Diamond et al. recently identified malaria and dengue as high-priority diseases in wastewater surveillance for climate-change-driven shifts in pathogen dynamics. When employing wastewater surveillance for vector-borne pathogens it is essential to take into account the geographical context, pathogen biology, and the availability of sewage networks for meaningful interventions.
Leishmaniasis is a zoonotic disease transmitted to humans by a protozoan parasite through sandfly vectors and multiple vertebrate hosts. The Pan American Health Organization reported a declining trend in cases, with Brazil, Colombia, Peru, Nicaragua, and Bolivia having the most cases in 2020. There are still knowledge gaps in transmission and the parasite-host relationship. Ecological niche modeling has been used to study host-vector relationships, disease dynamics, and the impact of climate change. Understanding these aspects can aid in early surveillance and vector control strategies. The potential distribution of five host species associated with the transmission of cutaneous leishmaniasis (CL) was modeled. Occurrence data were collected for each host species, and environmental variables were used to build the models. Climatic data from El Niño, La Niña, and Neutral episodes were used to compare the predicted distributions. Additionally, the potential distributions of four vector species were compared to identify overlaps with host species. Niche analysis was conducted to evaluate changes in vector niches across episodes and to identify host-vector pairs based on niche overlap in geographic and environmental spaces. After spatial thinning, 467 records were obtained, and 1,190 candidate models were evaluated for each species. Results showed the distribution of occurrences in the environmental space, highlighting a high risk of extrapolation beyond the calibration areas. Movement-Oriented Parity analysis revealed distinct distribution patterns under different climate conditions, with areas of environmental similarity identified. Bradypus variegatus exhibited a broad potential distribution, while Dasypus novemcinctus and Didelphis marsupialis had more restricted ranges. Sylvilagus braziliensis covered most of the Neotropics. Our study provides valuable insights into ecological niches and geographic ranges of these species, contributing to the understanding of cutaneous leishmaniasis transmission dynamics.
The adverse effects of climate change on human health are unfolding in real time. Environmental fragmentation is amplifying spillover of viruses from wildlife to humans. Increasing temperatures are expanding mosquito and tick habitats, introducing vector-borne viruses into immunologically susceptible populations. More frequent flooding is spreading water-borne viral pathogens, while prolonged droughts reduce regional capacity to prevent and respond to disease outbreaks with adequate water, sanitation, and hygiene resources. Worsening air quality and altered transmission seasons due to an increasingly volatile climate may exacerbate the impacts of respiratory viruses. Furthermore, both extreme weather events and long-term climate variation are causing the destruction of health systems and large-scale migrations, reshaping health care delivery in the face of an evolving global burden of viral disease. Because of their immunological immaturity, differences in physiology (e.g., size), dependence on caregivers, and behavioral traits, children are particularly vulnerable to climate change. This investigation into the unique pediatric viral threats posed by an increasingly inhospitable world elucidates potential avenues of targeted programming and uncovers future research questions to effect equitable, actionable change. IMPACT: A review of the effects of climate change on viral threats to pediatric health, including zoonotic, vector-borne, water-borne, and respiratory viruses, as well as distal threats related to climate-induced migration and health systems. A unique focus on viruses offers a more in-depth look at the effect of climate change on vector competence, viral particle survival, co-morbidities, and host behavior. An examination of children as a particularly vulnerable population provokes programming tailored to their unique set of vulnerabilities and encourages reflection on equitable climate adaptation frameworks.
The global temperature has been increasing resulting in climate change. This negatively impacts planetary health that disproportionately affects the most vulnerable among us, especially children. Extreme weather events, such as hurricanes, tornadoes, wildfires, flooding, and heatwaves, are becoming more frequent and severe, posing a significant threat to our patients’ health, safety, and security. Concurrently, shifts in environmental exposures, including air pollution, allergens, pathogenic vectors, and microplastics, further exacerbate the risks faced by children. In this paper, we provide an overview of pediatric illnesses that are becoming more prevalent and severe because of extreme weather events, global temperature increases, and shifts in environmental exposures. As members of pediatric health care teams, it is crucial for pediatric radiologists to be knowledgeable about the impacts of climate change on our patients, and continue to advocate for safe, healthier environments for our patients.
Visceral leishmaniasis or Kala-azar (KA) is a Vector-Borne Disease (VBD) that remains the second-largest parasitic killer across the globe (mortality rate: 75-95%). More than 60% of KA cases originate in South Asia, wherein India accounts for 2/3rd of the cases, and Bihar, a state in India, alone accounts for more than 50% of the Indian cases. Past studies suspected climate change vulnerabilities as a driving cause of KA outbreaks. The VBDs-based epidemic prediction systems have been developed to mitigate recurrent outbreaks; however, Machine Learning (ML) based approaches still need to be explored for modeling changing climate impacts on KA cases. This study, for the first time, develops a Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR), hereinafter RBF-kernel-based-SVR model for the most-affected endemic districts of Bihar (northern-India), using the data from 2016 and 2021. Forward selection, backward elimination, and stepwise regression procedures were adopted while selecting influential climatic variables, followed by the k-fold cross-validation technique and, then, the RBF-kernel-based-SVR algorithm for classification. Results suggested that temperature, wind speed, rainfall, and population density significantly contributed to the KA outbreaks. This study also developed Multiple Linear Regression (MLR) and Multilayer Perceptron (MLP) models to compare SVR with other classification models. Findings indicated that the proposed RBF-kernel-based-SVR model [Correlation Coefficient (CC) = 0.82, Root-Mean-Square Error (RMSE) = 12.20, and Nash-Sutcliffe Efficiency (NSE) = 0.66] outperformed MLR (0.81, 14.20, 0.48) and MLP (0.81, 12.95, 0.61). Study recommends using the RBF-kernel-based-SVR model as a quick and efficient model capable of detecting KA cases with high predictability even under limited data availability. Such models can assist public health authorities, given monitoring KA spread, learning the climate impacts of outbreaks, and ensuring timelier health services.
Ixodes scapularis (the blacklegged tick) is widely distributed in forested areas across the eastern United States. The public health impact of I. scapularis is greatest in the north, where nymphal stage ticks commonly bite humans and serve as primary vectors for multiple human pathogens. There were dramatic increases in the tick’s distribution and abundance over the last half-century in the northern part of the eastern US, and climate warming is commonly mentioned as a primary driver for these changes. In this review, we summarize the evidence for the observed spread and proliferation of I. scapularis being driven by climate warming. Although laboratory and small-scale field studies have provided insights into how temperature and humidity impact survival and reproduction of I. scapularis, using these associations to predict broad-scale distribution and abundance patterns is more challenging. Numerous efforts have been undertaken to model the distribution and abundance of I. scapularis at state, regional, and global scales based on climate and landscape variables, but outcomes have been ambiguous. Across the models, the functional relationships between seasonal or annual measures of heat, cold, precipitation, or humidity and tick presence or abundance were inconsistent. The contribution of climate relative to landscape variables was poorly defined. Over the last half-century, climate warming occurred in parallel with spread and population increase of the white-tailed deer, the most important reproductive host for I. scapularis adults, in the northern part of the eastern US. There is strong evidence for white-tailed deer playing a key role to facilitate spread and proliferation of I. scapularis in the US over the last century. However, due to a lack of spatially and temporally congruent data, climate, landscape, and host variables are rarely included in the same models, thus limiting the ability to evaluate their relative contributions or interactions in defining the geographic range and abundance patterns of ticks. We conclude that the role of climate change as a key driver for geographic expansion and population increase of I. scapularis in the northern part of the eastern US over the last half-century remains uncertain.
The twenty-first century has been marked by a surge in viral epidemics and pandemics, highlighting the global health challenge posed by emerging and re-emerging pediatric viral diseases. This review article explores the complex dynamics contributing to this challenge, including climate change, globalization, socio-economic interconnectedness, geopolitical tensions, vaccine hesitancy, misinformation, and disparities in access to healthcare resources. Understanding the interactions between the environment, socioeconomics, and health is crucial for effectively addressing current and future outbreaks. This scoping review focuses on emerging and re-emerging viral infectious diseases, with an emphasis on pediatric vulnerability. It highlights the urgent need for prevention, preparedness, and response efforts, particularly in resource-limited communities disproportionately affected by climate change and spillover events. Adopting a One Health/Planetary Health approach, which integrates human, animal, and ecosystem health, can enhance equity and resilience in global communities. IMPACT: We provide a scoping review of emerging and re-emerging viral threats to global pediatric populations This review provides an update on current pediatric viral threats in the context of the COVID-19 pandemic This review aims to sensitize clinicians, epidemiologists, public health practitioners, and policy stakeholders/decision-makers to the role these viral diseases have in persistent pediatric morbidity and mortality.
Leishmaniasis is a complex disease. Any change in weather conditions affects the humans’ social and agricultural expansion and, consequently, the parasite’s life cycle in terms of ecology, biodiversity, social stigma, and exclusion. This population-based prospective longitudinal investigation was conducted between 1991 and 2021 in a well-defined CL (cutaneous leishmaniasis) focus in Bam County, southeastern Iran. A robust health clinic and health surveillance system were responsible for the ongoing systematic documentation, detection, identification, and management of CL cases. The exponential smoothing method via the state space model was used in the univariate time series. The TTR, smooth, and forecast packages were used in R software. Landsat satellite images from 1991, 2001, 2011, and 2021 were employed in the physical development. During this period, the temperature increased while the rainfall and humidity decreased. The findings showed a downward trend in the standardized drought index. Also, the results showed that climate warming and ecological changes profoundly affected the area’s agricultural patterns and topographical features. Furthermore, the last three decades witnessed an elimination trend for zoonotic CL (ZCL) and the predominance of anthroponotic CL (ACL). The present findings showed that the critical factors in the predominance of ACL and elimination of ZCL were rising temperature, drought, migration, unplanned urbanization, earthquake, and agrarian reform. The wall-enclosed palm tree gardens excluded the primary ZCL reservoir host. They controlled the disease while providing suitable conditions for the emergence/re-emergence of ACL in the newly established settlements and the unplanned ecozone. Therefore, robust health infrastructures, sustained financial support, and evidence-based research studies are crucial to facilitating the necessary surveillance, monitoring, and evaluation to control and eliminate the disease.
Invasive hematophagous arthropods threaten planetary health by vectoring a growing diversity of pathogens and parasites which cause diseases. Efforts to limit human and animal morbidity and mortality caused by these disease vectors are dependent on understandings of their biology and ecology-from cellular to ecosystem levels. Here, we review research into the biology and ecology of invasive hematophagous arthropods globally, with a particular emphasis on mosquitoes, culminating towards management recommendations. Evolutionary history, genetics, and environmental filtering contribute to invasion success of these taxa, with life history trait and ecological niche shifts between native and invaded regions regularly documented. Pertinent vector species spread readily through active and passive means, via anthropogenic and natural mechanisms as climate changes. The rate and means of spread differ among taxa according to their capacity for entrainment in human vectors and physiology. It is critical to understand the role of these invaders in novel ecosystems, as biotic interactions, principally with their resources, competitors, and natural enemies, mediate patterns of invasion success. We further highlight recent advances in understanding interactions among arthropod-associated microbiota, and identify future research directions integrating arthropod microbiota to explain invasion success under changing environments. These biological and ecological facets provide an integrative perspective on the invasion history and dynamics of invasive hematophagous arthropods, helping inform on their management strategies. Genetic and microbiome features of invasive mosquitoes are reviewed.Movement patterns and geographic spread of mosquitoes are explored.A food-web approach to assess the impacts of invasive mosquitoes is presented.Novel perspectives for the management of invasive mosquitoes are considered.
Dengue fever (DF) is an arthropod-borne viral infection caused by four serotypes of dengue virus (DENV 1-4) transmitted to the host by the vector mosquito Aedes, which causes fever, vomiting, headache, joint pain, muscle pain, and a distinctive itching and skin rash, ultimately leading to dengue hemorrhagic fever and dengue shock syndrome. The first case of DF in Pakistan was documented in 1994, but outbreak patterns began in 2005. As of 20 August 2022, Pakistan has 875 confirmed cases, raising alarming concerns. Misdiagnosis due to mutual symptoms, lack of an effective vaccine, the weakened and overburdened health system of Pakistan, irrational urbanization, climate change in Pakistan, insufficient waste management system, and a lack of awareness are the significant challenges Pakistan faces and result in recurrent dengue outbreaks every year. The recent flood in Pakistan has caused massive destruction, and stagnant dirty water has facilitated mosquito breeding. Sanitization and spraying, proper waste management, an adequate and advanced diagnostic system, control of population size, public awareness, and promotion of medical research and global collaboration, especially amidst flood devastation, are recommended to combat this deadly infection in Pakistan. This article aims to comprehensively review the year-round DF in Pakistan, highlighting the surge amidst ongoing flood havoc and the coronavirus disease 2019 pandemic.
Rising temperatures are impacting the range and prevalence of mosquito-borne diseases. A promising biocontrol technology replaces wild mosquitoes with those carrying the virus-blocking Wolbachia bacterium. Because the most widely used strain, wMel, is adversely affected by heat stress, we examined how global warming may influence wMel-based replacement. We simulated interventions in two locations with successful field trials using Coupled Model Intercomparison Project Phase 5 climate projections and historical temperature records, integrating empirical data on wMel’s thermal sensitivity into a model of Aedes aegypti population dynamics to evaluate introgression and persistence over one year. We show that in Cairns, Australia, climatic futures necessitate operational adaptations for heatwaves exceeding two weeks. In Nha Trang, Vietnam, projected heatwaves of three weeks and longer eliminate wMel under the most stringent assumptions of that symbiont’s thermal limits. We conclude that this technology is generally robust to near-term (2030s) climate change. Accelerated warming may challenge this in the 2050s and beyond.
The cattle tick Rhipicephalus microplus (Acari: Ixodidae) has demonstrated its ability to increase its distribution raising spatially its importance as a vector for zoonotic hemotropic pathogens. In this study, a global ecological niche model of R. microplus was built in different scenarios using Representative Concentration Pathway (RCP), Socio-Economic Pathway (SSP), and a climatic dataset to determine where the species could establish itself and thus affect the variability in the presentation of the hemotropic diseases they transmit. America, Africa and Oceania showed a higher probability for the presence of R. microplus in contrast to some countries in Europe and Asia in the ecological niche for the current period (1970-2000), but with the climate change, there was an increase in the ratio between the geographic range preserved between the RCP and SSP scenarios obtaining the greatest gain in the interplay of RCP4.5-SSP245. Our results allow to determine future changes in the distribution of the cattle tick according to the increase in environmental temperature and socio-economic development influenced by human development activities and trends; this work explores the possibility of designing integral maps between the vector and specific diseases.
Ťahyňa virus (TAHV) is an orthobunyavirus and was the first arbovirus isolated from mosquitoes in Europe and is associated with floodplain areas as a characteristic biotope, hares as reservoir hosts and the mammal-feeding mosquitoes Aedes vexans as the main vector. The disease caused by TAHV (“Valtice fever”) was detected in people with acute flu-like illness in the 1960s, and later the medical significance of TAHV became the subject of many studies. Although TAHV infections are widespread, the prevalence and number of actual cases, clinical manifestations in humans and animals and the ecology of transmission by mosquitoes and their vertebrate hosts are rarely reported. Despite its association with meningitis in humans, TAHV is a neglected human pathogen with unknown public health importance in Central Europe, and a potential emerging disease threat elsewhere in Europe due to extreme summer flooding events.
Changing climatic conditions and other anthropogenic influences have altered tick distribution, abundance and seasonal activity over the last decades. In Germany, the two most important tick species are Ixodes ricinus and Dermacentor reticulatus, the latter of which has expanded its range across the country during the past three decades. While I. ricinus was rarely found during the colder months in the past, D. reticulatus is known to be active at lower temperatures. To quantify tick appearance during winter, specimens were monitored in quasi-natural tick plots three times a week. Additionally, the questing activities of these two tick species were observed throughout the year at nine field collection sites that were regularly sampled by the flagging method from April 2020 to April 2022. Furthermore, tick winter activity in terms of host infestation was analysed as part of a nationwide submission study from March 2020 to October 2021, in which veterinarians sent in ticks mainly collected from dogs and cats. All three study approaches showed a year-round activity of I. ricinus and D. reticulatus in Germany. During the winter months (December to February), on average 1.1% of the inserted I. ricinus specimens were observed at the tops of rods in the tick plots. The average questing activity of I. ricinus amounted to 2 ticks/100 m² (range: 1-17) in the flagging study, and 32.4% (211/651) of ticks found infesting dogs and cats during winter 2020/21 were I. ricinus. On average 14.7-20.0% of the inserted D. reticulatus specimens were observed at the tops of rods in the tick plots, while the average winter questing activity in the field study amounted to 23 specimens/100 m² (range: 0-62), and 49.8% (324/651) of all ticks collected from dogs and cats during winter 2020/21 were D. reticulatus. Additionally, the hedgehog tick Ixodes hexagonus was found to infest dogs and cats quite frequently during the winter months, accounting for 13.2% (86/651) of the collected ticks. A generalized linear mixed model identified significant correlations of D. reticulatus winter activity in quasi-natural plots with climatic variables. The combined study approaches confirmed a complementary main activity pattern of I. ricinus and D. reticulatus with climate change-driven winter activity of both species. Milder winters and a decrease of snowfall, and consequently high winter activity of D. reticulatus, among other factors, may have contributed to the rapid spread of this tick species throughout the country. Therefore, an effective year-round tick control is strongly recommended to not only efficiently protect dogs and cats with outdoor access from ticks and tick-borne pathogens (TBPs), but also to limit the further geographical spread of ticks and TBPs to so far non-endemic regions. Further measures, including information of the public, are necessary to protect both, humans and animals, in a One Health approach.
The diseases transmitted through vectors such as mosquitoes are named vector-borne diseases (VBDs), such as malaria, dengue, and leishmaniasis. Malaria spreads by a vector named Anopheles mosquitos. Dengue is transmitted through the bite of the female vector Aedes aegypti or Aedes albopictus mosquito. The female Phlebotomine sandfly is the vector that transmits leishmaniasis. The best way to control VBDs is to identify breeding sites for their vectors. This can be efficiently accomplished by the Geographical Information System (GIS). The objective was to find the relation between climatic factors (temperature, humidity, and precipitation) to identify breeding sites for these vectors. Our data contained imbalance classes, so data oversampling of different sizes was created. The machine learning models used were Light Gradient Boosting Machine, Random Forest, Decision Tree, Support Vector Machine, and Multi-Layer Perceptron for model training. Their results were compared and analyzed to select the best model for disease prediction in Punjab, Pakistan. Random Forest was the selected model with 93.97% accuracy. Accuracy was measured using an F score, precision, or recall. Temperature, precipitation, and specific humidity significantly affect the spread of dengue, malaria, and leishmaniasis. A user-friendly web-based GIS platform was also developed for concerned citizens and policymakers.
Dengue is a rapidly spreading viral disease transmitted to humans by Aedes mosquitoes. Due to global urbanization and climate change, the number of dengue cases are gradually increasing in recent decades. Hence, an early prediction of dengue continues to be a major concern for public health in countries with high prevalence of dengue. Creating a robust forecast model for the accurate prediction of dengue is a complex task and can be done through various data modelling approaches. In the present study, we have applied vector auto regression, generalized boosted models, support vector regression, and long short-term memory (LSTM) to predict the dengue prevalence in Kerala state of the Indian subcontinent. We consider the number of dengue cases as the target variable and weather variables viz., relative humidity, soil moisture, mean temperature, precipitation, and NINO3.4 as independent variables. Various analytical models have been applied on both datasets and predicted the dengue cases. Among all the models, the LSTM model was outperformed with superior prediction capability (RMSE: 0.345 and R(2):0.86) than the other models. However, other models are able to capture the trend of dengue cases but failed in predicting the outbreak periods when compared to LSTM. The findings of this study will be helpful for public health agencies and policymakers to draw appropriate control measures before the onset of dengue. The proposed LSTM model for dengue prediction can be followed by other states of India as well.
Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Deep learning applications across fields are proving valuable, however the field of epidemiological forecasting is still in its infancy with a lack of applied deep learning studies for malaria in southern Africa which leverage quality datasets. Using a novel high resolution malaria incidence dataset containing 23 years of daily data from 1998 to 2021, a statistical model and XGBOOST machine learning model were compared to a deep learning Transformer model by assessing the accuracy of their numerical predictions. A novel loss function, used to account for the variable nature of the data yielded performance around + 20% compared to the standard MSE loss. When numerical predictions were converted to alert thresholds to mimic use in a real-world setting, the Transformer’s performance of 80% according to AUROC was 20-40% higher than the statistical and XGBOOST models and it had the highest overall accuracy of 98%. The Transformer performed consistently with increased accuracy as more climate variables were used, indicating further potential for this prediction framework to predict malaria incidence at a daily level using climate data for southern Africa.
Ticks and tick-borne diseases are on the rise due to socioecosystemic changes and climate modification and are affecting human and animal health. Few vaccines are available. Two recent articles from Matias et al. and Pine et al. used mRNA technology to explore tick and pathogen proteins as vaccine candidates.
Vector-borne parasites may be transmitted by multiple vector species, resulting in an increased risk of transmission, potentially at larger spatial scales compared to any single vector species. Additionally, the different abilities of patchily distributed vector species to acquire and transmit parasites will lead to varying degrees of transmission risk. Investigation of how vector community composition and parasite transmission change over space due to variation in environmental conditions may help to explain current patterns in diseases but also informs our understanding of how patterns will change under climate and land-use change. We developed a novel statistical approach using a multi-year, spatially extensive case study involving a vector-borne virus affecting white-tailed deer transmitted by Culicoides midges. We characterized the structure of vector communities, established the ecological gradient controlling change in structure, and related the ecology and structure to the amount of disease reporting observed in host populations. We found that vector species largely occur and replace each other as groups, rather than individual species. Moreover, community structure is primarily controlled by temperature ranges, with certain communities being consistently associated with high levels of disease reporting. These communities are essentially composed of species previously undocumented as potential vectors, whereas communities containing putative vector species were largely associated with low levels, or even absence, of disease reporting. We contend that the application of metacommunity ecology to vector-borne infectious disease ecology can greatly aid the identification of transmission hotspots and an understanding of the ecological drivers of parasite transmission risk both now and in the future.
Vector-borne diseases are among the greatest causes of human suffering globally. Several studies have linked climate change and increasing temperature with rises in vector abundance, and in the incidence and geographical distribution of diseases. The microbiome of vectors can have profound effects on how efficiently a vector sustains pathogen development and transmission. Growing evidence indicates that the composition of vectors’ gut microbiome might change with shifts in temperature. Nonetheless, due to a lack of studies on vector microbiome turnover under a changing climate, the consequences for vector-borne disease incidence are still unknown. Here, we argue that climate change effects on vector competence are still poorly understood and the expected increase in vector-borne disease transmission might not follow a relationship as simple and straightforward as past research has suggested. Furthermore, we pose questions that are yet to be answered to enhance our current understanding of the effect of climate change on vector microbiomes, competence, and, ultimately, vector-borne diseases transmission.
Ecologies of zoonotic vector-borne diseases may shift with climate and land use change. As many urban-adapted mammals can host ectoparasites and pathogens of human and animal health concern, our goal was to compare patterns of arthropod-borne disease among medium-sized mammals across gradients of rural to urban landscapes in multiple regions of California. DNA of Anaplasma phagocytophilum was found in 1-5% of raccoons, coyotes, and San Joaquin kit foxes; Borrelia burgdorferi in one coyote, rickettsiae in two desert kit foxes, and Yersinia pestis in two coyotes. There was serological evidence of rickettsiae in 14-37% of coyotes, Virginia opossums, and foxes; and A. phagocytophilum in 6-40% of coyotes, raccoons, Virginia opossums, and foxes. Of six flea species, one Ctenocephalides felis from a raccoon was positive for Y. pestis, and Ct. felis and Pulex simulans fleas tested positive for Rickettsia felis and R. senegalensis. A Dermacentor similis tick off a San Joaquin kit fox was PCR-positive for A. phagocytophilum. There were three statistically significant risk factors: risk of A. phagocytophilum PCR-positivity was threefold greater in fall vs the other three seasons; hosts adjacent to urban areas had sevenfold increased A. phagocytophilum seropositivity compared with urban and rural areas; and there was a significant spatial cluster of rickettsiae within greater Los Angeles. Animals in areas where urban and rural habitats interconnect can serve as sentinels during times of change in disease risk.
