria and are preyed upon by other free-living amoebae. Additional laboratory and environmental studies are needed to fill in knowledge gaps, which are crucial for surveillance and management of N. fowleri in freshwaters. As surface water temperatures increase with climate change, it is likely that this amoeba will pose a greater threat to human health, suggesting that identifying its abiotic and biotic preferences is critical to mitigating this risk.
Efforts have been made to quantify the spatio-temporal malaria transmission intensity over India using the dynamical malaria model, namely, Vector-borne Disease Community Model of International Centre for Theoretical Physics Trieste (VECTRI). The likely effect of climate change in the variability of malaria transmission intensity over different parts of India is also investigated. The Historical data and future projection scenarios of the rainfall and temperature derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model output are used for this purpose. The Entomological Inoculation Rate (EIR) and Vector are taken as quantifiers of malaria transmission intensity. It is shown that the maximum number of malaria cases over India occur during the Sept-Oct months, whereas the minimum during the Feb-Apr months. The malaria transmission intensity as well as length of transmission season over India is likely to increase in the future climate as a result of global warming.
The geopolitics of pandemics and climate change intersect. Both are complex and urgent problems that demand collective action in the light of their global and trans-boundary scope. In this article we use a geopolitical framework to examine some of the tensions and contradictions in global governance and cooperation that are revealed by the pandemic of coronavirus disease 2019 (COVID-19). We argue that the pandemic provides an early warning of the dangers inherent in weakened international cooperation. The world’s states, with their distinct national territories, are reacting individually rather than collectively to the COVID-19 pandemic. Many countries have introduced extraordinary measures that have closed, rather than opened up, international partnership and cooperation. Border closures, restrictions on social mixing, domestic purchase of public health supplies and subsidies for local industry and commerce may offer solutions at the national level but they do not address the global strategic issues. For the poorest countries of the world, pandemics join a list of other challenges that are exacerbated by pressures of scarce resources, population density and climate disruption. COVID-19’s disproportionate impact on those living with environmental stresses, such as poor air quality, should guide more holistic approaches to the geopolitical intersection of public health and climate change. By discussing unhealthy geopolitics, we highlight the urgent need for a coordinated global response to addressing challenges that cannot be approached unilaterally.
Environmental variables related to vegetation and weather are some of the most influential factors that impacting Aedes (Stegomya) aegypti, a mosquito vector of dengue, chikungunya and Zika viruses. In this paper, we aim to develop temporal predictive models for Ae. aegypti oviposition activity utilizing vegetation and meteorological variables as predictors in Córdoba city (Argentina). Eggs were collected using ovitraps placed throughout the city from 2009 to 2012 that were replaced weekly. Temporal generalized linear mixed models were developed with negative binomial distributions of errors that model average number of eggs collected weekly as a function of vegetation and meteorological variables with time lags. The best model included a vegetation index, vapor pressure of water, precipitation and photoperiod. With each unit of increment in vegetation index per week the average number of eggs increased by 1.71 in the third week. Furthermore, each millimeter increase of accumulated rain during 4 weeks was associated with a decrease of 0.668 in the average number of eggs found in the following week. This negative effect of precipitation could occur during abundant rainfalls that fill containers completely, thereby depriving females of oviposition sites and leading them to search for other suitable breeding sites. Furthermore, the average number of eggs increased with the photoperiod at low values of mean vapor pressure; however the average number of eggs decreased at high values of mean vapor pressure, and the positive relationship between the response variable and mean vapor pressure was stronger at low values of photoperiod. Additionally, minimum temperature was associated positively with oviposition activity and that low minimum temperatures could be a limiting factor in Ae. aegypti oviposition activity. Our results emphasize the important role that climatic variables such as temperature, precipitation, and vapor pressure play in Ae. aegypti oviposition activity and how these variables along with vegetation indices can be used to inform predictive temporal models of Ae. aegypti population dynamics that can be used for informing mosquito population control and arbovirus mitigation strategies.
Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.
Drought is a devastating natural hazard that significantly affects human health and social and economic activities. This study aims to explore the short-term association between drought and outpatient visits for respiratory diseases (RDs) in four northwest cities, China. In this study, we obtained daily outpatient visits for RDs, meteorological factors, and air pollutant data in four cities (Lanzhou from 2014 to 2016, Wuwei from 2016 to 2018, Tianshui and Zhangye from 2015 to 2018) of northwest China. We used the daily Standardized Precipitation Index (SPI) as an indicator of drought and estimated the effects of drought on outpatient visits with RDs by using a generalized additive model (GAM) in each city, controlling for daily temperature, time trends, and other confounding factors. The city-specific estimates were pooled by random-effects meta-analysis. There were 1,134,577 RDs cases in the hospitals across the four cities. We found that a 1-unit decrease in daily exposure to SPI-1 was positively associated with daily outpatient visits for RDs, with estimated RR of 1.0230 (95% CIs: 1.0096, 1.0366). Compared to non-drought periods, the RR of daily outpatient visits for RDs for exposure to all drought conditions was 1.0431 (95% CIs: 1.0309, 1.0555). In subgroup analysis, the estimated effects of drought on outpatient visits for RDs appeared larger for males than females though not statistically different, and the estimated effects in children and adolescents were the greatest among different age groups. Drought likely increases the risk of respiratory diseases, particularly among children and adolescents. We highlight that public health adaptations to drought such as drought monitoring, mitigation measures, and adaptation strategies are necessary.
Seasonal changes in temperature, humidity, and rainfall affect vector survival and emergence of mosquitoes and thus impact the dynamics of vector-borne disease outbreaks. Recent studies of deterministic and stochastic epidemic models with periodic environments have shown that the average basic reproduction number is not sufficient to predict an outbreak. We extend these studies to time-nonhomogeneous stochastic dengue models with demographic variability wherein the adult vectors emerge from the larval stage vary periodically. The combined effects of variability and periodicity provide a better understanding of the risk of dengue outbreaks. A multitype branching process approximation of the stochastic dengue model near the disease-free periodic solution is used to calculate the probability of a disease outbreak. The approximation follows from the solution of a system of differential equations derived from the backward Kolmogorov differential equation. This approximation shows that the risk of a disease outbreak is also periodic and depends on the particular time and the number of the initial infected individuals. Numerical examples are explored to demonstrate that the estimates of the probability of an outbreak from that of branching process approximations agree well with that of the continuous-time Markov chain. In addition, we propose a simple stochastic model to account for the effects of environmental variability on the emergence of adult vectors from the larval stage.
Previous studies found non-linear mutual interactions among hydrometeorological factors on diarrheal disease. However, the complex interactions of the hydrometeorological, topographical and human activity factors need to be further explored. This study aimed to reveal how hydrological and other factors jointly influence bacillary dysentery in different geographical regions. Using Anhui Province in China, consisted of Huaibei plain, Jianghuai hilly and Wannan mountainous regions, we integrated multi-source data (6 meteorological, 3 hydrological, 2 topographic, and 9 socioeconomic variables) to explore the direct and interactive relationship between hydrological factors (quick flow, baseflow and local recharge) and other factors by combining the ecosystem model InVEST with spatial statistical analysis. The results showed hydrological factors had significant impact powers (q = 0.444 (Huaibei plain) for local recharge, 0.412 (Jianghuai hilly region) and 0.891 (Wannan mountainous region) for quick flow, respectively) on bacillary dysentery in different regions, but lost powers at provincial level. Land use and soil properties have created significant interactions with hydrological factors across Anhui province. Particularly, percentage of farmland in Anhui province can influence quick flow across Jianghuai, Wannan regions and the whole province, and it also has significant interactions with the baseflow and local recharge across the plain as well as the whole province. Percentage of urban areas had interactions with baseflow and local recharge in Jianghuai and Wannan regions. Additionally, baseflow and local recharge could be interacted with meteorological factors (e.g. temperature and wind speed), while these interactions varied in different regions. In conclusion, it was evident that hydrological factors had significant impacts on bacillary dysentery, and also interacted significantly with meteorological and socioeconomic factors. This study applying ecosystem model and spatial analysis help reveal the complex and nonlinear transmission of bacillary dysentery in different geographical regions, supporting the development of precise public health interventions with consideration of hydrological factors.
Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context-dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.
Aedes albopictus (Diptera: Culicidae) distribution is bounded to a subtropical area in Argentina, while Aedes aegypti (Diptera: Culicidae) covers both temperate and subtropical regions. We assessed thermal and photoperiod conditions on dormancy status, development time and mortality for these species from subtropical Argentina. Short days (8 light : 16 dark) significantly increased larval development time for both species, an effect previously linked to diapause incidence. Aedes albopictus showed higher mortality than Ae. aegypti at 16?°C under long day treatments (16 light : 8 dark), which could indicate a lower tolerance to a sudden temperature decrease during the summer season. Aedes albopictus showed a slightly higher percentage of dormant eggs from females exposed to a short day, relative to previous research in Brazilian populations. Since we employed more hours of darkness, this could suggest a relationship between day-length and dormancy intensity. Interestingly, local Ae. aegypti presented dormancy similar to Ae. albopictus, in accordance with temperate populations. The minimum dormancy in Ae. albopictus would not be sufficient to extend its bounded distribution. We believe that these findings represent a novel contribution to current knowledge about the ecophysiology of Ae. albopictus and Ae. aegypti, two species with great epidemiological relevance in this subtropical region.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman’s correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM(2.5), PM(10), NO(2), and SO(2)) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 ?g/m(3) increase during (Lag0-14) in PM(2.5), PM(10), and NO(2) resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO(2) and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO(2) and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.
AIM: As the COVID-19 pandemic has been spreading rapidly all over the world, there are plenty of ongoing works to shed on light to unknown factors related to disease. One of the factors questioned is also to be the factors affecting the disease course. In this study, our aim is to determine the factors that affect the course of the disease in the hospitalised patients because of COVID-19 infection and to reveal whether the seasonal change has an effect on the disease course. METHODS: Our study was conducted on 1950 PCR test positive patients who were hospitalised for COVID-19 disease between March 16 and July 15. RESULTS: As the seasonal temperature increases, decrease in WBC, PLT and albumin levels and increase in LDH and AST levels were observed. Risk of need for ICU has been found statistically significant (P < .05) with the increase in the age, LDH levels and CRP levels and with the decrease in the Ca and Albumin levels. CONCLUSIONS: It is predicted with these results that, seasonal change might have affects on the clinical course of the disease, although it has no affect on the spread of the disease. And it might beneficial to check biochemical parameters such as LDH, CRP, Ca and Albumin to predict the course of the disease.
The surface water is a significant feature in the hydrological system and is a vital compound for life growth. Assessment of trace elements in the water bodies is essential since it poses huge threats to aquatic organisms and humans if present in high concentrations. This study was carried out to assess the seasonal changes in the dissolved trace elements concentration in Bhavani river, which is one of the major rivers of Tamil Nadu, southern India and also to assess the human health risk due to its consumption. A total of 46 surface water samples were collected along the river during pre-monsoon and post-monsoon of 2018 and were analyzed for various trace elements such as Zn, Cu, Fe, Ni, and Pb. The variation in trace element concentration is observed spatially, where higher concentration is found in samples from agricultural and urban areas than the samples from the undisturbed natural-mountain terrains. The results highlighted that the concentrations of trace elements differ temporally where the concentration is greater during the monsoon due to increased discharge of sewage and agricultural run off to the river. Multivariate statistical analysis indicates stronger relationship between trace elements and other physio-chemical parameters hinting that natural and anthropogenic sources alters the riverine chemistry. Thus, the rainfall-runoff characteristics along with lithology, topography, and landuse of the basin plays a dominant role in the seasonal variation of dissolved trace elements. The water quality index value shows “good/excellent” during pre-monsoon and “marginal/fair” during monsoon season and the Heavy Metal Pollution Index values were also low during both the seasons. The river water samples which defy these indices were found to be either from urban or agricultural lands. The oral and dermal ingestion health risk to adults was assessed, which indicates that the risks posed to humans by consumption of water were minimal. The trace metal concentration of the river was then compared with the other rivers of world and India, where it shows that Zn, Cu, and Ni concentration was higher in Bhavani than in most of the rivers. Thus, the study highlighted that the urban settlements and agricultural lands have a considerable influence on river quality thereby triggering the increase in trace element concentrations. Therefore, the study necessitates on the continuous monitoring of river along with adoption of stringent discharge protocols.
Enteric infections and water-related illnesses are more frequent during times of relative water abundance, especially in regions that experience bimodal rainfall patterns. However, it is unclear how seasonal changes in water availability and drinking water source types affect enteric infections in young children. This study investigated seasonal shifts in primary drinking water source type and the effect of water source type on enteric pathogen prevalence in stool samples from 404 children below age 5 in rural communities in Limpopo Province, South Africa. From wet to dry season, 4.6% (n = 16) of households switched from a source with a higher risk of contamination to a source with lower risk, with the majority switching to municipal water during the dry season. In contrast, 2.6% (n = 9) of households switched from a source with a lower risk of contamination to a source with higher risk. 74.5% (n = 301) of the total households experienced interruptions in their water supply, regardless of source type. There were no significant differences in enteric pathogen prevalence between drinking water sources. Intermittent municipal water distribution and household water use and storage practices may have a larger impact on enteric infections than water source type. The limited differences in enteric pathogen prevalence in children by water source could also be due to other exposure pathways in addition to drinking water, for example through direct contact and food-borne transmission.
Secure potable water is indispensable to life. The presence of salinity in potable water has become a serious problem worldwide and it is essential to ensure secure potable water, particularly in the coastal areas of Bangladesh. In this work, 48 (forty-eight) harvested rainwater samples were assessed from Upazila (sub-district) of Mongla and Sarankhola, Bagerhat district, Bangladesh during the monsoon (May) and post-monsoon (October) periods. The objective was to examine the effect of seasonal variations on the quality of harvested rainwater. The harvested rainwater was analyzed for fecal coliform, total coliform, lead (Pb), zinc (Zn), pH, and turbidity. The mean pH in monsoon and post-monsoon periods was 6.93 and 7.24, respectively, which was within both the WHO guideline and Bangladesh Drinking Standard. In the monsoon season, turbidity levels in samples met the Bangladesh water quality standard but 10% of the harvested rainfall samples had Pb levels that exceeded the WHO drinking water limit. The turbidity of harvested rainwater in post-monsoon exceeded the WHO and Bangladesh Drinking Standard by 21% (10 out of 48) and 6% (3 out of 48), respectively. The fecal coliform of harvested rainwater exceeded both WHO and Bangladesh Drinking Standard by 56% (27 out of 48) and 67% (32 out of 48) in the monsoon and post-monsoon, correspondingly. Conversely, total coliform of harvested rainwater exceeded both the WHO and Bangladesh Drinking Standard by 67% (32 out of 48) and 79% (38 out of 48), accordingly in the monsoon and post-monsoon seasons. The Zn was below the WHO and Bangladesh Drinking Standard but Pb exceeded the WHO guideline in the monsoon and post-monsoon by 15% (7 out of 48) and 17% (8 out of 48), respectively. Pb is toxic to humans and children are especially vulnerable. The harvested rainwater should be treated effectively to reduce the toxicity and danger posed by Pb, fecal coliform, and total coliform before it is fit for drinking purposes.
In Colombia, little is known on the distribution of the Asian mosquito Aedes albopictus, main vector of dengue, chikungunya, and Zika in Asia and Oceania. Therefore, this work sought to estimate its current and future potential geographic distribution under the Representative Concentration Paths (RCP) 2.6 and 8.5 emission scenarios by 2050 and 2070, using ecological niche models. For this, predictions were made in MaxEnt, employing occurrences of A. albopictus from their native area and South America and bioclimatic variables of these places. We found that, from their invasion of Colombia to the most recent years, A. albopictus is present in 47% of the country, in peri-urban (20%), rural (23%), and urban (57%) areas between 0 and 1800 m, with Antioquia and Valle del Cauca being the departments with most of the records. Our ecological niche modelling for the currently suggests that A. albopictus is distributed in 96% of the Colombian continental surface up to 3000 m (p < 0.001) putting at risk at least 48 million of people that could be infected by the arboviruses that this species transmits. Additionally, by 2050 and 2070, under RCP 2.6 scenario, its distribution could cover to nearly 90% of continental extension up to 3100 m (?55 million of people at risk), while under RCP 8.5 scenario, it could decrease below 60% of continental extension, but expand upward to 3200 m (< 38 million of people at risk). These results suggest that, currently in Colombia, A. albopictus is found throughout the country and climate change could diminish eventually its area of distribution, but increase its altitudinal range. In Colombia, surveillance and vector control programs must focus their attention on this vector to avoid complications in the national public health setting.
BACKGROUND: Understanding and describing the regional and climatic patterns associated with increasing dengue epidemics in Nepal is critical to improving vector and disease surveillance and targeting control efforts. METHODS: We investigated the spatial and temporal patterns of annual dengue incidence in Nepal from 2010 to 2019, and the impacts of seasonal meteorological conditions (mean maximum, minimum temperature and precipitation) and elevation on those patterns. RESULTS: More than 25 000 laboratory-confirmed dengue cases were reported from 2010 to 2019. Epidemiological trends suggest that dengue epidemics are cyclical with major outbreaks occurring at 2- to 3-y intervals. A significant negative relationship between dengue incidence and increasing elevation (metres above sea level) driven by temperature was observed (p<0.05) with dengue risk being greatest below 500 m. Risk was moderate between 500 and 1500 m and decreased substantially above 1500 m. Over the last decade, increased nightly temperatures during the monsoon months correlated with increased transmission (p<0.05). No other significant relationship was observed between annual dengue cases or incidence and climatological factors. CONCLUSIONS: The spatial analysis and interpretation of dengue incidence over the last decade in Nepal confirms that dengue is now a well-established public health threat of increasing importance, particularly in low elevation zones and urbanised areas with a tropical or subtropical climate. Seasonal variations in temperature during the monsoon months are associated with increased transmission.
Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population. Field campaigns are expensive, and data from meteorological sites that feed models with the required environmental data often lack detail. As a consequence, we explore temporal modeling of the population of Ae. aegypti mosquito vector species and environmental conditions- temperature, moisture, precipitation, and vegetation- have been shown to have significant effects. We use earth observation (EO) data as our source for estimating these biotic and abiotic environmental variables based on proxy features, namely: Normalized difference vegetation index, Normalized difference water index, Precipitation, and Land surface temperature. We obtained our response variable from field-collected mosquito population measured weekly using 791 mosquito traps in Vila Velha city, Brazil, for 36 weeks in 2017, and 40 weeks in 2018. Recent similar studies have used machine learning (ML) techniques for this task. However, these techniques are neither intuitive nor explainable from an operational point of view. As a result, we use a Generalized Linear Model (GLM) to model this relationship due to its fitness for count response variable modeling, its interpretability, and the ability to visualize the confidence intervals for all inferences. Also, to improve our model, we use the Akaike Information Criterion to select the most informative environmental features. Finally, we show how to improve the quality of the model by weighting our GLM. Our resulting weighted GLM compares well in quality with ML techniques: Random Forest and Support Vector Machines. These results provide an advancement with regards to qualitative and explainable epidemiological risk modeling in urban environments.
Climate change is expected to increase the prevalence of water-borne diseases especially in developing countries. Climate-resilient drinking water supplies are critical to protect communities from faecal contamination and thus against increasing disease risks. However, no quantitative assessment exists for the impacts of short-term climate variability on faecal contamination at different drinking water sources in developing countries, while existing understanding remains largely conceptual. This critical gap limits the ability to predict drinking water quality under climate change or to recommend climate-resilient water sources for vulnerable communities. This study aims to provide such quantitative understanding by investigating the relationships between faecal contamination and short-term climate variability across different types of water sources. We collected a novel dataset with over 20 months’ monitoring of weather, Escherichia coli (E. coli) and total coliforms, at 233 different water sources in three climatically different regions in Tanzania. We then took a rigorous statistical analysis with Bayesian hierarchical models, to relate both contamination occurrence and amount to climate variability. The model results explained the temporal variability in drinking water faecal contamination using climate predictors, and also revealed the climate sensitivity of faecal contamination for individual water sources. We found that: a) short-term climate variability and baseline contamination levels can explain about half the observed variability in faecal contamination (R(2) ? 0.44); b) increased contamination was most consistently related to recent heavy rainfall and high temperature across different water sources; c) unimproved water sources such as the unprotected dug wells have substantially higher climate sensitivity. Based on these results, we can expect substantial increases in drinking water contamination risks across tropical Sub-Saharan Africa and South-East Asian developing countries under a warmer climate, which highlight the urgent need of protecting vulnerable communities from the severe climate impacts.
Rivers are important for drinking water supply worldwide. However, they are often impacted by pathogen discharges via wastewater treatment plants (WWTP) and combined sewer overflows (CSO). To date, accurate predictions of the effects of future changes and pollution control measures on the microbiological water quality of rivers considering safe drinking water production are hindered due to the uncertainty of the pathogen source and transport variables. The aim of this study was to test an integrative approach for an improved understanding of these effects, i.e. climate change and population growth as well as enhanced treatment at WWTPs and/or prevention of CSOs. We applied a significantly extended version of QMRAcatch (v1.0 Python), a probabilistic-deterministic model that combines fate and transport modelling with quantitative microbial infection risk assessment. The impact of climatic changes until the period 2035-2049 was investigated by a conceptual semi-distributed hydrological model, based on regional climate model outputs. QMRAcatch was calibrated and validated using site- and source-specific data (human-associated genetic microbial source tracking marker and enterovirus). The study showed that the degree to which future changes affect drinking water safety strongly depends on the type and magnitude of faecal pollution sources and are thus highly site- and scenario-specific. For example, if the load of pathogens from WWTPs is reduced through enhanced treatment, climate-change driven increases in CSOs had a considerable impact. Preventing CSOs and installing enhanced treatment at the WWTPs together had the most significant positive effect. The simultaneous consideration of source apportionment and concentrations of reference pathogens, focusing on human-specific viruses (enterovirus, norovirus) and cross-comparison with bacterial and protozoan pathogens (Campylobacter, Cryptosporidium), was found crucial to quantify these effects. While demonstrated here for a large, wastewater-impacted river, the approach is applicable at other catchments and pollution sources. It allows assessing future changes and selecting suitable pollution control measures for long-term water safety planning.
Cryptosporidium is a water-borne zoonotic parasite worldwide, usually found in lakes and rivers contaminated with sewage and animal wastes, causing outbreaks of cryptosporidiosis. In this study, 300 water samples were collected from four designated places of flood-affected district Nowshera consist of different water sources to find out the prevalence of Cryptosporidium via polymerase chain reaction (PCR). The overall prevalence of Cryptosporidium was 30.33% (91/300) with more prevalent 44% in drain water and low 5% in bore/tube well water. The prevalence in open well and tap water was recorded 33% and 20%, respectively. The highest prevalence was recorded in summer (June-September). The result of this study ensures enormous contamination of drinking water that requires appropriate treatment, cleaning and filtration to provide safe drinking water. Preventing water-borne disease and proper treatment of water supplies is essential to public health.
In early 2019, following the 2015-2016 severe drought, the provinces of Sofala and Cabo Delgado, Mozambique, were hit by Cyclones Idai and Kenneth, respectively. These were the deadliest and most destructive cyclones in the country’s history. Currently, these two provinces host tens of thousands of vulnerable households due to the climatic catastrophes and the massive influx of displaced people associated with violent terrorist attacks plaguing Cabo Delgado. The emergence of the COVID-19 pandemic added a new challenge to this already critical scenario, serving as a real test for Mozambique’s public health preparedness. On the planetary level, Mozambique can be viewed as a ‘canary in the coal mine’, harbingering to the world the synergistic effects of co-occurring anthropogenic and natural disasters. Herein, we discuss how the COVID-19 pandemic has accentuated the need for an effective and comprehensive public health response in a country already deeply impacted by health problems associated with natural disasters and population displacement.