Vector-borne diseases (VBDs) are caused by infectious pathogens that spread from an infected human or animal reservoir to an uninfected human via a vector (mosquito, tick, rodent, others) and remain an important cause of morbidity and mortality worldwide. Pregnant individuals and their fetuses are especially at risk, as certain pathogens, such as Zika virus, have specific implications in pregnancy and for neonatal health. Global climate change is affecting the incidence and geographic spread of many VBDs. Thus, it is important for clinicians in the fields of obstetrics/gynecology and newborn medicine, regardless of geographic location, to familiarize themselves with a basic understanding of these conditions and how climate change is altering their distributions. In this chapter, we review the incidence, clinical presentation, implications during pregnancy and intersection with climate change for four of the most important VBDs in pregnancy: malaria, Zika, dengue and Chagas disease. Although not exhaustive of all VBDs, a more extensive table is included for reference, and our discussion provides a helpful framework for understanding other vector-borne pathogens and perinatal health.
As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.
Arboviruses (arthropod-borne-viruses) are an emerging global health threat that are rapidly spreading as climate change, international business transport, and landscape fragmentation impact local ecologies. Since its initial detection in 1999, West Nile virus has shifted from being a novel to an established arbovirus in the United States of America. Subsequently, more than 25,000 cases of West Nile neuro-invasive disease have been diagnosed, cementing West Nile virus as an arbovirus of public health importance. Given its novelty in the United States of America, high-risk ecologies are largely underdefined making targeted population-level public health interventions challenging. Using the Centers for Disease Control and Prevention ArboNET neuroinvasive West Nile virus data from 2000-2021, this study aimed to predict neuroinvasive West Nile virus human cases at the county level for the contiguous USA using a spatio-temporal Bayesian negative binomial regression model. The model includes environmental, climatic, and demographic factors, as well as the distribution of host species. An integrated nested Laplace approximation approach was used to fit our model. To assess model prediction accuracy, annual counts were withheld, forecasted, and compared to observed values. The validated models were then fit to the entire dataset for 2022 predictions. This proof-of-concept mathematical, geospatial modelling approach has proven utility for national health agencies seeking to allocate funding and other resources for local vector control agencies tackling West Nile virus and other notifiable arboviral agents.
Leishmaniasis is a vector-borne disease of which the transmission is highly influenced by climatic factors, whereas the nature and magnitude differ between geographical regions. The effects of climatic variables on leishmaniasis in Sri Lanka are poorly investigated. The present study focused on time-series analysis of leishmaniasis cases reported from Sri Lanka with selected climatic variables. Variance stabilized time series of leishmaniasis patients of entire Sri Lanka and major districts from 2014 to 2018 was fitted to autoregressive integrated moving average (ARIMA) models. All the possible models were generated by assigning different values for autoregression and moving average terms using a function written in R statistical program. The top ten models with the lowest Akaike information criterion (AIC) values were selected by writing another function. These models were further evaluated using RMSE and MAPE error parameters to select the optimal model for each area. Cross-autocorrelation analyses were performed to assess the associations between climate and the leishmaniasis incidence. Most associated lags of each variable were integrated into the optimal models to determine the true effects imposed. The optimal models varied depending on the area. SARIMA (0,1,1) (1,0,0)(12) was optimal for the country level. All the forecasts were within the 95% confidence intervals. Humidity was the most notable factor associated with leishmaniasis, which could be attributed to the positive impacts on sand fly activity. Rainfall showed a negative impact probably as a result of flooding of sand fly larval habitats. The ARIMA-based models performed well for the prediction of leishmaniasis in the short term.
The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R(2): 0.944) and Tripura (RMSE: 0.0451; R(2): 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.
Given that no exact cause has been reported for the rapid increase of tick-borne infections in South Korea, the impact of climate and environmental changes on tick-borne infections is investigated, and potential high-risk areas are identified at the refined resolution. Ticks transmit a wide range of pathogens. The spread of tick-borne infections is an emerging, yet often overlooked, threat in the context of climate change. The infections have rapidly increased over the past few years in South Korea despite no significant changes in socioeconomic circumstances. We investigated the impact of climate change on the surge of tick-borne infections and identified potential disease hot spots at a resolution of 5 km by 5 km. A composite index was constructed based on multiple climate and environmental indicators and compared with the observed tick-borne infections. The surge of tick-borne episodes corresponded to the rising trend of the index over time. High-risk areas identified by the index can be used to prioritize locations for disease prevention activities. Monitoring climate risk factors may provide an opportunity to predict the spread of the infections in advance.
Despite considerable progress made over the past 20 years in reducing the global burden of malaria, the disease remains a major public health problem and there is concern that climate change might expand suitable areas for transmission. This study investigated the relative effect of climate variability on malaria incidence after scale-up of interventions in western Kenya. METHODS: Bayesian negative binomial models were fitted to monthly malaria incidence data, extracted from records of patients with febrile illnesses visiting the Lwak Mission Hospital between 2008 and 2019. Data pertaining to bed net use and socio-economic status (SES) were obtained from household surveys. Climatic proxy variables obtained from remote sensing were included as covariates in the models. Bayesian variable selection was used to determine the elapsing time between climate suitability and malaria incidence. RESULTS: Malaria incidence increased by 50% from 2008 to 2010, then declined by 73% until 2015. There was a resurgence of cases after 2016, despite high bed net use. Increase in daytime land surface temperature was associated with a decline in malaria incidence (incidence rate ratio [IRR] = 0.70, 95% Bayesian credible interval [BCI]: 0.59-0.82), while rainfall was associated with increased incidence (IRR = 1.27, 95% BCI: 1.10-1.44). Bed net use was associated with a decline in malaria incidence in children aged 6-59 months (IRR = 0.78, 95% BCI: 0.70-0.87) but not in older age groups, whereas SES was not associated with malaria incidence in this population. CONCLUSIONS: Variability in climatic factors showed a stronger effect on malaria incidence than bed net use. Bed net use was, however, associated with a reduction in malaria incidence, especially among children aged 6-59 months after adjusting for climate effects. To sustain the downward trend in malaria incidence, this study recommends continued distribution and use of bed nets and consideration of climate-based malaria early warning systems when planning for future control interventions.
Although single factors such as rainfall are known to affect the population dynamics of Aedes albopictus, the main vector of dengue fever in Eurasia, the synergistic effects of different meteorological factors are not fully understood. To address this topic, we used meteorological data and mosquito-vector association data including Breteau and ovitrap indices in key areas of dengue outbreaks in Guangdong Province, China, to formulate a five-stage mathematical model for Aedes albopictus population dynamics by integrating multiple meteorological factors. Unknown parameters were estimated using a genetic algorithm, and the results were analyzed by k-Shape clustering, random forest and grey correlation analysis. In addition, the population density of mosquitoes in 2022 was predicted and used for evaluating the effectiveness of the model. We found that there is spatiotemporal heterogeneity in the effects of temperature and rainfall and their distribution characteristics on the diapause period, the numbers of peaks in mosquito densities in summer and the annual total numbers of adult mosquitoes. Moreover, we identified the key meteorological indicators of the mosquito quantity at each stage and that rainfall (seasonal rainfall and annual total rainfall) was more important than the temperature distribution (seasonal average temperature and temperature index) and the uniformity of rainfall annual distribution (coefficient of variation) for most of the areas studied. The peak rainfall during the summer is the best indicator of mosquito population development. The results provide important theoretical support for the future design of mosquito vector control strategies and early warnings of mosquito-borne diseases.
Plasmodium vivax and Plasmodium falciparum cases have opposite trends in Anhui China in the past decade. Long term and seasonal trends in the transmission rate of P. falciparum in Africa has been well studied, however that of P. vivax transmitted by Anopheles sinensis in China has not been investigated. There is a lot of work on the relationship between P. vivax cases and climatic factors in China, with sometimes contradicting results. However, how climatic factors affect transmission rate of P. vivax in China is unknown. We used Anhui province as an example to analyze the recent transmission dynamics where two types of malaria have been reported with differing etiologies. We examined breakpoints of the P. vivax and P. falciparum malaria long term dynamics in the recent decade. For locally transmitted P. vivax malaria, we analyzed the transmission rate and its seasonality using the combined human and mosquitos SIR-SI model with time-varied mosquito biting rate. We identified the effects of meteorological factors on the seasonality in transmission rate using a GAM model. For the imported P. falciparum malaria, we analyzed the potential reason for the observed increase in cases. The breakpoints of P. vivax and P. falciparum dynamics happened in a same year, 2010. The seasonality in the transmission rate of P. vivax malaria was high (42.4%) and was linearly associated with temperature and nonlinearly with rainfall. The abrupt increase in imported P. falciparum cases after the breakpoint was significantly related to the increased annual Chinese investment in Africa. Under the conditions of the existing vectors of malaria, long-term trends in climatic factors, and increasing trend in migration to/from endemic areas and imported malaria cases, we should be cautious of the possibility of the reestablishment of malaria in regions where it has been eliminated or the establishment of other vector-borne diseases.
Over the last two decades, vector-borne pathogens (VBPs) have changed their distribution across the globe as a consequence of a variety of environmental, socioeconomic and geopolitical factors. Dirofilaria immitis and Dirofilaria repens are perfect exemplars of European VBPs of One Health concern that have undergone profound changes in their distribution, with new hotspots of infection appearing in previously non-endemic countries. Some areas, such as the United Kingdom, are still considered non-endemic. However, a combination of climate change and the potential spread of invasive mosquito species may change this scenario, exposing the country to the risk of outbreaks of filarial infections. Only a limited number of non-autochthonous cases have been recorded in the United Kingdom to date. These infections remain a diagnostic challenge for clinicians unfamiliar with these “exotic” parasites, which in turn complicates the approach to treatment and management. Therefore, this review aims to (i) describe the first case of D. repens infection in a dog currently resident in Scotland, (ii) summarise the available literature on Dirofilaria spp. infections in both humans and animals in the United Kingdom and (iii) assess the suitability of the United Kingdom for the establishment of these new VBPs.
Aedes albopictus (Skuse) (Diptera: Culicidae) is one of the 100 most invasive species in the world and represents a significant threat to public health. The distribution of Ae. albopictus has been expanding rapidly due to increased international trade, population movement, global warming and accelerated urbanization. Consequently, it is very important to know the potential distribution area of Ae. albopictus in advance for early warning and control of its spread and invasion. We randomly selected 282 distribution sites from 27 provincial-level administrative regions in China, and used the GARP and MaxEnt models to analyze and predict the current and future distribution areas of Ae. albopictus in China. The results showed that the current range of Ae. albopictus in China covers most provinces such as Yunnan and Guizhou Provinces, and the distribution of Ae. albopictus in border provinces such as Tibet, Gansu and Jilin Provinces tend to expand westwards. In addition, the potential distribution area of Ae. albopictus in China will continue to expand westwards due to future climate change under the SSP126 climate scenario. Furthermore, the results of environmental factor filtering showed that temperature and precipitation had a large effect on the distribution probability of Ae. albopictus.
Global warming contributes to the widespread spread of some of the main vectors of natural-focal infections. Ixodid ticks can inhabit large numbers both in woodlands and in meadow and pasture areas. Recent decades have seen a shift in the habitats of many parasites to the northern regions, which contributes to the survival and reproduction of not only the vectors themselves but also to the completion of the development cycle of ticks. The growth of the population size and duration of the spring-autumn period of tick activity increases the period of the epidemic season. The epidemiological situation is complicated by the persistence and almost constant activity of natural foci of arthropod-borne infections. Weather conditions, precipitation, humidity (relative humidity of at least 85%), and air temperature affect the life cycle and range of ixodid ticks. These factors make a certain contribution to geographical expansion due to changes in the habitats of vegetation and carriers in the wild (animals, birds, and rodents), which carry ticks to new territories. The northern border of the area of ixodid infections – viral tick-borne encephalitis and ixodid borreliosis – lies now beyond the borders of the Arctic. However, there is evidence of a possible movement of these boundaries to the north, so the southern part of the Arctic region may fall into the zone of potential risk of transmission of these infections.
Although malaria control investments worldwide have resulted in dramatic declines in transmission since 2000, progress has stalled. In the Amazon, malaria resurgence has followed withdrawal of Global Fund support of the Project for Malaria Control in Andean Border Areas (PAMAFRO). We estimate intervention-specific and spatially-explicit effects of the PAMAFRO program on malaria incidence across the Loreto region of Peru, and consider the influence of the environmental risk factors in the presence of interventions. METHODS: We conducted a retrospective, observational, spatial interrupted time series analysis of malaria incidence rates among people reporting to health posts across Loreto, Peru between the first epidemiological week of January 2001 and the last epidemiological week of December 2016. Model inference is at the smallest administrative unit (district), where the weekly number of diagnosed cases of Plasmodium vivax and Plasmodium falciparum were determined by microscopy. Census data provided population at risk. We include as covariates weekly estimates of minimum temperature and cumulative precipitation in each district, as well as spatially- and temporally-lagged malaria incidence rates. Environmental data were derived from a hydrometeorological model designed for the Amazon. We used Bayesian spatiotemporal modeling techniques to estimate the impact of the PAMAFRO program, variability in environmental effects, and the role of climate anomalies on transmission after PAMAFRO withdrawal. FINDINGS: During the PAMAFRO program, incidence of P. vivax declined from 42.8 to 10.1 cases/1000 people/year. Incidence for P. falciparum declined from 14.3 to 2.5 cases/1000 people/year over this same period. The effects of PAMAFRO-supported interventions varied both by geography and species of malaria. Interventions were only effective in districts where interventions were also deployed in surrounding districts. Further, interventions diminished the effects of other prevailing demographic and environmental risk factors. Withdrawal of the program led to a resurgence in transmission. Increasing minimum temperatures and variability and intensity of rainfall events from 2011 onward and accompanying population displacements contributed to this resurgence. INTERPRETATION: Malaria control programs must consider the climate and environmental scope of interventions to maximize effectiveness. They must also ensure financial sustainability to maintain local progress and commitment to malaria prevention and elimination efforts, as well as to offset the effects of environmental change that increase transmission risk. FUNDING: National Aeronautics and Space Administration, National Institutes of Health, Bill and Melinda Gates Foundation.
Ticks are obligatory hematophagous parasites that serve as vectors for a large amount of important human and livestock pathogens around the world, and their distribution and incidence of tick-associated diseases are currently increasing because of environmental biomass being modified by both climate change and other human activities. Hyalomma species are of major concern for public health, due to their important role as vectors of several diseases such as the Crimea-Congo hemorrhagic fever (CCHF) virus in humans or theileriosis in cattle. Characterizing the Hyalomma-associated microbiota and delving into the complex interactions between ticks and their bacterial endosymbionts for host survival, development, and pathogen transmission are fundamental, as it may provide new insights and spawn new paradigms to control tick-borne diseases. Francisella-like endosymbionts (FLEs) have recently gained importance, not only as a consequence of the public health concerns of the highly transmissible Francisella tularensis, but for the essential role of FLEs in tick homeostasis. In this comprehensive review, we discuss the growing importance of ticks associated with the genus Hyalomma, their associated tick-borne human and animal diseases in the era of climate change, as well as the role of the microbiome and the FLE in their ecology. We compile current evidence from around the world on FLEs in Hyalomma species and examine the impact of new molecular techniques in the study of tick microbiomes (both in research and in clinical practice). Lastly, we also discuss different endosymbiont-directed strategies for the control of tick populations and tick-borne diseases, providing insights into new evidence-based opportunities for the future.
As humans expand their territories across more and more regions of the planet, activities such as deforestation, urbanization, tourism, wildlife exploitation, and climate change can have drastic consequences for animal movements and animal-human interactions. These events, especially climate change, can also affect the arthropod vectors that are associated with the animals in these scenarios. As the COVID-19 pandemic and other various significant outbreaks throughout the centuries have demonstrated, when animal patterns and human interactions change, so does the exposure of humans to zoonotic pathogens potentially carried by wildlife. With approximately 60% of emerging human pathogens and around 75% of all emerging infectious diseases being categorized as zoonotic, it is of great importance to examine the impact of human activities on the prevalence and transmission of these infectious agents. A better understanding of the impact of human-related factors on zoonotic disease transmission and prevalence can help drive the preventative measures and containment policies necessary to improve public health.
Climate change has had a major impact on seasonal weather patterns, resulting in marked phenological changes in a wide range of taxa. However, empirical studies of how changes in seasonality impact the emergence and seasonal dynamics of vector-borne diseases have been limited. Lyme borreliosis, a bacterial infection spread by hard-bodied ticks, is the most common vector-borne disease in the northern hemisphere and has been rapidly increasing in both incidence and geographical distribution in many regions of Europe and North America. By analysis of long-term surveillance data (1995-2019) from across Norway (latitude 57°58′-71°08′ N), we demonstrate a marked change in the within-year timing of Lyme borreliosis cases accompanying an increase in the annual number of cases. The seasonal peak in cases is now six weeks earlier than 25 years ago, exceeding seasonal shifts in plant phenology and previous model predictions. The seasonal shift occurred predominantly in the first 10 years of the study period. The concurrent upsurgence in case number and shift in case timing indicate a major change in the Lyme borreliosis disease system over recent decades. This study highlights the potential for climate change to shape the seasonal dynamics of vector-borne disease systems.
Low socioeconomic status (SES), high temperature, and increasing rainfall patterns are associated with increased dengue case counts. However, the effect of climatic variables on individual dengue virus (DENV) serotypes and the extent to which serotype count affects the rate of severe dengue in Mexico have not been studied before. A principal components analysis was used to determine the poverty indices across Mexico. Conditional autoregressive Bayesian models were used to determine the effect of poverty and climatic variables on the rate of serotype distribution and severe dengue in Mexico. A unit increase in poverty increased the rate of DENV-1, DENV-2, DENV-3, and DENV-4 by 8.4%, 5%, 16%, and 13.8% respectively. An increase in one case attributable to DENV-1, DENV-2, DENV-3, and DENV-4 was independently associated with an increase in the rate of severe dengue by 0.02%, 0.1%, 0.03%, and 5.8% respectively. Hotspots of all DENV serotypes and severe dengue are found mostly in parts of the Northeastern, Center west, and Southeastern regions of Mexico. The association between climatic parameters predominant in the Southeast region and severe dengue leaves several states in this region at an increased risk of a higher number of severe dengue cases. Our study’s results may guide policies that help allocate public health resources to the most vulnerable municipalities in Mexico.
Climate change and globalization have raised the risk of vector-borne disease (VBD) introduction and spread in various European nations in recent years. In Italy, viruses carried by tropical vectors have been shown to cause viral encephalitis, one of the symptoms of arboviruses, a spectrum of viral disorders spread by arthropods such as mosquitoes and ticks. Arboviruses are currently causing alarm and attention, and the World Health Organization (WHO) has released recommendations to adopt essential measures, particularly during the hot season, to restrict the spreading of the infectious agents among breeding stocks. In this scenario, rapid analysis systems are required, because they can quickly provide information on potential virus-host interactions, the evolution of the infection, and the onset of disabling clinical symptoms, or serious illnesses. Such systems include bioinformatics approaches integrated with molecular evaluation. Viruses have co-evolved different strategies to transcribe their own genetic material, by changing the host’s transcriptional machinery, even in short periods of time. The introduction of genetic alterations, particularly in RNA viruses, results in a continuous adaptive fight against the host’s immune system. We propose an in silico pipeline method for performing a comprehensive motif analysis (including motif discovery) on entire genome sequences to uncover viral sequences that may interact with host RNA binding proteins (RBPs) by interrogating the database of known RNA binding proteins, which play important roles in RNA metabolism and biological processes. Indeed, viral RNA sequences, able to bind host RBPs, may compete with cellular RNAs, altering important metabolic processes. Our findings suggest that the proposed in silico approach could be a useful and promising tool to investigate the complex and multiform clinical manifestations of viral encephalitis, and possibly identify altered metabolic pathways as targets of pharmacological treatments and innovative therapeutic protocols.
Ticks are known to transmit a wide range of diseases, including those caused by bacteria, viruses, and protozoa. The expansion of tick habitats has been intensified in recent years due to various factors such as global warming, alterations in microclimate, and human activities. Consequently, the probability of human exposure to diseases transmitted by ticks has increased, leading to a higher degree of risk associated with such diseases. METHODS: In this study, we conducted a comprehensive review of domestic and international literature databases to determine the current distribution of tick species in Inner Mongolia. Next, we employed the MaxEnt model to analyze vital climatic and environmental factors influencing dominant tick distribution. Subsequently, we predicted the potential suitability areas of these dominant tick species under the near current conditions and the BCC-CSM2.MR model SSP245 scenario for the future periods of 2021-2040, 2041-2060, 2061-2080, and 2081-2100. RESULTS: Our study revealed the presence of 23 tick species from six genera in Inner Mongolia, including four dominant tick species (Dermacentor nuttalli, Ixodes persulcatus, Dermacentor silvarum, and Hyalomma asiaticum). Dermacentor nuttalli, D. silvarum, and I. persulcatus are predominantly found in regions such as Xilin Gol and Hulunbuir. Temperature seasonality (Bio4), elevation (elev), and precipitation seasonality (Bio15) were the primary variables impacting the distribution of three tick species. In contrast, H. asiaticum is mainly distributed in Alxa and Bayannur and demonstrates heightened sensitivity to precipitation and other climatic factors. Our modeling results suggested that the potential suitability areas of these tick species would experience fluctuations over the four future periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100). Specifically, by 2081-2100, the centroid of suitable habitat for D. nuttalli, H. asiaticum, and I. persulcatus was predicted to shift westward, with new suitability areas emerging in regions such as Chifeng and Xilin Gol. The centroid of suitable habitat for H. asiaticum will move northeastward, and new suitability areas are likely to appear in areas such as Ordos and Bayannur. CONCLUSIONS: This study provided a comprehensive overview of the tick species distribution patterns in Inner Mongolia. Our research has revealed a significant diversity of tick species in the region, exhibiting a wide distribution but with notable regional disparities. Our modeling results suggested that the dominant tick species’ suitable habitats will significantly expand in the future compared to their existing distribution under the near current conditions. Temperature and precipitation are the primary variables influencing these shifts in distribution. These findings can provide a valuable reference for future research on tick distribution and the surveillance of tick-borne diseases in the region.
Bangladesh reported the highest number of annual deaths (n = 281) related to dengue virus infection in 2022 since the virus reappeared in the country in 2000. Earlier studies showed that >92% of the annual cases occurred between the months of August and September. The 2022 outbreak is characterized by late onset of dengue cases with unusually higher deaths in colder months, that is, October-December. Here we present possible hypotheses and explanations for this late resurgence of dengue cases. First, in 2022, the rainfall started late in the season. Compared to the monthly average rainfall for September and October between 2003 and 2021, there was 137 mm of additional monthly rainfall recorded in September and October 2022. Furthermore, the year 2022 was relatively warmer with a 0.71°C increased temperature than the mean annual temperature of the past 20 yr. Second, a new dengue virus serotype, DENV-4, had recently reintroduced/reappeared in 2022 and become the dominant serotype in the country for a large naïve population. Third, the post-pandemic return of normalcy after 2 yr of nonpharmaceutical social measures facilitates extra mosquito breeding habitats, especially in construction sites. Community engagement and regular monitoring and destruction of Aedes mosquitoes’ habitats should be prioritized to control dengue virus outbreaks in Bangladesh.
Crimean-Congo Hemorrhagic Fever continues to be an important public health problem by expanding its borders. To evaluate the temporal trend, seasonality, and relationship with the climatic factors of Crimean-Congo Hemorrhagic Fever. Study data included cases treated in two different tertiary healthcare institutions between 2012 and 2021. The demographic characteristics of the cases and the dates of admission to the hospital were determined, and they were matched with the average of the measurements (temperature, cumulative precipitation, relative humidity, wind speed) of two different meteorology stations in the study area. By calculating the crude incidence rates, the trend in years was investigated. Estimates were created by removing the incidence rates, seasonality, and trend components using the additive decomposition technique. The temporal relationship between incidence rates and climatic factors was evaluated with the help of the Autoregressive Distributed Lag Bound Test. Toda Yamamoto test was used for causality verification. The mean age of the cases (n = 974) included in the study was 47.6 ± 17.7 years, and the majority (57.3%) were in the group above 45 years of age. 56.6% of the cases were male and there was a male predominance in all age groups. Incidence rates ranged from 5.5 to 23.1/100,000 over the ten-year period and there was a significant upward trend (R(2) = 0.691, p = 0.003). Cases of Crimean-Congo Hemorrhagic Fever that started in March, peaked in July and ended in October, showed a clear seasonality. A cointegration relationship was observed between case incidence rates and air temperature, cumulative precipitation, and relative humidity (p < 0.05 for all). Climatic factors can only indirectly affect the occurrence of Crimean-Congo Hemorrhagic Fever cases. However, climatic conditions that become progressively more favorable for vector ticks lead to the spread of the disease. The control measures to be taken should be prepared by considering the changing climatic conditions and prioritizing the risk groups. There is a need for information and awareness-raising studies about climate change and the growing dangers associated with it, also outside of endemic regions.
Mosquito-borne viruses are leading causes of morbidity and mortality in many parts of the world. In recent years, modelling studies have shown that climate change strongly influences vector-borne disease transmission, particularly rising temperatures. As a result, the risk of epidemics has increased, posing a significant public health risk. This review aims to summarize all published laboratory experimental studies carried out over the years to determine the impact of temperature on the transmission of arboviruses by the mosquito vector. Given their high public health importance, we focus on dengue, chikungunya, and Zika viruses, which are transmitted by the mosquitoes Aedes aegypti and Aedes albopictus. Following PRISMA guidelines, 34 papers were included in this systematic review. Most studies found that increasing temperatures result in higher rates of infection, dissemination, and transmission of these viruses in mosquitoes, although several studies had differing findings. Overall, the studies reviewed here suggest that rising temperatures due to climate change would alter the vector competence of mosquitoes to increase epidemic risk, but that some critical research gaps remain.
Leishmaniasis is a dynamic disease in which transmission conditions change due to environmental and human behavioral factors. Epidemiological analyses have shown modifications in the spread profile and growing urbanization of the disease, justifying the expansion of endemic areas and increasing number of cases in dogs and humans. In the city of Belo Horizonte, located in the southeastern state of Minas Gerais (Brazil), visceral leishmaniasis (VL) is endemic, with a typical urban transmission pattern, but with different regional prevalence. This study was conducted at the Zoo of the Foundation of Municipal Parks and Zoobotany of Belo Horizonte (FPMZB-BH), located in the Pampulha region, which is among the areas most severely affected by VL. This study aimed to determine the taxonomic diversity of native phlebotomine sand flies (Diptera: Psychodidae), identify climatic variables that potentially affect the phenology of these insects, and determine the blood meal sources for female phlebotomine sand flies. To achieve this, 10 mammal enclosures in the zoo were selected using the presence of possible leishmaniasis reservoirs as a selection criterion, and sampled using light traps between August 2019 and August 2021. A total of 6034 phlebotomine sand flies were collected, indicating nine species, with Lutzomyia longipalpis being the very abundant species (65.35% of the total). Of the 108 engorged phlebotomine collected females, seven samples (6.5%) were positive for blood meals from humans, marsupials, canids, and birds. Relative humidity and rainfall increased the phenology of phlebotomine sand flies, with population increases in the hottest and wettest months. The data obtained will provide guidelines for competent health agencies to implement vector control measures to reduce the risk of leishmaniasis transmission in the FPMZB-BH.
Social learning theory can support understanding of how a group of diverse actors addresses complex challenges related to public health adaptation. This study focuses on one specific issue of public health adaptation: oak processionary moth (OPM) adaptation. With a social learning framework, we examined how public health adaption strategies gradually develop and are adjusted on the basis of new knowledge and experiences. For this qualitative case study, data were collected through 27 meetings of the Processionary Moth Knowledge Platform in the Netherlands and six additional interviews. Results indicate that relations between stakeholders, including experts played a major role in the learning process, facilitating the development and implementation of OPM adaptation and connecting local challenges to national adaptation strategies. Uncertainties regarding knowledge and organization were recurrent topics of discussion, highlighting the iterative and adaptive nature of public health adaptation. The study emphasizes the importance of building relationships among stakeholders and small steps in the learning process that can lead to the creation of new strategies and, if successful, the prevention of negative health impacts.
Millions of dollars have been spent in fighting malaria in Namibia. However, malaria remains a major public health concern in Namibia, mostly in Kavango West and East, Ohangwena and Zambezi region. The primary goal of this study was to fit a spatio-temporal model that profiles spatial variation in malaria risk areas and investigate possible associations between disease risk and environmental factors at the constituency level in highly risk northern regions of Namibia. METHODS: Malaria data, climatic data, and population data were merged and Global spatial autocorrelation statistics (Moran’s I) was used to detect the spatial autocorrelation of malaria cases while malaria occurrence clusters were identified using local Moran statistics. A hierarchical Bayesian CAR model (Besag, York and Mollie’s model “BYM”) known to be the best model for modelling the spatial and temporal effects was then fitted to examine climatic factors that might explain spatial/temporal variation of malaria infection in Namibia. RESULTS: Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6%, same to average maximum temperature. The posterior means of the time main effect (year t) showed a visible slightly increase in global trend from 2018 to 2020. CONCLUSION: The study discovered that the spatial temporal model with both random and fixed effects best fit the model, which demonstrated a strong spatial and temporal heterogeneity distribution of malaria cases (spatial pattern) with high risk in most of the Kavango West and East outskirt constituencies, posterior relative risk (RR: 1.57 to 1.78).
Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale.
The majority of malaria cases in Southeast Asia occur in India. It is a major public health problem in India, which accounts for substantial morbidity, mortality, and economic loss. The spatial distribution of malaria widely varies due to geo-ecological diversity, multi-ethnicity, and wide distribution of the different anopheline vectors. The predominant malaria parasites in India for malaria are P. Falciparum (Pf) and P. Vivax (Pv). This study analyzes the spatial patterns of malaria cases, specifically the two dominant malaria vectors, at the regional level and its relation to seasonal precipitation. The results of our study revealed an overall decline in malaria cases in the later years. The spatial spread of malaria cases was more widespread during the normal monsoon years vs drought years, which can be attributed to more conducive environment for mosquitos to breed. The correlation analysis revealed a stronger correlation between malaria case burden and monsoon precipitation. Spatially, the strongest correlation between seasonal and annual precipitation, and malaria case burden were located across the northern plains and northeast India. The results of this research further our understanding of the relationship between seasonal precipitation and malaria case burden at the regional level across India.
This study examined the spatio-temporal dynamics of malaria epidemiological patterns considering environmental(vegetation, water bodies, slope, elevation) and climatic factors (rainfall, temperature and relative humidity) in Ondo State, Nigeria, from 2013 to 2017 using ArcGIS 10.4 and QGIS software. The factors influencing malaria were studied using a multi-criteria analysis (Analytical Hierarchical Process-AHP). The trend analysis revealed an increase in cases over time, indicating a significant increase in the occurrence of malaria in all study areas. The most important climatic variable impacting malaria transmission in the study was temperature. Nevertheless, other environmental and climatic factors causing transmission include vegetation, water bodies, slopes, elevation, rainfall, and relative humidity. With the exception of Okitipupa, the study identified high-risk locations (vulnerable areas/hot spots) in almost all of the local government areas, while Ondo East, Akure South, Akoko South West, and Owo are the most vulnerable areas. The findings reveal that the malaria incidence is high in the developed LGAs having more towns where temperature is higher due to several anthropogenesis activities, high population and increased land-use. Thus, in-depth epidemiological studies on malaria should be undertaken in Ondo State and other regions of Nigeria considering environmental factors impacting malaria incidence as this will enable one to ascertain the major factors influencing the disease, thereby taking adequate measures to curb the increase in incidence.
Dengue fever, a mosquito-borne fatal disease, brings a huge health burden in tropical regions. With global warming, rapid urbanization and the expansion of mosquitoes, dengue fever is expected to spread to many subtropical regions, leading to increased potential health risks on local populations. So far, limited studies assessed the dengue fever risk spatially for subtropical non-endemic regions hindering the development of related public health management. Therefore, we proposed a spatial hazard-exposure-vulnerability assessment framework for mapping the dengue fever risk in Hong Kong. Firstly, the spatial distribution of the habitat suitability for Aedes albopictus, the mosquito proxy for the dengue fever hazard, was predicted using a species distribution model (e.g., MaxEnt) relying on a list of variables related to local climate, urban morphology, and landscape metrics. Secondly, the spatial autocorrelation between high dengue hazard and high human popula-tion exposure in urban areas was measured. Finally, the dengue fever risk was assessed at community scale by integrating the results of vulnerability analysis basing on census data. This approach allowed the identification of 17 high-risk spots within Hong Kong. The landscape metrics about land utilities and vegetations, and urban morphological characteristics are the influential factors on the spatial distribution of dengue vector. In addition, the underlying factors behind each hot spot were investigated, and specific suggestions for dengue prevention were proposed accordingly. The findings provide a useful reference for developing local dengue fever risk pre-vention measures, with the proposed method easily exportable to other high-density cities within subtropical Asia and elsewhere.
Mosquitoes, including invasive species like the Asian tiger mosquito Aedes albopictus, alongside native species Culex pipiens s.l., pose a significant nuisance to humans and serve as vectors for mosquito-borne diseases in urban areas. Understanding the impact of water infrastructure characteristics, climatic conditions, and management strategies on mosquito occurrence and effectiveness of control measures to assess their implications on mosquito occurrence is crucial for effective vector control. In this study, we examined data collected during the local vector control program in Barcelona, Spain, focusing on 234,225 visits to 31,334 different sewers, as well as 1817 visits to 152 fountains between 2015 and 2019. We investigated both the colonization and recolonization processes of mosquito larvae within these water infrastructures. Our findings revealed higher larval presence in sandbox-sewers compared to siphonic or direct sewers, and the presence of vegetation and the use of naturalized water positively influenced larval occurrence in fountains. The application of larvicidal treatment significantly reduced larvae presence; however, recolonization rates were negatively affected by the time elapsed since treatment. Climatic conditions played a critical role in the colonization and recolonization of sewers and urban fountains, with mosquito occurrence exhibiting non-linear patterns and, generally, increasing at intermediate temperatures and accumulated rainfall levels. This study emphasizes the importance of considering sewers and fountains characteristics and climatic conditions when implementing vector control programs to optimize resources and effectively reduce mosquito populations.
BACKGROUND: This study aimed to examine the spatial variations in malaria hotspots along Dilla sub-watershed in western Ethiopia based on environmental factors for the prevalence; and compare the risk level along with districts and their respective kebele. The purpose was to identify the extent of the community’s exposure to the risk of malaria due to their geographical and biophysical situations, and the results contribute to proactive interventions to halt the impacts. METHODS: The descriptive survey design was used in this study. Ethiopia Central Statistical Agency based meteorological data, digital elevation model, and soil and hydrological data were integrated with other primary data such as the observations of the study area for ground truthing. The spatial analysis tools and software were used for watershed delineation, generating malaria risk map for all variables, reclassification of factors, weighted overlay analysis, and generation of risk maps. RESULTS: The findings of the study reveal that the significant spatial variations in magnitudes of malaria risk have persisted in the watershed due to discrepancy in their geographical and biophysical situations. Accordingly, significant areas in most of the districts in the watershed are characterized by high and moderate in malaria risks. In general, out of the total area of the watershed which accounts 2773 km2, about 54.8% (1522km2) identified as high and moderate malaria risk area. These areas are explicitly identified and mapped along with the districts and kebele in the watershed to make the result suitable for planning proactive interventions and other decision making. CONCLUSIONS: The research output may help the government and humanitarian organizations to prioritize the interventions based on identified spatial situations in severity of malaria risks. The study was aimed only for hotspot analysis which may not provide inclusive account for community’s vulnerability to malaria. Thus, the findings in this study needs to be integrated with the socio-economic and other relevant data for better malaria management in the area. Therefore, future research should comprehend the analysis of vulnerability to the impacts of malaria through integrating the level of exposure to the risk, for instance identified in this study, with factors of sensitivity and adaptation capacity of the local community.
BackgroundLymphatic filariasis (LF) is a vector-borne parasitic disease which affects 70 million people worldwide and causes life-long disabilities. In Bangladesh, there are an estimated 44,000 people suffering from clinical conditions such as lymphoedema and hydrocoele, with the greatest burden in the northern Rangpur division. To better understand the factors associated with this distribution, this study examined socio-economic and environmental factors at division, district, and sub-district levels. MethodologyA retrospective ecological study was conducted using key socio-economic (nutrition, poverty, employment, education, house infrastructure) and environmental (temperature, precipitation, elevation, waterway) factors. Characteristics at division level were summarised. Bivariate analysis using Spearman’s rank correlation coefficient was conducted at district and sub-district levels, and negative binomial regression analyses were conducted across high endemic sub-districts (n = 132). Maps were produced of high endemic sub-districts to visually illustrate the socio-economic and environmental factors found to be significant. ResultsThe highest proportion of rural population (86.8%), poverty (42.0%), tube well water (85.4%), and primary employment in agriculture (67.7%) was found in Rangpur division.Spearman’s rank correlation coefficient at district and sub-district level show that LF morbidity prevalence was significantly (p<0.05) positively correlated with households without electricity (district r(s) = 0.818; sub-district r(s) = 0.559), households with tube well water (sub-district r(s) = 0.291), households without toilet (district r(s) = 0.504; sub-district r(s) = 0.40), mean annual precipitation (district r(s) = 0.695; sub-district r(s) = 0.503), mean precipitation of wettest quarter (district r(s) = 0.707; sub-district r(s) = 0.528), and significantly negatively correlated with severely stunted children (district r(s) = -0.723; sub-district r(s) = -0.370), mean annual temperature (district r(s) = -0.633.; sub-district r(s) = 0.353) and mean temperature (wettest quarter) ((district r(s) = -0.598; sub-district r(s) = 0.316)Negative binomial regression analyses at sub-district level found severely stunted children (p = <0.001), rural population (p = 0.002), poverty headcount (p = 0.001), primary employment in agriculture (p = 0.018), households without toilet (p = <0.001), households without electricity (p = 0.002) and mean temperature (wettest quarter) (p = 0.045) to be significant. ConclusionsThis study highlights the value of using available data to identify key drivers associated with high LF morbidity prevalence, which may help national LF programmes better identify populations at risk and implement timely and targeted public health messages and intervention strategies. Author summaryLymphatic Filariasis (LF) is particularly associated with poverty and living in a rural area, where disease transmitting mosquito vectors thrive. To better understand the socio-economic and environmental factors associated with the LF morbidity prevalence distribution in Bangladesh, this study examined publicly available data and used descriptive, statistical, and mapping methods to highlight key associations. Results found that high risk populations were those living in rural areas, employed in agriculture, with high levels of poverty, and houses without electricity or toilets, and where temperatures in the rainy season were lower than other regions of the country. These findings will help to inform public health messages and implement interventions for people affected by LF morbidity, but also to help reduce any current and future risk of transmission. This will support progress to achieving WHO elimination targets, leading to a future free of suffering for people affected by LF associated morbidity.
Climate change can increase the spread of infectious diseases and public health concerns. Malaria is one of the endemic infectious diseases of Iran, whose transmission is strongly influenced by climatic conditions. The effect of climate change on malaria in the southeastern Iran from 2021 to 2050 was simulated by using artificial neural networks (ANNs). Gamma test (GT) and general circulation models (GCMs) were used to determine the best delay time and to generate the future climate model under two distinct scenarios (RCP2.6 and RCP8.5). To simulate the various impacts of climate change on malaria infection, ANNs were applied using daily collected data for 12 years (from 2003 to 2014). The future climate of the study area will be hotter by 2050. The simulation of malaria cases elucidated that there is an intense increasing trend in malaria cases under the RCP8.5 scenario until 2050, with the highest number of infections occurring in the warmer months. Rainfall and maximum temperature were identified as the most influential input variables. Optimum temperatures and increased rainfall provide a suitable environment for the transmission of parasites and cause an intense increase in the number of infection cases with a delay of approximately 90 days. ANNs were introduced as a practical tool for simulating the impact of climate change on the prevalence, geographic distribution, and biological activity of malaria and for estimating the future trend of the disease in order to adopt protective measures in endemic areas.
This paper summarises the lessons learnt in dengue epidemiology, risk factors, and prevention in Singapore over the last half a century, during which Singapore evolved from a city of 1.9 million people to a highly urban globalised city-state with a population of 5.6 million. Set in a tropical climate, urbanisation among green foliage has created ideal conditions for the proliferation of Aedes aegypti and Aedes albopictus, the mosquito vectors that transmit dengue. A vector control programme, largely for malaria, was initiated as early as 1921, but it was only in 1966 that the Vector Control Unit (VCU) was established to additionally tackle dengue haemorrhagic fever (DHF) that was first documented in the 1960s. Centred on source reduction and public education, and based on research into the bionomics and ecology of the vectors, the programme successfully reduced the Aedes House Index (HI) from 48% in 1966 to <5% in the 1970s. Further enhancement of the programme, including through legislation, suppressed the Aedes HI to around 1% from the 1990s. The current programme is characterised by 4 key features: (i) proactive inter-epidemic surveillance and control that is stepped up during outbreaks; (ii) risk-based prevention and intervention strategies based on advanced data analytics; (iii) coordinated inter-sectoral cooperation between the public, private, and people sectors; and (iv) evidence-based adoption of new tools and strategies. Dengue seroprevalence and force of infection (FOI) among residents have substantially and continuously declined over the 5 decades. This is consistent with the observation that dengue incidence has been delayed to adulthood, with severity highest among the elderly. Paradoxically, the number of reported dengue cases and outbreaks has increased since the 1990s with record-breaking epidemics. We propose that Singapore's increased vulnerability to outbreaks is due to low levels of immunity in the population, constant introduction of new viral variants, expanding urban centres, and increasing human density. The growing magnitude of reported outbreaks could also be attributed to improved diagnostics and surveillance, which at least partially explains the discord between rising trend in cases and the continuous reduction in dengue seroprevalence. Changing global and local landscapes, including climate change, increasing urbanisation and global physical connectivity are expected to make dengue control even more challenging. The adoption of new vector surveillance and control tools, such as the Gravitrap and Wolbachia technology, is important to impede the growing threat of dengue and other Aedes-borne diseases.
Simulium damnosum s.l., the most important vector of onchocerciasis in Africa, is a complex of sibling species described on the basis of differences in their larval polytene chromosomes. These (cyto) species differ in their geographical distributions, ecologies and epidemiological roles. In Togo and Benin, distributional changes have been recorded as a consequence of vector control and environmental changes (e.g. creation of dams, deforestation), with potential epidemiological consequences. We review the distribution of cytospecies in Togo and Benin and report changes observed from 1975 to 2018. The elimination of the Djodji form of S. sanctipauli in south-western Togo in 1988 seems to have had no long-term effects on the distribution of the other cytospecies, despite an initial surge by S. yahense. Although we report a general tendency for long-term stability in most cytospecies’ distributions, we also assess how the cytospecies’ geographical distributions have fluctuated and how they vary with the seasons. In addition to seasonal expansions of geographical ranges by all species except S. yahense, there are seasonal variations in the relative abundances of cytospecies within a year. In the lower Mono river, the Beffa form of S. soubrense predominates in the dry season but is replaced as the dominant taxon in the rainy season by S. damnosum s.str. Deforestation was previously implicated in an increase of savanna cytospecies in southern Togo (1975-1997), but our data had little power to support (or refute) suggestions of a continuing increase, partly because of a lack of recent sampling. In contrast, the construction of dams and other environmental changes including climate change seem to be leading to decreases in the populations of S. damnosum s.l. in Togo and Benin. If so, combined with the disappearance of the Djodji form of S. sanctipauli, a potent vector, plus historic vector control actions and community directed treatments with ivermectin, onchocerciasis transmission in Togo and Benin is much reduced compared with the situation in 1975.
The impacts of climate change and environmental predictors on malaria epidemiology remain unclear and not well investigated in the Sub-Sahara African region. This study was aimed to investigate the nonlinear effects of climate and environmental factors on monthly malaria cases in northwest Ethiopia, considering space-time interaction effects. The monthly malaria cases and populations sizes of the 152 districts were obtained from the Amhara public health institute and the central statistical agency of Ethiopia. The climate and environmental data were retrieved from US National Oceanic and Atmospheric Administration. The data were analyzed using a spatiotemporal generalized additive model. The spatial, temporal, and space-time interaction effects had higher contributions in explaining the spatiotemporal distribution of malaria transmissions. Malaria transmission was seasonal, in which a higher number of cases occurred from September to November. The long-term trend of malaria incidence has decreased between 2012 and 2018 and has turned to an increased pattern since 2019. Areas neighborhood to the Abay gorge and Benshangul-Gumuz, South Sudan, and Sudan border have higher spatial effects. Climate and environmental predictors had significant nonlinear effects, in which their effects are not stationary through the ranges of values of variables, and they had a smaller contributions in explaining the variabilities of malaria incidence compared to seasonal, spatial and temporal effects. Effects of climate and environmental predictors were nonlinear and varied across areas, ecology, and landscape of the study sites, which had little contribution to explaining malaria transmission variabilities with an account of space and time dimensions. Hence, exploring and developing an early warning system that predicts the outbreak of malaria transmission would have an essential role in controlling, preventing, and eliminating malaria in areas with lower and higher transmission levels and ultimately lead to the achievement of malaria GTS milestones.
Mosquitoes in the genus Culex are primary vectors in the US for West Nile virus (WNV) and other arboviruses. Climatic drivers such as temperature have differential effects on species-specific changes in mosquito range, distribution, and abundance, posing challenges for population modeling, disease forecasting, and subsequent public health decisions. Understanding these differences in underlying biological dynamics is crucial in the face of climate change. METHODS: We collected empirical data on thermal response for immature development rate, egg viability, oviposition, survival to adulthood, and adult lifespan for Culex pipiens, Cx. quinquefasciatus, Cx. tarsalis, and Cx. restuans from existing literature according to the PRISMA scoping review guidelines. RESULTS: We observed linear relationships with temperature for development rate and lifespan, and nonlinear relationships for survival and egg viability, with underlying variation between species. Optimal ranges and critical minima and maxima also appeared varied. To illustrate how model output can change with experimental input data from individual Culex species, we applied a modified equation for temperature-dependent mosquito type reproduction number for endemic spread of WNV among mosquitoes and observed different effects. CONCLUSIONS: Current models often input theoretical parameters estimated from a single vector species; we show the need to implement the real-world heterogeneity in thermal response between species and present a useful data resource for researchers working toward that goal.
The Anastasia Mosquito Control District, which manages mosquitoes in St. Johns County in northeastern Florida, has observed that the maximum numbers of the salt marsh mosquitoes, Aedes taeniorhynchus and Ae. sollicitan appeared to shift or change relative to each other, as evidenced by the Centers for Disease Control and Prevention (CDC) light trap data in the past 17 years. The aim of this study was to analyze environmental data to identify and explore these changes. Data from CDC light traps, temperature, rainfall, and tidal levels were analyzed using ANOVA. Analyses showed the 2 species had maximum abundance at different temperatures, which translated into seasonal differences with peaks of Ae. taeniorhynchus in the summer and, to a lesser extent, later in the year, and Ae. sollicitans with a peak in the autumn. This seasonal pattern was reflected in rainfall (more rain in autumn than in summer) and also, in the general area, in tidal levels (mean highest tide levels at the recording station were in autumn). The research demonstrated that simplifying the mosquito data, initially using only very high trap numbers (Mean ± 2 SD) that are important for control, identified, and made the seasonal pattern very obvious. The pattern was also observed using all the data but, although significant, was not as clear. Having identified tide as a potential driving variable, further research needs to detail spatial tidal patterns to identify areas and timing of flooding and explore the relationship between salinity and mosquito species and abundance. This is important as sea levels rise and climate changes, both potentially changing the mosquito situation and affecting control actions.