BACKGROUND: Global temperatures are projected to rise by ?2?°C by the end of the century, with expected impacts on infectious disease incidence. Establishing the historic relationship between temperature and childhood diarrhea is important to inform future vulnerability under projected climate change scenarios. METHODS: We compiled a national dataset from Peruvian government data sources, including weekly diarrhea surveillance records, annual administered doses of rotavirus vaccination, annual piped water access estimates, and daily temperature estimates. We used generalized estimating equations to quantify the association between ambient temperature and childhood (
The mosquito Aedes aegypti is the primary vector of many disease-causing viruses, including dengue (DENV), Zika, chikungunya, and yellow fever. As consequences of climate change, we expect an increase in both global mean temperatures and extreme climatic events. When temperatures fluctuate, mosquito vectors will be increasingly exposed to temperatures beyond their upper thermal limits. Here, we examine how DENV infection alters Ae. aegypti thermotolerance by using a high-throughput physiological ‘knockdown’ assay modeled on studies in Drosophila. Such laboratory measures of thermal tolerance have previously been shown to accurately predict an insect’s distribution in the field. We show that DENV infection increases thermal sensitivity, an effect that may ultimately limit the geographic range of the virus. We also show that the endosymbiotic bacterium Wolbachia pipientis, which is currently being released globally as a biological control agent, has a similar impact on thermal sensitivity in Ae. aegypti. Surprisingly, in the coinfected state, Wolbachia did not provide protection against DENV-associated effects on thermal tolerance, nor were the effects of the two infections additive. The latter suggests that the microbes may act by similar means, potentially through activation of shared immune pathways or energetic tradeoffs. Models predicting future ranges of both virus transmission and Wolbachia’s efficacy following field release may wish to consider the effects these microbes have on host survival.
BACKGROUND: Dengue is linked with climate change in tropical and sub-tropical countries including the Lao People’s Democratic Republic (Laos) and Thailand. Knowledge about these issues and preventive measures can affect the incidence and outbreak risk of dengue. Therefore, the present study was conducted to determine the knowledge, attitudes, and practices (KAP) among urban and rural communities and government officials about climate change and dengue in Laos and Thailand. METHODS: A cross-sectional KAP survey about climate change and dengue were conducted in 360 households in Laos (180 urban and 180 rural), 359 households in Thailand (179 urban and 180 rural), and 20 government officials (10 in each country) using structured questionnaires. Data analysis was undertaken using descriptive methods, principal component analysis (PCA), Chi-square test or Fisher’s exact test (as appropriate), and logistic regression. RESULTS: Significant differences among the selected communities in both countries were found in terms of household participant’s age, level of education, socioeconomic status, attitude level of climate change and KAP level of dengue (P < 0.05; 95% CI). Overall, participants’ KAP about climate change and dengue were low except the attitude level for dengue in both countries. The level of awareness among government officials regarding the climatic relationship with dengue was also low. In Lao households, participants’ knowledge about climate change and dengue was significantly associated with the level of education and socioeconomic status (SES) (P < 0.01). Their attitudes towards climate change and dengue were associated with educational level and internet use (P < 0.05). Householders’ climate change related practices were associated with SES (P < 0.01) and dengue related practices were associated with educational level, SES, previous dengue experience and internet use (P < 0.01). In Thailand, participants’ knowledge about climate change was associated with the level of education and SES (P < 0.01). Their attitudes towards climate change were associated with residence status (urban/rural) and internet use (P < 0.05); climate change related practices were associated with educational level and SES (P < 0.05). Dengue related knowledge of participants was associated with SES and previous dengue experience (P < 0.05); participants’ dengue related attitudes and practices were associated with educational level (P < 0.01). CONCLUSION: The findings call for urgently needed integrated awareness programs to increase KAP levels regarding climate change adaptation, mitigation and dengue prevention to improve the health and welfare of people in these two countries, and similar dengue-endemic countries.
Medical statistics collected by WHO indicates that dengue fever is still ravaging developing regions with climates befitting mosquito breeding amidst moderate-to-weak health systems. This work initiates a study over 2009-2017 panel data of dengue incidences and meteorological factors in Jakarta, Indonesia to bear particular understanding. Using a panel random-effect model joined by the pooled estimator, we show positively significant relationships between the incidence level and meteorological factors. We ideate a clustering strategy to decompose the meteorological datasets into several more datasets such that more explanatory variables are present and the zero-inflated problem from the incidence data can be handled properly. The resulting new model gives good agreement with the incidence data accompanied by a high coefficient of determination and normal zero-mean error in the prediction window. A risk measure is characterized from a one-step vector autoregression model relying solely on the incidence data and a threshold incidence level separating the low-risk and high-risk regime. Its magnitude greater than unity and the weak stochastic convergence to the endemic equilibrium mark a persistent cyclicality of the disease in all the five districts in Jakarta. Moreover, all districts are shown to co-vary profoundly positively in terms of epidemics occurrence, both generally and timely. We also show that the peak of incidences propagates almost periodically every year on the districts with the most to the least recurrence: Central, South, West, East, and North Jakarta.
Many deltas worldwide have increasingly faced extreme drought and salinity intrusion, which have adversely affected millions of coastal inhabitants in terms of lives and property. The Vietnamese Mekong Delta (VMD) is considered one of the world?s most vulnerable regions to drought and saline water intrusion, especially in the context of climate change. This study aims to assess livelihood vulnerability and adaptation of the coastal people of the VMD under the impacts of drought and saltwater intrusion. A multi-disciplinary approach was applied, including desktop literature reviews, field surveys, interviews, and focus group discussions with 120 farmers and 30 local officials in two representative hamlets of Soc Trang, a coastal province of the VMD. A vulnerability assessment tool in combination with a sustainable livelihood framework was used to evaluate livelihood vulnerability using the five capital resources to indicate the largest effects of drought and salinity intrusion on the migration of local young people to large cities for adaptation. Livelihood Vulnerability Indexes revealed higher vulnerability in terms of the five capitals of coastal communities living in Nam Chanh hamlet compared to Soc Leo. Results of interviews with officials indicated an optimized mechanism between social organizations and local communities before, at the time, and after being impacted by the drought and salinity intrusion. Our findings contribute evidence-based knowledge to decision-makers to enable coastal communities in the VMD and other deltas worldwide to effectively adapt to the impacts of drought and salinity intrusion.
Hydro-meteorological risks are a growing issue for societies, economies and environments around the world. An effective, sustainable response to such risks and their future uncertainty requires a paradigm shift in our research and practical efforts. In this respect, Nature-Based Solutions (NBSs) offer the potential to achieve a more effective and flexible response to hydro-meteorological risks while also enhancing human well-being and biodiversity. The present paper describes a new methodology that incorporates stakeholders’ preferences into a multi-criteria analysis framework, as part of a tool for selecting risk mitigation measures. The methodology has been applied to Tamnava river basin in Serbia and Nangang river basin in Taiwan within the EC-funded RECONECT project. The results highlight the importance of involving stakeholders in the early stages of projects in order to achieve successful implementation of NBSs. The methodology can assist decision-makers in formulating desirable benefits and co-benefits and can enable a systematic and transparent NBSs planning process.
Understanding the regional impact of future climate change is one of the major global challenges of this century. This study investigated possible effects of climate change on malaria in West Africa in the near future (2006-2035) and the far future (2036-2065) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), compared to an observed evaluation period (1981-2010). Projected rainfall and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4). The malaria model used is the Liverpool malaria model (LMM), a dynamical malaria model driven by daily time series of rainfall and temperature obtained from the CORDEX data. Our results highlight the unimodal shape of the malaria prevalence distribution, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall. Projections showed that the mean annual malaria prevalence would decrease in both climatological periods under both RCPs but with a larger magnitude of decreasing under the RCP8.5. We found that the mean malaria prevalence for the reference period is greater than the projected prevalence for 6 of the 8 downscaled GCMs. The study enhances understanding of how malaria is impacted under RCP4.5 and RCP8.5 emission scenarios. These results indicate that the southern area of West Africa is at most risk of epidemics, and the malaria control programs need extra effort and help to make the best use of available resources by stakeholders.
This study investigates the impact of future changes in climatic variables on dengue incidence in the region of the Tucurui dam in the Amazon. Tucurui dam is the one of the largest hydroelectric power stations in the Amazon. Correlations and regression analysis through least squares fitting between dengue cases and temperature, precipitation, and humidity are obtained. Positive correlations between dengue incidence and temperature are found for lags from 4 to 5 months (higher correlation for lag 5), dengue and precipitation for lags 0 up to 1, and dengue and humidity for lag 0. The positive correlations between dengue and precipitation and between dengue and humidity are higher for the simultaneous correlation. To investigate the impact of the future changes in these climatic variables in the region, projections of RegCM4 model simulations under the RCP 8.5 scenario are obtained. The model projections indicate a warming and moisture increase in the region near the dam at the end of the twenty-first century. Regression analysis using the model projections indicates that the dengue incidence may increase substantially in future climate scenarios in this region (more than fivefold compared with the present climate). This increase is between two and three times higher than the global estimates of dengue incidence in the future. It is suggested that the incidence of dengue cases is more sensitive to changes in temperature. Vector parameters increase with temperature in the future, indicating that the temperature conditions are highly favorable for the spread of the disease in the region. The results indicate that cities in the area surrounding the Tucurui hydroelectric dam are areas of potential dengue incidence in the future. These findings may be applied to hydroelectric dams in other areas of the world. However, future studies involving additional dams are necessary. The results suggest an increase in climate-driven risk of transmission from Aedes aegypti throughout the entire Amazon, and especially the eastern and southern parts.
Scholars argue that personal experience with climate change related impacts can increase public engagement, with mixed empirical evidence. Previous studies have almost exclusively focussed on individuals’ experience with extreme weather events, even as scientific research on health impacts of climate change is burgeoning. This article extends previous research in the domain of public perceptions about climate-related public health impacts. Results from a nationally representative sample survey in New Zealand indicates that subjective attribution of infectious disease outbreaks to climate change and to human impact on the environment is positively associated with mitigation behavioural intentions and climate-focussed COVID-19 economic recovery policies. In contrast, knowledge about COVID-19 and self-reported economic impact due to COVID-19 is not associated with policy support. Moreover, significant interaction between political affiliation and subjective attribution to climate change on policy support indicate that learning about the links between health and climate change will particularly help increase mitigation engagement among right-leaning individuals. Subjective attribution may be the key to help translate personal experience to personal engagement.
Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006-2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ?2 days with temperature ? 95th percentile, and extreme rainfall and humidity defined as daily values ?95th percentile during 2006-2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115-251% around 6 weeks after heatwaves, 173-258% around 6-13 weeks after extremely high rainfall, and 572-587% around 6-13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model’s sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.
This study focuses on Guizhou Province, a region with difficult geographical conditions and poor economic development, to examine the effect of rainfall shocks on contemporaneous infant health and long-run socioeconomic outcomes in China. The study results indicate that negative rainfall shocks are robustly correlated with higher infant mortality and lower birth weight. In the long run, early life rainfall shortages limit an individual’s income and housing conditions. The study findings indicate a significant interaction of rainfall shock with the severity of water scarcity. This result implies that drinking water safety is an essential channel through which early life rainfall shocks influence individual health endowments. However, agriculture production is not a likely channel for rainfall effects despite its association with infant mortality. Accordingly, our empirical results suggest that improving public facility coverage will reduce the vulnerability of infant health to adverse rainfall shocks in Guizhou and other developing areas.
This publication presents a comparison of the content of pollutants in groundwater samples taken at 117 measurement points in four regions of Poland during a drought period and in the reference period without drought. Based on the chemical analyses of water, an assessment of the health risk resulting from the use of underground water for consumption was carried out. The study aimed to determine whether drought affects the increase in health risk exposure of the population. It was found that despite the occurrence of drought, the expected increase in the concentration of pollutants in water does not take place in all locations. This study found that in some cases the occurrence of drought did not cause an increase in the non-cancerogenic threat expressed by the hazard index. There were also no clear changes in excess lifetime cancer risk values except for selected measurement points. On the other hand, the statistical analysis of all data collected in the regions where the research was conducted showed a general trend of increasing environmental health risk caused by changes in groundwater pollution during drought.
Leptospirosis is a disease usually acquired by humans through water contaminated with the urine of rodents that comes into direct contact with the cutaneous lesions, eyes, or mucous membranes. The disease has an important environmental component associated with climatic conditions and natural disasters, such as floods. We analyzed the relationship between rainfall and temperature and the incidence of leptospirosis in the top 30 municipalities with the highest numbers of cases of the disease in the period of 2007 to 2016. It was an ecological study of the time series of cases of leptospirosis, rainfall, and temperature with lags of 0, 1, 2, 3, and 4 weeks. A multilevel negative binomial regression model was implemented to evaluate the relationship between leptospirosis and both meteorological factors. In the 30 evaluated municipalities during the study period, a total of 5136 cases of leptospirosis were reported. According to the implemented statistical model, there was a positive association between the incidence of leptospirosis and rainfall with a lag of 1 week and a negative association with temperature with a lag of 4 weeks. Our results show the importance of short-term lags in rainfall and temperature for the occurrence of new cases of leptospirosis in Colombia.
The floods of 2018 caused havoc in the State of Kerala, situated in the extreme south-west of India, in terms of infrastructure and health. This research provides the first-ever assessment of the bacterial diversity and its antibiotic susceptibility of the inundated areas of Pampa, Periyar and Vembanad waterbodies by comparing the data collected in two different time intervals succeeding the calamitous floods that is, immediately after flood and 5 months post-flood. An elevated total coliform count was detected in the waterbodies after the flood thereby rendering it unsafe for drinking. Variation in bacterial diversity was observed in the river and lake water samples with a distinct increase in that of the river samples immediately after flood indicated by shannon diversity index (>5.5). Resistance to ampicillin and cefotaxime was observed in a major proportion of isolates from the three biotopes thus indicating the influence of antibiotic wastes accumulated from different sources of human interventions. Furthermore, operational taxonomic units clustering to Acinetobacter, Legionella, Pseudomonas and Burkholderia genera were detected by metataxonomic analysis which portray as a potential health risk in the future. The article emphasises the importance of adopting sanitation programmes for effective management of epidemic outbreaks post floods.
Malaria is a climate-sensitive infectious disease. Many ecological studies have investigated the independent impacts of ambient temperature on malaria. However, the optimal temperature measures of malaria and its interaction with other meteorological factors on malaria transmission are less understood. This study aims to investigate the effect of ambient temperature and its interactions with relative humidity and rainfall on malaria in Suzhou, a temperate climate city in Anhui Province, China. Weekly malaria and meteorological data from 2005 to 2012 were obtained for Suzhou. A distributed lag nonlinear model was conducted to quantify the effect of different temperature measures on malaria. The best measure was defined as that with the minimum quasi-Akaike information criterion. GeoDetector and Poisson regression models were employed to quantify the interactions of temperature, relative humidity, and rainfall on malaria transmission. A total of 13,382 malaria cases were notified in Suzhou from 2005 to 2012. Each 5 °C rise in average temperature over 10 °C resulted in a 22% (95% CI: 17%, 28%) increase in malaria cases at lag of 4 weeks. In terms of cumulative effects from lag 1 to 8 weeks, each 5 °C increase over 10 °C caused a 175% growth in malaria cases (95% CI: 139%, 216%). Average temperature achieved the best performance in terms of model fitting, followed by minimum temperature, most frequent temperature, and maximum temperature. Temperature had an interactive effect on malaria with relative humidity and rainfall. High temperature together with high relative humidity and high rainfall could accelerate the transmission of malaria. Meteorological factors may affect malaria transmission interactively. The research findings could be helpful in the development of weather-based malaria early warning system, especially in the context of climate change for the prevention of possible malaria resurgence.
The health, economic, and social impact of COVID-19 has been significant across the world. Our objective was to evaluate the association between air pollution (through NO(2) and PM(2.5) levels) and COVID-19 mortality in Spanish provinces from February 3, 2020, to July 14, 2020, adjusting for climatic parameters. An observational and ecological study was conducted with information extracted from Datadista repository (Datadista, 2020). Air pollutants (NO(2) and PM(2.5) levels) were analyzed as potential determinants of COVID-19 mortality. Multilevel Poisson regression models were used to analyze the risk of mortality after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Models were adjusted by four climatic variables (hours of solar radiation, precipitation, daily temperature and wind speed) and population size. The mean levels of PM(2.5) and NO(2) across all provinces and time in Spain were 8.7 ?g/m(3) (SD 9.7) and 8.7 ?g/m(3) (SD 6.2), respectively. High levels of PM(2.5) (IRR?=?1.016, 95% CI: 1.007-1.026), NO(2) (IRR?=?1.066, 95% CI: 1.058-1.075) and precipitation (IRR(NO2)?=?0.989, 95% CI: 0.981-0.997) were positively associated with COVID-19 mortality, whereas temperature (IRR(PM2.5)?=?0.988, 95% CI: 0.976-1.000; and IRR(NO2)?=?0.771, 95% CI: 0.761-0.782, respectively) and wind speed (IRR(NO2)?=?1.095, 95% CI: 1.061-1.131) were negatively associated with COVID-19 mortality. Air pollution can be a key factor to understand the mortality rate for COVID-19 in Spain. Furthermore, climatic variables could be influencing COVID-19 progression. Thus, air pollution and climatology ought to be taken into consideration in order to control the pandemic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-021-01062-2.
Since the early twentieth century, the intensity of malaria transmission has decreased sharply worldwide, although it is still an infectious disease with a yearly estimate of 228 million cases. The aim of this study was to expand our knowledge on the main drivers of malaria in Spain. In the case of autochthonous malaria, these drivers were linked to socioeconomic and hygienic and sanitary conditions, especially in rural areas due to their close proximity to the wetlands that provide an important habitat for anopheline reproduction. In the case of imported malaria, the main drivers were associated with urban areas, a high population density and international communication nodes (e.g. airports). Another relevant aspect is that the major epidemic episodes of the twentieth century were strongly influenced by war and military conflicts and overcrowding of the healthcare system due to the temporal overlap with the pandemic flu of 1918. Therefore, military conflicts and overlap with other epidemics or pandemics are considered to be drivers of malaria that can-in a temporary manner-exponentially intensify transmission of the disease. Climatic factors did not play a relevant role as drivers of malaria in Spain (at least directly). However, they did influence the seasonality of the disease and, during the epidemic outbreak of 1940-1944, the climate conditions favored or coadjuvated its spread. The results of this study provide additional knowledge on the seasonal and interannual variability of malaria that can help to develop and implement health risk control measures. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41207-021-00245-8.
Aedes aegypti is the main vector of dengue globally. The variables that influence the abundance of dengue vectors are numerous and complex. This has generated a need to focus on areas at risk of disease transmission, the spatial-temporal distribution of vectors, and the factors that modulate vector abundance. To help guide and improve vector-control efforts, this study identified the ecological, social, and other environmental risk factors that affect the abundance of adult female and immature Ae. aegypti in households in urban and rural areas of northeastern Thailand. A one-year entomological study was conducted in four villages of northeastern Thailand between January and December 2019. Socio-demographic; self-reported prior dengue infections; housing conditions; durable asset ownership; water management; characteristics of water containers; knowledge, attitudes, and practices (KAP) regarding climate change and dengue; and climate data were collected. Household crowding index (HCI), premise condition index (PCI), socio-economic status (SES), and entomological indices (HI, CI, BI, and PI) were calculated. Negative binomial generalized linear models (GLMs) were fitted to identify the risk factors associated with the abundance of adult females and immature Ae. aegypti. Urban sites had higher entomological indices and numbers of adult Ae. aegypti mosquitoes than rural sites. Overall, participants’ KAP about climate change and dengue were low in both settings. The fitted GLM showed that a higher abundance of adult female Ae. aegypti was significantly (p < 0.05) associated with many factors, such as a low education level of household respondents, crowded households, poor premise conditions, surrounding house density, bathrooms located indoors, unscreened windows, high numbers of wet containers, a lack of adult control, prior dengue infections, poor climate change adaptation, dengue, and vector-related practices. Many of the above were also significantly associated with a high abundance of immature mosquito stages. The GLM model also showed that maximum and mean temperature with four-and one-to-two weeks of lag were significant predictors (p < 0.05) of the abundance of adult and immature mosquitoes, respectively, in northeastern Thailand. The low KAP regarding climate change and dengue highlights the engagement needs for vector-borne disease prevention in this region. The identified risk factors are important for the critical first step toward developing routine Aedes surveillance and reliable early warning systems for effective dengue and other mosquito-borne disease prevention and control strategies at the household and community levels in this region and similar settings elsewhere.
Food poisoning is caused by pathogenic bacteria in water and aquatic products, especially bivalves (e.g., oysters, clams), which can bioaccumulate pathogenic bacteria. Polluted water and aquatic products thus pose a serious threat to human health and safety. In this study, the types of pathogenic bacteria in water samples and shellfish collected from the Dadeng offshore area in Xiamen were examined. We also analyzed the relationships between dominant pathogens and major climate and water quality parameters. Our objective was to provide reference data that may be used to help prevent bacterial infections and to improve aquatic food hygiene in Xiamen and its surrounding areas to safe levels, thus ensuring the health of Xiamen residents. We found that the main pathogenic bacteria were Vibrio and Bacillus, with the dominant pathogen being Vibrio parahaemolyticus. Physical and chemical indexes (water temperature, salinity, pH, dissolved oxygen, and turbidity) of water bodies and the 3-day accumulated rainfall were found to be important factors affecting the occurrence and abundance of V. parahaemolyticus.
BACKGROUND: Dengue is one of the most rapidly spreading vector-borne diseases, which is considered to be a major health concern in tropical and sub-tropical countries. It is strongly believed that the spread and abundance of vectors are related to climate. Construction of climate-based mathematical model that integrates meteorological factors into disease infection model becomes compelling challenge since the climate is positively associated with both incidence and vector existence. METHODS: A host-vector model is constructed to simulate the dynamic of transmission. The infection rate parameter is replaced with the time-dependent coefficient obtained by optimization to approximate the daily dengue data. Further, the optimized infection rate is denoted as a function of climate variables using the Autoregressive Distributed Lag (ARDL) model. RESULTS: The infection parameter can be extended when updated daily climates are known, and it can be useful to forecast dengue incidence. This approach provides proper prediction, even when tested in increasing or decreasing prediction windows. In addition, associations between climate and dengue are presented as a reversed slide-shaped curve for dengue-humidity and a reversed U-shaped curves for dengue-temperature and dengue-precipitation. The range of optimal temperature for infection is 24.3-30.5 °C. Humidity and precipitation are positively associated with dengue upper the threshold 70% at lag 38 days and below 50 mm at lag 50 days, respectively. CONCLUSION: Identification of association between climate and dengue is potentially useful to counter the high risk of dengue and strengthen the public health system and reduce the increase of the dengue burden.
Urbanization, livestock activities, and rainfall are factors that contribute to the contamination of inland water. This study aimed to determine the spatial and temporal variability of total coliforms (TCs) and fecal coliforms (FCs) in the surface water of San Pedro Lake as well as the gills and skin of Nile tilapia (Oreochromis niloticus) cultivated in the lake. The study consisted of seasonal sampling during an annual cycle. Using the multiple-tube fermentation technique, we quantified the microbial load of TCs in the lake and fish. The median of the TC and FC groups in surface water showed differences during the seasonal cycle, in which a significant correlation was observed between rainfall and bacterial load in the lake surface water. There was a significant seasonal difference between FCs and TCs in the gills as well as in skin FCs. Anthropogenic activities in the watershed combined with rainfall influence the bacterial load of San Pedro Lake. However, the water quality is still classified as excellent and uncontaminated according to Mexican regulations with lower FC values acceptable for higher FC values. In addition, the bacterial load in tilapia from San Pedro Lake does not pose a risk to human health. PRACTITIONER POINTS: Watershed livestock activities combined with rainfall increase fecal matter pollution in specific areas of the lake. San Pedro Lake displays satisfactory quality for aquatic life. The median fecal coliform population in lake fish (gills and skin) differs by season.
Around 27% of South Americans live in central and southern Brazil. Of 19,400 human malaria cases in Brazil in 2018, some were from the southern and southeastern states. High abundance of malaria vectors is generally positively associated with malaria incidence. Expanding geographic distributions of Anopheles vector mosquito species (e.g. A. cruzii) in the face of climate change processes would increase risk of such malaria transmission; such risk is of particular concern in regions that hold human population concentrations near present limits of vector species’ geographic distributions. We modeled effects of likely climate changes on the distribution of A. cruzii, evaluating two scenarios of future greenhouse gas emissions for 2050, as simulated in 21 general circulation models and two greenhouse gas scenarios (RCP 4.5 and RCP 8.5) for 2050. We tested 1305 candidate models, and chose among them based on statistical significance, predictive performance, and complexity. The models closely approximated the known geographic distribution of the species under current conditions. Under scenarios of future climate change, we noted increases in suitable area for the mosquito vector species in São Paulo and Rio de Janeiro states, including areas close to 30 densely populated cities. Under RCP 8.5, our models anticipate areal increases of >75% for this important malaria vector in the vicinity of 20 large Brazilian cities. We developed models that anticipate increased suitability for the mosquito species; around 50% of Brazilians reside in these areas, and ?89% of foreign tourists visit coastal areas in this region. Under climate change thereefore, the risk and vulnerability of human populations to malaria transmission appears bound to increase.
The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster.
Global warming and air pollution affect the transmission pathway and the survival of viruses, altering the human immune system as well. The first wave of the COVID-19 pandemic dramatically highlights the key roles of climate and air chemistry in viral epidemics. The elongated form of the Italian peninsula and the two major islands (the largest in Europe) is a perfect case study to assess some of these key roles, as the fate of the virus is mirroring the industrialization in the continental part of our country. Fine particulate matter (PM(2.5)), geography, and climate explain what is happening in Italy and support cleaner air actions to address efficiently other outbreaks. Besides the environmental factors, future works should also address the genetic difference among individuals to explain the spatial variability of the human response to viral infections.
The 2020 summer Olympic and Paralympic Games in Tokyo were postponed to July-September 2021 due to the coronavirus disease 2019 (COVID-19) pandemic. While COVID-19 has emerged as a monumental health threat for mass gathering events, heat illness must be acknowledged as a potentially large health threat for maintaining health services. We examined the number of COVID-19 admissions and the Tokyo rule for emergency medical care, in Tokyo, from March to September 2020, and investigated the weekly number of emergency transportations due to heat illness and weekly averages of the daily maximum Wet Bulb Globe Temperature (WBGT) in Tokyo in the summer (2016-2020). The peak of emergency transportations due to heat illness overlapped the resurgence of COVID-19 in 2020, and an increase of heat illness patients and WBGT has been observed. Respect for robust science is critical for the decision-making process of mass gathering events during the pandemic, and science-based countermeasures and implementations for COVID-19 will be warranted. Without urgent reconsiderations and sufficient countermeasures, the double burden of COVID-19 and heat-related illnesses in Tokyo will overwhelm the healthcare provision system, and maintaining essential health services will be challenging during the 2021 summer Olympic and Paralympic Games.
People with chronic obstructive pulmonary disease, cardiovascular disease, or hypertension have a high risk of developing severe coronavirus disease 2019 (COVID-19) and of COVID-19 mortality. However, the association between long-term exposure to air pollutants, which increases cardiopulmonary damage, and vulnerability to COVID-19 has not yet been fully established. We collected data of confirmed COVID-19 cases during the first wave of the epidemic in mainland China. We fitted a generalized linear model using city-level COVID-19 cases and severe cases as the outcome, and long-term average air pollutant levels as the exposure. Our analysis was adjusted using several variables, including a mobile phone dataset, covering human movement from Wuhan before the travel ban and movements within each city during the period of the emergency response. Other variables included smoking prevalence, climate data, socioeconomic data, education level, and number of hospital beds for 324 cities in China. After adjusting for human mobility and socioeconomic factors, we found an increase of 37.8% (95% confidence interval [CI]: 23.8%-52.0%), 32.3% (95% CI: 22.5%-42.4%), and 14.2% (7.9%-20.5%) in the number of COVID-19 cases for every 10-?g/m(3) increase in long-term exposure to NO(2), PM(2.5), and PM(10), respectively. However, when stratifying the data according to population size, the association became non-significant. The present results are derived from a large, newly compiled and geocoded repository of population and epidemiological data relevant to COVID-19. The findings suggested that air pollution may be related to population vulnerability to COVID-19 infection, although the extent to which this relationship is confounded by city population density needs further exploration.
INTRODUCTION: In 2016-18, the Democratic Republic of São Tomé and Príncipe suffered a necrotic skin infection epidemic. METHODS: A surveillance system was established after increased hospitalisations for this infection. Microbiology results were available for samples analysed in December 2016 and March 2017 using whole genome sequencing and metagenomics. Negative binomial regression was used to study the association of weather conditions with monthly case counts in a time-series analysis. RESULTS: From October 2016 to October 2018, the epidemic cumulative attack rate was 1.5%. The first peak lasted 5 months, accounting for one-third of total cases. We could not conclusively identify the aetiological agent(s) due to the country’s lack of microbiology capacity. Increased relative humidity was associated with increased monthly cases (incidence rate ratio (IRR) 1.05, 95% CI 1.02-1.09), and higher precipitation in the previous month with a higher number of cases in the following month (months with 0-49 mm rainfall compared with months with 50-149 mm and ?150 mm: IRR 1.44, 95 % CI 1.13-1.78 and 1.50, 95% CI 1.12-1.99, respectively). DISCUSSION: This epidemic was favoured by increased relative humidity and precipitation, potentially contributing to community-based transmission of ubiquitous bacterial strains superinfecting skin wounds. FUNDING: World Health Organization Regional Office for Africa, Ministry of Health.
OBJECTIVE: The wildfire allied environmental pollution is highly toxic and can cause significant wide-ranging damage to the regional environment, weather conditions, and it can facilitate the transmission of microorganisms and diseases. The present study aims to investigate the effect of wildfire allied pollutants, particulate matter (PM-2.5 ?m), and carbon monoxide (CO) on the dynamics of daily cases and deaths due to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in San Francisco, USA. MATERIALS AND METHODS: For this study, we selected San Francisco, one of the regions affected by the wildfires allied pollution in California, USA. The data on the COVID-19 pandemic in San Francisco, including daily new cases and new deaths were recorded from Worldometer Web. The daily environmental pollutants particulate matter (PM-2.5 ?m) and carbon monoxide (CO) were recorded from the metrological web “BAAQMD”. The daily cases, deaths, particulate matter (PM-2.5 ?m) and carbon monoxide were documented from the date of the occurrence of the first case of (SARS-CoV-2) in San Francisco, CA, USA, from March 20, 2020 to Sept 16, 2020. RESULTS: The results revealed a significant positive correlation between the environmental pollutants particulate matter (PM2.5 ?m) and the number of daily cases (r=0.203, p=0.007), cumulative cases (r=0.567, p<0.001) and cumulative deaths (r=0.562, p<0.001); whereas the PM2.5 ?m and daily deaths had no relationship (r=-0.015, p=0.842). In addition, CO was also positively correlated with cumulative cases (r=0.423, p<0.001) and cumulative deaths (r=0.315, p<0.001), however, CO had no correlation with the number of daily cases (r=0.134, p=0.075) and daily deaths (r=0.030, p=0.693). In San Francisco, one micrometer (?g/m3) increase in PM2.5 caused an increase in the daily cases, cumulative cases and cumulative deaths of SARS-COV-2 by 0.5%, 0.9% and 0.6%, respectively. Moreover, with a 1 part per million (ppm) increase in carbon monoxide level, the daily number of cases, cumulative cases and cumulative deaths increased by 5%, 9.3% and 5.3%, respectively. On the other hand, CO and daily deaths had no significant relationship. CONCLUSIONS: The wildfire allied pollutants, particulate matter PM-2.5?m and CO have a positive association with an increased number of SARS-COV-2 daily cases, cumulative cases and cumulative deaths in San Francisco. The metrological, disaster management and health officials must implement the necessary policies and assist in planning to minimize the wildfire incidences, environmental pollution and COVID-19 pandemic both at regional and international levels.
BACKGROUND: Zika virus (ZIKV) emerged in the Pacific Ocean and subsequently caused a dramatic Pan-American epidemic after its first appearance in the Northeast region of Brazil in 2015. The virus is transmitted by Aedes mosquitoes. We evaluated the role of temperature and infectious doses of ZIKV in vector competence of Brazilian populations of Ae. aegypti and Ae. albopictus. METHODOLOGY/PRINCIPAL FINDINGS: Two Ae. aegypti (Rio de Janeiro and Natal) and two Ae. albopictus (Rio de Janeiro and Manaus) populations were orally challenged with five viral doses (102 to 106 PFU / ml) of a ZIKV strain (Asian genotype) isolated in Northeastern Brazil, and incubated for 14 and 21 days in temperatures mimicking the spring-summer (28°C) and winter-autumn (22°C) mean values in Brazil. Detection of viral particles in the body, head and saliva samples was done by plaque assays in cell culture for determining the infection, dissemination and transmission rates, respectively. Compared with 28°C, at 22°C, transmission rates were significantly lower for both Ae. aegypti populations, and Ae. albopictus were not able to transmit the virus. Ae. albopictus showed low transmission rates even when challenged with the highest viral dose, while both Ae. aegypti populations presented higher of infection, dissemination and transmission rates than Ae. albopictus. Ae. aegypti showed higher transmission efficiency when taking virus doses of 105 and 106 PFU/mL following incubation at 28°C; both Ae. aegypti and Ae. albopictus were unable to transmit ZIKV with virus doses of 102 and 103 PFU/mL, regardless the incubation temperature. CONCLUSIONS/SIGNIFICANCE: The ingested viral dose and incubation temperature were significant predictors of the proportion of mosquito’s biting becoming infectious. Ae. aegypti and Ae. albopictus have the ability to transmit ZIKV when incubated at 28°C. However Brazilian populations of Ae. aegypti exhibit a much higher transmission potential for ZIKV than Ae. albopictus regardless the combination of infection dose and incubation temperature.
The global risks report of 2020 stated, climate-related issues dominate all of the top-five long-term critical global risks burning the planet and according to the report, “as existing health risks resurge and new ones emerge, humanity’s past successes in overcoming health challenges are no guarantee of future results.” Over the last few decades, the world has experienced several pandemic outbreaks of various pathogens and the frequency of the emergence of novel strains of infectious organisms has increased in recent decades. As per expert opinion, rapidly mutating viruses, emergence and re-emergence of epidemics with increasing frequencies, climate-sensitive vector-borne diseases are likely to be increasing over the years and the trends will continue and intensify. Susceptible disease hosts, anthropogenic activities and environmental changes contribute and trigger the ‘adaptive evolution’ of infectious agents to thrive and spread into different ecological niches and to adapt to new hosts. The overarching objective of this paper is to provide insight into the human actions which should be strictly regulated to help to sustain life on earth. To identify and categorize the triggering factors that contribute to disease ecology, especially repeated emergence of disease pandemics, a theory building approach, ‘Total Interpretive Structural Modeling’ (TISM) was used; also the tool, ‘Impact Matrix Cross-Reference Multiplication Applied to a Classification’ analysis (MICMAC) was applied to rank the risk factors based on their impacts on other factors and on the interdependence among them. This mathematical modeling tool clearly explains the strength, position and interconnectedness of each anthropogenic factor that contributes to the evolution of pathogens and to the frequent emergence of pandemics which needs to be addressed with immediate priority. As we are least prepared for another pandemic outbreak, significant policy attention must be focused on the causative factors to limit emerging outbreaks like COVID 19 in the future.
Due to population growth, urbanization and economic development, demand for freshwater in urban areas is increasing throughout Europe. At the same time, climate change, eutrophication and pollution are affecting the availability of water supplies. Sicily, a big island in southern Italy, suffers from an increasing drought and consequently water shortage. In the last decades, in Sicilian freshwater reservoirs several Microcystis aeruginosa and more recently Planktothrix rubescens blooms were reported. The aims of the study were: (1) identify and quantify the occurring species of cyanobacteria (CB), (2) identify which parameters, among those investigated in the waters, could favor their growth, (3) set up a model to identify reservoirs that need continuous monitoring due to the presences, current or prospected, of cyanobacterial blooms and of microcystins, relevant for environmental and, consequentially, for human health. Fifteen artificial reservoirs among the large set of Sicilian artificial water bodies were selected and examined for physicochemical and microbiological characterization. Additional parameters were assessed, including the presence, identification and count of the cyanobacterial occurring species, the measurement of microcystins (MCs) levels and the search for the genes responsible for the toxins production. Principal Component Analysis (PCA) was used to relate environmental condition to cyanobacterial growth. Water quality was poor for very few parameters, suggesting common anthropic pressures, and PCA highlighted clusters of reservoirs vulnerable to hydrological conditions, related to semi-arid Mediterranean climate and to the use of the reservoir. In summer, bloom was detected in only one reservoir and different species was highlighted among the Cyanobacteria community. The only toxins detected were microcystins, although always well below the WHO reference value for drinking waters (1.0 ?g/L). However, molecular analysis could not show the presence of potential cyanotoxins producers since a few numbers of cells among total could be sufficient to produce these low MCs levels but not enough high to be proved by the traditional molecular method applied. A simple environmental risk-based model, which accounts for the high variability of both cyanobacteria growth and cyanotoxins producing, is proposed as a cost-effective tool to evaluate the need for monitoring activities in reservoirs aimed to guarantee supplying waters safety.
Existing efforts to ensure safe water access in coastal Bangladesh are challenged by increasing freshwater salinity. This research explored/explores safe water consumption choices in coastal Bangladesh, which data are scarce to date, using a mixed-methods approach. In 2014, a cross-sectional survey was conducted in southwestern coastal Bangladesh (n=261) and data was generated on water supply and consumption. Data collection also involved 29 in-depth interviews of household care givers and focus group discussions were performed with three community groups. Descriptive statistics were applied to analyse quantitative data and thematic analysis was used for qualitative data. The survey showed that 60% of the study population used tube well water while 40% used pond water for drinking. It was observed that for cooking purposes, the use of pond water was slightly higher than the tube well water. Only 13% of the respondents mentioned that their drinking water tasted salty whereas 6% of the respondents reported health problem (diarrhoea, dysentery, gastric issues and skin problems) after using these water sources. The qualitative data reveals that water available for drinking and cooking is causing a serious threat to this coastal community, particularly during the dry season. In-depth assessments indicated that drinking water choices were less driven by concerns for health than practical issues such as travel distance and time taken and taste. The palatability of water was an important determinant of choice for drinking and other domestic uses. Furthermore, the utility of alternative options for safe drinking water is driven by beliefs and traditions and source maintenance. Given the increasing salinisation of freshwaters in many low-lying countries and likely exacerbation related to climate change-induced sea level rise, therefore, promotion of low saline drinking water along with salt reducing interventions consider that community beliefs and practices must be a made priority.
OBJECTIVES: The objective of this study is to describe an important waterborne outbreak of gastrointestinal illness observed in a rural municipality of Quebec. METHODS: A population-based retrospective cohort study was conducted to identify risk factors associated with acute gastroenteritis. Indirect surveillance data were used to estimate the extent and the resolution of the epidemic. RESULTS: The cohort consisted of 140 randomly selected individuals of whom 22 met the illness case definition (15.7% attack rate). The epidemic curve was similar to the evolution of antidiarrheal products sold by the only pharmacy in town and calls made to the Health Info Line. Bivariate analysis led to identifying five risk factors of gastrointestinal illness: consumption of municipal water, contact with someone with acute gastroenteritis (within and outside of the household), contact with a child in daycare, and being less than 35 years of age. Drinking municipal water had the highest risk ratio (RR?=?24.31; 95% CI?=?1.50-393.4). Drinking water from a private artesian well was a protective factor (RR?=?0.28; 95% CI?=?0.09-0.90). CONCLUSION: This study highlighted that managing the risks associated with the consumption of untreated drinking water remains an important public health challenge, particularly in small rural municipalities vulnerable to climate variability.
Since the first report in December 2019, the novel coronavirus (COVID-19) has spread to most parts of the world, with over 21.5 million people infected and nearly 768,000 deaths to date. Evidence suggests that transmission of the virus is primarily through respiratory droplets and contact routes, and airborne carriers such as atmospheric particulates and aerosols have also been proposed as important vectors for the environmental transmission of COVID-19. Sewage and human excreta have long been recognized as potential routes for transmitting human pathogens. The causative agent of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been detected in human feces and urine, where it could remain viable for days and show infectivity. Urban flooding, a common threat in summer caused by heavy rainfalls, is frequently reported in urban communities along with sewage overflows. With summer already underway and economy re-opening in many parts of the world, urban flooding and the often-accompanied sewage overflows could jeopardize previous mitigation efforts by posing renewed risks of virus spread in affected areas and communities. In this article, we present the up-to-date evidence and discussions on sewage-associated transmission of COVID-19, and highlighted the roles of sewage overflow and sewage-contaminated aerosols in two publicized events of community outbreaks. Further, we collected evidence in real-life environments to demonstrate the shortcuts of exposure to overflowed sewage and non-dispersed human excreta during a local urban flooding event. Given that communities serviced by combined sewer systems are particularly prone to such risks, local municipalities could prioritize wastewater infrastructure upgrades and consider combined sewer separations to minimize the risks of pathogen transmission via sewage overflows during epidemics.
BACKGROUND: In the Republic of Congo, hot temperature and seasons distortions observed may impact the development of malaria parasites. We investigate the variation of malaria cases, parasite density and the multiplicity of Plasmodium falciparum infection throughout the year in Brazzaville. METHODS: From May 2015 to May 2016, suspected patients with uncomplicated malaria were enrolled at the Hôpital de Mfilou, CSI « Maman Mboualé», and the Laboratoire National de Santé Publique. For each patient, thick blood was examined and parasite density was calculated. After DNA isolation, MSP1 and MSP2 genes were genotyped. RESULTS: A total of 416, 259 and 131 patients with suspected malaria were enrolled at the CSI «Maman Mboualé», Hôpital de Mfilou and the Laboratoire National de Santé Publique respectively. Proportion of malaria cases and geometric mean parasite density were higher at the CSI «Maman Mboualé» compared to over sites (P-value <0.001). However the multiplicity of infection was higher at the Hôpital de Mfilou (P-value <0.001). At the Laboratoire National de Santé Publique, malaria cases and multiplicity of infection were not influenced by different seasons. However, variation of the mean parasite density was statistically significant (P-value <0.01). Higher proportions of malaria cases were found at the end of main rainy season either the beginning of the main dry season at the Hôpital de Mfilou and the CSI «Maman Mboualé»; while, lowest proportions were observed in September and January and in September and March respectively. Higher mean parasite densities were found at the end of rainy seasons with persistence at the beginning of dry seasons. The lowest mean parasite densities were found during dry seasons, with persistence at the beginning of rainy seasons. Fluctuation of the multiplicity of infection throughout the year was observed without significance between seasons. CONCLUSION: The current study suggests that malaria transmission is still variable between the north and south parts of Brazzaville. Seasonal fluctuations of malaria cases and mean parasite densities were observed with some extension to different seasons. Thus, both meteorological and entomological studies are needed to update the season's periods as well as malaria transmission intensity in Brazzaville.
Climate change is imposing substantial consequences across physical and social infrastructures. The extent of social disruption and risk to human health are, however, potentially much broader than these general consequences, taken individually, would suggest. To address this gap, we assess the distribution of contaminated sites in the United States (US) and then estimate the impact that flood hazards in urban areas will have on these contaminated sites. Using these measures, we draw inferences about the risk of contamination from climate impacted extreme weather events, climate adaptation at the local level, social risk and how it is distributed, and a broader understanding of the potential global consequences of climate change. In this paper we address three critical points: 1) the role classification of contaminated sites on our understanding of risk due to climate change; 2) the relationship between contaminated sites and flood risk; and 3) the potential for climate adaption strategies to mediate this risk. We estimate that of the roughly one-third of the US population living in urban areas, up to 3,338,518 people, are living in high-risk flood zones near contaminated sites. Our results suggest severe potential implications for estimates of the negative consequences from climate change and contamination and provide critical insights into the relationship between climate change and the built environment for urban planners and environmental policy makers and managers alike.
Malaria is a vector-borne disease of significant public health concern. Despite widespread success of many elimination initiatives, elimination efforts in some regions of the world have stalled. Barriers to malaria elimination include climate and land use changes, such as warming temperatures and urbanization, which can alter mosquito habitats. Socioeconomic factors, such as political instability and regional migration, also threaten elimination goals. This is particularly relevant in areas where local elimination has been achieved and consequently surveillance and control efforts are dwindling and are no longer a priority. Understanding how environmental change, impacts malaria elimination has important practical implications for vector control and disease surveillance strategies. It is important to consider climate change when monitoring the threat of malaria resurgence due to socioeconomic influences. However, there is limited assessment of how the combination of climate variation, interventions and socioeconomic pressures influence long-term trends in malaria transmission and elimination efforts. In this study, we used Bayesian hierarchical mixed models and malaria case data for a 29-year period to disentangle the impacts of climate variation and malaria control efforts on malaria risk in the Ecuadorian province of El Oro, which achieved local elimination in 2011. We found shifting patterns of malaria between rural and urban areas, with a relative increase ofPlasmodium vivaxin urbanized areas. Minimum temperature was an important driver of malaria seasonality and the association between warmer minimum temperatures and malaria incidence was greater forPlasmodium falciparumcompared toP. vivaxmalaria. There was considerable heterogeneity in the impact of three chemical vector control measures on bothP. falciparumandP. vivaxmalaria. We found statistically significant associations between two of the three measures [indoor residual spraying (IRS) and space spraying] and a reduction in malaria incidence, which varied between malaria type. We also found environmental suitability for malaria transmission is increasing in El Oro, which could limit future elimination efforts if malaria is allowed to re-establish. Our findings have important implications for understanding environmental obstacles to malaria elimination and highlights the importance of designing and sustaining elimination efforts in areas that remain vulnerable to resurgence.
BACKGROUND: In Thailand, dengue fever is one of the most well-known public health problems. The objective of this study was to examine the epidemiology of dengue and determine the seasonal pattern of dengue and its associate to climate factors in Bangkok, Thailand, from 2003 to 2017. METHODS: The dengue cases in Bangkok were collected monthly during the study period. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The Spearman correlation analysis and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok. RESULTS: The seasonal-decomposition procedure showed that the seasonal component was weaker than the trend component for dengue cases during the study period. The Spearman correlation analysis showed that rainfall and humidity played a role in dengue transmission with correlation efficiency equal to 0.396 and 0.388, respectively. ANN showed that precipitation was the most crucial factor. The time series multivariate Poisson regression model revealed that increasing 1% of rainfall corresponded to an increase of 3.3% in the dengue cases in Bangkok. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study. CONCLUSION: This work demonstrates the significance of weather in dengue transmission in Bangkok and compares the accuracy of the different mathematical approaches to predict the dengue case. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.
Previous studies that observed the fact that Middle Palaeolithic sites mainly were concentrated in arid and semi-arid areas in Africa and Southwest Asia, concluded that climate factors determined the distribution patterns. We argue that biological factors could have been equally important. In present-day sub-Saharan Africa, mosquito borne diseases and especially falciparum malaria have a serious impact on human populations. This study was aimed to investigate the possible former effect of falciparum malaria on Middle Palaeolithic site distribution patterns and explain why ancient humans avoided the humid areas in the tropical and subtropical regions. It was found that the early human settlements situated in those regions of Africa and Southwest Asia where the potential annual development period of falciparum parasites was short in the mosquitoes, the area was not too humid, and the potential falciparum malaria incidence values were low or moderate. In the Indian Peninsula, precipitation played a less significant role in determining human settlements. The number of the months when the extrinsic development of Plasmodium falciparum parasites was possible showed the strongest structural overlap with the modelled malaria incidences according to the spatial occurrence of the Middle Paleolithic archaeological sites in the case of Africa and in Southwest Asia. In the Indian Peninsula, climatic factors showed the strongest structural overlap with the modelled malaria incidences according to the occurrence patterns of the Middle Palaeolithic archaeological sites.