Air pollution may be involved in spreading dengue fever (DF) besides rainfalls and warmer temperatures. While particulate matter (PM), especially those with diameter of 10 μm (PM10) or 2.5 μm or less (PM25), and NO2 increase the risk of coronavirus 2 infection, their roles in triggering DF remain unclear. We explored if air pollution factors predict DF incidence in addition to the classic climate factors. Public databases and DF records of two southern cities in Taiwan were used in regression analyses. Month order, PM10 minimum, PM2.5 minimum, and precipitation days were retained in the enter mode model, and SO2 minimum, O3 maximum, and CO minimum were retained in the stepwise forward mode model in addition to month order, PM10 minimum, PM2.5 minimum, and precipitation days. While PM2.5 minimum showed a negative contribution to the monthly DF incidence, other variables showed the opposite effects. The sustain of month order, PM10 minimum, PM2.5 minimum, and precipitation days in both regression models confirms the role of classic climate factors and illustrates a potential biological role of the air pollutants in the life cycle of mosquito vectors and dengue virus and possibly human immune status. Future DF prevention should concern the contribution of air pollution besides the classic climate factors.
Lutzomyia longipalpis is known as one of the primary insect vectors of visceral leishmaniasis. For such ectothermic organisms, the ambient temperature is a critical life factor. However, the impact of temperature has been ignored in many induced-stress situations of the vector life. Therefore, this study explored the interaction of Lu. longipalpis with temperature by evaluating its behaviour across a thermal gradient, thermographic recordings during blood-feeding on mice, and the gene expression of heat shock proteins (HSP) when insects were exposed to extreme temperature or infected. The results showed that 72 h after blood ingestion, Lu. longipalpis became less active and preferred relatively low temperatures. However, at later stages of blood digestion, females increased their activity and remained at higher temperatures. Real-time imaging showed that the body temperature of females can adjust rapidly to the host and remain constant until the end of blood-feeding. Insects also increased the expression of HSP90(83) during blood-feeding. Our findings suggest that Lu. longipalpis interacts with temperature by using its behaviour to avoid temperature-induced physiological damage during the gonotrophic cycle. However, the expression of certain HSP might be triggered to mitigate thermal stress in situations where a behavioural response is not the best option.
Climate change is an important driver of the increased spread of dengue from tropical and subtropical regions to temperate areas around the world. Climate variables such as temperature and precipitation influence the dengue vector’s biology, physiology, abundance, and life cycle. Thus, an analysis is needed of changes in climate change and their possible relationships with dengue incidence and the growing occurrence of epidemics recorded in recent decades. OBJECTIVES: This study aimed to assess the increasing incidence of dengue driven by climate change at the southern limits of dengue virus transmission in South America. METHODS: We analyzed the evolution of climatological, epidemiological, and biological variables by comparing a period of time without the presence of dengue cases (1976-1997) to a more recent period of time in which dengue cases and important outbreaks occurred (1998-2020). In our analysis, we consider climate variables associated with temperature and precipitation, epidemiological variables such as the number of reported dengue cases and incidence of dengue, and biological variables such as the optimal temperature ranges for transmission of dengue vector. RESULTS: The presence of dengue cases and epidemic outbreaks are observed to be consistent with positive trends in temperature and anomalies from long-term means. Dengue cases do not seem to be associated with precipitation trends and anomalies. The number of days with optimal temperatures for dengue transmission increased from the period without dengue cases to the period with occurrences of dengue cases. The number of months with optimal transmission temperatures also increased between periods but to a lesser extent. CONCLUSIONS: The higher incidence of dengue virus and its expansion to different regions of Argentina seem to be associated with temperature increases in the country during the past two decades. The active surveillance of both the vector and associated arboviruses, together with continued meteorological data collection, will facilitate the assessment and prediction of future epidemics that use trends in the accelerated changes in climate. Such surveillance should go hand in hand with efforts to improve the understanding of the mechanisms driving the geographic expansion of dengue and other arboviruses beyond the current limits. https://doi.org/10.1289/EHP11616.
Facing a warming climate, many tropical species-including the arthropod vectors of several infectious diseases-will be displaced to higher latitudes and elevations. These shifts are frequently projected for the future, but rarely documented in the present day. Here, we use one of the most comprehensive datasets ever compiled by medical entomologists to track the observed range limits of African malaria mosquito vectors (Anopheles spp.) from 1898 to 2016. Using a simple regression approach, we estimate that these species’ ranges gained an average of 6.5 m of elevation per year, and the southern limits of their ranges moved polewards 4.7 km per year. These shifts would be consistent with the local velocity of recent climate change, and might help explain the incursion of malaria transmission into new areas over the past few decades. Confirming that climate change underlies these shifts, and applying similar methods to other disease vectors, are important directions for future research.
Climate-related phenomena in Peru have been slowly but continuously changing in recent years beyond historical variability. These include sea surface temperature increases, irregular precipitation patterns and reduction of glacier-covered areas. In addition, climate scenarios show amplification in rainfall variability related to the warmer conditions associated with El Niño events. Extreme weather can affect human health, increase shocks and stresses to the health systems, and cause large economic losses. In this article, we study the characteristics of El Niño events in Peru, its health and economic impacts and we discuss government preparedness for this kind of event, identify gaps in response, and provide evidence to inform adequate planning for future events and mitigating impacts on highly vulnerable regions and populations. This is the first case study to review the impact of a Coastal El Niño event on Peru’s economy, public health, and governance. The 2017 event was the third strongest El Niño event according to literature, in terms of precipitation and river flooding and caused important economic losses and health impacts. At a national level, these findings expose a need for careful consideration of the potential limitations of policies linked to disaster prevention and preparedness when dealing with El Niño events. El Niño-related policies should be based on local-level risk analysis and efficient preparedness measures in the face of emergencies.
The expansion of tick-borne diseases challenges ecologists, epidemiologists, and public health professionals to understand the mechanisms underlying its emergence. The vast majority of tick-borne disease research emphasizes Ixodes spp. and Borrelia burgdorferi, with less known about other Ixodidae ticks that serve as vectors for an increasing number of pathogens of public health concern. Here, we review and discuss the current knowledge of tick and tick-borne pathogens in an undersurveilled region of the United States. We discuss how landscape shifts may potentially influence tick vector dynamics and expansion. We also discuss the impact of climate change on the phenology of ticks and subsequent disease transmission. Increased efforts in the Central Plains to conduct basic science will help understand the patterns of tick distribution and pathogen prevalence. It is crucial to develop intensive datasets that may be used to generate models that can aid in developing mitigation strategies.
Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.
The Asian tiger mosquito Aedes albopictus is currently spreading across Europe, facilitated by climate change and global transportation. It is a vector of arboviruses causing human diseases such as chikungunya, dengue hemorrhagic fever and Zika fever. For the majority of these diseases, no vaccines or therapeutics are available. Options for the control of Ae. albopictus are limited by European regulations introduced to protect biodiversity by restricting or phasing out the use of pesticides, genetically modified organisms (GMOs) or products of genome editing. Alternative solutions are thus urgently needed to avoid a future scenario in which Europe faces a choice between prioritizing human health or biodiversity when it comes to Aedes-vectored pathogens. To ensure regulatory compliance and public acceptance, these solutions should preferably not be based on chemicals or GMOs and must be cost-efficient and specific. The present review aims to synthesize available evidence on RNAi-based mosquito vector control and its potential for application in the European Union. The recent literature has identified some potential target sites in Ae. albopictus and formulations for delivery. However, we found little information concerning non-target effects on the environment or human health, on social aspects, regulatory frameworks, or on management perspectives. We propose optimal designs for RNAi-based vector control tools against Ae. albopictus (target product profiles), discuss their efficacy and reflect on potential risks to environmental health and the importance of societal aspects. The roadmap from design to application will provide readers with a comprehensive perspective on the application of emerging RNAi-based vector control tools for the suppression of Ae. albopictus populations with special focus on Europe.
Malaria remains a major public health burden to children under five, especially in Eastern Africa (E.A), -a region that is also witnessing the increasing occurrence of floods and extreme climate change. The present study, therefore, explored the trends in floods, as well as the association of their occurrence and duration with the malaria incidence in children < 5 years in five E.A partner countries of Forum for China-Africa Cooperation (FOCAC), including Ethiopia, Kenya, Somalia, Sudan, and Tanzania between 1990 and 2019. METHODS: A retrospective analysis of data retrieved from two global sources was performed: the Emergency Events Database (EM-DAT) and the Global Burden of Diseases Study (GBD) between 1990 and 2019. Using SPSS 20.0, a correlation was determined based on ρ= -1 to + 1, as well as the statistical significance of P = < 0.05. Time plots of trends in flooding and malaria incidence were generated in 3 different decades using R version 4.0. RESULTS: Between 1990 and 2019, the occurrence and duration of floods among the five E.A partner countries of FOCAC increased and showed an upward trend. On the contrary, however, this had an inverse and negative, as well as a weak correlation on the malaria incidence in children under five years. Only Kenya, among the five countries, showed a perfect negative correction of malaria incidence in children under five with flood occurrence (ρ = -0.586**, P-value = 0.001) and duration (ρ = -0.657**, P-value = < 0.0001). CONCLUSIONS: This study highlights the need for further research to comprehensively explore how different climate extreme events, which oftentimes complement floods, might be influencing the risk of malaria in children under five in five E.A malaria-endemic partner countries of FOCAC. Similarly, it ought to consider investigating the influence of other attributes apart from flood occurrence and duration, which also compound floods like displacement, malnutrition, and water, sanitation and hygiene on the risk and distribution of malaria and other climate-sensitive diseases.
BACKGROUND: Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India. METHODS: We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk. FINDINGS: The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model. INTERPRETATION: This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts. FUNDING: US National Institutes of Health.
The recent global expansion of dengue has been facilitated by changes in urbanisation, mobility, and climate. In this work, we project future changes in dengue incidence and case burden to 2099 under the latest climate change scenarios. We fit a statistical model to province-level monthly dengue case counts from eight countries across Southeast Asia, one of the worst affected regions. We project that dengue incidence will peak this century before declining to lower levels with large variations between and within countries. Our findings reveal that northern Thailand and Cambodia will show the biggest decreases and equatorial areas will show the biggest increases. The impact of climate change will be counterbalanced by income growth, with population growth having the biggest influence on increasing burden. These findings can be used for formulating mitigation and adaptation interventions to reduce the immediate growing impact of dengue virus in the region.
We have limited knowledge on the impact of hydrometeorological conditions on dengue incidence in China and its associated disease burden in a future with a changed climate. This study projects the excess risk of dengue caused by climate change-induced hydrometeorological conditions across mainland China. METHODS: In this modelling study, the historical association between the Palmer drought severity index (PDSI) and dengue was estimated with a spatiotemporal Bayesian hierarchical model from 70 cities. The association combined with the dengue-transmission biological model was used to project the annual excess risk of dengue related to PDSI by 2100 across mainland China, under three representative concentration pathways ([RCP] 2·6, RCP 4·5, and RCP 8·5). FINDINGS: 93 101 dengue cases were reported between 2013 and 2019 in mainland China. Dry and wet conditions within 3 months lag were associated with increased risk of dengue. Locations with potential dengue risk in China will expand in the future. The hydrometeorological changes are projected to substantially affect the risk of dengue in regions with mid-to-low latitudes, especially the coastal areas under high emission scenarios. By 2100, the annual average increased excess risk is expected to range from 12·56% (95% empirical CI 9·54-22·24) in northwest China to 173·62% (153·15-254·82) in south China under the highest emission scenario. INTERPRETATION: Hydrometeorological conditions are predicted to increase the risk of dengue in the future in the south, east, and central areas of mainland China in disproportionate patterns. Our findings have implications for the preparation of public health interventions to minimise the health hazards of non-optimal hydrometeorological conditions in a context of climate change. FUNDING: National Natural Science Foundation of China.
Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with increasing incidence and geographic extent. The extent to which global climate change affects the incidence of SFTS disease remains obscure. We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in China. The spatial distribution of habitat suitability for the tick Haemaphysalis longicornis was predicted by applying a boosted regression tree model under four alternative climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) for the periods 2030-2039, 2050-2059, and 2080-2089. We incorporate the SFTS cases in the mainland of China from 2010 to 2019 with environmental variables and the projected distribution of H. longicornis into a generalized additive model to explore the current and future spatiotemporal dynamics of SFTS. Our results demonstrate an expanded geographic distribution of H. longicornis toward Northern and Northwestern China, showing a more pronounced change under the RCP8.5 scenario. In contrast, the environmental suitability of H. longicornis is predicted to be reduced in Central and Eastern China. The SFTS incidence in three time periods (2030-2039, 2050-2059, and 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. A heterogeneous trend across provinces, however, was observed, when an increased incidence in Liaoning and Shandong provinces, while decreased incidence in Henan province is predicted. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for tick control and population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.
Dengue is a global problem that seems to be worsening, as hyper-urbanization associated with climate change has led to a significant increase in the abundance and geographical spread of its principal vector, the Aedes aegypti mosquito. Currently available solutions have not been able to stop the spread of dengue which shows the urgent need to implement alternative technologies as practical solutions. In a previous pilot trial, we demonstrated the efficacy and safety of the method ‘Natural Vector Control’ (NVC) in suppressing the Ae. aegypti vector population and in blocking the occurrence of an outbreak of dengue in the treated areas. Here, we expand the use of the NVC program in a large-scale 20 months intervention period in an entire city in southern Brazil. METHODS: Sterile male mosquitoes were produced from locally sourced Ae. aegypti mosquitoes by using a treatment that includes double-stranded RNA and thiotepa. Weekly massive releases of sterile male mosquitoes were performed in predefined areas of Ortigueira city from November 2020 to July 2022. Mosquito monitoring was performed by using ovitraps during the entire intervention period. Dengue incidence data was obtained from the Brazilian National Disease Surveillance System. FINDINGS: During the two epidemiological seasons, the intervention in Ortigueira resulted in up to 98.7% suppression of live progeny of field Ae. aegypti mosquitoes recorded over time. More importantly, when comparing the 2020 and 2022 dengue outbreaks that occurred in the region, the post-intervention dengue incidence in Ortigueira was 97% lower compared to the control cities. INTERPRETATION: The NVC method was confirmed to be a safe and efficient way to suppress Ae. aegypti field populations and prevent the occurrence of a dengue outbreak. Importantly, it has been shown to be applicable in large-scale, real-world conditions. FUNDING: This study was funded by Klabin S/A and Forrest Innovations Ltd.
Ixodes ricinus ticks are Scandinavia’s main vector for tick-borne encephalitis virus (TBEV), which infects many people annually. The aims of the present study were (i) to obtain information on the TBEV prevalence in host-seeking I. ricinus collected within the Øresund-Kattegat-Skagerrak (ØKS) region, which lies in southern Norway, southern Sweden and Denmark; (ii) to analyse whether there are potential spatial patterns in the TBEV prevalence; and (iii) to understand the relationship between TBEV prevalence and meteorological factors in southern Scandinavia. Tick nymphs were collected in 2016, in southern Scandinavia, and screened for TBEV, using pools of 10 nymphs, with RT real-time PCR, and positive samples were confirmed with pyrosequencing. Spatial autocorrelation and cluster analysis was performed with Global Moran’s I and SatScan to test for spatial patterns and potential local clusters of the TBEV pool prevalence at each of the 50 sites. A climatic analysis was made to correlate parameters such as minimum, mean and maximum temperature, relative humidity and saturation deficit with TBEV pool prevalence. The climatic data were acquired from the nearest meteorological stations for 2015 and 2016. This study confirms the presence of TBEV in 12 out of 30 locations in Denmark, where six were from Jutland, three from Zealand and two from Bornholm and Falster counties. In total, five out of nine sites were positive from southern Sweden. TBEV prevalence of 0.7%, 0.5% and 0.5%, in nymphs, was found at three sites along the Oslofjord (two sites) and northern Skåne region (one site), indicating a potential concern for public health. We report an overall estimated TBEV prevalence of 0.1% in questing I. ricinus nymphs in southern Scandinavia with a region-specific prevalence of 0.1% in Denmark, 0.2% in southern Sweden and 0.1% in southeastern Norway. No evidence of a spatial pattern or local clusters was found in the study region. We found a strong correlation between TBEV prevalence in ticks and relative humidity in Sweden and Norway, which might suggest that humidity has a role in maintaining TBEV prevalence in ticks. TBEV is an emerging tick-borne pathogen in southern Scandinavia, and we recommend further studies to understand the TBEV transmission potential with changing climate in Scandinavia.
The emergence of potentially life-threatening zoonotic malaria caused by Plasmodium knowlesi nearly two decades ago has continued to challenge Malaysia healthcare. With a total of 376 P. knowlesi infections notified in 2008, the number increased to 2,609 cases in 2020 nationwide. Numerous studies have been conducted in Malaysian Borneo to determine the association between environmental factors and knowlesi malaria transmission. However, there is still a lack of understanding of the environmental influence on knowlesi malaria transmission in Peninsular Malaysia. Therefore, our study aimed to investigate the ecological distribution of human P. knowlesi malaria in relation to environmental factors in Peninsular Malaysia. A total of 2,873 records of human P. knowlesi infections in Peninsular Malaysia from 1st January 2011 to 31st December 2019 were collated from the Ministry of Health Malaysia and geolocated. Three machine learning-based models, maximum entropy (MaxEnt), extreme gradient boosting (XGBoost), and ensemble modeling approach, were applied to predict the spatial variation of P. knowlesi disease risk. Multiple environmental parameters including climate factors, landscape characteristics, and anthropogenic factors were included as predictors in both predictive models. Subsequently, an ensemble model was developed based on the output of both MaxEnt and XGBoost. Comparison between models indicated that the XGBoost has higher performance as compared to MaxEnt and ensemble model, with AUC(ROC) values of 0.933 ± 0.002 and 0.854 ± 0.007 for train and test datasets, respectively. Key environmental covariates affecting human P. knowlesi occurrence were distance to the coastline, elevation, tree cover, annual precipitation, tree loss, and distance to the forest. Our models indicated that the disease risk areas were mainly distributed in low elevation (75-345 m above mean sea level) areas along the Titiwangsa mountain range and inland central-northern region of Peninsular Malaysia. The high-resolution risk map of human knowlesi malaria constructed in this study can be further utilized for multi-pronged interventions targeting community at-risk, macaque populations, and mosquito vectors.
Climate change will affect the distribution of species in the future. To determine the vulnerable areas relating to CL in Iran, we applied two models, MaxEnt and RF, for the projection of the future distribution of the main vectors and reservoirs of CL. The results of the models were compared in terms of performance, species distribution maps, and the gain, loss, and stable areas. The models provided a reasonable estimate of species distribution. The results showed that the Northern and Southern counties of Iran, which currently do not have a high incidence of CL may witness new foci in the future. The Western, and Southwestern regions of the Country, which currently have high habitat suitability for the presence of some vectors and reservoirs, will probably significantly decrease in the future. Furthermore, the most stable areas are for T. indica and M. hurrianae in the future. So that, this species may remain a major reservoir in areas that are present under current conditions. With more local studies in the field of identifying vulnerable areas to CL, it can be suggested that the national CL control guidelines should be revised to include a section as a climate change adaptation plan.
Background The ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.Methods A series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.Results The performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller’s calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.Conclusions This comprehensive analysis suggests that there is no single 'best practice' climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.
Climate change may influence the incidence of infectious diseases including those transmitted by ticks. Rhipicephalus sanguineus complex has a worldwide distribution and transmits Rickettsial infections that could cause high mortality rates if untreated. We assessed the potential effects of climate change on the distribution of R. sanguineus in the Americas in 2050 and 2070 using the general circulation model CanESM5 and two shared socioeconomic pathways (SSPs), SSP2-4.5 (moderate emissions) and SSP2-8.5 (high emissions). A total of 355 occurrence points of R. sanguineus and eight uncorrelated bioclimatic variables were entered into a maximum entropy algorithm (MaxEnt) to produce 50 replicates per scenario. The area under the curve (AUC) value for the consensus model (>0.90) and the partial ROC value (>1.28) indicated a high predictive capacity. The models showed that the geographic regions currently suitable for R. sanguineus will remain stable in the future, but also predicted increases in habitat suitability in the Western U.S., Venezuela, Brazil and Bolivia. Scenario 4.5 showed an increase in habitat suitability for R. sanguineus in tropical and subtropical regions in both 2050 and 2070. Habitat suitability is predicted to remain constant in moist broadleaf forests and deserts but is predicted to decrease in flooded grasslands and savannas. Using the high emissions SSP5-8.5 scenario, habitat suitability in tropical and subtropical coniferous forests and temperate grasslands, savannas, and shrublands was predicted to be constant in 2050. In 2070, however, habitat suitability was predicted to decrease in tropical and subtropical moist broadleaf forests and increase in tropical and subtropical dry broadleaf forests. Our findings suggest that the current and potential future geographic distributions can be used in evidence-based strategies in the design of control plans aimed at reducing the risk of exposure to zoonotic diseases transmitted by R. sanguineus.
OBJECTIVES: Scrub typhus is an increasingly serious public health problem, which is becoming the most common vector-borne disease in Guangzhou. This study aimed to analyse the correlation between scrub typhus incidence and potential factors and rank the importance of influential factors. METHODS: We collected monthly scrub typhus cases, meteorological variables, rodent density (RD), Normalised Difference Vegetation Index (NDVI), and land use type in Guangzhou from 2006 to 2019. Correlation analysis and a random forest model were used to identify the risk factors for scrub typhus and predict the importance rank of influencing factors related to scrub typhus incidence. RESULTS: The epidemiological results of the scrub typhus cases in Guangzhou between 2006 and 2019 showed that the incidence rate was on the rise. The results of correlation analysis revealed that a positive relationship between scrub typhus incidence and meteorological factors of mean temperature (T(mean) ), accumulative rainfall (RF), relative humidity (RH), sunshine hours (SH), and NDVI, RD, population density, and green land coverage area (all p < 0.001). Additionally, we tested the relationship between the incidence of scrub typhus and the lagging meteorological factors through cross-correlation function, and found that incidence was positively correlated with 1-month lag T(mean) , 2-month lag RF, 2-month lag RH, and 6-month lag SH (all p < 0.001). Based on the random forest model, we found that the T(mean) was the most important predictor among the influential factors, followed by NDVI. CONCLUSIONS: Meteorological factors, NDVI, RD, and land use type jointly affect the incidence of scrub typhus in Guangzhou. Our results provide a better understanding of the influential factors correlated with scrub typus, which can improve our capacity for biological monitoring and help public health authorities to formulate disease control strategies.
Ross River virus (RRV) is the most common mosquito-borne infection in Australia. RRV disease is characterised by joint pain and lethargy, placing a substantial burden on individual patients, the healthcare system and economy. This burden is compounded by a lack of effective treatment or vaccine for the disease. The complex RRV disease ecology cycle includes a number of reservoirs and vectors that inhabit a range of environments and climates across Australia. Climate is known to influence humans, animals and the environment and has previously been shown to be useful to RRV prediction models. We developed a negative binomial regression model to predict monthly RRV case numbers and outbreaks in the Darling Downs region of Queensland, Australia. Human RRV notifications and climate data for the period July 2001 – June 2014 were used for model training. Model predictions were tested using data for July 2014 – June 2019. The final model was moderately effective at predicting RRV case numbers (Pearson’s r = 0.427) and RRV outbreaks (accuracy = 65%, sensitivity = 59%, specificity = 73%). Our findings show that readily available climate data can provide timely prediction of RRV outbreaks.
Leptotrombidium scutellare is one of the six main vectors of scrub typhus in China and is a putative vector of hemorrhagic fever with renal syndrome (HFRS). This mite constitutes a large portion of the chigger mite community in southwest China. Although empirical data on its distribution are available for several investigated sites, knowledge of the species’ association with human well-being and involvement in the prevalence of mite-borne diseases remains scarce. METHODS: Occurrence data on the chigger mite were obtained from 21 years (2001-2021) of field sampling. Using boosted regression tree (BRT) ecological models based on climate, land cover and elevation variables, we predicted the environmental suitability for L. scutellare in Yunnan and Sichuan Provinces. The potential distribution range and shifts in the study area for near-current and future scenarios were mapped and the scale of L. scutellare interacting with human activities was evaluated. We tested the explanatory power of the occurrence probability of L. scutellare on incidences of mite-borne diseases. RESULTS: Elevation and climate factors were the most important factors contributing to the prediction of the occurrence pattern of L. scutellare. The most suitable habitats for this mite species were mainly concentrated around high-elevation areas, with predictions for the future showing a trend towards a reduction. Human activity was negatively correlated with the environmental suitability of L. scutellare. The occurrence probability of L. scutellare in Yunnan Province had a strong explanatory power on the epidemic pattern of HFRS but not scrub typhus. CONCLUSIONS: Our results emphasize the exposure risks introduced by L. scutellare in the high-elevation areas of southwest China. Climate change may lead to a range contraction of this species towards areas of higher elevation and lessen the associated exposure risk. A comprehensive understanding of the transmission risk requires more surveillance efforts.