Harmful algal blooms (HABs) can have dire repercussions on aquatic wildlife and human health, and may negatively affect recreational uses, aesthetics, taste, and odor in drinking water. The factors that influence the occurrence and magnitude of harmful algal blooms and toxin production remain poorly understood and can vary in space and time. It is within this context that we use machine learning (ML) and two 14-year (2005-2018) data sets on water quality and meteorological conditions of China’s lakes and reservoirs to shed light on the magnitude and associated drivers of HAB events. General regression neural network (GRNN) models are developed to predict chlorophyll a concentrations for each lake and reservoir during two study periods (2005-2010 and 2011-2018). The developed models with an acceptable model fit are then analyzed by two indices to determine the areal HAB magnitudes and associated drivers. Our national assessment suggests that HAB magnitudes for China’s lakes and reservoirs displayed a decreasing trend from 2006 (1363.3 km(2)) to 2013 (665.2 km(2)), and a slightly increasing trend from 2013 to 2018 (775.4 km(2)). Among the 142 studied lakes and reservoirs, most severe HABs were found in Lakes Taihu, Dianchi and Chaohu with their contribution to the total HAB magnitude varying from 89.2% (2013) to 62.6% (2018). HABs in Lakes Taihu and Chaohu were strongly associated with both total phosphorus and nitrogen concentrations, while our results were inconclusive with respect to the predominant environmental factors shaping the eutrophication phenomena in Lake Dianchi. The present study provides evidence that effective HAB mitigation may require both nitrogen and phosphorus reductions and longer recovery times; especially in view of the current climate-change projections. ML represents a robust strategy to elucidate water quality patterns in lakes, where the available information is sufficient to train the constructed algorithms. Our mapping of HAB magnitudes and associated environmental/meteorological drivers can help managers to delineate hot-spots at a national scale, and comprehensively design the best management practices for mitigating the eutrophication severity in China’s lakes and reservoirs.
According to the World Health Organization, about 20 million people are infected with Hepatitis E every year. In 2015, there were 44,000 deaths due to HEV infection worldwide. Food, water and climate are key factors that affect the outbreak of Hepatitis E. This paper presents an ensemble learning model for Hepatitis E prediction by studying the correlation between historical epidemic cases of hepatitis E and environmental factors (water quality and meteorological data). Environmental factors include many features, and ones that are most relevant to HEV are selected and input into the ensemble learning model composed by Gradient Boosting Decision Tree (GBDT) and Random Forest for training and prediction. Three indicators, root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), are used to evaluate the effectiveness of the ensemble learning model against the classical time series prediction model. It is concluded that the ensemble learning model has a better prediction effect than the classical model, and the prediction effectiveness can be improved by exploiting water quality and meteorological factors (radiation, air pressure, precipitation).
Objective: To determine the significance of temperature, rainfall and humidity in the seasonal abundance of Anopheles stephensi in southern Iran. Methods: Data on the monthly abundance of Anopheles stephensi larvae and adults were gathered from earlier studies conducted between 2002 and 2019 in malaria prone areas of southeastern Iran. Climatic data for the studied counties were obtained from climatology stations. Generalized estimating equations method was used for cluster correlation of data for each study site in different years. Results: A significant relationship was found between monthly density of adult and larvae of Anopheles stephensi and precipitation, max temperature and mean temperature, both with simple and multiple generalized estimating equations analysis (P<0.05). But when analysis was done with one month lag, only relationship between monthly density of adults and larvae of Anopheles stephensi and max temperature was significant (P<0.05). Conclusions: This study provides a basis for developing multivariate time series models, which can be used to develop improved appropriate epidemic prediction systems for these areas. Long-term entomological study in the studied sites by expert teams is recommended to compare the abundance of malaria vectors in the different areas and their association with climatic variables.
Introduction: Altered weather patterns and changes in precipitation, temperature and humidity resulting from climate change could affect the distribution and incidence of cholera. This study is to quantify climate-induced increase in morbidity rates of cholera. Material and Methods: Monthly cholera cases and monthly temperature, precipitation, and relative humidity data from 2004 to 2014 were obtained from the Malaysian Ministry of Health and Malaysian Meteorological Department, respectively. Poisson generalized linear models were developed to quantify the relationship between meteorological parameters and the number of reported cholera cases. Results: The findings revealed that the total number of cholera cases in Malaysia during the 11 year study period was 3841 cases with 32 deaths. Out of these, 45.1% of the cases were among children below 12 years old and 75% of the cases were from Sabah. Temperature and precipitation gave significant impact on the cholera cases in Sabah, (p<0.001) while precipitation were significant in Terengganu (p<0.001), and Sarawak (p=0.013). Monthly lag temperature data at Lag 0, 1, and 2 months were associated with the cholera cases in Sabah (p<0.001). The change in odds of having cholera cases were by the factor of 3.5 for every 1 degrees C increase in temperature. However, the contribution of rainfall was very mild, whereby an increase of 1 mm in precipitation will increase the excess risk of cholera by up to 0.8%. Conclusion: This study concludes that climate does influence the number of cholera cases in Malaysia.
In recent years, dengue has been rapidly spreading and growing in the tropics and subtropics. Located in southern China, Hong Kong’s subtropical monsoon climate may favour dengue vector populations and increase the chance of disease transmissions during the rainy summer season. An increase in local dengue incidence has been observed in Hong Kong ever since the first case in 2002, with an outbreak reaching historically high case numbers in 2018. However, the effects of seasonal climate variability on recent outbreaks are unknown. As the local cases were found to be spatially clustered, we developed a Poisson generalized linear mixed model using pre-summer monthly total rainfall and mean temperature to predict annual dengue incidence (the majority of local cases occur during or after the summer months), over the period 2002-2018 in three pre-defined areas of Hong Kong. Using leave-one-out cross-validation, 5 out of 6 observations of area-specific outbreaks during the major outbreak years 2002 and 2018 were able to be predicted. 42 out of a total of 51 observations (82.4%) were within the 95% confidence interval of the annual incidence predicted by our model. Our study found that the rainfall before and during the East Asian monsoon (pre-summer) rainy season is negatively correlated with the annual incidence in Hong Kong while the temperature is positively correlated. Hence, as mosquito control measures in Hong Kong are intensified mainly when heavy rainfalls occur during or close to summer, our study suggests that a lower-than-average intensity of pre-summer rainfall should also be taken into account as an indicator of increased dengue risk.
INTRODUCTION: Bacterial meningitis still constitutes an important threat in Africa. In the meningitis belt, a clear seasonal pattern in the incidence of meningococcal disease during the dry season has been previously correlated with several environmental parameters like dust and sand particles as well as the Harmattan winds. In parallel, the evidence of seasonality in meningitis dynamics and its environmental variables remain poorly studied outside the meningitis belt. This study explores several environmental factors associated with meningitis cases in the Democratic Republic of Congo (DRC), central Africa, outside the meningitis belt area. METHODS: Non-parametric Kruskal-Wallis’ tests were used to establish the difference between the different health zones, climate and vegetation types in relation to both the number of cases and attack rates for the period 2000-2018. The relationships between the number of meningitis cases for the different health zones and environmental and socio-economical parameters collected were modeled using different generalized linear (GLMs) and generalized linear mixed models (GLMMs), and different error structure in the different models, i.e., Poisson, binomial negative, zero-inflated binomial negative and more elaborated multi-hierarchical zero-inflated binomial negative models, with randomization of certain parameters or factors (health zones, vegetation and climate types). Comparing the different statistical models, the model with the smallest Akaike’s information criterion (AIC) were selected as the best ones. 515 different health zones from 26 distinct provinces were considered for the construction of the different GLM and GLMM models. RESULTS: Non-parametric bivariate statistics showed that there were more meningitis cases in urban health zones than in rural conditions (?2 = 6.910, p-value = 0.009), in areas dominated by savannah landscape than in areas with dense forest or forest in mountainous areas (?2 = 15.185, p-value = 0.001), and with no significant difference between climate types (?2 = 1.211, p-value = 0,449). Additionally, no significant difference was observed for attack rate between the two types of heath zones (?2 = 0.982, p-value = 0.322). Conversely, strong differences in attack rate values were obtained for vegetation types (?2 = 13.627, p-value = 0,001) and climate types (?2 = 13.627, p-value = 0,001). This work demonstrates that, all other parameters kept constant, an urban health zone located at high latitude and longitude eastwards, located at low-altitude like in valley ecosystems predominantly covered by savannah biome, with a humid tropical climate are at higher risk for the development of meningitis. In addition, the regions with mean range temperature and a population with a low index of economic well-being (IEW) constitute the perfect conditions for the development of meningitis in DRC. CONCLUSION: In a context of global environmental change, particularly climate change, our findings tend to show that an interplay of different environmental and socio-economic drivers are important to consider in the epidemiology of bacterial meningitis epidemics in DRC. This information is important to help improving meningitis control strategies in a large country located outside of the so-called meningitis belt.
Hurricane Katrina caused unprecedented flood damage to New Orleans, Louisiana, and has been the costliest hurricane in US history. We analyzed the environmental and public health outcomes of Hurricane Katrina by using Internet searches to identify epidemiological, sociodemographic, and toxicological measurements provided by regulatory agencies.Atmospheric scientists have now warned that global warming will increase the proportion of stronger hurricanes (categories 4-5) by 25% to 30% compared with weaker hurricanes (categories 1-2).With the new $14.6 billion Hurricane Storm Damage Risk Reduction System providing a 100-year storm surge-defensive wall across the Southeast Louisiana coast, New Orleans will be ready for stronger storms in the future.
The northeast (NE) region of Brazil commonly goes through drought periods, which favor cyanobacterial blooms, capable of producing neurotoxins with implications for human and animal health. The most severe dry spell in the history of Brazil occurred between 2012 and 2016. Coincidently, the highest incidence of microcephaly associated with the Zika virus (ZIKV) outbreak took place in the NE region of Brazil during the same years. In this work, we tested the hypothesis that saxitoxin (STX), a neurotoxin produced in South America by the freshwater cyanobacteria Raphidiopsis raciborskii, could have contributed to the most severe Congenital Zika Syndrome (CZS) profile described worldwide. Quality surveillance showed higher cyanobacteria amounts and STX occurrence in human drinking water supplies of NE compared to other regions of Brazil. Experimentally, we described that STX doubled the quantity of ZIKV-induced neural cell death in progenitor areas of human brain organoids, while the chronic ingestion of water contaminated with STX before and during gestation caused brain abnormalities in offspring of ZIKV-infected immunocompetent C57BL/6J mice. Our data indicate that saxitoxin-producing cyanobacteria is overspread in water reservoirs of the NE and might have acted as a co-insult to ZIKV infection in Brazil. These results raise a public health concern regarding the consequences of arbovirus outbreaks happening in areas with droughts and/or frequent freshwater cyanobacterial blooms.
Flooding displaces large populations each season, which potentially increases the exposure of the vulnerable societies. Having failed to curve down the number of people infected with COVID-19 in the first wave of the pandemic, many states in the United States (U.S.) are now at high risk of the concurrence of the two disasters. Assessing this compound risk before the country enters the flood season is of vital importance. Therefore, we provide a prompt tool to assess the compound risk of COVID-19 at the county level over the U.S. We find that (1) the number of flood insurance house claims can proxy the displaced population accurately with more spatiotemporal detail, and (2) the high-risk areas of both flooding and COVID-19 are concentrated along the southern and eastern coasts and some parts of the Mississippi River. Our findings may trigger the interest of further exploring the topics related to the concurrence of COVID-19 and flooding.
Global warming results in a slow expansion of habitat range of mosquitoes, an important vector of dengue virus. To understand the impact of this changing environment on the transmission of dengue virus, we develop a dengue model on a growing domain under the framework of reaction diffusion equations. By overcoming some difficulties of dynamical behaviors caused by diffusion terms with variable-dependent coefficients, we investigate the stabilities of the disease-free and endemic equilibria in terms of the associated basic reproduction number. Comparing our dengue model on a growing domain to the model on a fixed domain in terms of the basic reproduction number, we conclude that habitat expansion resulting from global warming catalyzes the spread of dengue fever, and it is negative to the control of dengue fever. Finally, numerical simulations are performed and show a good agreement with our analytical results. (C) 2020 Elsevier Inc. All rights reserved.
BACKGROUND: Cercarial dermatitis (swimmer’s itch) caused by bird schistosome cercariae, released from intermediate host snails, is a common disorder also at higher latitudes. Several cases were observed in the artificial Danish freshwater Ringen Lake frequently used by the public for recreational purposes. The lake may serve as a model system when establishing a risk analysis for this zoonotic disease. In order to explain high risk periods we determined infection levels of intermediate host snails from early spring to late summer (March, June and August) and elucidated the effect of temperature and light on parasite shedding, behavior and life span. RESULTS: Field studies revealed no shedding snails in March and June but in late summer the prevalence of Trichobilharzia szidati infection (in a sample of 226 pulmonate Lymnaea stagnalis snails) reached 10%. When investigated under laboratory conditions the cercarial shedding rate (number of cercariae shed per snail per day) was positively correlated to temperature raising from a mean of 3000 (SD 4000) at 7 °C to a mean of 44,000 (SD 30,000) at 27 °C). The cercarial life span was inversely correlated to temperature but the parasites remained active for up to 60 h at 20 °C indicating accumulation of cercariae in the lake during summer periods. Cercariae exhibited positive phototaxy suggesting a higher pathogen concentration in surface water of the lake during daytime when the public visits the lake. CONCLUSION: The only causative agent of cercarial dermatitis in Ringen Lake detected was T. szidati. The infection risk associated with aquatic activities is low during spring and early summer (March-June). In late summer the risk of infection is high since the release, behavior and life span of the infective parasite larvae have optimal conditions.
Thermal comfort standards are essential to ensure comfortable and enjoyable indoor conditions, and they also help in optimizing energy use. Thermal comfort studies, either climate chamber-based or field investigation, are conducted across the globe in order to ascertain the comfort limits as per the climatic and other adaptive features. However, very few studies are conducted when the occupants are subjected to a stressed condition, like the COVID-19 lockdown, which may not only have the health impacts but also have psychological impacts on the adaptation. In this paper, we present the results of the online study conducted regarding the status of thermal comfort during the COVID-19 lockdown in India. A total of 406 complete responses were collected from subjects located across 3 different climatic regions of India, that is, cold climate, composite climate, and hot and humid climate. Variations in clothing insulation, thermal sensation, and preference were noted across the different climatic regions. We also present the variation in opening of windows and running of fans with the variation in outdoor mean air temperature. The self-judged productivity, comfort, desire to go outdoors, and effectiveness of working from home were seen to vary with the increase in the days of lockdown.
BACKGROUND: Between 2014 and 2017, successive cholera epidemics occurred in South Sudan within the context of civil war, population displacement, flooding, and drought. We aim to describe the spatiotemporal and molecular features of the three distinct epidemic waves and explore the role of vaccination campaigns, precipitation, and population movement in shaping cholera spread in this complex setting. METHODS: In this descriptive epidemiological study, we analysed cholera linelist data to describe the spatiotemporal progression of the epidemics. We placed whole-genome sequence data from pandemic Vibrio cholerae collected throughout these epidemics into the global phylogenetic context. Using whole-genome sequence data in combination with other molecular attributes, we characterise the relatedness of strains circulating in each wave and the region. We investigated the association of rainfall and the instantaneous basic reproduction number using distributed lag non-linear models, compared county-level attack rates between those with early and late reactive vaccination campaigns, and explored the consistency of the spatial patterns of displacement and suspected cholera case reports. FINDINGS: The 2014 (6389 cases) and 2015 (1818 cases) cholera epidemics in South Sudan remained spatially limited whereas the 2016-17 epidemic (20?438 cases) spread among settlements along the Nile river. Initial cases of each epidemic were reported in or around Juba soon after the start of the rainy season, but we found no evidence that rainfall modulated transmission during each epidemic. All isolates analysed had similar genotypic and phenotypic characteristics, closely related to sequences from Uganda and Democratic Republic of the Congo. Large-scale population movements between counties of South Sudan with cholera outbreaks were consistent with the spatial distribution of cases. 21 of 26 vaccination campaigns occurred during or after the county-level epidemic peak. Counties vaccinated on or after the peak incidence week had 2·2 times (95% CI 2·1-2·3) higher attack rates than those where vaccination occurred before the peak. INTERPRETATION: Pandemic V cholerae of the same clonal origin was isolated throughout the study period despite interepidemic periods of no reported cases. Although the complex emergency in South Sudan probably shaped some of the observed spatial and temporal patterns of cases, the full scope of transmission determinants remains unclear. Timely and well targeted use of vaccines can reduce the burden of cholera; however, rapid vaccine deployment in complex emergencies remains challenging. FUNDING: The Bill & Melinda Gates Foundation.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), universally recognized as COVID-19, is currently is a global issue. Our study uses multivariate regression for determining the relationship between the ambient environment and COVID-19 cases in Lima. We also forecast the pattern trajectory of COVID-19 cases with variables using an Auto-Regressive Integrated Moving Average Model (ARIMA). There is a significant association between ambient temperature and PM10 and COVID-19 cases, while no significant correlation has been seen for PM2.5. All variables in the multivariate regression model have R-2 = 0.788, which describes a significant exposure to COVID-19 cases in Lima. ARIMA (1,1,1), during observation time of PM2.5, PM10, and average temperature, is found to be suitable for forecasting COVID-19 cases in Lima. This result indicates that the expected high particle concentration and low ambient temperature in the coming season will further facilitate the transmission of the coronavirus if there is no other policy intervention. A suggested sustainable policy related to ambient environment and the lessons learned from different countries to prevent future outbreaks are also discussed in this study.
BACKGROUND: As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. METHODS: Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. RESULTS: An estimated 38.8 million (95% Credible Interval [CI]: 37.9-40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9-21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7-9.4) to 36.6 (95% CI: 35.7-38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0-50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I?=?0.3 (p <?0.001) and districts Moran’s I?=?0.4 (p <?0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions. CONCLUSION: Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.
BACKGROUND: In malaria endemic areas, identifying spatio-temporal hotspots is becoming an important element of innovative control strategies targeting transmission bottlenecks. The aim of this work was to describe the spatio-temporal variation of malaria hotspots in central Senegal and to identify the meteorological, environmental, and preventive factors that influence this variation. METHODS: This study analysed the weekly incidence of malaria cases recorded from 2008 to 2012 in 575 villages of central Senegal (total population approximately 500,000) as part of a trial of seasonal malaria chemoprevention (SMC). Data on weekly rainfall and annual vegetation types were obtained for each village through remote sensing. The time series of weekly malaria incidence for the entire study area was divided into periods of high and low transmission using change-point analysis. Malaria hotspots were detected during each transmission period with the SaTScan method. The effects of rainfall, vegetation type, and SMC intervention on the spatio-temporal variation of malaria hotspots were assessed using a General Additive Mixed Model. RESULTS: The malaria incidence for the entire area varied between 0 and 115.34 cases/100,000 person weeks during the study period. During high transmission periods, the cumulative malaria incidence rate varied between 7.53 and 38.1 cases/100,000 person-weeks, and the number of hotspot villages varied between 62 and 147. During low transmission periods, the cumulative malaria incidence rate varied between 0.83 and 2.73 cases/100,000 person-weeks, and the number of hotspot villages varied between 10 and 43. Villages with SMC were less likely to be hotspots (OR?=?0.48, IC95%: 0.33-0.68). The association between rainfall and hotspot status was non-linear and depended on both vegetation type and amount of rainfall. The association between village location in the study area and hotspot status was also shown. CONCLUSION: In our study, malaria hotspots varied over space and time according to a combination of meteorological, environmental, and preventive factors. By taking into consideration the environmental and meteorological characteristics common to all hotspots, monitoring of these factors could lead targeted public health interventions at the local level. Moreover, spatial hotspots and foci of malaria persisting during LTPs need to be further addressed. TRIAL REGISTRATION: The data used in this work were obtained from a clinical trial registered on July 10, 2008 at www.clinicaltrials.gov under NCT00712374.
Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June-August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005-1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928-0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.
Fecal coliform bacteria are a key indicator of human health risks; however, the spatiotemporal variability and key influencing factors of river fecal coliform have yet to be explored in a rural-suburban-urban watershed with multiple land uses. In this study, the fecal coliform concentrations in 21 river sections were monitored for 20 months, and 441 samples were analyzed. Multivariable regressions were used to evaluate the spatiotemporal dynamics of fecal coliform. The results showed that spatial differences were mainly dominated by urbanization level, and environmental factors could explain the temporal dynamics of fecal coliform in different urban patterns except in areas with high urbanization levels. Reducing suspended solids is a direct way to manage fecal coliform in the Beiyun River when the natural factors are difficulty to change, such as temperature and solar radiation. The export of fecal coliform from urban areas showed a quick and sensitive response to rainfall events and increased dozens of times in the short term. Landscape patterns, such as the fragmentation of impervious surfaces and the overall landscape, were identified as key factors influencing urban non-point source bacteria. The results obtained from this study will provide insight into the management of river fecal pollution.
Dengue is an important emerging vector-borne disease in Bhutan. This study aimed to quantify the spatial and temporal patterns of dengue and their relationship to environmental factors in dengue-affected areas at the sub-district level. A multivariate zero-inflated Poisson regression model was developed using a Bayesian framework with spatial and spatiotemporal random effects modelled using a conditional autoregressive prior structure. The posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. A total of 708 dengue cases were notified through national surveillance between January 2016 and June 2019. Individuals aged ?14 years were found to be 53% (95% CrI: 42%, 62%) less likely to have dengue infection than those aged >14 years. Dengue cases increased by 63% (95% CrI: 49%, 77%) for a 1°C increase in maximum temperature, and decreased by 48% (95% CrI: 25%, 64%) for a one-unit increase in normalized difference vegetation index (NDVI). There was significant residual spatial clustering after accounting for climate and environmental variables. The temporal trend was significantly higher than the national average in eastern sub-districts. The findings highlight the impact of climate and environmental variables on dengue transmission and suggests prioritizing high-risk areas for control strategies.
BACKGROUND: Malaria continues to be a disease of massive burden in Africa, and the public health resources targeted at surveillance, prevention, control, and intervention comprise large outlays of expense. Malaria transmission is largely constrained by the suitability of the climate for Anopheles mosquitoes and Plasmodium parasite development. Thus, as climate changes, shifts in geographic locations suitable for transmission, and differing lengths of seasons of suitability will occur, which will require changes in the types and amounts of resources. METHODS: The shifting geographic risk of malaria transmission was mapped, in context of changing seasonality (i.e. endemic to epidemic, and vice versa), and the number of people affected. A published temperature-dependent model of malaria transmission suitability was applied to continental gridded climate data for multiple future AR5 climate model projections. The resulting outcomes were aligned with programmatic needs to provide summaries at national and regional scales for the African continent. Model outcomes were combined with population projections to estimate the population at risk at three points in the future, 2030, 2050, and 2080, under two scenarios of greenhouse gas emissions (RCP4.5 and RCP8.5). RESULTS: Estimated geographic shifts in endemic and seasonal suitability for malaria transmission were observed across all future scenarios of climate change. The worst-case regional scenario (RCP8.5) of climate change predicted an additional 75.9 million people at risk from endemic (10-12 months) exposure to malaria transmission in Eastern and Southern Africa by the year 2080, with the greatest population at risk in Eastern Africa. Despite a predominance of reduction in season length, a net gain of 51.3 million additional people is predicted be put at some level of risk in Western Africa by midcentury. CONCLUSIONS: This study provides an updated view of potential malaria geographic shifts in Africa under climate change for the more recent climate model projections (AR5), and a tool for aligning findings with programmatic needs at key scales for decision-makers. In describing shifting seasonality, it was possible to capture transitions between endemic and epidemic risk areas, to facilitate the planning for interventions aimed at year-round risk versus anticipatory surveillance and rapid response to potential outbreak locations.
The recent emergence and established presence of Aedes aegypti in the Autonomous Region of Madeira, Portugal, was responsible for the first autochthonous outbreak of dengue in Europe. The island has not reported any dengue cases since the outbreak in 2012. However, there is a high risk that an introduction of the virus would result in another autochthonous outbreak given the presence of the vector and permissive environmental conditions. Understanding the dynamics of a potential epidemic is critical for targeted local control strategies. Here, we adopt a deterministic model for the transmission of dengue in Aedes aegypti mosquitoes. The model integrates empirical and mechanistic parameters for virus transmission, under seasonally varying temperatures for Funchal, Madeira Island. We examine the epidemic dynamics as triggered by the arrival date of an infectious individual; the influence of seasonal temperature mean and variation on the epidemic dynamics; and performed a sensitivity analysis on the following quantities of interest: the epidemic peak size, time to peak, and the final epidemic size. Our results demonstrate the potential for summer and autumn season transmission of dengue, with the arrival date significantly affecting the distribution of the timing and peak size of the epidemic. Late-summer arrivals were more likely to produce large epidemics within a short peak time. Epidemics within this favorable period had an average of 11% of the susceptible population infected at the peak, at an average peak time of 95 days. We also demonstrated that seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with a short peak time and vice versa. Overall, our quantities of interest were most sensitive to variance in the date of arrival, seasonal temperature, transmission rates, mortality rate, and the mosquito population; the magnitude of sensitivity differs across quantities. Our model could serve as a useful guide in the development of effective local control and mitigation strategies for dengue fever in Madeira Island.
Climate change affects individual life-history characteristics and species interactions, including predator-prey interactions. While effects of warming on Aedes aegypti adults are well known, clarity the interactive effects of climate change (temperature and CO2 concentration) and predation risk on the larval stage remains unexplored. In this study, we performed a microcosm experiment simulating temperature and CO2 changes in Manaus, Amazonas, Brazil, for the year 2100. Simulated climate change scenarios (SCCS) were in accordance with the Fourth Assessment Report of Intergovernmental Panel on Climate Change (IPCC). Used SCCS were: Control (real-time current conditions in Manaus: average temperature is ~25.76°C ± 0.71°C and ~477.26 ± 9.38 parts per million by volume (ppmv) CO2); Light: increase of ~1,7°C and ~218 ppmv CO2; Intermediate: increase of ~2.4°C and ~446 ppmv CO2; and Extreme: increase of ~4.5°C and ~861 ppmv CO2, all increases were relative to a Control SCCS. Light, Intermediate and Extreme SCCS reproduced, respectively, the B1, A1B, and A2 climatic scenarios predicted by IPCC (2007). We analyzed Aedes aegypti larval survivorship and adult emergence pattern with a factorial design combining predation risk (control and predator presence-Toxorhynchites haemorrhoidalis larvae) and SCCS. Neither SCCS nor predation risk affected Aedes aegypti larval survivorship, but adult emergence pattern was affected by SCCS. Accordingly, our results did not indicate interactive effects of SCCS and predation risk on larval survivorship and emergence pattern of Aedes aegypti reared in SCCS in western Amazonia. Aedes aegypti is resistant to SCCS conditions tested, mainly due to high larval survivorship, even under Extreme SCCS, and warmer scenarios increase adult Aedes aegypti emergence. Considering that Aedes aegypti is a health problem in western Amazonia, an implication of our findings is that the use of predation cues as biocontrol strategies will not provide a viable means of controlling the accelerated adult emergence expected under the IPCC climatic scenarios.
INTRODUCTION: Malaria is an infectious disease of high transmission in the Amazon region, but its dynamics and spatial distribution may vary depending on the interaction of environmental, socio-cultural, economic, political and health services factors. OBJECTIVE: To verify the existence of malaria case patterns in consonance with the fluviometric regimes in Amazon basin. METHOD: Methods of descriptive and inferential statistics were used in malaria and water level data for 35 municipalities in the Amazonas State, in the period from 2003 to 2014. RESULTS: The existence of a tendency to modulate the seasonality of malaria cases due to distinct periods of rivers flooding has been demonstrated. Differences were observed in the annual hydrological variability accompanied by different patterns of malaria cases, showing a trend of remodeling of the epidemiological profile as a function of the flood pulse. CONCLUSION: The study suggests the implementation of regional and local strategies considering the hydrological regimes of the Amazon basin, enabling municipal actions to attenuate the malaria in the Amazonas State.
Many of the six million residents of unincorporated communities in the United States depend on well-water to meet their needs. One group of unincorporated communities is the colonias, located primarily in several southwestern U.S. states. Texas is home to the largest number of these self-built communities, of mostly low-income families, lacking basic infrastructure. While some states have regulations that mandate minimum infrastructure for these communities, water and sewage systems are still lacking for many of their residents. Unprotected wells and self-built septic/cesspool systems serve as the primary infrastructure for many such colonias. This research was designed to probe how wells and septic/cesspool systems are influenced by heavy rainfall events. Such events are hypothesized to impact water quality with regard to human health. Inorganic and microbiological water quality of the wells in nine colonias located in Nueces County, Texas, were evaluated during dry and wet periods. Nueces County was selected as an example based on its flooding history and the fact that many colonias there depend entirely on well-water and septic/cesspool systems. The results demonstrate that well-water quality in these communities varies seasonally with respect to arsenic (up to 35 ?g/L) and bacterial contamination (Escherichia coli), dependent on the amount of rainfall, which leaves this population vulnerable to health risks during both wet and dry periods. Microbial community analyses were also conducted on selected samples. To explore similar seasonal contamination of well-water, an analysis of unincorporated communities, flooding frequency, and arsenic contamination in wells was conducted by county throughout the United States. This nationwide analysis indicates that unincorporated communities elsewhere in the United States are likely experiencing comparable challenges for potable water access because of a confluence of socioeconomic, infrastructural, and policy realities.
Aedes albopictus is an invasive mosquito that has colonized several European countries as well as Portugal, where it was detected for the first time in 2017. To increase the knowledge of Ae. albopictus population dynamics, a survey was carried out in the municipality of Loulé, Algarve, a Southern temperate region of Portugal, throughout 2019, with Biogents Sentinel traps (BGS traps) and ovitraps. More than 19,000 eggs and 400 adults were identified from May 9 (week 19) and December 16 (week 50). A positive correlation between the number of females captured in the BGS traps and the number of eggs collected in ovitraps was found. The start of activity of A. albopictus in May corresponded to an average minimum temperature above 13.0 °C and an average maximum temperature of 26.2 °C. The abundance peak of this A. albopictus population was identified from September to November. The positive effect of temperature on the seasonal activity of the adult population observed highlight the importance of climate change in affecting the occurrence, abundance, and distribution patterns of this species. The continuously monitoring activities currently ongoing point to an established population of A. albopictus in Loulé, Algarve, in a dispersion process to other regions of Portugal and raises concern for future outbreaks of mosquito-borne diseases associated with this invasive mosquito species.
BACKGROUND: Between 1999 and 2008 Russia experienced a flare-up of transmission of vivax malaria following its massive importation with more than 500 autochthonous cases in European Russia, the Moscow region being the most affected. The outbreak waned soon after a decrease in importation in mid-2000s and strengthening the control measures. Compared with other post-eradication epidemics in Europe this one was unprecedented by its extension and duration. METHODS: The aim of this study is to identify geographical determinants of transmission. The degree of favourability of climate for vivax malaria was assessed by measuring the sum of effective temperatures and duration of season of effective infectivity using data from 22 weather stations. For geospatial analysis, the locations of each of 405 autochthonous cases detected in Moscow region have been ascertained. A MaxEnt method was used for modelling the territorial differentiation of Moscow region according to the suitability of infection re-emergence based on the statistically valid relationships between the distribution of autochthonous cases and environmental and climatic factors. RESULTS: In 1999-2004, in the beginning of the outbreak, meteorological conditions were extremely favourable for malaria in 1999, 2001 and 2002, especially within the borders of the city of Moscow and its immediate surroundings. The greatest number of cases occurred at the northwestern periphery of the city and in the adjoining rural areas. A significant role was played by rural construction activities attracting migrant labour, vegetation density and landscape division. A cut-off altitude of 200 m was observed, though the factor of altitude did not play a significant role at lower altitudes. Most likely, the urban heat island additionally amplified malaria re-introduction. CONCLUSION: The malariogenic potential in relation to vivax malaria was high in Moscow region, albeit heterogeneous. It is in Moscow that the most favourable conditions exist for vivax malaria re-introduction in the case of a renewed importation. This recent event of large-scale re-introduction of vivax malaria in a temperate area can serve as a case study for further research.
This study aimed to evaluate the relationship between weather factors (temperature, humidity, solar radiation, wind speed, and rainfall) and COVID-19 infection in the State of Rio de Janeiro, Brazil. Solar radiation showed a strong (-0.609, p < 0.01) negative correlation with the incidence of novel coronavirus (SARS-CoV-2). Temperature (maximum and average) and wind speed showed negative correlation (p < 0.01). Therefore, in this studied tropical state, high solar radiation can be indicated as the main climatic factor that suppress the spread of COVID-19. High temperatures, and wind speed also are potential factors. Therefore, the findings of this study show the ability to improve the organizational system of strategies to combat the pandemic in the State of Rio de Janeiro, Brazil, and other tropical countries around the word.
The relationship between the fires occurrences and diseases is an essential issue for making public health policy and environment protecting strategy. Thanks to the Internet, today, we have a huge amount of health data and fire occurrence reports at our disposal. The challenge, therefore, is how to deal with 4 Vs (volume, variety, velocity and veracity) associated with these data. To overcome this problem, in this paper, we propose a method that combines techniques based on Data Mining and Knowledge Discovery from Databases (KDD) to discover spatial and temporal association between diseases and the fire occurrences. Here, the case study was addressed to Malaria, Leishmaniasis and respiratory diseases in Brazil. Instead of losing a lot of time verifying the consistency of the database, the proposed method uses Decision Tree, a machine learning-based supervised classification, to perform a fast management and extract only relevant and strategic information, with the knowledge of how reliable the database is. Namely, States, Biomes and period of the year (months) with the highest rate of fires could be identified with great success rates and in few seconds. Then, the K-means, an unsupervised learning algorithms that solves the well-known clustering problem, is employed to identify the groups of cities where the fire occurrences is more expressive. Finally, the steps associated with KDD is perfomed to extract useful information from mined data. In that case, Spearman’s rank correlation coefficient, a nonparametric measure of rank correlation, is computed to infer the statistical dependence between fire occurrences and those diseases. Moreover, maps are also generated to represent the distribution of the mined data. From the results, it was possible to identify that each region showed a susceptible behaviour to some disease as well as some degree of correlation with fire outbreak, mainly in the drought period.
Non-point stormwater runoff is a major contamination source of receiving waterbodies. Heightened incidence of waterborne disease outbreaks related to recreational use and source water contamination is associated with extreme rainfall events. Such extreme events are predicted to increase in some regions due to climate change. Consequently, municipal separate storm sewer systems (MS4s) conveying pathogens to receiving waters are a growing public health concern. In addition, the spread of antibiotic resistance genes (ARGs) and antibiotic resistant bacteria in various environmental matrices, including urban runoff, is an emerging threat. The resistome and microbiota profile of MS4 discharges has yet to be fully characterized. To address this knowledge gap, we first analyzed the relationship between rainfall depth and intensity and E. coli densities (fecal indicator) in stormwater from four MS4 outflows in Columbus, Ohio, USA during the spring and summer of 2017. Microbial source tracking (MST) was conducted to examine major fecal contamination sources in the study sewersheds. A subset of samples was analyzed for microbial and resistome profiles using a metagenomic approach. The results showed a significant positive relationship between outflow E. coli density and rainfall intensity. MST results indicate prevalent fecal contamination from ruminant populations in the study sites (91% positive among the samples tested). Protobacteria and Actinobacteria were two dominant bacteria at a phylum level. A diverse array of ARGs and potentially pathogenic bacteria (e.g. Salmonella enterica Typhimurium), fungi (e.g. Scedosporium apiospermum), and protists (e.g. Acanthamoeba palestinensis) were found in urban stormwater outflows that discharge into adjacent streams. The most prevalent ARGs among samples were ?-lactam resistance genes and the most predominant virulence genes within bacterial community were related with Staphylococcus aureus. A comprehensive contamination profile indicates a need for sustainable strategies to manage urban stormwater runoff amid increasingly intense rainfall events to protect public and environmental health.
Mosquito-borne diseases affect millions of individuals worldwide; the area of endemic transmission has been increasing due to several factors linked to globalization, urban sprawl, and climate change. The Aedes aegypti mosquito plays a central role in the dissemination of dengue, Zika, chikungunya, and urban yellow fever. Current preventive measures include mosquito control programs; however, identifying high-risk areas for mosquito infestation over a large geographic region based only on field surveys is labor-intensive and time-consuming. Thus, the objective of this study was to assess the potential of remote satellite images (WorldView) for determining land features associated with Ae. aegypti adult infestations in São José do Rio Preto/SP, Brazil. We used data from 60 adult mosquito traps distributed along four summers; the remote sensing images were classified by land cover types using a supervised classification method. We modeled the number of Ae. aegypti using a Poisson probability distribution with a geostatistical approach. The models were constructed in a Bayesian context using the Integrated nested Laplace Approximations and Stochastic Partial Differential Equation method. We showed that an infestation of Ae. aegypti adult mosquitoes was positively associated with the presence of asbestos roofing and roof slabs. This may be related to several other factors, such as socioeconomic or environmental factors. The usage of asbestos roofing may be more prevalent in socioeconomically poor areas, while roof slabs may retain rainwater and contribute to the generation of temporary mosquito breeding sites. Although preliminary, our results demonstrate the utility of satellite remote sensing in identifying landscape differences in urban environments using a geostatistical approach, and indicated directions for future research. Further analyses including other variables, such as land surface temperature, may reveal more complex relationships between urban mosquito micro-habitats and land cover features.
Pollen is an important component of bioaerosol and the distribution of pollen and its relationship with meteorological parameters can be analyzed to better prevent hay fever. Pollen assemblages can also provide basic data for analyzing the relationship between bioaerosol and PM. We collected 82 samples of airborne pollen using a TSP large flow pollen collector from June 1, 2015 to June 1, 2016, from central Zhanjiang city in South China. We also conducted a survey of the nearby vegetation at the same time, in order to characterize the major plant types and their flowering times. We then used data on daily temperature, relative humidity, precipitation, vapor pressure and wind speed from a meteorological station in the center of Zhanjiang City to assess the relationship between the distribution of airborne pollen and meteorological parameters. Our main findings and conclusions are as follows: (1) We identified 15 major pollen types, including Pinus, Castanopsis, Myrica, Euphorbiaceae, Compositae, Gramineae, Microlepia and Polypodiaceae. From the vegetation survey, we found that the pollen from these taxa represented more than 75% of local pollen, while the pollen of Podocarpus, Dacrydium and other regional pollen types represented less than 25%. (2) The pollen concentrations varied significantly in different seasons. The pollen concentrations were at a maximum in spring, consisting mainly of tree pollen; the pollen concentrations were at an intermediate level in autumn and winter, consisting mainly of herb pollen and fern spores; and the pollen concentrations in summer were the lowest, consisting mainly of fern spores. (3) Analysis of the relationship between airborne pollen concentrations and meteorological parameters showed that variations in the pollen concentrations were mainly affected by temperature and relative humidity. In addition, there were substantial differences in these relationships in different seasons. In spring, pollen concentrations were mainly affected by temperature; in summer, they were mainly affected by the direction of the maximum wind speed; in autumn, they were mainly affected by relative humidity and temperature; and in winter, they were mainly affected by relative humidity and wind speed. Temperature and relative humidity promote plant growth and flowering. Notably, the variable wind direction in summer and the increased wind speed in winter and spring are conductive to pollen transmission. (4) Of the 15 major pollen types, Moraceae, Artemisia and Gramineae are the main allergenic pollen types, with peaks in concentration during April-May, August-September, and October-December, respectively. (5) Atypical weather conditions have substantial effects on pollen dispersal. In South China, the pollen concentrations in the sunny day were usually significantly higher than that of the rainy day. The pollen concentrations increased in short rainy days, which usually came from the Herb and Fern pollen. The pollen concentrations decreased in continuous rainy days especially for the Tree and Shrub pollen. the pollen concentrations in the sunny days were usually significantly higher than that in the rainy days. The pollen concentrations increased in short and strong rainfall.
Knowing how people perceive multiple risks is essential to the management and promotion of public health and safety. Here we present a dataset based on a survey (N?=?4,154) of public risk perception in Italy and Sweden during the COVID-19 pandemic. Both countries were heavily affected by the first wave of infections in Spring 2020, but their governmental responses were very different. As such, the dataset offers unique opportunities to investigate the role of governmental responses in shaping public risk perception. In addition to epidemics, the survey considered indirect effects of COVID-19 (domestic violence, economic crises), as well as global (climate change) and local (wildfires, floods, droughts, earthquakes, terror attacks) threats. The survey examines perceived likelihoods and impacts, individual and authorities’ preparedness and knowledge, and socio-demographic indicators. Hence, the resulting dataset has the potential to enable a plethora of analyses on social, cultural and institutional factors influencing the way in which people perceive risk.
Paralytic shellfish poison toxins (PSTs) produced by the dinoflagellate in the genus Alexandrium are a threat to human health and subsistence lifestyles in Southeast Alaska. It is important to understand the drivers of Alexandrium blooms to inform shellfish management and aquaculture, as well as to predict trends of PST in a changing climate. In this study, we aggregate environmental data sets from multiple agencies and tribal partners to model and predict concentrations of PSTs in Southeast Alaska from 2016 to 2019. We used daily PST concentrations interpolated from regularly sampled blue mussels (Mytilus trossulus) analyzed for total PSTs using a receptor binding assay. We then created random forest models to classify shellfish above and below a threshold of toxicity (80 µg 100 g(-1)) and used two methods to determine variable importance. We obtained a multivariate model with key variables being sea surface temperature, salinity, freshwater discharge, and air temperature. We then used a similar model trained using lagged environmental variables to hindcast out-of-sample (OOS) shellfish toxicities during April-October in 2017, 2018, and 2019. Hindcast OOS accuracies were low (37-50%); however, we found forecasting using environmental variables may be useful in predicting the timing of early summer blooms. This study reinforces the efficacy of machine learning to determine important drivers of harmful algal blooms, although more complex models incorporating other parameters such as toxicokinetics are likely needed for accurate regional forecasts.
Aedes aegypti is the main vector of dengue, Zika, chikungunya, and yellow fever viruses. Controlling populations of vector mosquito species in urban environments is a major challenge and being able to determine what aquatic habitats should be prioritized for controlling Ae. aegypti populations is key to the development of more effective mosquito control strategies. Therefore, our objective was to leverage on the Miami-Dade County, Florida immature mosquito surveillance system based on requested by citizen complaints through 311 calls to determine what are the most important aquatic habitats in the proliferation of Ae. aegypti in Miami. We used a tobit model for Ae. aegypti larvae and pupae count data, type and count of aquatic habitats, and daily rainfall. Our results revealed that storm drains had 45% lower percentage of Ae. aegypti larvae over the total of larvae and pupae adjusted for daily rainfall when compared to tires, followed by bromeliads with 33% and garbage cans with 17%. These results are indicating that storm drains, bromeliads and garbage cans had significantly more pupae in relation to larvae when compared to tires, traditionally know as productive aquatic habitats for Ae. aegypti. Ultimately, the methodology and results from this study can be used by mosquito control agencies to identify habitats that should be prioritized in mosquito management and control actions, as well as to guide and improve policies and increase community awareness and engagement. Moreover, by targeting the most productive aquatic habitats this approach will allow the development of critical emergency outbreak responses by directing the control response efforts to the most productive aquatic habitats.
The coronavirus disease 2019 (COVID-19) pandemic is the most severe global health and socioeconomic crisis of our time, and represents the greatest challenge faced by the world since the end of the Second World War. The academic literature indicates that climatic features, specifically temperature and absolute humidity, are very important factors affecting infectious pulmonary disease epidemics – such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS); however, the influence of climatic parameters on COVID-19 remains extremely controversial. The goal of this study is to individuate relationships between several climate parameters (temperature, relative humidity, accumulated precipitation, solar radiation, evaporation, and wind direction and intensity), local morphological parameters, and new daily positive swabs for COVID-19, which represents the only parameter that can be statistically used to quantify the pandemic. The daily deaths parameter was not considered, because it is not reliable, due to frequent administrative errors. Daily data on meteorological conditions and new cases of COVID-19 were collected for the Lombardy Region (Northern Italy) from 1 March, 2020 to 20 April, 2020. This region exhibited the largest rate of official deaths in the world, with a value of approximately 1700 per million on 30 June 2020. Moreover, the apparent lethality was approximately 17% in this area, mainly due to the considerable housing density and the extensive presence of industrial and craft areas. Both the Mann-Kendall test and multivariate statistical analysis showed that none of the considered climatic variables exhibited statistically significant relationships with the epidemiological evolution of COVID-19, at least during spring months in temperate subcontinental climate areas, with the exception of solar radiation, which was directly related and showed an otherwise low explained variability of approximately 20%. Furthermore, the average temperatures of two highly representative meteorological stations of Molise and Lucania (Southern Italy), the most weakly affected by the pandemic, were approximately 1.5 °C lower than those in Bergamo and Brescia (Lombardy), again confirming that a significant relationship between the increase in temperature and decrease in virulence from COVID-19 is not evident, at least in Italy.
The number of dengue fever incidence and its distribution has increased considerably in recent years in Africa. However, due to inadequate research at the continental level, there is a limited understanding regarding the current and future spatial distribution of the main vector, the mosquitoAedes aegypti, and the associated dengue risk due to climate change. To fill this gap we used reported dengue fever incidences, the presence of Ae. aegypti, and bioclimatic variables in a species distribution model to assess the current and future (2050 and 2070) climatically suitable areas. High temperatures and with high moisture levels are climatically suitable for the distribution of Ae. aegypti related to dengue fever. Under the current climate scenario indicated that 15.2% of the continent is highly suitable for dengue fever outbreaks. We predict that climatically suitable areas for Ae. aegypti related to dengue fever incidences in eastern, central and western part of Africa will increase in the future and will expand further towards higher elevations. Our projections provide evidence for the changing continental threat of vector-borne diseases and can guide public health policy decisions in Africa to better prepare for and respond to future changes in dengue fever risk.
Climate change is postulated to alter the distribution and abundance of species which serve as vectors for pathogens and is thus expected to affect the transmission of infectious, vector-borne diseases such as malaria. The ability to project and therefore, to mitigate the risk of potential expansion of infectious diseases requires an understanding of how vectors respond to environmental change. Here, we used an extensive dataset on the distribution of the mosquito Anopheles sacharovi, a vector of malaria parasites in Greece, southeast Europe, to build a modeling framework that allowed us to project the potential species range within the next decades. In order to account for model uncertainty, we employed a multi-model approach, combining an ensemble of diverse correlative niche models and a mechanistic model to project the potential expansion of species distribution and to delineate hotspots of potential malaria risk areas. The performance of the models was evaluated using official records on autochthonous malaria incidents. Our projections demonstrated a gradual increase in the potential range of the vector distribution and thus, in the malaria receptive areas over time. Linking the model outputs with human population inhabiting the study region, we found that population at risk increases, relative to the baseline period. The methodological framework proposed and applied here, offers a solid basis for a climate change impact assessment on malaria risk, facilitating informed decision making at national and regional scales.
BACKGROUND AND AIMS: As, the COVID-19 has been deemed a pandemic by World Health Organization (WHO), and since it spreads everywhere throughout the world, investigation in relation to this disease is very much essential. Investigation of pattern in the occurrence of COVID-19, to check the influence of different meteorological factors on the incidence of COVID-19 and prediction of incidence of COVID-19 are the objectives of this paper. METHODS: For trend analysis, Sen’s Slope and Man-Kendall test have been used, Generalized Additive Model (GAM) of regression has been used to check the influence of different meteorological factors on the incidence and to predict the frequency of COVID-19, and Verhulst (Logistic) Population Model has been used. RESULTS: Statistically significant linear trend found for the daily-confirmed cases of COVID-19. The regression analysis indicates that there is some influence of the interaction of average temperature (AT) and average relative humidity (ARH) on the incidence of COVID-19. However, this result is not consistent throughout the study area. The projections have been made up to 21st May, 2020. CONCLUSIONS: Trend and regression analysis give an idea of the incidence of COVID-19 in India while projection made by Verhulst (Logistic) Population Model for the confirmed cases of the study area are encouraging as the sample prediction is as same as the actual number of confirmed COVID-19 cases.
Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012-2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.
Identifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.
Over the years, Jamaica has experienced sporadic cases of dengue fever. Even though the island is vulnerable to dengue, there is paucity in the spatio-temporal analysis of the disease using Geographic Information Systems (GIS) and remote sensing tools. Further, access to time series dengue data at the community level is a major challenge on the island. This study therefore applies the Water-Associated Disease Index (WADI) framework to analyze vulnerability to dengue in Jamaica based on past, current and future climate change conditions using three scenarios: (1) WorldClim rainfall and temperature dataset from 1970 to 2000; (2) Climate Hazard Group InfraRed Precipitation with Station data (CHIRPS) rainfall and land surface temperature (LST) as proxy for air temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2002 to 2016, and (3) maximum temperature and rainfall under the Representative Concentration Pathway (RCP) 8.5 climate change scenario for 2030 downscaled at 25 km based on the Regional Climate Model, RegCM4.3.5. Although vulnerability to dengue varies spatially and temporally, a higher vulnerability was depicted in urban areas in comparison to rural areas. The results also demonstrate the possibility for expansion in the geographical range of dengue in higher altitudes under climate change conditions based on scenario 3. This study provides an insight into the use of data with different temporal and spatial resolution in the analysis of dengue vulnerability.