The spatio-temporal distribution of leishmaniasis, a parasitic vector-borne zoonotic disease, is significantly impacted by land-use change and climate warming in the Americas. However, predicting and containing outbreaks is challenging as the zoonotic Leishmania system is highly complex: leishmaniasis (visceral, cutaneous and muco-cutaneous) in humans is caused by up to 14 different Leishmania species, and the parasite is transmitted by dozens of sandfly species and is known to infect almost twice as many wildlife species. Despite the already broad known host range, new hosts are discovered almost annually and Leishmania transmission to humans occurs in absence of a known host. As such, the full range of Leishmania hosts is undetermined, inhibiting the use of ecological interventions to limit pathogen spread and the ability to accurately predict the impact of global change on disease risk. Here, we employed a machine learning approach to generate trait profiles of known zoonotic Leishmania wildlife hosts (mammals that are naturally exposed and susceptible to infection) and used trait-profiles of known hosts to identify potentially unrecognized hosts. We found that biogeography, phylogenetic distance, and study effort best predicted Leishmania host status. Traits associated with global change, such as agricultural land-cover, urban land-cover, and climate, were among the top predictors of host status. Most notably, our analysis suggested that zoonotic Leishmania hosts are significantly undersampled, as our model predicted just as many unrecognized hosts as unknown hosts. Overall, our analysis facilitates targeted surveillance strategies and improved understanding of the impact of environmental change on local transmission cycles.
Dengue fever is a tropical disease and a major public health concern, and almost half of the world’s population lives in areas at risk of contracting this disease. Climate change is identified by WHO and other international health authorities as one of the primary factors that contribute to the rapid spread of dengue fever. METHODS: We evaluated the effect of sanitation on the cross-correlation between rainfall and the first symptoms of dengue in the city of Mato Grosso do Sul, which is in a state in the Midwest region of Brazil, and employed the time-lagged detrended cross-correlation analysis (DCCAC) method. RESULTS: Co-movements were obtained through the time-phased DCCAC to analyze the effects of climatic variables on arboviruses. The use of a time-lag analysis was more robust than DCCAC without lag to present the behavior of dengue cases in relation to accumulated precipitation. Our results show that the cross-correlation between rain and dengue increased as the city implemented actions to improve basic sanitation in the city. CONCLUSION: With climate change and the increase in the global average temperature, mosquitoes are advancing beyond the tropics, and our results show that cities with improved sanitation have a high correlation between dengue and annual precipitation. Public prevention and control policies can be targeted according to the period of time and the degree of correlation calculated to structure vector control and prevention work in places where sanitation conditions are adequate.
Mosquito-borne flaviviruses are emerging pathogens with zoonotic potential. Due to the recent climate and environmental changes, they are spreading across Europe, becoming a major threat for public and veterinary health. West Nile virus (WNV) and Usutu virus (USUV) are arboviruses that are responsible for multiple disease outbreaks in different species of birds, reptiles, and mammals, including humans. This review reports and compares the clinical signs as well as the gross and microscopic pathological features during natural infection with WNV and USUV in wild and domestic animals, as well as in humans. The main objective of this comparative review is to delineate the common features and the specific differences that characterize WNV- and USUVinduced diseases in each group of species and to highlight the main gaps in knowledge that could provide insight for further investigation on the pathogenesis and neurovirulence of these viruses.
West Nile virus (WNV) primarily infects birds and mosquitoes but has also caused over 2,000 human deaths, and >50,000 reported human cases in the United States. Expected numbers of WNV neuroinvasive cases for the present were described for the Northeastern United States, using a negative binomial model. Changes in temperature-based suitability for WNV due to climate change were examined for the next decade using a temperature-trait model. WNV suitability was generally expected to increase over the next decade due to changes in temperature, but the changes in suitability were generally small. Many, but not all, populous counties in the northeast are already near peak suitability. Several years in a row of low case numbers is consistent with a negative binomial, and should not be interpreted as a change in disease dynamics. Public health budgets need to be prepared for the expected infrequent years with higher-than-average cases. Low-population counties that have not yet had a case are expected to have similar probabilities of having a new case as nearby low-population counties with cases, as these absences are consistent with a single statistical distribution and random chance.
Vector-borne diseases, including those transmitted by mosquitoes, account for more than 17% of infectious diseases worldwide. This number is expected to rise with an increased spread of vector mosquitoes and viruses due to climate change and man-made alterations to ecosystems. Among the most common, medically relevant mosquito-borne infections are those caused by arthropod-borne viruses (arboviruses), especially members of the genera Flavivirus and Alphavirus. Arbovirus infections can cause severe disease in humans, livestock and wildlife. Severe consequences from infections include congenital malformations as well as arthritogenic, haemorrhagic or neuroinvasive disease. Inactivated or live-attenuated vaccines (LAVs) are available for a small number of arboviruses; however there are no licensed vaccines for the majority of these infections. Here we discuss recent developments in pan-arbovirus LAV approaches, from site-directed attenuation strategies targeting conserved determinants of virulence to universal strategies that utilize genome-wide re-coding of viral genomes. In addition to these approaches, we discuss novel strategies targeting mosquito saliva proteins that play an important role in virus transmission and pathogenesis in vertebrate hosts. For rapid pre-clinical evaluations of novel arbovirus vaccine candidates, representative in vitro and in vivo experimental systems are required to assess the desired specific immune responses. Here we discuss promising models to study attenuation of neuroinvasion, neurovirulence and virus transmission, as well as antibody induction and potential for cross-reactivity. Investigating broadly applicable vaccination strategies to target the direct interface of the vertebrate host, the mosquito vector and the viral pathogen is a prime example of a One Health strategy to tackle human and animal diseases.
OBJECTIVE: Nurses are well positioned to play an integral role in the mitigation of climate change and climate-driven vector-borne diseases, however, they lack awareness and knowledge about their role. The purpose of this scoping review was to map existing literature on nurses’ perceptions, knowledge, attitudes, and experiences with vector-borne diseases, specifically Lyme disease and West Nile virus. DESIGN: A scoping review was conducted using Joanna Briggs Institute (JBI) scoping review methodology. CINAHL, ProQuest Nursing & Allied Health Premium, MEDLINE, APA PsycINFO, ProQuest Dissertations & Theses, and Web of Science were searched for English-language publications. The PRISMA-ScR was used. After initial screening as per study protocol, a total of 33 items were reviewed independently by four reviewers. RESULTS: Thirty-three articles, including seven sources from grey literature, met the criteria for this scoping review. Results were mapped according to the five domains of the Guidelines for Undergraduate Nursing Education on Climate-Driven Vector-Borne Diseases. CONCLUSIONS: Findings from the review indicate that nurses play a role in climate-related health effects and should be knowledgeable about vector-borne diseases. However, scant literature exists on nurses’ knowledge, perceptions, attitudes toward vector-borne diseases, and practice readiness, signifying a need for further research on this emerging topic.
Geographic isolation and strict control limits in border areas have kept Chile free from various pathogens, including Flavivirus. However, the scenario is changing mainly due to climate change, the reintroduction of more aggressive mosquitoes, and the great wave of migration of people from endemic countries in recent years. Hence, it is necessary to surveillance mosquitoes to anticipate a possible outbreak in the population and take action to control it. This study aimed to investigate the presence of Flavivirus RNA by molecular tools with consensus primers in mosquitoes collected in the extreme north and central Chile. From 2019 to 2021, a prospective study was carried out in localities of Northern and part of Central Chile. Larvae, pupae, and adults of mosquitoes were collected in rural and urban sites in each locality. The collected samples were pooled by species and geographical location and tested using RT-PCR and RT-qPCR to determine presence of Flavivirus. 3085 specimens were collected, the most abundant specie Culex quinquefasciatus in the North and Aedes (Ochlerotatus) albifasciatus in the Center of Chile. Both genera are associated with Flavivirus transmission. However, PCR and RT-PCR did not detect Flavivirus RNA in the mosquitoes studied. These negative results indicate we are still a free Flavivirus country, which is reaffirmed by the non-existence of endemic human cases. Despite this, routine surveillance of mosquitoes and the pathogens they carry is highly recommended to evaluate each area-specific risk of vector-borne transmission.
Dengue is a vector-borne disease that is endemic to several countries, including the Dominican Republic, which has experienced dengue outbreaks for over four decades. With outbreaks growing in incidence in recent years, it is becoming increasingly important to develop better tools to understand drivers of dengue transmission. Such tools are critical for providing timely information to assist healthcare authorities in preparing human, material, and medical resources for outbreaks. Here, we investigate associations between meteorological variables and dengue transmission in the Dominican Republic in 2019, the year in which the country’s largest outbreak to date ocurred. We apply generalized linear mixed modelling with gamma family and log link to model the weekly dengue incidence rate. Because correlations in lags between climate variables and dengue cases exhibited different behaviour among provinces, a backward-type selection method was executed to find a final model with lags in the explanatory variables. We find that in the best models, meteorological conditions such as temperature and rainfall have an impact with a delay of 2-5 weeks in the development of an outbreak, ensuring breeding conditions for mosquitoes.
Global dengue incidence has increased dramatically over the past few decades from approximately 500 000 reported cases in 2000 to over 5 million in 2019. This trend has been attributed to population growth in endemic areas, rapid unplanned urbanization, increasing global connectivity, and climate change expanding the geographic range of the Aedes spp. mosquito, among other factors. Reporting dengue surveillance data is key to understanding the scale of the problem, identifying important changes in the landscape of disease, and developing policies for clinical management, vector control and vaccine rollout. However, surveillance practices are not standardized, and data may be difficult to interpret particularly in low- and middle-income countries with fragmented health-care systems. The latest national dengue surveillance data for Cambodia was published in 2010. Since its publication, the country experienced marked changes in health policies, population demographics, climate and urbanization. How these changes affected dengue control remains unknown. In this article, we summarize two decades of policy changes, published literature, country statistics, and dengue case data collected by the Cambodia National Dengue Control Programme to: (i) identify important changes in the disease landscape; and (ii) derive lessons to inform future surveillance and disease control strategies. We report that while dengue case morbidity and mortality rates in Cambodia fell between 2002 and 2020, dengue incidence doubled and age at infection increased. Future national surveillance, disease prevention and treatment, and vector control policies will have to account for these changes to optimize disease control.
PURPOSE OF REVIEW: The climate change (CC) or global warming (GW) modifies environment that favors vectors’ abundance, growth, and reproduction, and consequently, the rate of development of pathogens within the vectors. This review highlights the threats of GW-induced vector-borne diseases (VBDs) in Southern Europe (SE) and the need for mitigation efforts to prevent potential global health catastrophe. RECENT FINDINGS: Reports showed astronomical surges in the incidences of CC-induced VBDs in the SE. The recently (2022) reported first cases of African swine fever in Northern Italy and West Nile fever in SE are linked to the CC-modified environmental conditions that support vectors and pathogens’ growth and development, and disease transmission. SUMMARY: VBDs endemic to the tropics are increasingly becoming a major health challenge in the SE, a temperate region, due to the favorable environmental conditions caused by CC/GW that support vectors and pathogens’ biology in the previously non-endemic temperate regions.
Climate change represents an unprecedented threat to humanity and will be the ultimate challenge of the 21st century. As a public health consequence, the World Health Organization estimates an additional 250,000 deaths annually by 2030, with resource-poor countries being predominantly affected. Although climate change’s direct and indirect consequences on human health are manifold and far from fully explored, a growing body of evidence demonstrates its potential to exacerbate the frequency and spread of transmissible infectious diseases. Effective, high-impact mitigation measures are critical in combating this global crisis. While vaccines and vaccination are among the most cost-effective public health interventions, they have yet to be established as a major strategy in climate change-related health effect mitigation. In this narrative review, we synthesize the available evidence on the effect of climate change on vaccine-preventable diseases. This review examines the direct effect of climate change on water-related diseases such as cholera and other enteropathogens, helminthic infections and leptospirosis. It also explores the effects of rising temperatures on vector-borne diseases like dengue, chikungunya, and malaria, as well as the impact of temperature and humidity on airborne diseases like influenza and respiratory syncytial virus infection. Recent advances in global vaccine development facilitate the use of vaccines and vaccination as a mitigation strategy in the agenda against climate change consequences. A focused evaluation of vaccine research and development, funding, and distribution related to climate change is required.
The geographic range of the blacklegged tick, Ixodes scapularis, is expanding northward from the United States into southern Canada, and studies suggest that the lone star tick, Amblyomma americanum, will follow suit. These tick species are vectors for many zoonotic pathogens, and their northward range expansion presents a serious threat to public health. Climate change (particularly increasing temperature) has been identified as an important driver permitting northward range expansion of blacklegged ticks, but the impacts of host movement, which is essential to tick dispersal into new climatically suitable regions, have received limited investigation. Here, a mechanistic movement model was applied to landscapes of eastern North America to explore 1) relationships between multiple ecological drivers and the speed of the northward invasion of blacklegged ticks infected with the causative agent of Lyme disease, Borrelia burgdorferi sensu stricto, and 2) its capacity to simulate the northward range expansion of infected blacklegged ticks and uninfected lone star ticks under theoretical scenarios of increasing temperature. Our results suggest that the attraction of migratory birds (long-distance tick dispersal hosts) to resource-rich areas during their spring migration and the mate-finding Allee effect in tick population dynamics are key drivers for the spread of infected blacklegged ticks. The modeled increases in temperature extended the climatically suitable areas of Canada for infected blacklegged ticks and uninfected lone star ticks towards higher latitudes by up to 31% and 1%, respectively, and with an average predicted speed of the range expansion reaching 61 km/year and 23 km/year, respectively. Differences in the projected spatial distribution patterns of these tick species were due to differences in climate envelopes of tick populations, as well as the availability and attractiveness of suitable habitats for migratory birds. Our results indicate that the northward invasion process of lone star ticks is primarily driven by local dispersal of resident terrestrial hosts, whereas that of blacklegged ticks is governed by long-distance migratory bird dispersal. The results also suggest that mechanistic movement models provide a powerful approach for predicting tick-borne disease risk patterns under complex scenarios of climate, socioeconomic and land use/land cover changes.
Vector-borne viral diseases (VBVDs) continue to pose a considerable public health risk to animals and humans globally. Vectors have integral roles in autochthonous circulation and dissemination of VBVDs worldwide. The interplay of agricultural activities, population expansion, urbanization, host/pathogen evolution, and climate change, all contribute to the continual flux in shaping the epidemiology of VBVDs. In recent decades, VBVDs, once endemic to particular countries, have expanded into new regions such as Iran and its neighbors, increasing the risk of outbreaks and other public health concerns. Both Iran and its neighboring countries are known to host a number of VBVDs that are endemic to these countries or newly circulating. The proximity of Iran to countries hosting regional diseases, along with increased global socioeconomic activities, e.g., international trade and travel, potentially increases the risk for introduction of new VBVDs into Iran. In this review, we examined the epidemiology of numerous VBVDs circulating in Iran, such as Chikungunya virus, Dengue virus, Sindbis virus, West Nile virus, Crimean-Congo hemorrhagic fever virus, Sandfly-borne phleboviruses, and Hantavirus, in relation to their vectors, specifically mosquitoes, ticks, sandflies, and rodents. In addition, we discussed the interplay of factors, e.g., urbanization and climate change on VBVD dissemination patterns and the consequent public health risks in Iran, highlighting the importance of a One Health approach to further surveil and to evolve mitigation strategies.
Dengue fever is a rapidly spreading mosquito-borne contagion. However, the effects of extreme rainfall events on dengue occurrences have not been widely evaluated. With their immense precipitation and high winds, typhoons may have distinct effects on dengue occurrence from those during other heavy rain events. Frequented by typhoons and situated in the tropical climate zone, southern Taiwan is an appropriate study area due to its isolated geographic environment. Each subject to distinct orographic effects on typhoon structure and typhoon-induced precipitation, 9 typhoon trajectories around Taiwan have not been observed until now. This study analyzes typhoon-induced precipitation and examines historical typhoon events by trajectory to determine the effects of typhoons on dengue occurrences in different urban contexts of Tainan and Kaohsiung in high-epidemic southern Taiwan. We employed data from 1998 to 2019 and developed logistic regression models for modeling dengue occurrence while taking 28-day lag effects into account. We considered factors including typhoon trajectory, occurrence, and typhoon-induced precipitation to dengue occurrences. Our results indicate that typhoon trajectories are a significant risk factor for dengue occurrence. Typhoons affect dengue occurrence differently by trajectory. One out of four northbound (along the Taiwan Strait) and four out of five westbound (across Taiwan) typhoons were found to be positively correlated with dengue occurrences in southern Taiwan. We observe that typhoon-induced precipitation is not associated with dengue occurrence in southern Taiwan, which suggests that wind destruction during typhoon events may serve as the primary cause for their positive effects by leaving debris suitable for mosquito habitats. Our findings provide insights into the impact of typhoons by trajectory on dengue occurrence, which can improve the accuracy of future dengue forecasts in neighboring regions with similar climatic contexts.
India has made tremendous progress in reducing malaria mortality and morbidity in the last decade. Mizoram State in North-East India is one of the few malaria-endemic regions where malaria transmission has continued to remain high. As Mizoram shares international borders with Bangladesh and Myanmar, malaria control in this region is critical for malaria elimination efforts in all the three countries. For identifying hotspots for targeted intervention, malaria data from 385 public health sub-centers across Mizoram were analyzed in the Geographic Information System. Almost all the sub-centers reporting high Annual Parasite Index (> 10) are located in Mizoram’s districts that border Bangladesh. Getis-Ord G(i)* statistic shows most of the sub-centers located along the Bangladesh border in the Lawngtlai and Lunglei districts to be the malaria hotspots. The hotspots also extended into the Mamit and Siaha districts, especially along the borders of Lawngtlai and Lunglei. Analysis of terrain, climatic, and land use/land cover datasets obtained from the Global Modelling and Assimilation Office and satellite images show Mizoram’s western part (Lawngtlai, Lunglei, and Mamit districts) to experience similar topographic and climatic conditions as the bordering Rangamati district in the Chittagong division of Bangladesh. Climatic trends in this region from 1981 to 2021, estimated by the Mann-Kendall test and Sen’s slope estimates, show an increasing trend in minimum temperature, relative humidity, rainfall, and the associated shift of climatic pattern (temperate to tropical monsoon) could facilitate malaria transmission. The quasi-Poisson regression model estimates a strong association (p < 0.001) between total malaria cases, temperature range, and elevation. The Kruskal-Wallis H test shows a statistically significant association between malaria cases and forest classes (p < 0.001). A regional coordination and strategic plan are required to eliminate malaria from this hyper-endemic malaria region of North-East India.
The number of malaria cases worldwide has increased, with over 241 million cases and 69,000 more deaths in 2020 compared to 2019. Burkina Faso recorded over 11 million malaria cases in 2020, resulting in nearly 4,000 deaths. The overall incidence of malaria in Burkina Faso has been steadily increasing since 2016. This study investigates the spatiotemporal pattern and environmental and meteorological determinants of malaria incidence in Burkina Faso. METHODS: We described the temporal dynamics of malaria cases by detecting the transmission periods and the evolution trend from 2013 to 2018. We detected hotspots using spatial scan statistics. We assessed different environmental zones through a hierarchical clustering and analyzed the environmental and climatic data to identify their association with malaria incidence at the national and at the district’s levels through generalized additive models. We also assessed the time lag between malaria peaks onset and the rainfall at the district level. The environmental and climatic data were synthetized into indicators. RESULTS: The study found that malaria incidence had a seasonal pattern, with high transmission occurring during the rainy seasons. We also found an increasing trend in the incidence. The highest-risk districts for malaria incidence were identified, with a significant expansion of high-risk areas from less than half of the districts in 2013-2014 to nearly 90% of the districts in 2017-2018. We identified three classes of health districts based on environmental and climatic data, with the northern, south-western, and western districts forming separate clusters. Additionally, we found that the time lag between malaria peaks onset and the rainfall at the district level varied from 7 weeks to 17 weeks with a median at 10 weeks. Environmental and climatic factors have been found to be associated with the number of cases both at global and districts levels. CONCLUSION: The study provides important insights into the environmental and spatiotemporal patterns of malaria in Burkina Faso by assessing the spatio temporal dynamics of Malaria cases but also linking those dynamics to the environmental and climatic factors. The findings highlight the importance of targeted control strategies to reduce the burden of malaria in high-risk areas as we found that Malaria epidemiology is complex and linked to many factors that make some regions more at risk than others.
Evidence on the trends of the proportion of malaria infections detected by routine passive case detection at health facilities is important for public health decision making especially in areas moving towards elimination. The objective was to assess nine years of trends on clinical malaria infections detected at health facility and its associated climate factors, in the water resource development set up of Wonji sugar estate, Oromia, Ethiopia. Retrospective data were collected from malaria-suspected patient recording logbook at Wonji sugar factory’s primary hospital. Monthly average meteorological data were obtained from the estate meteorological station. Data were collected from April through June 2018 and January 2022. The data were analyzed using Stata version 16.0 software for Chi-square and regression analysis. Over the last nine years, 34,388 cases were legible for analysis with complete data. Of these, 11.75% (4039/34388) were positive for clinical malaria. Plasmodium vivax test positivity was the highest proportion (8.2%, n = 2820) followed by Plasmodium falciparum (3.48%, n = 1197) and mixed infections (P. falciparum and P. vivax, 0.06%, n = 21). The odds of being positive for malaria was highest in males (AOR = 1.46; 95%CI = 1.36-1.52; P < 0.001) compared to females and in older individuals of above 15 years old (AOR = 4.55, 95%CI = 4.01-5.17, P < 0.001) followed by school-aged children (5-15 years old) (AOR = 2.16; 95%CI = 1.88-2.49, P < 0.001). There was no significant variation in the proportion of malaria-positive cases in the dry and wet seasons (P = 0.059). Malaria test positivity rates were associated with average monthly rainfall (AdjIRR = 1.00; 95%CI = 1.00-1.001, P < 0.001) while negatively associated with average monthly minim temperature (adjIRR = 0.94; 95%CI = 0.94-0.95; P < 0.001) and average monthly relative humidity (adjIRR = 0.99, 95%CI = 0.99-1.00, P = 0.023). There was year-round malaria transmission, adults especially males and school children frequently tested malaria positive. Hence, alternative vector management tools like larval source management have to be deployed besides ITNs and IRS in such water development areas to achieve the malaria elimination goal.
Assessment of the relative impact of climate change on malaria dynamics is a complex problem. Climate is a well-known factor that plays a crucial role in driving malaria outbreaks in epidemic transmission areas. However, its influence in endemic environments with intensive malaria control interventions is not fully understood, mainly due to the scarcity of high-quality, long-term malaria data. The demographic surveillance systems in Africa offer unique platforms for quantifying the relative effects of weather variability on the burden of malaria. Here, using a process-based stochastic transmission model, we show that in the lowlands of malaria endemic western Kenya, variations in climatic factors played a key role in driving malaria incidence during 2008-2019, despite high bed net coverage and use among the population. The model captures some of the main mechanisms of human, parasite, and vector dynamics, and opens the possibility to forecast malaria in endemic regions, taking into account the interaction between future climatic conditions and intervention scenarios.
BACKGROUND: Anopheles stephensi is a malaria-transmitting mosquito that has recently expanded from its primary range in Asia and the Middle East, to locations in Africa. This species is a competent vector of both Plasmodium falciparum and Plasmodium vivax malaria. Perhaps most alarming, the characteristics of An. stephensi, such as container breeding and anthropophily, make it particularly adept at exploiting built environments in areas with no prior history of malaria risk. METHODS: In this paper, global maps of thermal transmission suitability and people at risk (PAR) for malaria transmission by An. stephensi were created, under current and future climate. Temperature-dependent transmission suitability thresholds derived from recently published species-specific thermal curves were used to threshold gridded, monthly mean temperatures under current and future climatic conditions. These temperature driven transmission models were coupled with gridded population data for 2020 and 2050, under climate-matched scenarios for future outcomes, to compare with baseline predictions for 2020 populations. RESULTS: Using the Global Burden of Disease regions approach revealed that heterogenous regional increases and decreases in risk did not mask the overall pattern of massive increases of PAR for malaria transmission suitability with An. stephensi presence. General patterns of poleward expansion for thermal suitability were seen for both P. falciparum and P. vivax transmission potential. CONCLUSIONS: Understanding the potential suitability for An. stephensi transmission in a changing climate provides a key tool for planning, given an ongoing invasion and expansion of the vector. Anticipating the potential impact of onward expansion to transmission suitable areas, and the size of population at risk under future climate scenarios, and where they occur, can serve as a large-scale call for attention, planning, and monitoring.
BACKGROUND: Climate change is increasing the dispersion of mosquitoes and the spread of viruses of which some mosquitoes are the main vectors. In Quebec, the surveillance and management of endemic mosquito-borne diseases, such as West Nile virus or Eastern equine encephalitis, could be improved by mapping the areas of risk supporting vector populations. However, there is currently no active tool tailored to Quebec that can predict mosquito population abundances, and we propose, with this work, to help fill this gap. METHODS: Four species of mosquitos were studied in this project for the period from 2003 to 2016 for the southern part of the province of Quebec: Aedes vexans (VEX), Coquillettidia perturbans (CQP), Culex pipiens-restuans group (CPR) and Ochlerotatus stimulans group (SMG) species. We used a negative binomial regression approach, including a spatial component, to model the abundances of each species or species group as a function of meteorological and land-cover variables. We tested several sets of variables combination, regional and local scale variables for landcover and different lag period for the day of capture for weather variables, to finally select one best model for each species. RESULTS: Models selected showed the importance of the spatial component, independently of the environmental variables, at the larger spatial scale. In these models, the most important land-cover predictors that favored CQP and VEX were ‘forest’, and ‘agriculture’ (for VEX only). Land-cover ‘urban’ had negative impact on SMG and CQP. The weather conditions on the trapping day and previous weather conditions summarized over 30 or 90 days were preferred over a shorter period of seven days, suggesting current and long-term previous weather conditions effects on mosquito abundance. CONCLUSIONS: The strength of the spatial component highlights the difficulties in modelling the abundance of mosquito species and the model selection shows the importance of selecting the right environmental predictors, especially when choosing the temporal and spatial scale of these variables. Climate and landscape variables were important for each species or species group, suggesting it is possible to consider their use in predicting long-term spatial variationsin the abundance of mosquitoes potentially harmful to public health in southern Quebec.
Crimean-Congo haemorrhagic fever (CCHF) is the most widely distributed tick-borne viral disease in humans and is caused by the Crimean-Congo haemorrhagic fever virus (CCHFV). The virus has a broader distribution, expanding from western China and South Asia to the Middle East, southeast Europe, and Africa. The historical known distribution of the CCHFV vector Hyalomma marginatum in Europe includes most of the Mediterranean and the Balkan countries, Ukraine, and southern Russia. Further expansion of its potential distribution may have occurred in and out of the Mediterranean region. This study updated the distributional map of the principal vector of CCHFV, H. marginatum, in the Old World using an ecological niche modeling approach based on occurrence records from the Global Biodiversity Information Facility (GBIF) and a set of covariates. The model predicted higher suitability of H. marginatum occurrences in diverse regions of Africa and Asia. Furthermore, the model estimated the environmental suitability of H. marginatum across Europe. On a continental scale, the model anticipated a widespread potential distribution encompassing the southern, western, central, and eastern parts of Europe, reaching as far north as the southern regions of Scandinavian countries. The distribution of H. marginatum also covered countries across Central Europe where the species is not autochthonous. All models were statistically robust and performed better than random expectations (p < 0.001). Based on the model results, climatic conditions could hamper the successful overwintering of H. marginatum and their survival as adults in many regions of the Old World. Regular updates of the models are still required to continually assess the areas at risk using up-to-date occurrence and climatic data in present-day and future conditions.
West Nile virus (WNV) is a re-emerging zoonotic pathogen with increasing incidence in Europe, producing a recent outbreak in 2020 in Spain with 77 human cases and eight fatalities. However, the factors explaining the observed changes in the incidence of WNV in Europe are not completely understood. Longitudinal monitoring of WNV in wild animals across Europe is a useful approach to understand the eco-epidemiology of WNV in the wild and the risk of spillover into humans. However, such studies are very scarce up to now. Here, we analysed the occurrence of WNV and Usutu virus (USUV) antibodies in 2102 samples collected between 2005 and 2020 from a population of feral horses in Doñana National Park. The prevalence of WNV antibodies varied between years, with a mean seroprevalence of 8.1% (range 0%-25%) and seasonally. Climate conditions including mean minimum annual temperatures and mean rainy days per year were positively correlated with WNV seroprevalence, while the annual rainfall was negatively. We also detected the highest incidence of seroconversions in 2020 coinciding with the human outbreak in southern Spain. Usutu virus-specific antibodies were detected in the horse population since 2011. The WNV outbreak in humans was preceded by a long period of increasing circulation of WNV among horses with a very high exposure in the year of the outbreak. These results highlight the utility of One Health approaches to better understand the transmission dynamics of zoonotics pathogens.
The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.
Cyclone Idai in 2019 was one of the worst tropical cyclones recorded in the Southern Hemisphere. The storm caused catastrophic damage and led to a humanitarian crisis in Mozambique. The affected population suffered a cholera epidemic on top of housing and infrastructure damage and loss of life. The housing and infrastructure damage sustained during Cyclone Idai still has not been addressed in all affected communities. This is of grave concern because storm damage results in poor housing conditions which are known to increase the risk of malaria. Mozambique has the 4th highest malaria prevalence in sub-Saharan Africa and is struggling to control malaria in most of the country. We conducted a community-based cross-sectional survey in Sussundenga Village, Manica Province, Mozambique in December 2019-February 2020. We found that most participants (64%) lived in households that sustained damage during Cyclone Idai. The overall malaria prevalence was 31% measured by rapid diagnostic test (RDT). When controlling for confounding variables, the odds of malaria infection was nearly threefold higher in participants who lived in households damaged by Cyclone Idai nearly a year after the storm. This highlights the need for long-term disaster response to improve the efficiency and success of malaria control efforts.
For vector-borne diseases the basic reproduction number [Formula: see text], a measure of a disease’s epidemic potential, is highly temperature-dependent. Recent work characterizing these temperature dependencies has highlighted how climate change may impact geographic disease spread. We extend this prior work by examining how newly emerging diseases, like Zika, will be impacted by specific future climate change scenarios in four diverse regions of Brazil, a country that has been profoundly impacted by Zika. We estimated a [Formula: see text], derived from a compartmental transmission model, characterizing Zika (and, for comparison, dengue) transmission potential as a function of temperature-dependent biological parameters specific to Aedes aegypti. We obtained historical temperature data for the five-year period 2015-2019 and projections for 2045-2049 by fitting cubic spline interpolations to data from simulated atmospheric data provided by the CMIP-6 project (specifically, generated by the GFDL-ESM4 model), which provides projections under four Shared Socioeconomic Pathways (SSP). These four SSP scenarios correspond to varying levels of climate change severity. We applied this approach to four Brazilian cities (Manaus, Recife, Rio de Janeiro, and São Paulo) that represent diverse climatic regions. Our model predicts that the [Formula: see text] for Zika peaks at 2.7 around 30°C, while for dengue it peaks at 6.8 around 31°C. We find that the epidemic potential of Zika will increase beyond current levels in Brazil in all of the climate scenarios. For Manaus, we predict that the annual [Formula: see text] range will increase from 2.1-2.5, to 2.3-2.7, for Recife we project an increase from 0.4-1.9 to 0.6-2.3, for Rio de Janeiro from 0-1.9 to 0-2.3, and for São Paulo from 0-0.3 to 0-0.7. As Zika immunity wanes and temperatures increase, there will be increasing epidemic potential and longer transmission seasons, especially in regions where transmission is currently marginal. Surveillance systems should be implemented and sustained for early detection.
Leishmaniasis epidemiology is currently undergoing substantial transformations in both Turkey and Europe, signifying potential implications for public health. This review analyzes the evolving patterns within Turkey and their potential ramifications for Europe. Within Turkey, the dynamics of leishmaniasis are undergoing noteworthy alterations, manifesting in a rise in cutaneous leishmaniasis (CL) cases and the emergence of Leishmania major and Leishmania donovani. These transformations are predominantly driven by factors such as the distribution of vectors, human activities, climate fluctuations, and migration. Across Europe, particularly in countries within the Mediterranean basin, leishmaniasis is endemic, primarily attributed to Leishmania infantum. Recent evidence suggests a resurgence of the disease even in previously non-endemic areas, propelled by climate change, urbanization, and migration. The changing landscape of leishmaniasis in Turkey carries direct implications for Europe. The presence and distribution of Leishmania tropica, L. major, and L. donovani raise concerns regarding cross-border transmission. Turkey’s strategic position along migration routes further compounds the risk, alongside the facilitative effects of climate change and host mobility. Embracing a One Health approach with public awareness campaigns should be a priority. To ensure the protection of public health in Europe, it is imperative to adopt a proactive approach by establishing robust surveillance mechanisms, implementing preventive measures, and cultivating collaboration with Turkey. The invaluable experience, strategic geographical location, and well-established infrastructure of Turkey make this collaboration crucial in effectively addressing the evolving dynamics of leishmaniasis and its potential impacts on Europe.
Leishmaniasis is the second deadliest vector-borne, neglected tropical zoonotic disease and is found in a variety of clinical forms based on genetic background. Its endemic type is present in tropical, sub-tropical and Mediterranean areas around the world which accounts for a lot of deaths every year. Currently, a variety of techniques are available for detection of leishmaniasis each technique having it’s own pros and cons. The advancing next-generation sequencing (NGS) techniques are employed to find out novel diagnostic markers based on single nucleotide variants. A total of 274 NGS studies are available in European Nucleotide Archive (ENA) portal (https://www.ebi.ac.uk/ena/browser/home) that focused on wild-type and mutated Leishmania, differential gene expression, miRNA expression, and detection of aneuploidy mosaicism by omics approaches. These studies have provided insights into the population structure, virulence, and extensive structural variation, including known and suspected drug resistance loci, mosaic aneuploidy and hybrid formation under stressed conditions and inside the midgut of the sandfly. The complex interactions occurring within the parasite-host-vector triangle can be better understood by omics approaches. Further, advanced CRISPR technology allows researchers to delete and modify each gene individually to know the importance of genes in the virulence and survival of the disease-causing protozoa. In vitro generation of Leishmania hybrids are helping to understand the mechanism of disease progression in its different stages of infection. This review will give a comprehensive picture of the available omics data of various Leishmania spp. which helped to reveal the effect of climate change on the spread of its vector, the pathogen survival strategies, emerging antimicrobial resistance and its clinical importance.
BACKGROUND: Urbanization has led to the proliferation of high-rise buildings, which have substantially influenced the distribution of dengue vectors, such as Aedes aegypti (L.). However, knowledge gaps exist regarding the individual and combined effects of architectural and spatiotemporal factors on dengue vector. This study investigated the interrelationship between Ae. aegypti presence, building architectural features, and spatiotemporal factors in urban environments. RESULTS: The mosquito Ae. aegypti presence varied by location and seasons, being higher in outdoor environments than in indoor environments. Lingya (Kaohsiung City, Taiwan) had the highest mosquito numbers, particularly in basement and first floor areas. Ae. aegypti was found on multiple floors within buildings, and their presence was greater in summer and autumn. The XGBoost model revealed that height within a building, temperature, humidity, resident density, and rainfall were key factors influencing mosquito presence, whereas openness had a relatively minor impact. CONCLUSION: To effectively address the problems caused by urbanization, the three-dimensional distribution of Ae. aegypti, including their spatial distribution across heights and areas within the urban environment, must be considered. By incorporating these multiple factors, this approach provides valuable insights for those responsible for urban planning and disease management strategies. Understanding the interplay between architectural features, environmental conditions, and the presence of Ae. aegypti is essential for developing targeted interventions and mitigating the adverse impacts of urbanization on public health. © 2023 Society of Chemical Industry.
Leishmania is a genus of parasitic protozoa that causes a disease called leishmaniasis. Leishmaniasis is transmitted to humans through the bites of infected female sandflies. There are several different species of Leishmania that can cause various forms of the disease, and the symptoms can range from mild to severe, depending on species of Leishmania involved and the immune response of the host. Leishmania parasites have a variety of reservoirs, including humans, domestic animals, horses, rodents, wild animals, birds, and reptiles. Leishmaniasis is endemic of 90 countries, mainly in South American, East and West Africa, Mediterranean region, Indian subcontinent, and Central Asia. In recent years, cases have been detected in other countries, and it is already an infection present throughout the world. The increase in temperatures due to climate change makes it possible for sandflies to appear in countries with traditionally colder regions, and the easy movement of people and animals today, facilitate the appearance of Leishmania species in new countries. These data mean that leishmaniasis will probably become an emerging zoonosis and a public health problem in the coming years, which we must consider controlling it from a One Health point of view. This review summarizes the prevalence of Leishmania spp. around the world and the current knowledge regarding the animals that could be reservoirs of the parasite.
BACKGROUND: Mosquito research in Europe has a long history, primarily focused on malaria vectors. In recent years, invasive mosquito species like the Asian tiger mosquito (Aedes albopictus) and the spread of arboviruses like dengue virus, chikungunya virus or bluetongue virus have led to an intensification of research and monitoring in Europe. The risk of further dissemination of exotic species and mosquito-borne pathogens is expected to increase with ongoing globalization, human mobility, transport geography, and climate warming. Researchers have conducted various studies to understand the ecology, biology, and effective control strategies of mosquitoes and associated pathogens. MAIN BODY: Three invasive mosquito species are established in Europe: Asian tiger mosquito (Aedes albopictus), Japanese bush mosquito (Ae. japonicus), and Korean bush mosquito (Aedes koreicus). Ae. albopictus is the most invasive species and has been established in Europe since 1990. Over the past two decades, there has been an increasing number of outbreaks of infections by mosquito-borne viruses in particular chikungunya virus, dengue virus or Zika virus in Europe primary driven by Ae. albopictus. At the same time, climate change with rising temperatures results in increasing threat of invasive mosquito-borne viruses, in particular Usutu virus and West Nile virus transmitted by native Culex mosquito species. Effective mosquito control programs require a high level of community participation, going along with comprehensive information campaigns, to ensure source reduction and successful control. Control strategies for container breeding mosquitoes like Ae. albopictus or Culex species involve community participation, door-to-door control activities in private areas. Further measures can involve integration of sterile insect techniques, applying indigenous copepods, Wolbachia sp. bacteria, or genetically modified mosquitoes, which is very unlike to be practiced as standard method in the near future. CONCLUSIONS: Climate change and globalization resulting in the increased establishment of invasive mosquitoes in particular of the Asian tiger mosquito Ae. albopictus in Europe within the last 30 years and increasing outbreaks of infections by mosquito-borne viruses warrants intensification of research and monitoring. Further, effective future mosquito control programs require increase in intense community and private participation, applying physical, chemical, biological, and genetical control activities.
Biological invasions have increased significantly with the tremendous growth of international trade and transport. Hematophagous arthropods can be vectors of infectious and potentially lethal pathogens and parasites, thus constituting a growing threat to humans-especially when associated with biological invasions. Today, several major vector-borne diseases, currently described as emerging or re-emerging, are expanding in a world dominated by climate change, land-use change and intensive transportation of humans and goods. In this review, we retrace the historical trajectory of these invasions to better understand their ecological, physiological and genetic drivers and their impacts on ecosystems and human health. We also discuss arthropod management strategies to mitigate future risks by harnessing ecology, public health, economics and social-ethnological considerations. Trade and transport of goods and materials, including vertebrate introductions and worn tires, have historically been important introduction pathways for the most prominent invasive hematophagous arthropods, but sources and pathways are likely to diversify with future globalization. Burgeoning urbanization, climate change and the urban heat island effect are likely to interact to favor invasive hematophagous arthropods and the diseases they can vector. To mitigate future invasions of hematophagous arthropods and novel disease outbreaks, stronger preventative monitoring and transboundary surveillance measures are urgently required. Proactive approaches, such as the use of monitoring and increased engagement in citizen science, would reduce epidemiological and ecological risks and could save millions of lives and billions of dollars spent on arthropod control and disease management. Last, our capacities to manage invasive hematophagous arthropods in a sustainable way for worldwide ecosystems can be improved by promoting interactions among experts of the health sector, stakeholders in environmental issues and policymakers (e.g. the One Health approach) while considering wider social perceptions.
West Nile virus is the most common mosquito borne disease in North America and the leading cause of viral encephalitis. West Nile virus is primarily transmitted between birds and mosquitoes while humans are incidental, dead-end hosts. Climate change may increase the risk of human infections as climatic variables have been shown to affect the mosquito life cycle, biting rate, incubation period of the disease in mosquitoes, and bird migration patterns. We develop a zero-inflated Poisson model to investigate how human West Nile virus case counts vary with respect to mosquito abundance and infection rates, bird abundance, and other environmental covariates. We use a Bayesian paradigm to fit our model to data from 2010-2019 in Ontario, Canada. Our results show mosquito infection rate, temperature, precipitation, and crow abundance are positively correlated with human cases while NDVI and robin abundance are negatively correlated with human cases. We find the inclusion of spatial random effects allows for more accurate predictions, particularly in years where cases are higher. Our model is able to accurately predict the magnitude and timing of yearly West Nile virus outbreaks and could be a valuable tool for public health officials to implement prevention strategies to mitigate these outbreaks.
Infectious diseases are emerging at an unprecedented rate while food production intensifies to keep pace with population growth. Large-scale irrigation schemes have the potential to permanently transform the landscape with health, nutritional and socio-economic benefits; yet, this also leads to a shift in land-use patterns that can promote endemic and invasive insect vectors and pathogens. The balance between ensuring food security and preventing emerging infectious disease is a necessity; yet the impact of irrigation on vector-borne diseases at the epidemiological, entomological and economic level is uncertain and depends on the geographical and climatological context. Here, we highlight the risk factors and challenges facing vector-borne disease surveillance and control in an emerging agricultural ecosystem in the lower Shire Valley region of southern Malawi. A phased large scale irrigation programme (The Shire Valley Transformation Project, SVTP) promises to transform over 40,000 ha into viable and resilient farmland, yet the valley is endemic for malaria and schistosomiasis and experiences frequent extreme flooding events following tropical cyclones. The latter exacerbate vector-borne disease risk while simultaneously making any empirical assessment of that risk a significant hurdle. We propose that the SVTP provides a unique opportunity to take a One Health approach at mitigating vector-borne disease risk while maintaining agricultural output. A long-term and multi-disciplinary approach with buy-in from multiple stakeholders will be needed to achieve this goal.
Introduction. Dengue Hemorrhagic Fever (DHF) is still a public health problem even in the era of the COVID-19 pandemic in 2020, including in Indonesia. This study aimed to analyze the incidence of DHF based on the integration of climatic factors, including rainfall, humidity, air temperature, and duration of sun-light and their distribution.Materials and Methods. This was an ecological time series study with secondary data from the Surabaya City Health Office covering the incidence of DHF and larva-free rate and climate data on rainfall, humidity, air temperature, and duration of sunlight obtained from the Meteorology and Geophysics Agency (BMKG). Silver station in Surabaya, the distribution of dengue incidence during 2018-2020.Results and Discussion. The results showed that humidity was correlated with the larvae-free rate. Meanwhile, the larva-free rate did not correlate with the number of DHF cases. DHF control is estimated due to the correlation of climatic factors and the inci-dence of DHF, control of vectors and disease agents, control of transmission media, and exposure to the community.Conclusions. The integration of DHF control can be used for early precautions in the era of the COVID-19 pandemic by control-ling DHF early in the period from January to June in Surabaya. It is concluded that humidity can affect the dengue outbreak and it can be used as an early warning system and travel warning regarding the relative risk of DHF outbreak.
Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue’s distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue’s expansion throughout Vietnam.
The incidence of West Nile fever (WNF) is highly variable in emerging areas, making it difficult to identify risk periods. Using clinical case records has important biases in understanding the transmission dynamics of West Nile virus (WNV) because asymptomatic infections are frequent. However, estimating virus exposure in sentinel species could help achieve this goal at varying spatiotemporal scales. To identify the determinants of inter-annual variation in WNV transmission rates, we designed a 15-year longitudinal seroepidemiological study (2005-2020) in five environmentally diverse areas of southwestern Spain. We modeled individual annual area-dependent exposure risk based on potential environmental and host predictors using generalized linear mixed models. Further, we analyzed the weight of predictors on exposure probability by variance partitioning of the model components. The analysis of 2418 wild ungulate sera (1168 red deer – Cervus elaphus – and 1250 Eurasian wild boar – Sus scrofa) with a highly sensitive commercial blocking ELISA identified an average seroprevalence of 24.9% (95% confidence interval (CI): 23.2-26.7%). Antibody prevalence was slightly higher in wild boar (27.5%; CI: 25.1-30.1%) than in deer (22.2%; CI: 19.8-24.7%). We observed a spatial trend in exposure, with higher frequency in the southernmost areas and a slight, although area-dependent, increasing temporal trend. Host-related predictors were important drivers of exposure risk. The environmental predictor with the highest weight was annual cumulative precipitation, while temperature variations were also relevant but with less weight. We observed a coincidence of spatiotemporal changes in exposure with the notification of WNF outbreaks in horses and humans. That indicates the usefulness of wild ungulates as sentinels for WNV transmission and as models to understand its spatiotemporal dynamics. These results will allow the development of more accurate predictive models of spatiotemporal variations in transmission risk that can inform health authorities to take appropriate action.
Climate sensitivity of infectious diseases is discussed in many studies. A quantitative basis for distinguishing and predicting the disease impacts of climate and other environmental and anthropogenic driver-pressure changes, however, is often lacking. To assess research effort and identify possible key gaps that can guide further research, we here apply a scoping review approach to two widespread infectious diseases: Lyme disease (LD) as a vector-borne and cryptosporidiosis as a water-borne disease. Based on the emerging publication data, we further structure and quantitatively assess the driver-pressure foci and interlinkages considered in the published research so far. This shows important research gaps for the roles of rarely investigated water-related and socioeconomic factors for LD, and land-related factors for cryptosporidiosis. For both diseases, the interactions of host and parasite communities with climate and other driver-pressure factors are understudied, as are also important world regions relative to the disease geographies; in particular, Asia and Africa emerge as main geographic gaps for LD and cryptosporidiosis research, respectively. The scoping approach developed and gaps identified in this study should be useful for further assessment and guidance of research on infectious disease sensitivity to climate and other environmental and anthropogenic changes around the world.
Arboviral infections such as Zika, chikungunya, dengue, and yellow fever pose significant health problems globally. The population at risk is expanding with the geographical distribution of the main transmission vector of these viruses, the Aedes aegypti mosquito. The global spreading of this mosquito is driven by human migration, urbanization, climate change, and the ecological plasticity of the species. Currently, there are no specific treatments for Aedes-borne infections. One strategy to combat different mosquito-borne arboviruses is to design molecules that can specifically inhibit a critical host protein. We obtained the crystal structure of 3-hydroxykynurenine transaminase (AeHKT) from A. aegypti, an essential detoxification enzyme of the tryptophan metabolism pathway. Since AeHKT is found exclusively in mosquitoes, it provides the ideal molecular target for the development of inhibitors. Therefore, we determined and compared the free binding energy of the inhibitors 4-(2-aminophenyl)-4-oxobutyric acid (4OB) and sodium 4-(3-phenyl-1,2,4-oxadiazol-5-yl)butanoate (OXA) to AeHKT and AgHKT from Anopheles gambiae, the only crystal structure of this enzyme previously known. The cocrystallized inhibitor 4OB binds to AgHKT with K (i) of 300 μM. We showed that OXA binds to both AeHKT and AgHKT enzymes with binding energies 2-fold more favorable than the crystallographic inhibitor 4OB and displayed a 2-fold greater residence time τ upon binding to AeHKT than 4OB. These findings indicate that the 1,2,4-oxadiazole derivatives are inhibitors of the HKT enzyme not only from A. aegypti but also from A. gambiae.