A probe of a patient, seeking help in an emergency ward of a French hospital in late December 2019 because of Influenza like symptoms, was retrospectively tested positive to COVID-19. Despite the early appearance of the virus in Europe, the prevalence and virulence appeared to be low for several weeks, before the spread and severity of symptoms increased exponentially, yet with marked spatial and temporal differences. Here, we compare the possible linkages between peaks of fine particulate matter (PM2.5) and the sudden, explosive increase of hospitalizations and mortality rates in the Swiss Canton of Ticino, and the Greater Paris and London regions. We argue that these peaks of fine particulate matter are primarily occurring during thermal inversion of the boundary layer of the atmosphere. We also discuss the influence of Saharan dust intrusions on the COVID-19 outbreak observed in early 2020 on the Canary Islands. We deem it both reasonable and plausible that high PM2.5 concentrations-favored by air temperature inversions or Saharan dust intrusions-are not only modulating but even more so boosting severe outbreaks of COVID-19. Moreover, desert dust events-besides enhancing PM2.5 concentrations-can be a vector for fungal diseases, thereby exacerbating COVID-19 morbidity and mortality. We conclude that the overburdening of the health services and hospitals as well as the high over-mortality observed in various regions of Europe in spring 2020 may be linked to peaks of PM2.5 and likely particular weather situations that have favored the spread and enhanced the virulence of the virus. In the future, we recommended to monitor not only the prevalence of the virus, but also to consider the occurrence of weather situations that can lead to sudden, very explosive COVID-19 outbreaks.
Rivers are representative of the overall contamination found in their catchment area. Contaminant concentrations in watercourses depend on numerous factors including land use and rainfall events. Globally, in Mediterranean regions, rainstorms are at the origin of fluvial multipollution phenomena as a result of Combined Sewer Overflows (CSOs) and floods. Large loads of urban-associated microorganisms, including faecal bacteria, are released from CSOs which place public health – as well as ecosystems – at risk. The impacts of freshwater contamination on river ecosystems have not yet been adequately addressed, as is the case for the release of pollutant mixtures linked to extreme weather events. In this context, microbial communities provide critical ecosystem services as they are the only biological compartment capable of degrading or transforming pollutants. Through the use of 16S rRNA gene metabarcoding of environmental DNA at different seasons and during a flood event in a typical Mediterranean coastal river, we show that the impacts of multipollution phenomena on structural shifts in the particle-attached riverine bacteriome were greater than those of seasonality. Key players were identified via multivariate statistical modelling combined with network module eigengene analysis. These included species highly resistant to pollutants as well as pathogens. Their rapid response to contaminant mixtures makes them ideal candidates as potential early biosignatures of multipollution stress. Multiple resistance gene transfer is likely enhanced with drastic consequences for the environment and human-health, particularly in a scenario of intensification of extreme hydrological events.
Water deficit, exacerbated by global population increases and climate change, necessitates the investigation of alternative non-traditional water sources to augment existing supplies. Indirect potable reuse (IPR) represents a promising alternative water source in water-stressed regions. Of high concern is the presence of pathogenic microorganisms in wastewater, such as enteric viruses, protozoa and bacteria. Therefore, a greater understanding of the potential impact to human health is required. The aim of this research was to use a quantitative microbial risk assessment (QMRA) approach to calculate the probability of potential pathogen infection risk to the public in surface waters used for a range of recreational activities under scenarios: 1) existing de facto wastewater reuse conditions; 2) after augmentation with conventionally treated wastewater; and 3) after augmentation with reclaimed wastewater from proposed IPR schemes. Forty-four 31 l samples were collected from river sites and a coastal wastewater treatment works from July 2016-May 2017. Concentrations of faecal indicator organisms (enterococci, faecal coliforms, somatic coliphages and Bacteroides phages) determined using culture-based approaches and selected pathogens (adenovirus, Salmonella and Cryptosporidium) determined using molecular approaches (qPCR) were used to inform QMRA. The mean probability of infection from adenovirus under de facto conditions was high (>0.90) for all recreational activities, per single event. The risk of adenovirus and Cryptosporidium infection increased under augmentation scenario (2) (mean probability 0.95-1.00 and 0.01-0.06 per single event, respectively). Adenovirus and Cryptosporidium infection risk decreased under reclaimed water augmentation scenario (3) (mean probability <0.79, excluding swimming, which remained 1.00 and <0.01 per single event, respectively). Pathogen reduction after reclaimed water augmentation in surface waters impacted by de facto reuse, provides important evidence for alternative water supply option selection. As such, this evidence may inform water managers and the public of the potential benefits of IPR and improve acceptance of such practices in the future.
Extreme floods pose multiple direct and indirect health risks. These risks include contamination of water, food, and the environment, often causing outbreaks of diarrheal disease. Evidence regarding the effects of flooding on individual diarrhea-causing pathogens is limited, but is urgently needed in order to plan and implement interventions and prioritize resources before climate-related disasters strike. This study applied a causal inference approach to data from a multisite study that deployed broadly inclusive diagnostics for numerous high-burden common enteropathogens. Relative risks (RRs) of infection with each pathogen during a flooding disaster that occurred at one of the sites-Loreto, Peru-were calculated from generalized linear models using a comparative interrupted time series framework with the other sites as a comparison group and adjusting for background seasonality. During the early period of the flood, increased risk of heat-stable enterotoxigenic E. coli (ST-ETEC) was identified (RR = 1.73 [1.10, 2.71]) along with a decreased risk of enteric adenovirus (RR = 0.36 [0.23, 0.58]). During the later period of the flood, sharp increases in the risk of rotavirus (RR = 5.30 [2.70, 10.40]) and sapovirus (RR = 2.47 [1.79, 3.41]) were observed, in addition to increases in transmission of Shigella spp. (RR = 2.86 [1.81, 4.52]) and Campylobacter spp. (RR = 1.41 (1.01, 1.07). Genotype-specific exploratory analysis reveals that the rise in rotavirus transmission during the flood was likely due to the introduction of a locally atypical, non-vaccine (G2P[4]) strain of the virus. Policy-makers should target interventions towards these pathogens-including vaccines as they become available-in settings where vulnerability to flooding is high as part of disaster preparedness strategies, while investments in radical, transformative, community-wide, and locally-tailored water and sanitation interventions are also needed.
Rural, natural resource dependent communities are especially vulnerable to climate change, and their input is critical in developing solutions, but the study of risk perception within and among vulnerable communities remains underdeveloped. Our multi-disciplinary research team used a mixed-methods approach to document, analyze, and conceptualize the interacting factors that shape vulnerability and to explore community members’ perceptions of the role and relative importance of climate change compared to other factors in three rural communities in Ecuador. Economic instability, lack of access to basic services, and environmental degradation are perceived as greater threats to community well being than increasing seasonal variability and flooding. Programs and policies directed at climate change adaptation should integrate climate and non-climate related stressors. Our findings also point to a greater need for collaboration across public health, poverty alleviation, and environmental management fields through practical research targeting assistance to vulnerable populations.
Malaria is a Plasmodium parasitic disease transmitted by infected female Anopheles mosquitoes. Climatic factors, such as temperature, humidity, rainfall, and wind, have significant effects on the incidence of most vector-borne diseases, including malaria. The mosquito behavior, life cycle, and overall fitness are affected by these climatic factors. This paper presents the results obtained from investigating the optimal control strategies for malaria in the presence of temperature variation using a temperature-dependent malaria model. The study further identified the temperature ranges in four different geographical regions of sub-Saharan Africa, suitable for mosquitoes. The optimal control strategies in the temperature suitable ranges suggest, on average, a high usage of both larvicides and adulticides followed by a moderate usage of personal protection such as bednet. The average optimal bednet usage mimics the solution profile of the mosquitoes as the mosquitoes respond to changes in temperature. Following the results from the optimal control, this study also investigates using a temperature-dependent model with insecticide-sensitive and insecticide-resistant mosquitoes the impact of insecticide-resistant mosquitoes on disease burden when temperature varies. The results obtained indicate that optimal bednet usage on average is higher when insecticide-resistant mosquitoes are present. Besides, the average bednet usage increases as temperature increases to the optimal temperature suitable for mosquitoes, and it decreases after that, a pattern similar to earlier results involving insecticide-sensitive mosquitoes. Thus, personal protection, particularly the use of bednets, should be encouraged not only at low temperatures but particularly at high temperatures when individuals avoid the use of bednets. Furthermore, control and reduction of malaria may be possible even when mosquitoes develop resistance to insecticides.
Marine biotoxins are naturally existing chemicals produced by toxic algae and can accumulate in marine biota. When consumed with seafood, these phycotoxins can cause human intoxication with symptoms varying from barely-noticed illness to death depending on the type of toxin and its concentration. Recently, the occurrence of marine biotoxins has been given special attention in the Mediterranean as it increased in frequency and severity due to anthropogenic pressures and climate change. Up to our knowledge, no previous study reported the presence of lipophilic toxins (LTs) and cyclic imines (CIs) in marine biota in Lebanon. Hence, this study reports LTs and CIs in marine organisms: one gastropod (Phorcus turbinatus), two bivalves (Spondylus spinosus and Patella rustica complex) and one fish species (Siganus rivulatus), collected from various Lebanese coastal areas. The results show values below the limit of detection (LOD) for okadaic acid, dinophysistoxin-1 and 2, pectenotoxin-1 and 2, yessotoxins, azaspiracids and saxitoxins. The spiny oyster (S. spinosus) showed the highest levels of domoic acid (DA; 3.88 mg kg(-1)), gymnodimine (GYM-B) and spirolide (SPX) (102.9 and 15.07 ?g kg(-1), respectively) in congruence with the occurrence of high abundance of Pseudo-nitzchia spp., Gymnodinium spp., and Alexandrium spp. DA levels were below the European Union (EU) regulatory limit, but higher than the Lowest Observed Adverse Effect Level (0.9 ?g g(-1)) for neurotoxicity in humans and lower than the Acute Reference Dose (30 ?g kg(-1) bw) both set by the European Food Safety Authority (EFSA, 2009). Based on these findings, it is unlikely that a health risk exists due to the exposure to these toxins through seafood consumption in Lebanon. Despite this fact, the chronic toxicity of DA, GYMs and SPXs remains unclear and the effect of the repetitive consumption of contaminated seafood needs to be more investigated.
Environmental water quality issues have dominated global discourse and studies over the past five decades. Significant parameters of environmental water quality include changes in biological and physical parameters. Some of the biological parameters of significance include occurrence of enteric viruses. Enteric viruses can affect both human and animal’s health by causing diseases such as gastrointestinal and respiratory infections. In this study, the relationship between the occurrence of enteric viruses with reference to adenoviruses and enteroviruses and the physical water quality characteristics was assessed from water samples collected from Lake Victoria (LV) in Kenya. In order to understand the dynamics of season driven enteric viruses’ contamination of the lake waters, we additionally analysed seasonal behavior of the lake’s catchment area in terms of rainfall effects. Physical quality parameters were measured on-site while viral analysis was carried out by molecular methods using the nested polymerase chain reaction (nPCR). From 216 samples that were analysed for viral contamination, enteric viral genomes were discovered in 18 (8.3%) of the samples. Out of half of the samples (108) collected during the rainy season, enteric viral genomes were detected in 9.26% (10) while 8 (7.41%) samples tested positive from the other half of the samples (108) collected during the dry season. There was, however, no significant correlation noted between the physical water quality characteristics and the enteric viruses’ occurrence. Neither wet season nor dry season was significantly associated with the prevalence of the viruses. In Lake Victoria waters, most of the samples had an average of physical water quality parameters that were within the range accepted by the World Health Organization (WHO) for surface waters with exemption of turbidity which was above the recommended 5 NTU as recorded from some sampling sites. Continuous and long-term surveillance of the lake water to accurately monitor the contaminants and possible correlation between chemical, physical, and biological characteristics is recommended. This would be important in continuous understanding of the hydrological characteristics changes of the lake for proper management of its quality with reference to the WHO standards. A multiple varied-sampling approach in different geographical regions during different seasons is recommended to establish the geographical distribution and relatedness to seasonal distribution patterns of the viruses. The data generated from this study will be useful in providing a basis for assessment of seasonally driven fecal pollution load of the lake and enteric virus contamination for proper management of the sanitary situation around the lake.
Reported dengue fever cases are increasing day by day in the world as well as in Sri Lanka. Model, Prediction and Control are three major parts of the process of analysis of the dengue incidence which leads to reduce the burden of the dengue. There is an increasing trend in the applications and developments in interval-valued data analysis over recent years. Particularly, under regressions there have being developed various techniques to handle interval-valued dependent and independent variables. Representation of data as intervals is very much useful to capture uncertainty and missing details associated with variables. Further, the predictions in intervals suit well when the situations of exact forecasts may not necessary. In this study interval-valued dengue data with interval-valued minimum temperature, maximum temperature and rainfall from 2009 to 2015 in the Colombo district, Sri Lanka were model using three interval valued regression procedures, namely, Center Method (CM), Center and Range Method (CRM) and Constrained Center and Range Method (CCRM). Predicted dengue cases in a range is particularly important because actions taking towards controlling the dengue do not depend on exact number but on magnitude of the values represent in the interval. Data in the year 2016 used for the validation of the models which is developed under three methods. Root of the mean square error, coefficient of determination as well as square root of variance of the models were used to select the best procedure to predict dengue cases. Among the three regression procedures both CRM and CCRM perform well in predicting monthly dengue cases in Colombo.
BACKGROUND: More than 80,000 dengue cases including 215 deaths were reported nationally in less than 7 months between 2016 and 2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission. METHODS: We present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1?km?×?1?km spatial resolution and a weekly temporal resolution. RESULTS: Our results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions. CONCLUSIONS: Our study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.
Malaria, a vector-borne disease, is a significant public health problem in Keonjhar district of Odisha (the malaria capital of India). Prediction of malaria, in advance, is an urgent need for reporting rolling cases of disease throughout the year. The climate condition do play an essential role in the transmission of malaria. Hence, the current study aims to develop and assess a simple and straightforward statistical model of an association between malaria cases and climate variates. It may help in accurate predictions of malaria cases given future climate conditions. For this purpose, a Bayesian Gaussian time series regression model is adopted to fit a relationship of the square root of malaria cases with climate variables with practical lag effects. The model fitting is assessed using a Bayesian version of R(2) (RsqB). Whereas, the predictive ability of the model is measured using a cross-validation technique. As a result, it is found that the square root of malaria cases with lag 1, maximum temperature, and relative humidity with lag 3 and 0 (respectively), are significantly positively associated with the square root of the cases. However, the minimum and average temperatures with lag 2, respectively, are observed as negatively (significantly) related. The considered model accounts for moderate amount of variation in the square root of malaria cases as received through the results for RsqB. We also present Absolute Percentage Errors (APE) for each of the 12 months (January-December) for a better understanding of the seasonal pattern of the predicted (square root of) malaria cases. Most of the APEs obtained corresponding to test data points is reasonably low. Further, the analysis shows that the considered model closely predicted the actual (square root of) malaria cases, except for some peak cases during the particular months. The output of the current research might help the district to develop and strengthen early warning prediction of malaria cases for proper mitigation, eradication, and prevention in similar settings.
Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.
Floods are a prominent risk factor in the world of public health, as there is a risk of dispersal of harmful biological and chemical contaminants in floodwater. As climate change increases, the occurrence of natural disasters and risk of adverse health outcomes due to flash flooding also increases. Fecal indicator bacteria, such as Escherichia coli and Enterococci, are often encountered in contaminated floodwater and can cause gastrointestinal illnesses as well as a variety of infections. In August 2016, East Baton Rouge and surrounding parishes in Louisiana suffered heavy floods due to intense rainfall. No study of water quality during flooding has been conducted previously in Baton Rouge, Louisiana. Twenty-three pre-flush and post-flush water samples were collected immediately from accessible homes that had been affected by the floods in order to quantify concentrations of fecal indicator bacteria. These samples were analyzed for the presence of E. coli and Enterococci through both quantitative polymerase chain reaction (qPCR) and the IDEXX enzyme substrate method. The qPCR results indicated that 30% of the samples contained Enterococci and 61% of the samples contained E. coli, with the highest concentrations found in the pre-flush outdoor hose and the pre-flush kitchen tap. The IDEXX method yielded total coliforms in 65% of the samples, E. coli in 4%, and Enterococci in 35%, with the highest concentrations in the pre-flush outdoor faucet and the pre-flush post-filtration kitchen tap. Physical parameters including temperature, barometer pressure, dissolved oxygen, oxidation reduction potential, pH, conductivity, and salinity of these samples were also recorded. Of these parameters, conductivity and salinity were significant, suggesting they may positively influence E. coli and Enterococci growth.
The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department’s data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.
It is often difficult to define the relationship and the influence of climate on the occurrence and distribution of disease. To examine this issue, the effects of climate indices on the distributions of malaria and meningitis in Nigeria were assessed over space and time. The main purpose of the study was to evaluate the relationships between climatic variables and the prevalence of malaria and meningitis, and develop an early warning system for predicting the prevalence of malaria and meningitis as the climate varies. An early warning system was developed to predetermine the months in a year that people are vulnerable to malaria and meningitis. The results revealed a significant positive relationship between rainfall and malaria, especially during the wet season with correlation coefficient R-2 >= 60.0 in almost all the ecological zones. In the Sahel, Sudan and Guinea, there appears to be a strong relationship between temperature and meningitis with R-2 > 60.0. In all, the results further reveal that temperatures and aerosols have a strong relationship with meningitis. The assessment of these initial data seems to support the finding that the occurrence of meningitis is higher in the northern region, especially the Sahel and Sudan. In contrast, malaria occurrence is higher in the southern part of the study area. We suggest that a thorough investigation of climate parameters is critical for the reallocation of clinical resources and infrastructures in economically underprivileged regions.
BACKGROUND AND OBJECTIVE: Malaria is an arthropod-borne infectious disease transmitted by the mosquito Anopheles and claims millions of lives globally every year. Reasons for failure to eradicate this disease are multifactorial. The seasonality of the malaria is principally determined by climatic factors conducive for breeding of the vector. We aimed to study the relationship between climatic variability and the seasonality of malaria over an eight-year duration. METHODS: This was a retrospective medical chart review of 8,844 confirmed cases of malaria which presented to The Indus Hospital, Karachi from January 2008 to November 2015. Cases were plotted against meteorological data for Karachi to elicit monthly variation. RESULTS: A secular incline and seasonality in malaria cases over the duration of eight years was seen. More cases were reported in the summer, rainy season compared with the other three seasons in each year. There was significant association with specific climate variables such as temperature, moisture, and humidity. CONCLUSION: There is a marked seasonal variation of malaria in Karachi, influenced by various environmental factors. Identification of the ‘the concentrated period’ of malaria can be helpful for policymakers to deploy malaria control interventions.
BACKGROUND: Malaria remains a major tropical vector-borne disease of immense public health concern owing to its debilitating effects in sub-Saharan Africa. Over the past 30?years, the high altitude areas in Eastern Africa have been reported to experience increased cases of malaria. Governments including that of the Republic of Uganda have responded through intensifying programs that can potentially minimize malaria transmission while reducing associated fatalities. However, malaria patterns following these intensified control and prevention interventions in the changing climate remains widely unexplored in East African highland regions. This study thus analyzed malaria patterns across altitudinal zones of Mount Elgon, Uganda. METHODS: Times-series data on malaria cases (2011-2017) from five level III local health centers occurring across three altitudinal zones; low, mid and high altitude was utilized. Inverse Distance Weighted (IDW) interpolation regression and Mann Kendall trend test were used to analyze malaria patterns. Vegetation attributes from the three altitudinal zones were analyzed using Normalized Difference Vegetation Index (NDVI) was used to determine the Autoregressive Integrated Moving Average (ARIMA) model was used to project malaria patterns for a 7 year period. RESULTS: Malaria across the three zones declined over the study period. The hotspots for malaria were highly variable over time in all the three zones. Rainfall played a significant role in influencing malaria burdens across the three zones. Vegetation had a significant influence on malaria in the higher altitudes. Meanwhile, in the lower altitude, human population had a significant positive correlation with malaria cases. CONCLUSIONS: Despite observed decline in malaria cases across the three altitudinal zones, the high altitude zone became a malaria hotspot as cases variably occurred in the zone. Rainfall played the biggest role in malaria trends. Human population appeared to influence malaria incidences in the low altitude areas partly due to population concentration in this zone. Malaria control interventions ought to be strengthened and strategically designed to achieve no malaria cases across all the altitudinal zones. Integration of climate information within malaria interventions can also strengthen eradication strategies of malaria in such differentiated altitudinal zones.
BACKGROUND: Climate change and extreme weather poses significant threats to community health, which need to be addressed by local health workforce. This study investigated the perceptions of primary healthcare professionals in Southern China on individual and institutional strategies for actions on health impacts of climate change and the related barriers. METHODS: A mixed methodological approach was adopted, involving a cross-sectional questionnaire survey of 733 primary healthcare professionals (including medical doctors, nurses, public health practitioners, allied health workers and managers) selected through a multistage cluster randomized sampling strategy, and in-depth interviews of 25 key informants in Guangdong Province, China. The questionnaire survey investigated the perceptions of respondents on the health impacts of climate change and the individual and institutional actions that need to be taken in response to climate change. Multivariate logistic regression models were established to determine sociodemographic factors associated with the perceptions. The interviews tapped into coping strategies and perceived barriers in primary health care to adapt to tackle challenges of climate change. Contents analyses were performed to extract important themes. RESULTS AND CONCLUSION: The majority (64%) of respondents agreed that climate change is happening, but only 53.6% believed in its human causes. Heat waves and infectious diseases were highly recognized as health problems associated with climate change. There was a strong consensus on the need to strengthen individual and institutional capacities in response to health impacts of climate change. The respondents believed that it is important to educate the public, take active efforts to control infectious vectors, and pay increased attention to the health care of vulnerable populations. The lack of funding and limited local workforce capacity is a major barrier for taking actions. Climate change should be integrated into primary health care development through sustainable governmental funding and resource support.
On 6 June 2019, the Norwegian Institute of Public Health was notified of?more than?50 cases of gastroenteritis in Askøy. A reservoir in a water supply system was suspected as the source of the outbreak because of the acute onset and geographical distribution of cases. We investigated the outbreak to confirm the source, extent of the outbreak and effect of control measures. A case was defined as a person in a household served by Water Supply System A (WSS-A) who had gastroenteritis for more than?24 h between 1 and 19 June 2019. We conducted pilot interviews, a telephone survey and an SMS-based cohort study of residents served by WSS-A. System information of WSS-A was collected. Whole genome sequencing on human and environmental isolates was performed. Among 6,108 individuals, 1,573 fulfilled the case definition. Residents served by the reservoir had a 4.6× higher risk of illness than others. Campylobacter jejuni isolated from cases (n?=?24) and water samples (n?=?4) had identical core genome MLST profiles. Contamination through cracks in the reservoir most probably occurred during heavy rainfall. Water supply systems are susceptible to contamination, particularly to certain weather conditions. This highlights the importance of water safety planning and risk-based surveillance to mitigate risks.
Leptospirosis is one of the most common and neglected tropical waterborne diseases in China, causing serious economic losses, and constituting a significant public health threat. Leptospirosis has recently received increased attention and is considered a re-emerging infectious disease in many countries. The incidence of leptospirosis among people suggests that occupation, age, season, sex and water recreational activities are significant risk factors. The aim of this study was to describe the epidemiological profiles of leptospirosis in China during the 2007-2018 period. The morbidity data of leptospirosis by age, season (month), gender, occupation and geographic location (different provinces) were obtained from the public health science data centre of China for subsequent epidemiological analysis. The results indicate that the incidence of leptospirosis has shown a slow downward trend from 2007 to 2018, but morbidity rates were still relatively high (0.0660-0.0113). The incidence of leptospirosis varied in different provinces of China; cases localized mainly to the Southern and Central provinces, areas with warm weather and ample rainfall. Older people (aged 60-75), males, farmers, students and field workers were high-risk populations. During the 2007-2018 observation period, morbidity rates increased beginning in May, remained at high levels in August and September and decreased after November. The present investigation highlights the re-emergence of leptospirosis in some provinces of China (especially in Yunnan and Fujian) and shows that leptospirosis remains a serious public health threat. The results of this study should enhance measures taken for the prevention, control, and surveillance of leptospirosis in China.