Traditional ecological knowledge of indigenous groups in the southeastern Colombian Amazon coincides in identifying the two main hydrological transition periods (wet-dry: August-November; dry-wet: March-April) as those with greater susceptibility to disease in humans. Here we analyze the association between indigenous knowledge about these two periods and the incidence of two vector-borne diseases: malaria and dengue. We researched seven “ecological calendars” from three regions in the Colombian Amazon, malaria and dengue cases reported from 2007 to 2019 by the Colombian National Institute of Health, and daily temperature and precipitation data from eight meteorological stations in the region from 1990-2019 (a climatological normal). Malaria and dengue follow a seasonal pattern: malaria has a peak from August to November, corresponding with the wet-dry transition (the “season of the worms” in the indigenous calendars), and dengue has a peak in March and April, coinciding with the dry-wet transition. Previous studies have shown a positive correlation between rainfall and dengue and a negative correlation between rainfall and malaria. However, as the indigenous ecological knowledge codified in the calendars suggests, disease prediction cannot be reduced to a linear correlation with a single environmental variable. Our data show that two major aspects of the indigenous calendars (the time of friaje as a critical marker of the year and the hydrological transition periods as periods of greater susceptibility to diseases) are supported by meteorological data and by the available information about the incidence of malaria and dengue.
Scrub typhus is emerging as a global public health threat owing to its increased prevalence and remarkable geographic expansion. However, it remains a neglected disease, and possible influences of meteorological factors on its risk are poorly understood. We conducted the largest-scale research to assess the impact of meteorological factors on scrub typhus in China. Weekly data on scrub typhus cases and meteorological factors were collected across 59 prefecture-level administrative regions from 2006 to 2020. First, we divided these regions into 3 regions and analyzed the epidemiological characteristics of scrub typhus. We then applied the distributed lag nonlinear model, combined with multivariate meta-analysis, to examine the associations between meteorological factors and scrub typhus incidence at the total and regional levels. Subsequently, we identified the critical meteorological predictors of scrub typhus incidence and extracted climate risk windows. We observed distinct epidemiological characteristics across regions, featuring obvious clustering in the East and Southwest with more even distribution and longer epidemic duration in the South. The mean temperature and relative humidity had profound effects on scrub typhus with initial-elevated-descendent patterns. Weather conditions of weekly mean temperatures of 25-33°C and weekly relative humidity of 60-95% were risk windows for scrub typhus. Additionally, the heavy rainfall was associated with sharp increase in scrub typhus incidence. We identified specific climatic signals to detect the epidemic of scrub typhus, which were easily monitored to generalize. Regional heterogeneity should be considered for targeted monitoring and disease control strategies.
The Asian summer monsoon impacts the human lives and agrarian economies throughout Asia. These impacts are driven by monsoon anomalies which are manifested in terms of the seasonal precipitation, surface temperatures and the occurrences of floods, droughts and tropical cyclones. A strong monsoon results in various positive outcomes like increased agricultural produce, economic growth, reduced commodity prices and national inflationary levels as well as increased ground water and restored reservoirs. While predicting the Asian summer monsoon has been prioritized by decision-makers across sectors in Asia, impact forecasting must gain greater significance as it is particularly important to tackle disaster risks. The paradigm shifts from ‘what monsoon will be to what monsoon will do provides valuable insights to better prepare Asian countries for managing impending extreme events. The paper brings out how impact outlook for Asian monsoon can be effectively utilized. It shows how seasonal forecasts overlaid with risk and hazards maps and indicators on exposure and vulnerability can enhance understanding of potential risk scenarios for various sectors, including agriculture, energy, health, water, and disaster management. Noting the limitations of accuracy and information available from seasonal forecasts, the information provided from impact outlook should be understood as preliminary assessments. The paper makes a case for seamless integration of seasonal, sub-seasonal, medium, and short terms forecasts with the data on potential impact. This is aimed at enabling close monitoring and targeted policy actions.
The recent expansion of Aedes albopictus across continents in both tropical and temperate regions and the exponential growth of dengue cases over the past 50 years represent a significant risk to human health. Although climate change is not the only factor responsible for the increase and spread of dengue cases worldwide, it might increase the risk of disease transmission at global and regional scale. Here we show that regional and local variations in climate can induce differential impacts on the abundance of Ae. albopictus. We use the instructive example of Réunion Island with its varied climatic and environmental conditions and benefiting from the availability of meteorological, climatic, entomological and epidemiological data. Temperature and precipitation data based on regional climate model simulations (3 km × 3 km) are used as inputs to a mosquito population model for three different climate emission scenarios. Our objective is to study the impact of climate change on the life cycle dynamics of Ae. albopictus in the 2070-2100 time horizon. Our results show the joint influence of temperature and precipitation on Ae. albopictus abundance as a function of elevation and geographical subregion. At low-elevations areas, decreasing precipitation is expected to have a negative impact on environmental carrying capacity and, consequently, on Ae. albopictus abundance. At mid- and high-elevations, decreasing precipitation is expected to be counterbalanced by a significant warming, leading to faster development rates at all life stages, and consequently increasing the abundance of this important dengue vector in 2070-2100.
The survival of life on earth depends on equilibrium between the organisms and the environment. The monsoon is a seasonal variation prevailing in the Indian sub-continent. Monsoon has two seasons which are separated by a transition. The infectious diseases epidemiology is affected by both climatic and societal influences. An interaction of climatic and societal influence favours the infectious disease exposure in a population. The infectious diseases affecting the population can be broadly classified as vector borne diseases, food borne diseases, water borne diseases, and respiratory diseases. The rainfall associated change in temperature and floods favours the survival of infectious diseases and their transmitting vectors. The changing global climatic trends including the EL nino Southern oscillation bring undue rainfall during other seasons. The drastic events associated with these climatic changes affect the heath and sanitation infrastructure. India being a developing country has more vulnerability to such infections. A better strengthening of the infrastructure and health policies is the need of the hour to curb the infections.
The potential for dengue fever epidemic due to climate change remains uncertain in tropical areas. This study aims to assess the impact of climate change on dengue fever transmission in four South and Southeast Asian settings. We collected weekly data of dengue fever incidence, daily mean temperature and rainfall from 2012 to 2020 in Singapore, Colombo, Selangor, and Chiang Mai. Projections for temperature and rainfall were drawn for three Shared Socioeconomic Pathways (SSP126, SSP245, and SSP585) scenarios. Using a disease transmission model, we projected the dengue fever epidemics until 2090s and determined the changes in annual peak incidence, peak time, epidemic size, and outbreak duration. A total of 684,639 dengue fever cases were reported in the four locations between 2012 and 2020. The projected change in dengue fever transmission would be most significant under the SSP585 scenario. In comparison to the 2030s, the peak incidence would rise by 1.29 times in Singapore, 2.25 times in Colombo, 1.36 times in Selangor, and >10 times in Chiang Mai in the 2090s under SSP585. Additionally, the peak time was projected to be earlier in Singapore, Colombo, and Selangor, but be later in Chiang Mai under the SSP585 scenario. Even in a milder emission scenario of SSP126, the epidemic size was projected to increase by 5.94%, 10.81%, 12.95%, and 69.60% from the 2030s-2090s in Singapore, Colombo, Selangor, and Chiang Mai, respectively. The outbreak durations in the four settings were projected to be prolonged over this century under SSP126 and SSP245, while a slight decrease is expected in 2090s under SSP585. The results indicate that climate change is expected to increase the risk of dengue fever transmission in tropical areas of South and Southeast Asia. Limiting greenhouse gas emissions could be crucial in reducing the transmission of dengue fever in the future.
Endemic and imported vector- and rodent-borne infectious agents can be linked to high morbidity and mortality. Therefore, vector- and rodent-borne human diseases and the effects of climate change are important public health issues. METHODS: For this review, the relevant literature was identified and evaluated according to the thematic aspects and supplemented with an analysis of surveillance data for Germany. RESULTS: Factors such as increasing temperatures, changing precipitation patterns, and human behaviour may influence the epidemiology of vector- and rodent-borne infectious diseases in Germany. CONCLUSIONS: The effects of climatic changes on the spread of vector- and rodent-borne infectious diseases need to be further studied in detail and considered in the context of climate adaptation measures.
An effort is made to understand the role of El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on the malaria transmission intensity over India during the period 1951-2020 (70 years) with the help of a realistically simulated dynamical malaria model. The results suggest that the La Nina years pose a greater threat of malaria disease, especially in the densely populated Indian states. During El Nino years, the malaria transmission intensity and distribution over India greatly reduce, except in the regions such as Orissa, Chhattisgarh, Jharkhand, Western Ghats, parts of Madhya Pradesh, and Andhra Pradesh. It is found that in the positive IOD years, the malaria transmission intensity increases (decreases) over the entire central Indian region and along coastal regions of Tamil Nadu and Kerala (southern peninsular states of India and northeast India). An almost opposite behavior is seen during the negative IOD years. The malaria transmission variability over India is becoming increasingly heterogeneous in recent decades during the El Nino and La Nina years as a result of global warming. The period of 1986-2020 witnessed a substantial decrease (increase) in the malaria transmission intensity during the positive (negative) IOD years, except for a few regions of India. The implications of the results presented in the paper linking the ENSO and IOD signals with the intensity and distribution of malaria over India in a warming world are enormous, especially for the densely populated Indian states.
Temperature is a well-known effector of several transmission factors of mosquito-borne viruses, including within mosquito dynamics. These dynamics are often characterized by vector competence and the extrinsic incubation period (EIP). Vector competence is the intrinsic ability of a mosquito population to become infected with and transmit a virus, while EIP is the time it takes for the virus to reach the salivary glands and be expectorated following an infectious bloodmeal. Temperatures outside the optimal range act on life traits, decreasing transmission potential, while increasing temperature within the optimal range correlates to increasing vector competence and a decreased EIP. These relatively well-studied effects of other Aedes borne viruses (dengue and Zika) are used to make predictions about transmission efficiency, including the challenges presented by urban heat islands and climate change. However, the knowledge of temperature and chikungunya (CHIKV) dynamics within its two primary vectors-Ae. aegypti and Ae. albopictus-remains less characterized, even though CHIKV remains a virus of public-health importance. Here, we review the literature and summarize the state of the literature on CHIKV and temperature dependence of vector competence and EIP and use these data to demonstrate how the remaining knowledge gap might confound the ability to adequately predict and, thus, prepare for future outbreaks.
Protozoan parasites of the genus Plasmodium cause malaria, a mosquito borne disease responsible for substantial health and economic costs throughout the developing world. During transition from human host to insect vector, the parasites undergo profound changes in morphology, host cell tropism and gene expression. Unique among eukaryotes, Plasmodium differentiation through each stage of development includes differential expression of singular, stage-specific ribosomal RNAs, permitting real-time adaptability to major environmental changes. In the mosquito vector, these Plasmodium parasites respond to changes in temperature by modulating transcriptional activities, allowing real-time responses to environmental cues. Here, we identify a novel form of long noncoding RNA: a temperature-regulated untranslated lncRNA (tru-lncRNA) that influences the Plasmodium parasite’s ability to respond to changes in its local environment. Expression of this tru-lncRNA is specifically induced by shifts in temperature from 37 °C to ambient temperature that parallels the transition from mammalian host to insect vector. Interestingly, deletion of tru-lncRNA from the genome may prevent processing of S-type rRNA thereby affecting the protein synthesis machinery. Malaria prevention and mitigation strategies aimed at disrupting the Plasmodium life cycle will benefit from the characterization of ancillary biomolecules (including tru-lncRNAs) that are constitutively sensitive to micro- environmental parameters.
North Africa is home to more than 200 million people living across five developing economies (Egypt, Libya, Tunisia, Algeria, and Morocco) and two Spanish exclaves (Ceuta and Melilla), many of whom are impacted by ticks and tick-borne zoonoses. Populations in Europe are also increasingly vulnerable to North African ticks and tick-borne zoonoses due to a combination of climate change and the movement of ticks across the Mediterranean on migratory birds, human travellers, and trafficked wildlife. The human-biting ticks and tick-borne zoonoses in North Africa are reviewed along with their distribution in the region. We also assess present and future challenges associated with ticks and tick-borne zoonoses in North African and highlight opportunities for collaboration and coordination between governments in Europe and North Africa to address public health challenges posed by North African ticks and tick-borne zoonoses.
Chagas disease, considered a neglected disease, was initially confined to rural localities in endemic areas; however, in recent years through the process of urbanization and migration of infected people, the disease is gaining importance in urban environments. The presence of the vector in urban areas in most cases is due to the passive transport of vectors, but recently, its presence seems to be linked to vector adaptation processes associated with climate change. This paper reports the occurrence of an infected triatomine in the peridomicile of a house in an urban area of Córdoba, Veracruz, Mexico, where the species found is described, the molecular characteristics and resistance to BZN and NFX of the Trypanosoma cruzi isolate obtained, as well as serological data of the dwelling inhabitants. These urban disease scenarios make it possible to generate new scientific knowledge and enable the creation of new control strategies for Chagas disease vectors.
Dengue remains an important public health issue in South China. In this study, we aim to quantify the effect of climatic factors on dengue in nine cities of the Pearl River Delta (PRD) in South China. Monthly dengue cases, climatic factors, socio-economic, geographical, and mosquito density data in nine cities of the PRD from 2008 to 2019 were collected. A generalized additive model (GAM) was applied to investigate the exposure-response relationship between climatic factors (temperature and precipitation) and dengue incidence in each city. A spatio-temporal conditional autoregressive model (ST-CAR) with a Bayesian framework was employed to estimate the effect of temperature and precipitation on dengue and to explore the temporal trend of the dengue risk by adjusting the socioeconomic and geographical factors. There was a positive non-linear association between the temperature and dengue incidence in the nine cities in south China, while the approximate linear negative relationship between precipitation and dengue incidence was found in most of the cities. The ST-CAR model analysis showed the risk of dengue transmission increased by 101.0% (RR: 2.010, 95% CI: 1.818 to 2.151) for 1 degrees C increase in monthly temperature at 2 months lag in the overall nine cities, while a 3.2% decrease (relative risk (RR): 0.968, 95% CI: 0.946 to 0.985) and a 2.1% decrease (RR: 0.979, 95% CI: 0.975 to 0.983) for 10 mm increase in monthly precipitation at present month and 3 months lag. The expected incidence of dengue has risen again since 2015, and the highest incidence was in Guangzhou City. Our study showed that climatic factors, including temperature and precipitation would drive the dengue transmission, and the dengue epidemic risk has been increasing. The findings may contribute to the climate-driven dengue prediction and dengue risk projection for future climate scenarios.
Arboviruses represent a public health concern in many European countries, including Italy, mostly because they can infect humans, causing potentially severe emergent or re-emergent diseases, with epidemic outbreaks and the introduction of endemic circulation of new species previously confined to tropical and sub-tropical regions. In this review, we summarize the Italian epidemiology of arboviral infection over the past 10 years, describing both endemic and imported arboviral infections, vector distribution, and the influence of climate change on vector ecology. Strengthening surveillance systems at a national and international level is highly recommended to be prepared to face potential threats due to arbovirus diffusion.
The factors that drive dengue virus (DENV) evolution, and selection of virulent variants are yet not clear. Higher environmental temperature shortens DENV extrinsic incubation period in mosquitoes, increases human transmission, and plays a critical role in outbreak dynamics. In the present study, we looked at the effect of temperature in altering the virus virulence. We found that DENV cultured at a higher temperature in C6/36 mosquito cells was significantly more virulent than the virus grown at a lower temperature. In a mouse model, the virulent strain induced enhanced viremia and aggressive disease with a short course, hemorrhage, severe vascular permeability, and death. Higher inflammatory cytokine response, thrombocytopenia, and severe histopathological changes in vital organs such as heart, liver, and kidney were hallmarks of the disease. Importantly, it required only a few passages for the virus to acquire a quasi-species population harboring virulence-imparting mutations. Whole genome comparison with a lower temperature passaged strain identified key genomic changes in the structural protein-coding regions as well as in the 3’UTR of the viral genome. Our results point out that virulence-enhancing genetic changes could occur in the dengue virus genome under enhanced growth temperature conditions in mosquito cells.
The coronavirus (SARS-CoV-2) is the latest but not the first deadly pathogen to jump from animals to humans. The history of pandemics is replete with such events. The convergence of animal health, human health, and ecosystem health is a twenty-first century reality, as human activities that drive climate change also contribute to pandemic risk. Understanding the past and future of zoonotic diseases requires new models in the way we research human-animal-environment interconnections. This bibliographic essay discusses the historical development of these zoonotic diseases and incorporates sources from the history of science and medicine, environmental science, animal science, disease ecology, politics, and anthropology. Contributing to deeper understandings of zoonotic diseases, historians and anthropologists have viewed pandemics as social and biological phenomena. However, viewpoints differ whether scholars routinely examine disease links between animals and humans. These links include the ecological aspects of infectious diseases’ history and the role of wildlife as vectors of zoonotic disease. In addition, challenges persist in integrating social sciences and humanities, the environmental sector, and scientific research. Ideally, historiographies of zoonotic diseases would include societies’ responses and the social, cultural, political, economic, and ecological contexts. This bibliographic essay assembles resources that would benefit such an integrated approach.
Climate change impacts on the transmission and epidemics of vector-borne diseases (VBDs), hence an understanding of the institutional determinants that influence the response of national health systems is important. This study explored how institutional determinants influence health outcomes of malaria and bilharzia using the case study of Gwanda district, Zimbabwe, in the advent of climate change. Qualitative data were collected using in-depth interviews from representatives of public and private institutions; and organisations involved in the prevention and control of malaria and bilharzia. Results from the study showed that the Ministry of Health and Child Care of Zimbabwe and other relevant government ministries and departments involved in environmental and social issues, constituted the primary network in the control and prevention of malaria and bilharzia. Non-governmental organisations (NGOs) formed the secondary network that mainly mobilized resources or complimented the primary networks in the delivery of services. It was noted that there was an institutional structure primarily responsible for responding to malaria and bilharzia but it was not adequately prepared to address climate change-induced VBDs changes. Based on our findings, a framework for reducing vulnerability and enhancing resilience among populations affected by VBDs in the context of climate change was developed.
Due to climate changes, there has been a large expansion of emerging tick-borne zoonotic viruses, including Heartland bandavirus (HRTV) and Dabie bandavirus (DBV). As etiologic agents of hemorrhagic fever with high fatality, HRTV and DBV have been recognized as dangerous viral pathogens that likely cause future wide epidemics. Despite serious health concerns, the mechanisms underlying viral infection are largely unknown. HRTV and DBV Gn and Gc are viral surface glycoproteins required for early entry events during infection. Glycosphingolipids, including galactosylceramide (GalCer), glucosylceramide (GlcCer) and lactosylceramide (LacCer), are a class of membrane lipids that play essential roles in membrane structure and viral lifecycle. Here, our genome-wide CRISPR/Cas9 knockout screen identifies that glycosphingolipid biosynthesis pathway is essential for HRTV and DBV infection. The deficiency of UDP-glucose ceramide glucosyltransferase (UGCG) that produces GlcCer resulted in the loss of infectivity of recombinant viruses pseudotyped with HRTV or DBV Gn/Gc glycoproteins. Conversely, exogenous supplement of GlcCer, but not GalCer or LacCer, recovered viral entry of UGCG-deficient cells in a dose-dependent manner. Biophysical analyses showed that GlcCer targeted the lipid-head-group binding pocket of Gc to form a stable protein-lipid complex, which allowed the insertion of Gc protein into host lysosomal membrane lipid bilayers for viral fusion. Mutagenesis showed that D841 residue at the Gc lipid binding pocket was critical for GlcCer interaction and thereby, viral entry. These findings reveal detailed mechanism of GlcCer glycosphingolipid in HRTV and DBV Gc-mediated membrane fusion and provide a potential therapeutic target for tickborne virus infection. Author summaryHeartland bandavirus (HRTV) and Dabie bandavirus (DBV) were recently identified as emerging tick-borne zoonotic viruses in the United States and Asia, respectively. As etiologic agents of hemorrhagic fever with high fatality, HRTV and DBV have been recognized as dangerous viral pathogens that likely cause future wide epidemics. Despite serious health concerns, the mechanisms underlying viral infection are largely unknown. Here, we use genome-wide CRISPR/Cas9 knockout screens to determine the requirements of HRTV entry in mammalian cells. We found that the glycosphingolipid biosynthesis pathway is essential for HRTV and DBV infection. The infectivity of HRTV and DBV in glycosphingolipid biosynthesis-deficient cells was drastically reduced. We also found that glucosylceramide (GlcCer) plays a vital role in HRTV glycoproteins-mediated membrane fusion. The GlcCer targets the lipid-head-group binding pocket of HRTV glycoprotein in the host lysosomal membrane to form a stable lipid-protein complex, thereby facilitating viral fusion and entry. Our study reveals the detailed molecular mechanism of GlcCer glycosphingolipid in HRTV and DBV Gc-mediated membrane fusion and provides a potential therapeutic target for tick-borne virus infection.
Arboviral mosquito vectors are key targets for the surveillance and control of vector-borne diseases worldwide. In recent years, changes to the global distributions of these species have been a major research focus, aimed at predicting outbreaks of arboviral diseases. In this study, we analyzed a global scenario of climate change under regional rivalry to predict changes to these species’ distributions over the next century. Using occurrence data from VectorMap and environmental variables (temperature and precipitation) from WorldClim v. 2.1, we first built fundamental niche models for both species with the boosted regression tree modelling approach. A scenario of climate change on their fundamental niche was then analyzed. The shared socioeconomic pathway scenario 3 (regional rivalry) and the global climate model Geophysical Fluid Dynamics Laboratory Earth System Model v. 4.1 (GFDL-ESM4.1; gfdl.noaa.gov) were utilized for all analyses, in the following time periods: 2021-2040, 2041-2060, 2061-2080, and 2081-2100. Outcomes from these analyses showed that future climate change will affect Ae. aegypti and Ae. albopictus distributions in different ways across the globe. The Northern Hemisphere will have extended Ae. aegypti and Ae. albopictus distributions in future climate change scenarios, whereas the Southern Hemisphere will have the opposite outcomes. Europe will become more suitable for both species and their related vector-borne diseases. Loss of suitability in the Brazilian Amazon region further indicated that this tropical rainforest biome will have lower levels of precipitation to support these species in the future. Our models provide possible future scenarios to help identify locations for resource allocation and surveillance efforts before a significant threat to human health emerges.
Mosquito-borne viruses increasingly threaten human populations due to accelerating changes in climate, human and mosquito migration, and land use practices. Over the last three decades, the global distribution of dengue has rapidly expanded, causing detrimental health and economic problems in many areas of the world. To develop effective disease control measures and plan for future epidemics, there is an urgent need to map the current and future transmission potential of dengue across both endemic and emerging areas. Expanding and applying Index P, a previously developed mosquito-borne viral suitability measure, we map the global climate-driven transmission potential of dengue virus transmitted by Aedes aegypti mosquitoes from 1981 to 2019. This database of dengue transmission suitability maps and an R package for Index P estimations are offered to the public health community as resources towards the identification of past, current and future transmission hotspots. These resources and the studies they facilitate can contribute to the planning of disease control and prevention strategies, especially in areas where surveillance is unreliable or non-existent.
Culex tritaeniorhynchus is the primary vector of Japanese encephalitis (JE) and has a wide global distribution. However, the current and future geographic distribution maps of Cx. tritaeniorhynchus in global are still incomplete. Our study aims to predict the potential distribution of Cx. tritaeniorhynchus in current and future conditions to provide a guideline for the formation and implementation of vector control strategies all over the world. We collected and screened the information on the occurrence of Cx. tritaeniorhynchus by searching the literature and online databases and used ten algorithms to investigate its global distribution and impact factors. Cx. tritaeniorhynchus had been detected in 41 countries from 5 continents. The final ensemble model (TSS = 0.864 and AUC = 0.982) indicated that human footprint was the most important factor for the occurrence of Cx. tritaeniorhynchus. The tropics and subtropics, including southeastern Asia, Central Africa, southeastern North America and eastern South America, showed high habitat suitability for Cx. tritaeniorhynchus. Cx. tritaeniorhynchus is predicted to have a wider distribution in all the continents, especially in Western Europe and South America in the future under two extreme emission scenarios (SSP5-8.5 and SSP1-2.6). Targeted strategies for the control and prevention of Cx. tritaeniorhynchus should be further strengthened.