SARS CoV-2 (COVID-19) Coronavirus cases are confirmed throughout the world and millions of people are being put into quarantine. A better understanding of the effective parameters in infection spreading can bring about a logical measurement toward COVID-19. The effect of climatic factors on spreading of COVID-19 can play an important role in the new Coronavirus outbreak. In this study, the main parameters, including the number of infected people with COVID-19, population density, intra-provincial movement, and infection days to end of the study period, average temperature, average precipitation, humidity, wind speed, and average solar radiation investigated to understand how can these parameters effects on COVID-19 spreading in Iran? The Partial correlation coefficient (PCC) and Sobol’-Jansen methods are used for analyzing the effect and correlation of variables with the COVID-19 spreading rate. The result of sensitivity analysis shows that the population density, intra-provincial movement have a direct relationship with the infection outbreak. Conversely, areas with low values of wind speed, humidity, and solar radiation exposure to a high rate of infection that support the virus’s survival. The provinces such as Tehran, Mazandaran, Alborz, Gilan, and Qom are more susceptible to infection because of high population density, intra-provincial movements and high humidity rate in comparison with Southern provinces.
The transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the severity of the related disease (COVID-19) are influenced by a large number of factors. This study aimed to investigate the correlation of COVID-19 case and death rates with possible causal climatological and sociodemographic factors for the March to May 2020 (first wave) period in a worldwide scale by statistically processing data for over one hundred countries. The weather parameters considered herein were air temperature, relative humidity, cumulative precipitation, and cloud cover, while sociodemographic factors included population density, median age, and government measures in response to the pandemic. The results of this study indicate that there is a statistically significant correlation between average atmospheric temperature and the COVID-19 case and death rates, with chi-square test p-values in the 0.001-0.02 range. Regarding sociodemographic factors, there is an even stronger dependence of the case and death rates on the population median age (p = 0.0006-0.0012). Multivariate linear regression analysis using Lasso and the forward stepwise approach revealed that the median age ranks first in importance among the examined variables, followed by the temperature and the delays in taking first governmental measures or issuing stay-at-home orders.
Kerteszia cruzii is a sylvatic mosquito and the primary vector of Plasmodium spp., which can cause malaria in humans in areas outside the Amazon River basin in Brazil. Anthropic changes in the natural environments are the major drivers of massive deforestation and local climate change, with serious impacts on the dynamics of mosquito communities and on the risk of acquiring malaria. Considering the lack of information on the dynamics of malaria transmission in areas across the Atlantic Forest biome, where Ke. cruzii is the dominant vector, and the impact of climate drivers of malaria, the present study aimed to: (i) investigate the occurrence and survival rate of Ke. cruzii based on the distinct vegetation profiles found in areas across the coastal region of the Brazilian Atlantic Forest biome; (ii) estimate the extrinsic incubation period (EIP) and survival rates of P. vivax and P. falciparum parasites in Ke. cruzii under current and future scenarios. The potential distribution of Plasmodium spp. was estimated using simulation analyses under distinct scenarios of average temperature increases from 1 °C to 3.7 °C. Our results showed that two conditions are necessary to explain the occurrence and survival of Ke. cruzii: warm temperature and presence of the Atlantic Forest biome. Moreover, both Plasmodium species showed a tendency to decrease their EIP and increase their estimated survival rates in a scenario of higher temperature. Our findings support that the high-risk malaria areas may include the southern region of the distribution range of the Atlantic Forest biome in the coming years. Despite its limitations and assumptions, the present study provides robust evidence of areas with potential to be impacted by malaria incidence in a future scenario. These areas should be monitored in the next decades regarding the occurrence of the mosquito vector and the potential for malaria persistence and increased occurrence.
Dengue, a mosquito-borne infectious disease caused by the dengue viruses, is present in many parts of the tropical and subtropical regions of the world. All four serotypes of dengue viruses are endemic in Singapore, an equatorial city-state. Frequent outbreaks occur, sometimes leading to national epidemics. However, few studies have attempted to characterize breakpoints which precede large rises in dengue case counts. In this paper, Bayesian regime switching (BRS) models were employed to infer epidemic and endemic regimes of dengue transmissions, each containing regime specific autoregressive processes which drive the growth and decline of dengue cases, estimated using a custom built multi-move Gibbs sampling algorithm. Posterior predictive checks indicate that BRS replicates temporal trends in Dengue transmissions well and nowcast accuracy assessed using a post-hoc classification scheme showed that BRS classification accuracy is robust even under limited data with the AUC-ROC at 0.935. LASSO-based regression and bootstrapping was used to account for plausibly high dimensions of climatic factors affecting Dengue transmissions, which was then estimated using cross-validation to conduct statistical inference on long-run climatic effects on the estimated regimes. BRS estimates epidemic and endemic regimes of dengue in Singapore which are characterized by persistence across time, lasting an average of 20 weeks and 66 weeks respectively, with a low probability of transitioning away from their regimes. Climate analysis with LASSO indicates that long-run climatic effects up to 20 weeks ago do not differentiate epidemic and endemic regimes. Lastly, by fitting BRS to simulated disease data generated from a stochastic Susceptible-Infected-Recovered model, mechanistic links between infectivity and regimes classified using BRS were provided. The model proposed could be applied to other localities and diseases under minimal data requirements where transmission counts over time are collected.
BACKGROUND: This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue. METHODS: A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation. RESULTS: The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income. CONCLUSIONS: Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.
In the freshwater environment of north India, cholera appears seasonally in form of clusters as well as sporadically, accounting for a significant piece of the puzzle of cholera epidemiology. We describe a number of cholera outbreaks with an average attack rate of 96.5/1000 but an overall low case fatality (0.17). Clinical cholera cases coincided with high rainfall and elevated temperatures, whereas isolation of V. cholerae non-O1 non-O139 from water was dependent on temperature (p??0.05). However, isolation from plankton samples correlated with increased temperature and pH (p?
Rapid and significant range expansion of both Zika virus (ZIKV) and its Aedes vector species has resulted in ZIKV being declared a global health threat. Mean temperatures are projected to increase globally, likely resulting in alterations of the transmission potential of mosquito-borne pathogens. To understand the effect of diurnal temperature range on the vectorial capacity of Ae. aegypti and Ae. albopictus for ZIKV, longevity, blood-feeding and vector competence were assessed at two temperature regimes following feeding on infectious blood meals. Higher temperatures resulted in decreased longevity of Ae. aegypti [Log-rank test, ?2, df 35.66, 5, P < 0.001] and a decrease in blood-feeding rates of Ae. albopictus [Fisher's exact test, P < 0.001]. Temperature had a population and species-specific impact on ZIKV infection rates. Overall, Ae. albopictus reared at the lowest temperature regime demonstrated the highest vectorial capacity (0.53) and the highest transmission efficiency (57%). Increased temperature decreased vectorial capacity across groups yet more significant effects were measured with Ae. aegypti relative to Ae. albopictus. The results of this study suggest that future increases in temperature in the Americas could significantly impact vector competence, blood-feeding and longevity, and potentially decrease the overall vectorial capacity of Aedes mosquitoes in the Americas.
OBJECTIVE: The weather-related conditions change the ecosystem and pose a threat to social, economic and environmental development. It creates unprecedented or unanticipated human health problems in various places or times of the year. Africa is the world’s second largest and most populous continent and has relatively changeable weather conditions. The present study aims to investigate the impact of weather conditions, heat and humidity on the incidence and mortality of COVID-19 pandemic in various regions of Africa. MATERIALS AND METHODS: In this study, 16 highly populated countries from North, South, East, West, and Central African regions were selected. The data on COVID-19 pandemic including daily new cases and new deaths were recorded from World Health Organization. The daily temperature and humidity figures were obtained from the weather web “Time and Date”. The daily cases, deaths, temperature and humidity were recorded from the date of appearance of first case of “Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)” in the African region, from Feb 14 to August 2, 2020. RESULTS: In African countries, the daily basis mean temperature from Feb 14, 2020 to August 2, 2020 was 26.16±0.12°C, and humidity was 57.41±0.38%. The overall results revealed a significant inverse correlation between humidity and the number of cases (r= -0.192, p<0.001) and deaths (r= -0.213, p<0.001). Similarly, a significant inverse correlation was found between temperature and the number of cases (r= -0.25, p<0.001) and deaths (r=-0.18, p<0.001). Furthermore, the regression results showed that with 1% increase in humidity the number of cases and deaths was significantly reduced by 3.6% and 3.7% respectively. Congruently, with 1°C increase in temperature, the number of cases and deaths was also significantly reduced by 15.1% and 10.5%, respectively. CONCLUSIONS: Increase in relative humidity and temperature was associated with a decrease in the number of daily cases and deaths due to COVID-19 pandemic in various African countries. The study findings on weather events and COVID-19 pandemic have an impact at African regional levels to project the incidence and mortality trends with regional weather events which will enhance public health readiness and assist in planning to fight against this pandemic.
The ongoing pandemic of 2019 novel coronavirus disease (COVID-19) is challenging global public health response system. We aim to identify the risk factors for the transmission of COVID-19 using data on mainland China. We estimated attack rate (AR) at county level. Logistic regression was used to explore the role of transportation in the nationwide spread. Generalized additive model and stratified linear mixed-effects model were developed to identify the effects of multiple meteorological factors on local transmission. The ARs in affected counties ranged from 0.6 to 9750.4 per million persons, with a median of 8.8. The counties being intersected by railways, freeways, national highways or having airports had significantly higher risk for COVID-19 with adjusted odds ratios (ORs) of 1.40 (p = 0.001), 2.07 (p < 0.001), 1.31 (p = 0.04), and 1.70 (p < 0.001), respectively. The higher AR of COVID-19 was significantly associated with lower average temperature, moderate cumulative precipitation and higher wind speed. Significant pairwise interactions were found among above three meteorological factors with higher risk of COVID-19 under low temperature and moderate precipitation. Warm areas can also be in higher risk of the disease with the increasing wind speed. In conclusion, transportation and meteorological factors may play important roles in the transmission of COVID-19 in mainland China, and could be integrated in consideration by public health alarm systems to better prevent the disease.
BACKGROUND: Dengue is an arbovirus that has caused serious problem in Brazil, putting the public health system under severe stress. Understanding its incidence and spatial distribution is essential for disease control and prevention. OBJECTIVE: To perform an analysis on dengue incidence and spatial distribution in a medium-sized, cool-climate and high-altitude city. DESIGN AND SETTING: Ecological study carried out in a public institution in the city of Garanhuns, Pernambuco, Brazil. METHODS: Secondary data provided by specific agencies in each area were used for spatial analysis and elaboration of kernel maps, incidence calculations, correlations and percentages of dengue occurrence. The Geocentric Reference System for the Americas (Sistema de Referência Geocêntrico para as Américas, SIRGAS), 2000, was the software of choice. RESULTS: The incidence rates were calculated per 100,000 inhabitants. Between 2010 and 2019, there were 6,504 cases and the incidence was 474.92. From 2010 to 2014, the incidence was 161.46 for a total of 1,069 cases. The highest incidence occurred in the period from 2015 to 2019: out of a total of 5,435 cases, the incidence was 748.65, representing an increase of 485.97%. Population density and the interaction between two climatic factors, i.e. atypical temperature above 31 °C and relative humidity above 31.4%, contributed to the peak incidence of dengue, although these variables were not statistically significant (P > 0.05). CONCLUSION: The dengue incidence levels and spatial distribution reflected virus and vector adjustment to the local climate. However, there was no correlation between climatic factors and occurrences of dengue in this city.
Continental-scale models of malaria climate suitability typically couple well-established temperature-response models with basic estimates of vector habitat availability using rainfall as a proxy. Here we show that across continental Africa, the estimated geographic range of climatic suitability for malaria transmission is more sensitive to the precipitation threshold than the thermal response curve applied. To address this problem we use downscaled daily climate predictions from seven GCMs to run a continental-scale hydrological model for a process-based representation of mosquito breeding habitat availability. A more complex pattern of malaria suitability emerges as water is routed through drainage networks and river corridors serve as year-round transmission foci. The estimated hydro-climatically suitable area for stable malaria transmission is smaller than previous models suggest and shows only a very small increase in state-of-the-art future climate scenarios. However, bigger geographical shifts are observed than with most rainfall threshold models and the pattern of that shift is very different when using a hydrological model to estimate surface water availability for vector breeding.
Since Zika virus (ZIKV) emerged as a global human health threat, numerous studies have pointed to Aedes aegypti as the primary vector due to its high competence and propensity to feed on humans. The majority of vector competence studies have been conducted between 26-28°C, but arboviral extrinsic incubation periods (EIPs), and therefore transmission efficiency, are known to be affected strongly by temperature. To better understand the relationship between ZIKV EIPs and temperature, we evaluated the effect of adult mosquito exposure temperature on ZIKV infection, dissemination, and transmission in Ae. aegypti at four temperatures: 18°C, 21°C, 26°C, and 30°C. Mosquitoes were exposed to viremic mice infected with a 2015 Puerto Rican ZIKV strain, and engorged mosquitoes were sorted into the four temperatures with 80% RH and constant access to 10% sucrose. ZIKV infection, dissemination, and transmission rates were assessed via RT-qPCR from individual mosquito bodies, legs and wings, and saliva, respectively, at three to five time points per temperature from three to 31 days, based on expectations from other flavivirus EIPs. The median time from ZIKV ingestion to transmission (median EIP, EIP50) at each temperature was estimated by fitting a generalized linear mixed model for each temperature. EIP50 ranged from 5.1 days at 30°C to 24.2 days at 21°C. At 26°C, EIP50 was 9.6 days. At 18°C, only 15% transmitted by day 31 so EIP50 could not be estimated. This is among the first studies to characterize the effects of temperature on ZIKV EIP in Ae. aegypti, and the first to do so based on feeding of mosquitoes on a live, viremic host. This information is critical for modeling ZIKV transmission dynamics to understand geographic and seasonal limits of ZIKV risk; it is especially relevant for determining risk in subtropical regions with established Ae. aegypti populations and relatively high rates of return travel from the tropics (e.g. California or Florida), as these regions typically experience cooler temperature ranges than tropical regions.
In recent decades, the occurrence and distribution of arboviral diseases transmitted by Aedes aegypti mosquitoes has increased. In a new control strategy, populations of mosquitoes infected with Wolbachia are being released to replace existing populations and suppress arboviral disease transmission. The success of this strategy can be affected by high temperature exposure, but the impact of low temperatures on Wolbachia-infected Ae. aegypti is unclear, even though low temperatures restrict the abundance and distribution of this species. In this study, we considered low temperature cycles relevant to the spring season that are close to the distribution limits of Ae. aegypti, and tested the effects of these temperature cycles on Ae. aegypti, Wolbachia strains wMel and wAlbB, and Wolbachia phage WO. Low temperatures influenced Ae. aegypti life-history traits, including pupation, adult eclosion, and fertility. The Wolbachia-infected mosquitoes, especially wAlbB, performed better than uninfected mosquitoes. Temperature shift experiments revealed that low temperature effects on life history and Wolbachia density depended on the life stage of exposure. Wolbachia density was suppressed at low temperatures but densities recovered with adult age. In wMel Wolbachia there were no low temperature effects specific to Wolbachia phage WO. The findings suggest that Wolbachia-infected Ae. aegypti are not adversely affected by low temperatures, indicating that the Wolbachia replacement strategy is suitable for areas experiencing cool temperatures seasonally.
Dengue fever is an important arboviral disease in many countries. Its incidence has increased during the last decade in central Vietnam. Most dengue studies in Vietnam focused on the northern area (Hanoi) and southern regions but not on central Vietnam. Dengue transmission dynamics and relevant environmental risk factors in central Vietnam are not understood. This study aimed to evaluate spatiotemporal patterns of dengue fever in central Vietnam and effects of climatic factors and abundance of mosquitoes on its transmission. Dengue and mosquito surveillance data were obtained from the Department of Vector Control and Border Quarantine at Nha Trang Pasteur Institute. Geographic Information System and satellite remote sensing techniques were used to perform spatiotemporal analyses and to develop climate models using generalized additive models. During 2005-2018, 230,458 dengue cases were reported in central Vietnam. Da Nang and Khanh Hoa were two major hotspots in the study area. The final models indicated the important role of Indian Ocean Dipole, multivariate El Niño-Southern Oscillation index, and vector index in dengue transmission in both regions. Regional climatic variables and mosquito population may drive dengue transmission in central Vietnam. These findings provide important information for developing an early dengue warning system in central Vietnam.
Coronavirus Disease 2019 (COVID-19) pandemic poses extreme threat to public health and economy, particularly to the nations with higher population density. The disease first reported in Wuhan, China; later, it spreads elsewhere, and currently, India emerged as COVID-19 hotspot. In India, we selected 20 densely populated cities having infection counts higher than 500 (by 15 May) as COVID-19 epicenters. Daily COVID-19 count has strong covariability with local temperature, which accounts approximately 65-85% of the explained variance; i.e., its spread depends strongly on local temperature rise prior to community transmission phase. The COVID-19 cases are clustered at temperature and humidity ranging within 27-32°C and 25-45%, respectively. We introduce a combined temperature and humidity profile, which favors rapid COVID-19 growth at the initial phase. The results are highly significant for predicting future COVID-19 outbreaks and modeling cities based on environmental conditions. On the other hand, CO(2) emission is alarmingly high in South Asia (India) and entails high risk of climate change and extreme hot summer. Zoonotic viruses are sensitive to warming induced climate change; COVID-19 epicenters are collocated on CO(2) emission hotspots. The COVID-19 count distribution peaks at 31.0°C, which is 1.0°C higher than current (2020) and historical (1961-1990) mean, value. Approximately, 72% of the COVID-19 cases are clustered at severe to record-breaking hot extremes of historical temperature distribution spectrum. Therefore, extreme climate change has important role in the spread of COVID-19 pandemic. Hence, a strenuous mitigation measure to abate greenhouse gas (GHG) emission is essential to avoid such pandemics in future.
The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID-19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below 0 °C, 12 cities had average AH below 4 g/m(3), and one city (Haikou) had the highest AH (14.05 g/m(3)). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition, we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission.
It is an overwhelming concern that increases in global average temperature lead to serious consequences on the natural environment in the form of deteriorating water resource quality and damaging healthcare sustainability agenda. The sustainable innovation forum (COP21) shows a high concern on climate changes and suggested to reduce global average temperature less than 2 °C. The study brings an idea from the stated theme and analyzed the relationship between climate change and water resource quality in order to redesign economic and environmental policies to improve water quality and healthcare sustainability in the context of Pakistan. The country has serious issues regarding the provision of safe drinking water, improved water resource quality, and healthcare sustainability, which can be achieved by sustainable policies to handle the extreme temperature in Pakistan. The study employed simultaneous generalized method of moments (GMM) technique in order to estimate parameters of the study during the period of 1980-2016. The results show that energy demand and industry value added substantially decrease water resource quality (WRQ), while agriculture value added and per capita income significantly increase WRQ in a country. The other regression apparatus, where health expenditures serve as the response variable, shows that average temperature, industry value added, population growth, and foreign direct investment (FDI) inflows significantly increase healthcare expenditures while WRQ has a negative impact on healthcare expenditures in a country. The final regression model shows that average temperature and per capita income decrease, while WRQ and industrial value added increase mortality rate in a country. The overall results confirm that WRQ affected by climate change, energy demand, and population growth that need sustainable water resource policies in order to achieve long-term sustained growth. The climate actions required more policy instruments to combat environmental challenges that should support healthcare sustainability agenda across the globe.
Major perturbations in soil and water quality are factors that can negatively impact human health. In soil environments of urban areas, changes in antibiotic-resistance profiles may represent an increased risk of exposure to antibiotic-resistant bacteria via oral, dermal, or inhalation routes. We studied the perturbation of antibiotic-resistance profiles and microbial communities in soils following a major flooding event in Houston, Texas, caused by Hurricane Harvey. The main objective of this study was to examine the presence of targeted antibiotic-resistance genes and changes in the diversity of microbial communities in soils a short time (3-5?months) and a long time (18?months) after the catastrophic flooding event. Using polymerase chain reaction, we surveyed fourteen antibiotic-resistance elements: intI1, intI2, sul1, sul2, tet(A) to (E), tet(M), tet(O), tet(W), tet(X), and bla(CMY-2). The number of antibiotic-resistance genes detected were higher in short-time samples compared to samples taken a long time after flooding. From all the genes surveyed, only tet(E), bla(CMY-2), and intI1 were prevalent in short-time samples but not observed in long-time samples; thus, we propose these genes as indicators of exogenous antibiotic resistance in the soils. Sequencing of the V3-V4 region of the bacterial 16S rRNA gene was used to find that flooding may have affected bacterial community diversity, enhanced differences among bacterial lineages profiles, and affected the relative abundance of Actinobacteria, Verrucomicrobia, and Gemmatimonadetes. A major conclusion of this study is that antibiotic resistance profiles of soil bacteria are impacted by urban flooding events such that they may pose an enhanced risk of exposure for up to three to five months following the hurricane. The occurrence of targeted antibiotic-resistance elements decreased eighteen months after the hurricane indicating a reduction of the risk of exposure long time after Harvey.
Background: Malaria remains a global challenge with approximately 228 million cases and 405,000 malaria-related deaths reported in 2018 alone; 93% of which were in sub-Saharan Africa. Aware of the critical role than environmental factors play in malaria transmission, this study aimed at assessing the relationship between precipitation, temperature, and clinical malaria cases in East Africa and how the relationship may change under 1.5 C and 2.0 C global warming levels (hereinafter GWL1.5 and GWL2.0, respectively). Methods: A correlation analysis was done to establish the current relationship between annual precipitation, mean temperature, and clinical malaria cases. Differences between annual precipitation and mean temperature value projections for periods 2008-2037 and 2023-2052 (corresponding to GWL1.5 and GWL2.0, respectively), relative to the control period (1977-2005), were computed to determine how malaria transmission may change under the two global warming scenarios. Results: A predominantly positive/negative correlation between clinical malaria cases and temperature/precipitation was observed. Relative to the control period, no major significant changes in precipitation were shown in both warming scenarios. However, an increase in temperature of between 0.5 C and 1.5 C and 1.0 C to 2.0 C under GWL1.5 and GWL2.0, respectively, was recorded. Hence, more areas in East Africa are likely to be exposed to temperature thresholds favourable for increased malaria vector abundance and, hence, potentially intensify malaria transmission in the region. Conclusions: GWL1.5 and GWL2.0 scenarios are likely to intensify malaria transmission in East Africa. Ongoing interventions should, therefore, be intensified to sustain the gains made towards malaria elimination in East Africa in a warming climate.
BACKGROUND: Many studies have shown associations between rising temperatures, El Niño events and dengue incidence, but the effect of sustained periods of extreme high temperatures (i.e., heatwaves) on dengue outbreaks has not yet been investigated. This study aimed to compare the short-term temperature-dengue associations during different dengue outbreak periods, estimate the dengue cases attributable to temperature, and ascertain if there was an association between heatwaves and dengue outbreaks in Hanoi, Vietnam. METHODOLOGY/PRINCIPAL FINDINGS: Dengue outbreaks were assigned to one of three categories (small, medium and large) based on the 50th, 75th, and 90th percentiles of distribution of weekly dengue cases during 2008-2016. Using a generalised linear regression model with a negative binomial link that controlled for temporal trends, temperature variation, rainfall and population size over time, we examined and compared associations between weekly average temperature and weekly dengue incidence for different outbreak categories. The same model using weeks with or without heatwaves as binary variables was applied to examine the potential effects of extreme heatwaves, defined as seven or more days with temperatures above the 95th percentile of daily temperature distribution during the study period. This study included 55,801 dengue cases, with an average of 119 (range: 0 to 1454) cases per week. The exposure-response relationship between temperature and dengue risk was non-linear and differed with dengue category. After considering the delayed effects of temperature (one week lag), we estimated that 4.6%, 11.6%, and 21.9% of incident cases during small, medium, and large outbreaks were attributable to temperature. We found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks (around 14 weeks later) than small and medium outbreaks (4 to 9 weeks later). Compared with non-heatwave years, dengue outbreaks (i.e., small, moderate and large outbreaks combined) in heatwave years had higher weekly number of dengue cases (p<0.05). Findings were robust under different sensitivity analyses. CONCLUSIONS: The short-term association between temperature and dengue risk varied by the level of outbreaks and temperature seems more likely affect large outbreaks. Moreover, heatwaves may delay the timing and increase the magnitude of dengue outbreaks.
Runoff from heavy precipitation events can lead to microbiological contamination of source waters for public drinking water supplies. Philadelphia is a city of interest for a study of waterborne acute gastrointestinal illness (AGI) because of frequent heavy precipitation, extensive impervious landcover, and combined sewer systems that lead to overflows. We conducted a time-series analysis of the association between heavy precipitation and AGI incidence in Philadelphia, served by drinking water from Delaware River and Schuylkill River source waters. AGI cases on each day during the study period (2015-2017) were captured through syndromic surveillance of patients’ chief complaint upon presentation at local emergency departments. Daily precipitation was represented by measurements at the Philadelphia International Airport and by modeled precipitation within the watershed boundaries, and we also evaluated stream flowrate as a proxy of precipitation. We estimated the association using distributed lag nonlinear models, assuming a quasi-Poisson distribution of the outcome variable and with adjustment for potential confounding by seasonal and long-term time trends, ambient temperature, day-of-week, and major holidays. We observed an association between heavy precipitation and AGI incidence in Philadelphia that was primarily limited to the spring season, with significant increases in AGI that peaked from 8 to 16 days following a heavy precipitation event. For example, the increase in AGI incidence related to airport precipitation above the 95th percentile (vs no precipitation) during spring reached statistical significance on lag day 7, peaked on day 16 (102% increase, 95% confidence interval: 16%, 252%), and declined while remaining significantly elevated through day 28. Similar associations were observed in analyses of watershed-specific precipitation in relation to AGI cases within the populations served by drinking water from each river. Our results suggest that heavy precipitation events in Philadelphia result in detectable local increases in waterborne AGI.
The need for healthcare workers (HCWs) to wear personal protective equipment (PPE) during the coronavirus disease 2019 (COVID-19) pandemic heightens their risk of thermal stress. We assessed the knowledge, attitudes, and practices of HCWs from India and Singapore regarding PPE usage and heat stress when performing treatment and care activities. One hundred sixty-five HCWs from India (n = 110) and Singapore (n = 55) participated in a survey. Thirty-seven HCWs from Singapore provided thermal comfort ratings before and after ice slurry ingestion. Differences in responses between India and Singapore HCWs were compared. A p-value cut-off of 0.05 depicted statistical significance. Median wet-bulb globe temperature was higher in India (30.2 °C (interquartile range [IQR] 29.1-31.8 °C)) than in Singapore (22.0 °C (IQR 18.8-24.8 °C)) (p < 0.001). Respondents from both countries reported thirst (n = 144, 87%), excessive sweating (n = 145, 88%), exhaustion (n = 128, 78%), and desire to go to comfort zones (n = 136, 84%). In Singapore, reports of air-conditioning at worksites (n = 34, 62%), dedicated rest area availability (n = 55, 100%), and PPE removal during breaks (n = 54, 98.2%) were higher than in India (n = 27, 25%; n = 46, 42%; and n = 66, 60%, respectively) (p < 0.001). Median thermal comfort rating improved from 2 (IQR 1-2) to 0 (IQR 0-1) after ice slurry ingestion in Singapore (p < 0.001). HCWs are cognizant of the effects of heat stress but might not adopt best practices due to various constraints. Thermal stress management is better in Singapore than in India. Ice slurry ingestion is shown to be practical and effective in promoting thermal comfort. Adverse effects of heat stress on productivity and judgment of HCWs warrant further investigation.
BACKGROUND: Malaria is a mosquito-borne infectious disease known to cause significant numbers of morbidities and mortalities across the globe. In Ethiopia, its transmission is generally seasonal and highly unstable due to variations in topography and rainfall patterns. Studying the trends in malaria in different setups is crucial for area-specific evidence-based interventions, informed decisions, and to track the effectiveness of malaria control programs. The trend in malaria infections in the area has not been documented. Hence, this study aimed to assess the five-year trend in microscopically confirmed malaria cases in Dembecha Health Center, West Gojjam Zone, Amhara national regional state, Ethiopia. METHODS: A health facility-based retrospective study was conducted in Dembecha Health Center from February to April 2018. All microscopically confirmed malaria cases registered between 2011/12 and 2015/16 were carefully reviewed from laboratory record books and analyzed accordingly. RESULTS: A total of 12,766 blood films were requested over the last five years at Dembecha Health Center. The number of microscopically confirmed malaria cases was 2086 (16.34%). The result showed a fluctuating yet declining trend in malaria infections. The highest number of cases was registered in 2012/13, while the lowest was in 2015/16. Males and age groups >20 constituted 58.9% and 44.2% of the patients, respectively, being the hardest hit by malaria in the area. Malaria existed in almost every month and seasons. Plasmodium falciparum was the predominant species. The highest peak of malaria infections was observed in the late transition (October-December) 799 (38.3%) and early transition (May-June) 589 (28.2%) seasons. CONCLUSION: Although the results indicate a fluctuating yet declining trend, the prevalence of confirmed malaria cases in the area remains alarming and indicates a major public health burden. Therefore, close monitoring and intervention measures to control malaria infections in the area and also to tackle the dominant species, Plasmodium falciparum, are necessitated accordingly.
Drinking water quality is of vital importance for the healthy life of a community especially if consumer is a teenager. In order to compare groundwater profile of flooded area (FA) and non-flooded area (NFA) of district Sanghar, 120 water samples from public schools were collected and investigated for physico-chemical parameters, essential metals, trace elements and microbiological indicators. Analysis data revealed that 47% samples in FA were contaminated with faecal coliform bacteria as compared to only 8.3% in NFA. On the other hand, chemical indicators like TDS, Ca, Na, K, SO4, Mg and hardness were higher in FA. Comparison of trace elements content with WHO guidelines revealed that concentration of Fe, As and Zn was higher in 66.7%, 31.7% and 13.3% water samples, respectively in FA whereas content of these elements was also on higher side in 3.3%, 23.3% and 1.7% samples in NFA, respectively. Health risk assessment due to high concentration of Fe, As and Zn showed that As HRI>1, for children in 35 and 23% water samples in FA and NFA, respectively.
BACKGROUND: As a mosquito-borne infectious disease, dengue fever (DF) has spread through tropical and subtropical regions worldwide in recent decades. Dengue forecasting is essential for enhancing the effectiveness of preventive measures. Current studies have been primarily conducted at national, sub-national, and city levels, while an intra-urban dengue forecasting at a fine spatial resolution still remains a challenging feat. As viruses spread rapidly because of a highly dynamic population flow, integrating spatial interactions of human movements between regions would be potentially beneficial for intra-urban dengue forecasting. METHODOLOGY: In this study, a new framework for enhancing intra-urban dengue forecasting was developed by integrating the spatial interactions between urban regions. First, a graph-embedding technique called Node2Vec was employed to learn the embeddings (in the form of an N-dimensional real-valued vector) of the regions from their population flow network. As strongly interacting regions would have more similar embeddings, the embeddings can serve as “interaction features.” Then, the interaction features were combined with those commonly used features (e.g., temperature, rainfall, and population) to enhance the supervised learning-based dengue forecasting models at a fine-grained intra-urban scale. RESULTS: The performance of forecasting models (i.e., SVM, LASSO, and ANN) integrated with and without interaction features was tested and compared on township-level dengue forecasting in Guangzhou, the most threatened sub-tropical city in China. Results showed that models using both common and interaction features can achieve better performance than that using common features alone. CONCLUSIONS: The proposed approach for incorporating spatial interactions of human movements using graph-embedding technique is effective, which can help enhance fine-grained intra-urban dengue forecasting.
Although only a small proportion of the landmass of South Africa is classified as high risk for malaria, the country experiences on-going challenges relating to malaria outbreaks. Climate change poses a growing threat to this already dire situation. While considerable effort has been placed in public health campaigns in the highest-risk regions, and national malaria maps are updated to account for changing climate, malaria cases have increased. This pilot study considers the sub-population of South Africans who reside outside of the malaria area, yet have the means to travel into this high-risk region for vacation. Through the lens of the governmental “ABC of malaria prevention”, we explore this sub-population’s awareness of the current boundaries to the malaria area, perceptions of the future boundary under climate change, and their risk-taking behaviours relating to malaria transmission. Findings reveal that although respondents self-report a high level of awareness regarding malaria, and their boundary maps reveal the broad pattern of risk distribution, their specifics on details are lacking. This includes over-estimating both the current and future boundaries, beyond the realms of climate-topographic possibility. Despite over-estimating the region of malaria risk, the respondents reveal an alarming lack of caution when travelling to malaria areas. Despite being indicated for high-risk malaria areas, the majority of respondents did not use chemoprophylaxis, and many relied on far less-effective measures. This may in part be due to respondents relying on information from friends and family, rather than medical or governmental advice.
Background: Determination of the key factors affecting dengue occurrence is of significant importance for the successful response to its outbreak. Yunnan and Guangdong Provinces in China are hotspots of dengue outbreak during recent years. However, few studies focused on the drive of multi-dimensional factors on dengue occurrence failing to consider the possible multicollinearity of the studied factors, which may bias the results. Methods: In this study, multiple linear regression analysis was utilized to explore the effect of multicollinearity among dengue occurrences and related natural and social factors. A principal component regression (PCR) analysis was utilized to determine the key dengue-driven factors in Guangzhou city of Guangdong Province and Xishuangbanna prefecture of Yunnan Province, respectively. Results: The effect of multicollinearity existed in both Guangzhou city and Xishuangbanna prefecture, respectively. PCR model revealed that the top three contributing factors to dengue occurrence in Guangzhou were Breteau Index (BI) (positive correlation), the number of imported dengue cases lagged by 1 month (positive correlation), and monthly average of maximum temperature lagged by 1 month (negative correlation). In contrast, the top three factors contributing to dengue occurrence in Xishuangbanna included monthly average of minimum temperature lagged by 1 month (positive correlation), monthly average of maximum temperature (positive correlation), monthly average of relative humidity (positive correlation), respectively. Conclusion: Meteorological factors presented stronger impacts on dengue occurrence in Xishuangbanna, Yunnan, while BI and the number of imported cases lagged by 1 month played important roles on dengue transmission in Guangzhou, Guangdong. Our findings could help to facilitate the formulation of tailored dengue response mechanism in representative areas of China in the future.
Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1-7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8-7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7-27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9-25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar settings.
Dengue is a re-emerging global public health problem, the most common arbovirus causing human disease in the world, and a major cause of hospitalization in endemic countries causing significant economic burden. Data were analyzed from passive surveillance of hospital-attended dengue cases from 2002 to 2018 at Phramongkutklao Hospital (PMKH) located in Bangkok, Thailand, and Kamphaeng Phet Provincial Hospital (KPPH) located in the lower northern region of Thailand. At PMKH, serotype 1 proved to be the most common strain of the virus, whereas at KPPH, serotypes 1, 2, and 3 were the most common strains from 2006 to 2008, 2009 to 2012, and 2013 to 2015, respectively. The 11-17 years age-group made up the largest proportion of patients impacted by dengue illnesses during the study period at both sites. At KPPH, dengue virus (DENV)-3 was responsible for most cases of dengue fever (DF), whereas it was DENV-1 at PMKH. In cases where dengue hemorrhagic fever was the clinical diagnosis, DENV-2 was the predominant serotype at KPPH, whereas at PMKH, it was DENV-1. The overall disease prevalence remained consistent across the two study sites with DF being the predominant clinical diagnosis as the result of an acute secondary dengue infection, representing 40.7% of overall cases at KPPH and 56.8% at PMKH. The differences seen between these sites could be a result of climate change increasing the length of dengue season and shifts in migration patterns of these populations from rural to urban areas and vice versa.
The 2020 Atlantic hurricane season was extremely active and included, as of early November, six hurricanes that made landfall in the United States during the global coronavirus disease 2019 (COVID-19) pandemic. Such an event would necessitate a large-scale evacuation, with implications for the trajectory of the pandemic. Here we model how a hypothetical hurricane evacuation from four counties in southeast Florida would affect COVID-19 case levels. We find that hurricane evacuation increases the total number of COVID-19 cases in both origin and destination locations; however, if transmission rates in destination counties can be kept from rising during evacuation, excess evacuation-induced case numbers can be minimized by directing evacuees to counties experiencing lower COVID-19 transmission rates. Ultimately, the number of excess COVID-19 cases produced by the evacuation depends on the ability of destination counties to meet evacuee needs while minimizing virus exposure through public health directives. These results are relevant to disease transmission during evacuations stemming from additional climate-related hazards such as wildfires and floods.
Aims: Leptospirosis is an infectious disease that is endemic to many tropical regions. Large epidemics usually happen after heavy rainfall and flooding. This potentially fatal zoonosis is caused by pathogenic bacteria belonging to the genus Leptospira. Leptospirosis can be diagnosed using specific biomarkers such as target genes and virulence indicators that are well preserved across various Leptospira spp., including those that are prevalent in clinical samples and in the environment. To date, several pathogenicity-determinant genes, including lipL32 and lipL41, have been described and used for diagnosing leptospirosis. However, prevalence of these genes in leptospiral strains is unclear. Methodology and results: In the present study, we assessed the distribution of eight pathogenicity-determinant genes in reference Leptospira strains and environmental isolates in Malaysia, by polymerase chain reaction (PCR). We found that only lipL32 and ligB were consistently expressed in all pathogenic Leptospira strains compared with the other tested genes. Moreover, our results suggested that the use of lipL41, lipL21, ompL1, lfb1, ligA, and ligC as biomarkers could incorrectly misdetect pathogenic Leptospira strains present in the environment. Conclusion: Thus, our results suggest that the pathogenicity-determinant genes lipL32 and ligB can be used as biomarkers for detection pathogenic Leptospira.
We investigated the geographical character of the COVID-19 infection in China and correlated it with satellite- and ground-based measurements of air quality. Controlling for population density, we found more viral infections in those prefectures (U.S. county equivalent) afflicted by high Carbon Monoxide, Formaldehyde, PM 2.5, and Nitrogen Dioxide values. Higher mortality was also correlated with relatively poor air quality. When summarizing the results at a greater administrative level, we found that the 10 provinces (U.S. state equivalent) with the highest rate of mortality by COVID-19, were often the most polluted but not the most densely populated. Air pollution appears to be a risk factor for the incidence of this disease, despite the conventionally apprehended influence of human mobility on disease dynamics from the site of first appearance, Wuhan. The raw correlations reported here should be interpreted in a broader context, accounting for the growing evidence reported by several other studies. These findings warn communities and policymakers on the implications of long-term air pollution exposure as an ecological, multi-scale public health issue.
BACKGROUND: The World Health Organization (WHO) promotes long-lasting insecticidal nets (LLIN) and indoor residual house-spraying (IRS) for malaria control in endemic countries. However, long-term impact data of vector control interventions is rarely measured empirically. METHODS: Surveillance data was collected from paediatric admissions at Tororo district hospital for the period January 2012 to December 2019, during which LLIN and IRS campaigns were implemented in the district. Malaria test positivity rate (TPR) among febrile admissions aged 1 month to 14 years was aggregated at baseline and three intervention periods (first LLIN campaign; Bendiocarb IRS; and Actellic IRS?+?second LLIN campaign) and compared using before-and-after analysis. Interrupted time-series analysis (ITSA) was used to determine the effect of IRS (Bendiocarb?+?Actellic) with the second LLIN campaign on monthly TPR compared to the combined baseline and first LLIN campaign periods controlling for age, rainfall, type of malaria test performed. The mean and median ages were examined between intervention intervals and as trend since January 2012. RESULTS: Among 28,049 febrile admissions between January 2012 and December 2019, TPR decreased from 60% at baseline (January 2012-October 2013) to 31% during the final period of Actellic IRS and LLIN (June 2016-December 2019). Comparing intervention intervals to the baseline TPR (60.3%), TPR was higher during the first LLIN period (67.3%, difference 7.0%; 95% CI 5.2%, 8.8%, p?<?0.001), and lower during the Bendiocarb IRS (43.5%, difference -?16.8%; 95% CI -?18.7%, -?14.9%) and Actellic IRS (31.3%, difference -?29.0%; 95% CI -?30.3%, -?27.6%, p?<?0.001) periods. ITSA confirmed a significant decrease in the level and trend of TPR during the IRS (Bendicarb?+?Actellic) with the second LLIN period compared to the pre-IRS (baseline?+?first LLIN) period. The age of children with positive test results significantly increased with time from a mean of 24 months at baseline to 39 months during the final IRS and LLIN period. CONCLUSION: IRS can have a dramatic impact on hospital paediatric admissions harbouring malaria infection. The sustained expansion of effective vector control leads to an increase in the age of malaria positive febrile paediatric admissions. However, despite large reductions, malaria test-positive admissions continued to be concentrated in children aged under five years. Despite high coverage of IRS and LLIN, these vector control measures failed to interrupt transmission in Tororo district. Using simple, cost-effective hospital surveillance, it is possible to monitor the public health impacts of IRS in combination with LLIN.
Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010-2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.
BACKGROUND: Dengue, a febrile illness, is caused by a Flavivirus transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Climate influences the ecology of the vectors. We aimed to identify the influence of climatic variability on the occurrence of clinical dengue requiring hospitalization in Zone-5, a high incidence area of Dhaka City Corporation (DCC), Bangladesh. METHODS AND FINDINGS: We retrospectively identified clinical dengue cases hospitalized from Zone-5 of DCC between 2005 and 2009. We extracted records of the four major catchment hospitals of the study area. The Bangladesh Meteorological Department (BMD) provided data on temperature, rainfall, and humidity of DCC for the study period. We used autoregressive integrated moving average (ARIMA) models for the number of monthly dengue hospitalizations. We also modeled all the climatic variables using Poisson regression. During our study period, dengue occurred throughout the year in Zone-5 of DCC. The median number of hospitalized dengue cases was 9 per month. Dengue incidence increased sharply from June, and reached its peak in August. One additional rainy day per month increased dengue cases in the succeeding month by 6% (RR = 1.06, 95% CI: 1.04-1.09). CONCLUSIONS: Dengue is transmitted throughout the year in Zone-5 of DCC, with seasonal variation in incidence. The number of rainy days per month is significantly associated with dengue incidence in the subsequent month. Our study suggests the initiation of campaigns in DCC for controlling dengue and other Aedes mosquito borne diseases, including Chikunguniya from the month of May each year. BMD rainfall data may be used to determine campaign timing.
INTRODUCTION: Environmental factors such as wind, temperature, humidity, and sun exposure are known to affect influenza and viruses such as severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome (MERS) transmissions. COVID-19 is a new pandemic with very little information available about its transmission and association with environmental factors. The goal of this paper is to explore the association of environmental factors on daily incidence rate, mortality rate, and recoveries of COVID-19. METHODS: The environmental data for humidity, temperature, wind, and sun exposure were recorded from metrological websites and COVID-19 data such as the daily incidence rate, death rate, and daily recovery were extracted from the government’s official website available to the general public. The analysis for each outcome was adjusted for factors such as lock down status, nationwide events, and the number of daily tests performed. Analysis was completed with negative binominal regression log link using generalised linear modelling. RESULTS: Daily temperature, sun exposure, wind, and humidity were not significantly associated with daily incidence rate. Temperature and nationwide social gatherings, although non-significant, showed trends towards a higher chance of incidence. An increase in the number of daily testing was significantly associated with higher COVID-19 incidences (effect size ranged from 2.17-9.96). No factors were significantly associated with daily death rates. Except for the province of Balochistan, a lower daily temperature was associated with a significantly higher daily recovery rate. DISCUSSION: Environmental factors such as temperature, humidity, wind, and daily sun exposure were not consistently associated with COVID-19 incidence, death rates, or recovery. More policing about precautionary measures and ensuring diagnostic testing and accuracy are needed.
Human actions intensify the greenhouse effect, aggravating climate changes in the Amazon and elsewhere in the world. The Intergovernmental Panel on Climate Change (IPCC) foresees a global increase of up to 4.5 °C and 850 ppm CO(2) (above current levels) by 2100. This will impact the biology of the Aedes aegypti mosquito, vector of Dengue, Zika, urban Yellow Fever and Chikungunya. Heat shock proteins are associated with adaptations to anthropic environments and the interaction of some viruses with the vector. The transcription of the hsp26, hsp83 and hsc70 genes of an A. aegypti population, maintained for more than forty-eight generations, in the Current, Intermediate and Extreme climatic scenario predicted by the IPCC was evaluated with qPCR. In females, highest levels of hsp26, hsp83 and hsc70 expression occurred in the Intermediate scenario, while in males, levels were high only for hsp26 gene in Current and Extreme scenarios. Expression of hsp83 and hsc70 genes in males was low under all climatic scenarios, while in the Extreme scenario females had lower expression than in the Current scenario. The data suggest compensatory or adaptive processes acting on heat shock proteins, which can lead to changes in the mosquito’s biology, altering vectorial competence.
BACKGROUND: Since the huge epidemic of Zika virus (ZIKV) in Brazil in 2015, questions were raised to understand which mosquito species could transmit the virus. Aedes aegypti has been described as the main vector. However, other Aedes species (e.g. Ae. albopictus and Ae. japonicus) proven to be competent for other flaviviruses (e.g. West Nile, dengue and yellow fever), have been described as potential vectors for ZIKV under laboratory conditions. One of these, the Asian bush mosquito, Ae. japonicus, is widely distributed with high abundances in central-western Europe. In the present study, infection, dissemination and transmission rates of ZIKV (Dak84 strain) in two populations of Ae. japonicus from Switzerland (Zürich) and France (Steinbach, Haut-Rhin) were investigated under constant (27 °C) and fluctuating (14-27 °C, mean 23 °C) temperature regimes. RESULTS: The two populations were each able to transmit ZIKV under both temperature regimes. Infectious virus particles were detected in the saliva of females from both populations, regardless of the incubation temperature regime, from 7 days post-exposure to infectious rabbit blood. The highest amount of plaque forming units (PFU) (400/ml) were recorded 14 days post-oral infection in the Swiss population incubated at a constant temperature. No difference in terms of infection, dissemination and transmission rate were found between mosquito populations. Temperature had no effect on infection rate but the fluctuating temperature regime resulted in higher dissemination rates compared to constant temperature, regardless of the population. Finally, transmission efficiency ranged between 7-23% and 7-10% for the constant temperature and 0-10% and 3-27% under fluctuating temperatures for the Swiss and the French populations, respectively. CONCLUSIONS: To the best of our knowledge, this is the first study confirming vector competence for ZIKV of Ae. japonicus originating from Switzerland and France at realistic summer temperatures under laboratory conditions. Considering the continuous spread of this species in the northern part of Europe and its adaptation at cooler temperatures, preventative control measures should be adopted to prevent possible ZIKV epidemics.
Harris County, Texas, is home to thousands of documented sources of environmental pollution. It is also highly vulnerable to impacts from natural hazards, including floods. Building on the Toxics Mobility Inventory (TMI), this article discusses how the authors developed a Toxics Mobility Vulnerability Index (TMVI) and applied it to Harris County to assess potential exposure risks to residents from the transfer of toxic materials during flood events. The TMI concept was operationalized and standardized by combining multiple spatial data sets to simultaneously evaluate various factors in the weather hazards-extant toxics-social vulnerability nexus (e.g., floodplain area, industrial land use, social vulnerability measures). Findings indicated hot spots of vulnerability to hazard-induced toxics transfer concentrated in Northeast Houston US Census tracts in Harris County. The main drivers of increased risk in these areas include the proportion of the area that is impervious surface, consistently high social vulnerabilities, and poor health. However, the most vulnerable areas also have overlapping exposure to both industrial land use and floodplains. Assessing the contribution of a set of industrial land use, social vulnerability, natural hazard, emergency response, and topography variables in a single index on the same spatial scale (e.g., US Census tract) provides detailed information for policy makers tasked with mitigating risk. Applying tools such as the TMVI to highly vulnerable urban and coastal locations may help identify changes needed for preparedness and mitigation planning and highlight areas where limited resources for investment- and policy-related remediation should be focused, both before and after disasters.