Human activity has a direct influence on the climate on our planet. In recent decades, the greater part of the scientific community has united around the concept of Global Warming (GW). This process highly impacts the geographical distribution of mosquitoes and Mosquito-Borne Diseases (MBD). The examined scientific publications show that Africa, especially sub-Saharan countries were and still hot spot of MBD globally. The economic, social, and environmental conditions prevailing in most African countries have effectively contributed to the spread of MBD. The current situation is very worrying, and it will get even more complicated as GW gets worse. In this regard, health systems in developing countries will have serious difficulties in health policies and public health activities to control the spread on MBD. Therefore, the governments of African countries should do more to combat MBD. However, a part of the responsibility lies with the international community, especially countries that contribute to GW. In conclusion, the analysis of the scientific literature showed that with increasing importance of GW leads to an increase in the prevalence of MBD.
Affecting millions of individuals yearly, malaria is one of the most dangerous and deadly tropical diseases. It is a major global public health problem, with an alarming spread of parasite transmitted by mosquito (Anophele). Various studies have emerged that construct a mathematical and statistical model for malaria incidence forecasting. In this study, we formulate a generalized linear model based on Poisson and negative binomial regression models for forecasting malaria incidence, taking into account climatic variables (such as the monthly rainfall, average temperature, relative humidity), other predictor variables (the insecticide-treated bed-nets (ITNs) distribution and Artemisinin-based combination therapy (ACT)) and the history of malaria incidence in Dakar, Fatick and Kedougou, three different endemic regions of Senegal. A forecasting algorithm is developed by taking the meteorological explanatory variable Xj at time t-?j, where t is the observation time and ?j is the lag in Xj that maximizes its correlation with the malaria incidence. We saturated the rainfall in order to reduce over-forecasting. The results of this study show that the Poisson regression model is more adequate than the negative binomial regression model to forecast accurately the malaria incidence taking into account some explanatory variables. The application of the saturation where the over-forecasting was observed noticeably increases the quality of the forecasts.
One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16 degrees C to <= 20 degrees C), (>20 degrees C to <= 25 degrees C), (>25 degrees C to <= 28 degrees C), and (>28 degrees C to <= 30 degrees C). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28 degrees C to <= 30 degrees C) and minimum if the average temperature falls into the threshold (16 degrees C to <= 20 degrees C). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.
Driven by globalization, urbanization and climate change, the distribution range of invasive vector species has expanded to previously colder ecoregions. To reduce health-threatening impacts on humans, insect vectors are extensively studied. Population genomics can reveal the genomic basis of adaptation and help to identify emerging trends of vector expansion. By applying whole genome analyses and genotype-environment associations to populations of the main dengue vector Aedes aegypti, sampled along an altitudinal gradient in Nepal (200-1300 m), we identify putatively adaptive traits and describe the species’ genomic footprint of climate adaptation to colder ecoregions. We found two differentiated clusters with significantly different allele frequencies in genes associated to climate adaptation between the highland population (1300 m) and all other lowland populations (≤800 m). We revealed nonsynonymous mutations in 13 of the candidate genes associated to either altitude, precipitation or cold tolerance and identified an isolation-by-environment differentiation pattern. Other than the expected gradual differentiation along the altitudinal gradient, our results reveal a distinct genomic differentiation of the highland population. Local high-altitude adaptation could be one explanation of the population’s phenotypic cold tolerance. Carrying alleles relevant for survival under colder climate increases the likelihood of this highland population to a worldwide expansion into other colder ecoregions.
BACKGROUND: Rapid adaptation to new environments can facilitate species invasions and range expansions. Understanding the mechanisms of adaptation used by invasive disease vectors in new regions has key implications for mitigating the prevalence and spread of vector-borne disease, although they remain relatively unexplored. RESULTS: Here, we integrate whole-genome sequencing data from 96 Aedes aegypti mosquitoes collected from various sites in southern and central California with 25 annual topo-climate variables to investigate genome-wide signals of local adaptation among populations. Patterns of population structure, as inferred using principal components and admixture analysis, were consistent with three genetic clusters. Using various landscape genomics approaches, which all remove the confounding effects of shared ancestry on correlations between genetic and environmental variation, we identified 112 genes showing strong signals of local environmental adaptation associated with one or more topo-climate factors. Some of them have known effects in climate adaptation, such as heat-shock proteins, which shows selective sweep and recent positive selection acting on these genomic regions. CONCLUSIONS: Our results provide a genome wide perspective on the distribution of adaptive loci and lay the foundation for future work to understand how environmental adaptation in Ae. aegypti impacts the arboviral disease landscape and how such adaptation could help or hinder efforts at population control.
Geographic information systems (GIS) can be used to map mosquito larval and adult habitats and human populations at risk for mosquito exposure and possible arbovirus transmission. Along with traditional methods of surveillance-based targeted mosquito control, GIS can help simplify and target efforts during routine surveillance and post-disaster (e.g., hurricane-related flooding) to protect emergency workers and public health. A practical method for prioritizing areas for emergency mosquito control has been developed and is described here. North Carolina (NC) One Map was used to identify state-level data layers of interest based on human population distribution and mosquito habitat in Brunswick, Columbus, Onslow, and Robeson Counties in eastern NC. Relevant data layers were included to create mosquito control treatment areas for targeted control and an 18-step protocol for map development is discussed. This protocol is expected to help state, territorial, tribal, and/or local public health officials and associated mosquito control programs efficiently create treatment area maps to improve strategic planning in advance of a disaster. This protocol may be applied to any NC county and beyond, thereby increasing local disaster preparedness.
Climate change is arguably one of the most pressing issues affecting the world today and requires the fusion of disparate data streams to accurately model its impacts. Mosquito populations respond to temperature and precipitation in a nonlinear way, making predicting climate impacts on mosquito-borne diseases an ongoing challenge. Data-driven approaches for accurately modeling mosquito populations are needed for predicting mosquito-borne disease risk under climate change scenarios. Many current models for disease transmission are continuous and autonomous, while mosquito data is discrete and varies both within and between seasons. This study uses an optimization framework to fit a non-autonomous logistic model with periodic net growth rate and carrying capacity parameters for 15 years of daily mosquito time-series data from the Greater Toronto Area of Canada. The resulting parameters accurately capture the inter-annual and intra-seasonal variability of mosquito populations within a single geographic region, and a variance-based sensitivity analysis highlights the influence each parameter has on the peak magnitude and timing of the mosquito season. This method can easily extend to other geographic regions and be integrated into a larger disease transmission model. This method addresses the ongoing challenges of data and model fusion by serving as a link between discrete time-series data and continuous differential equations for mosquito-borne epidemiology models.
The parasitoid wasp, Ixodiphagus hookeri (Hymenoptera: Encyrtidae), is the natural enemy of a wide range of hard and soft tick species. While these encyrtid wasps are supposed to be distributed worldwide, only a few studies report on their actual distribution around the globe. Within a shotgun sequencing-based metagenome analysis, the occurrence of I. hookeri was screened at multiple Ixodes ricinus (Acari: Ixodidae) tick sampling points in Hungary to contribute to the assessment of the distribution patterns of the parasitoid wasps in Central Europe. To our knowledge, the first report of the species in Hungary and the description of the southernmost I. hookeri associated geoposition in Central Europe took place within our study. I. hookeri infested I. ricinus nymphs were detected at five sampling points in Hungary. The results show that the exact distribution range of I. hookeri is still barely studied. At the same time, unprecedented public health issues being brought about by climate change might require steps toward the exploitation of the tick biocontrol potential and as an ecological bioindicator role of the parasitoid wasp in the future.
Global warming is caused by human activity, such as the burning of fossil fuels, which produces high levels of greenhouse gasses. As a consequence, climate change impacts all organisms and the greater ecosystem through changing conditions from weather patterns to the temperature, pH and salt concentrations found in waterways and soil. These environmental changes fundamentally alter many parameters of the living world, from the kinetics of chemical reactions and cellular signaling pathways to the accumulation of unforeseen chemicals in the environment, the appearance and dispersal of new diseases, and the availability of traditional foods. Some organisms adapt to extremes, while others cannot. This article asks five questions that prompt us to consider the foundational knowledge that biochemistry can bring to the table as we meet the challenge of climate change. We approach climate change from the molecular point of view, identifying how cells and organisms – from microbes to plants and animals – respond to changing environmental conditions. To embrace the concept of “one health” for all life on the planet, we argue that we must leverage biochemistry, cell biology, molecular biophysics and genetics to fully understand the impact of climate change on the living world and to bring positive change.
Malaria epidemics result from extreme precipitation and flooding, which are increasing with global climate change. Local adaptation and mitigation strategies will be essential to preventing excess morbidity and mortality. METHODS: We investigated the spatial risk of malaria infection at multiple timepoints after severe flooding in rural western Uganda employing longitudinal household surveys measuring parasite prevalence and leveraging remotely-sensed information to inform spatial models of malaria risk in the three months after flooding. RESULTS: We identified clusters of malaria riskemerging in areas that (i) showed the greatest changes in NDVI from pre- to post-flood and (ii) residents were displaced for longer periods of time and had lower access to long-lasting insecticidal nets, both of which were associated with a positive malaria rapid diagnostic test result. The disproportionate risk persisted despite a concurrent chemoprevention program that achieved high coverage. CONCLUSIONS: The findings enhance our understanding not only of the spatial evolution of malaria risk after flooding, but also in the context of an effective intervention. The results provide a “proof-of-concept” for programs aiming to prevent malaria outbreaks after flooding using a combination of interventions. Further study of mitigation strategies – and particularly studies of implementation – is urgently needed.
West Nile virus (WNV), a mosquito-borne zoonosis, has emerged as a disease of public health concern in Europe. Recent outbreaks have been attributed to suitable climatic conditions for its vectors favoring transmission. However, to date, projections of the risk for WNV expansion under climate change scenarios is lacking. Here, we estimate the WNV-outbreaks risk for a set of climate change and socioeconomic scenarios. We delineate the potential risk-areas and estimate the growth in the population at risk (PAR). We used supervised machine learning classifier, XGBoost, to estimate the WNV-outbreak risk using an ensemble climate model and multi-scenario approach. The model was trained by collating climatic, socioeconomic, and reported WNV-infections data (2010-22) and the out-of-sample results (1950-2009, 2023-99) were validated using a novel Confidence-Based Performance Estimation (CBPE) method. Projections of area specific outbreak risk trends, and corresponding population at risk were estimated and compared across scenarios. Our results show up to 5-fold increase in West Nile virus (WNV) risk for 2040-60 in Europe, depending on geographical region and climate scenario, compared to 2000-20. The proportion of disease-reported European land areas could increase from 15% to 23-30%, putting 161 to 244 million people at risk. Across scenarios, Western Europe appears to be facing the largest increase in the outbreak risk of WNV. The increase in the risk is not linear but undergoes periods of sharp changes governed by climatic thresholds associated with ideal conditions for WNV vectors. The increased risk will require a targeted public health response to manage the expansion of WNV with climate change in Europe.
West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. METHODS: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. RESULTS: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. CONCLUSIONS: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).
BACKGROUND: Dengue is currently a global concern. The range of dengue vectors is expanding with climate change, yet United States of America (USA) studies on dengue epidemiology and burden are limited. This systematic review sought to characterize the epidemiology and disease burden of dengue within the USA. METHODS: Studies evaluating travel-related and endemic dengue in US states and territories were identified and qualitatively summarized. Commentaries and studies on ex-US cases were excluded. MEDLINE, Embase, Cochrane Library, Latin American and Caribbean Center of Health Sciences Information, Centre for Reviews and Dissemination and Clinicaltrials.gov were searched through January 2022. RESULTS: 116 studies were included. In US states, dengue incidence was generally low, with spikes occurring in recent years in 2013-16 (0.17-0.31 cases/100,000) and peaking in 2019 (0.35 cases/100,000). Most cases (94%, n = 7895, 2010-21) were travel related. Dengue was more common in Puerto Rico (cumulative average: 200 cases/100,000, 1980-2015); in 2010-21, 99.9% of cases were locally acquired. There were <50 severe cases in US states (2010-17); fatal cases were even rarer. Severe cases in Puerto Rico peaked in 1998 (n = 173) and 2021 (n = 76). Besides lower income, risk factors in US states included having birds in residence, suggesting unspecified environmental characteristics favourable to dengue vectors. Commonly reported symptoms included fever, headache and rash; median disease duration was 3.5-11 days. Hospitalization rates increased following 2009 World Health Organization disease classification changes (pre-2009: 0-54%; post-2009: 14-75%); median length of stay was 2.7-8 days (Puerto Rico) and 2-3 days (US states). Hospitalization costs/case (2010 USD) were$14 350 (US states),$1764-$5497 (Puerto Rico) and$4207 (US Virgin Islands). In Puerto Rico, average days missed were 0.2-5.3 (work) and 2.5 (school). CONCLUSIONS: Though dengue risk is ongoing, treatments are limited, and dengue's economic burden is high. There is an urgent need for additional preventive and therapeutic interventions.
In sub-Saharan Africa, temperatures are generally conducive to malaria transmission, and rainfall provides mosquitoes with optimal breeding conditions. The objective of this study is to assess the impact of future climate change on malaria transmission in West Africa using community-based vector-borne disease models, TRIeste (VECTRI). This VECTRI model, based on bias-corrected data from the Phase 6 Coupled Model Intercomparison Project (CMIP6), was used to simulate malaria parameters, such as the entomological inoculation rate (EIR). Due to the lack of data on confirmed malaria cases throughout West Africa, we first validated the forced VECTRI model with CMIP6 data in Senegal. This comparative study between observed malaria data from the National Malaria Control Program in Senegal (Programme National de Lutte contre le Paludisme, PNLP, PNLP) and malaria simulation data with the VECTRI (EIR) model has shown the ability of the biological model to simulate malaria transmission in Senegal. We then used the VECTRI model to reproduce the historical characteristics of malaria in West Africa and quantify the projected changes with two Shared Socio-economic Pathways (SSPs). The method adopted consists of first studying the climate in West Africa for a historical period (1950-2014), then evaluating the performance of VECTRI to simulate malaria over the same period (1950-2014), and finally studying the impact of projected climate change on malaria in a future period (2015-2100) according to the ssp245 ssp585 scenario. The results showed that low-latitude (southern) regions with abundant rainfall are the areas most affected by malaria transmission. Two transmission peaks are observed in June and October, with a period of high transmission extending from May to November. In contrast to regions with high latitudes in the north, semi-arid zones experience a relatively brief transmission period that occurs between August, September, and October, with the peak observed in September. Regarding projections based on the ssp585 scenario, the results indicate that, in general, malaria prevalence will gradually decrease in West Africa in the distant future. But the period of high transmission will tend to expand in the future. In addition, the shift of malaria prevalence from already affected areas to more suitable areas due to climate change is observed. Similar results were also observed with the ssp245 scenario regarding the projection of malaria prevalence. In contrast, the ssp245 scenario predicts an increase in malaria prevalence in the distant future, while the ssp585 scenario predicts a decrease. These findings are valuable for decision makers in developing public health initiatives in West Africa to mitigate the impact of this disease in the region in the context of climate change.
This study identifies the environmental and socio-economic determinants of clusters of high malaria incidence in Colombia during the period of 2008-2019. The malaria cases were obtained from the National System of Surveillance in Public Health, with 798,897 cases reported in the 986 Colombian municipalities evaluated during the study period. Spatial autocorrelation of incidence was examined with global and local indices. Clusters were identified in the Amazon, Pacific, and Uraba-Bajo Cauca-Alto Sinú regions. The factors associated with a municipality belonging to a high-incidence cluster were identified using a logistic regression model with mixed effects and showed a positive association for the variables (forest coverage and minimum multi-year average rainfall). An inverse relationship was observed for aqueduct coverage and the odds of belonging to a cluster. A 1% increase in forest coverage was associated with a 4.2% increase in the odds of belonging to a malaria cluster. The association with minimum multi-year average rainfall was positive (OR = 1.0011; 95% CI 1.0005-1.0027). A 1% increase in aqueduct coverage was associated with a 4.3% decrease in the odds of belonging to malaria cluster. The identification of malaria cluster determinants in Colombia could help guide surveillance and disease control policies.
Thailand has made substantial progress towards malaria elimination, with 46 of the country’s 77 provinces declared malaria-free as part of the subnational verification program. Nonetheless, these areas remain vulnerable to the reintroduction of malaria parasites and the reestablishment of indigenous transmission. As such, prevention of reestablishment (POR) planning is of increasing concern to ensure timely response to increasing cases. A thorough understanding of both the risk of parasite importation and receptivity for transmission is essential for successful POR planning. Routine geolocated case- and foci-level epidemiological and case-level demographic data were extracted from Thailand’s national malaria information system for all active foci from October 2012 to September 2020. A spatial analysis examined environmental and climate factors associated with the remaining active foci. A logistic regression model collated surveillance data with remote sensing data to investigate associations with the probability of having reported an indigenous case within the previous year. Active foci are highly concentrated along international borders, particularly Thailand’s western border with Myanmar. Although there is heterogeneity in the habitats surrounding active foci, land covered by tropical forest and plantation was significantly higher for active foci than other foci. The regression results showed that tropical forest, plantations, forest disturbance, distance from international borders, historical foci classification, percentage of males, and percentage of short-term residents were associated with the high probability of reporting indigenous cases. These results confirm that Thailand’s emphasis on border areas and forest-going populations is well placed. The results suggest that environmental factors alone are not driving malaria transmission in Thailand; rather, other factors, including demographics and behaviors that intersect with exophagic vectors, may also be contributors. However, these factors are syndemic, so human activities in areas covered by tropical forests and plantations may result in malaria importation and, potentially, local transmission, in foci that had previously been cleared. These factors should be addressed in POR planning.
Ethiopia has a history of climate related malaria epidemics. An improved understanding of malaria-climate interactions is needed to inform malaria control and national adaptation plans. METHODS: Malaria-climate associations in Ethiopia were assessed using (a) monthly climate data (1981-2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950-2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored. RESULTS: El Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July-August-September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October-November-December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall. CONCLUSION: Malaria-climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.
Global changes have influenced our societies in several ways with both positive (e.g., technology, transportation, and food security), and negative impacts (e.g., mental health problems, spread of diseases, and pandemics). Overall, these changes have affected the distribution patterns of parasites and arthropod vectors with the introduction and spreading of alien species in new geographical areas, eventually posing new challenges in public health. In this framework, the Acta Tropica Special Issue “Emerging parasites and vectors in a rapidly changing world: from ecology to management” provides a focus on the biology, ecology and management of emerging parasites and vectors of human and veterinary importance. Herein we review and discuss novel studies dealing with interactions of parasites and vectors with animals in changing environmental settings. In our opinion, a special focus on the implementation of management strategies of parasitic diseases to face anthropogenic environmental changes still represent a priority for public health. In the final section, key research challenges in this rapidly changing scenario are outlined.
Studies have shown that dengue virus transmission increases in association with ambient temperature. We performed a systematic review and meta-analysis to assess the effect of both high temperatures and heatwave events on dengue transmission in different climate zones globally. METHODS: A systematic literature search was conducted in PubMed, Scopus, Embase, and Web of Science from January 1990 to September 20, 2022. We included peer reviewed original observational studies using ecological time series, case crossover, or case series study designs reporting the association of high temperatures and heatwave with dengue and comparing risks over different exposures or time periods. Studies classified as case reports, clinical trials, non-human studies, conference abstracts, editorials, reviews, books, posters, commentaries; and studies that examined only seasonal effects were excluded. Effect estimates were extracted from published literature. A random effects meta-analysis was performed to pool the relative risks (RRs) of dengue infection per 1 °C increase in temperature, and further subgroup analyses were also conducted. The quality and strength of evidence were evaluated following the Navigation Guide systematic review methodology framework. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO). FINDINGS: The study selection process yielded 6367 studies. A total of 106 studies covering more than four million dengue cases fulfilled the inclusion criteria; of these, 54 studies were eligible for meta-analysis. The overall pooled estimate showed a 13% increase in risk of dengue infection (RR = 1.13; 95% confidence interval (CI): 1.11-1.16, I(2) = 98.0%) for each 1 °C increase in high temperatures. Subgroup analyses by climate zones suggested greater effects of temperature in tropical monsoon climate zone (RR = 1.29, 95% CI: 1.11-1.51) and humid subtropical climate zone (RR = 1.20, 95% CI: 1.15-1.25). Heatwave events showed association with an increased risk of dengue infection (RR = 1.08; 95% CI: 0.95-1.23, I(2) = 88.9%), despite a wide confidence interval. The overall strength of evidence was found to be “sufficient” for high temperatures but “limited” for heatwaves. Our results showed that high temperatures increased the risk of dengue infection, albeit with varying risks across climate zones and different levels of national income. INTERPRETATION: High temperatures increased the relative risk of dengue infection. Future studies on the association between temperature and dengue infection should consider local and regional climate, socio-demographic and environmental characteristics to explore vulnerability at local and regional levels for tailored prevention. FUNDING: Australian Research Council Discovery Program.
This study investigated associations between climate variables (average temperature and cumulative rainfall), and El Niño Southern Oscillation (ENSO) and dengue-like-illness (DLI) incidence in two provinces (Western and Guadalcanal Provinces) in Solomon Islands (SI). METHODS: Weekly DLI and meteorological data were obtained from the Ministry of Health and Medical Services SI and the Ministry of Environment, Climate Change, Disaster Management and Meteorology from 2015 to 2018, respectively. We used negative binomial generalized estimating equations to assess the effects of climate variables up to a lag of 2 months and ENSO on DLI incidence in SI. RESULTS: We captured an upsurge in DLI trend between August 2016 and April 2017. We found the effects of average temperature on DLI in Guadalcanal Province at lag of one month (IRR: 2.186, 95% CI: 1.094-4.368). Rainfall had minor but consistent effect in all provinces. La Niña associated with increased DLI risks in Guadalcanal Province (IRR: 4.537, 95% CI: 2.042-10.083), whereas El Niño associated with risk reduction ranging from 72.8% to 76.7% in both provinces. CONCLUSIONS: Owing to the effects of climate variability and ENSO on DLI, defining suitable and sustainable measures to control dengue transmission and enhancing community resilience against climate change in low- and middle-developed countries are important.
Malaria continues to be a major public health concern with a substantial burden in Africa. Even though it has been widely demonstrated that malaria transmission is climate-driven, there have been very few studies assessing the relationship between climate variables and malaria transmission in Côte d’Ivoire. We used the VECTRI model to predict malaria transmission in southern Côte d’Ivoire. First, we tested the suitability of VECTRI in modeling malaria transmission using ERA5 temperature data and ARC2 rainfall data. We then used the projected climatic data pertaining to 2030, 2050, and 2080 from a set of 14 simulations from the CORDEX-Africa database to compute VECTRI outputs. The entomological inoculation rate (EIR) from the VECTRI model was well correlated with the observed malaria cases from 2010 to 2019, including the peaks of malaria cases and the EIR. However, the correlation between the two parameters was not statistically significant. The VECTRI model predicted an increase in malaria transmissions in both scenarios (RCP8.5 and RCP4.5) for the time period 2030 to 2080. The monthly EIR for RCP8.5 was very high (1.74 to 1131.71 bites/person) compared to RCP4.5 (0.48 to 908 bites/person). These findings call for greater efforts to control malaria that take into account the impact of climatic factors.
The circulation of both West Nile Virus (WNV) and Chikungunya Virus (CHIKV) in humans and animals, coupled with a favorable tropical climate for mosquito proliferation in Zambia, call for the need for a better understanding of the ecological and epidemiological factors that govern their transmission dynamics in this region. This study aimed to examine the contribution of climatic variables to the distribution of Culex and Aedes mosquito species, which are potential vectors of CHIKV, WNV, and other arboviruses of public-health concern. Mosquitoes collected from Lusaka as well as from the Central and Southern provinces of Zambia were sorted by species within the Culex and Aedes genera, both of which have the potential to transmit viruses. The MaxEnt software was utilized to predict areas at risk of WNV and CHIKV based on the occurrence data on mosquitoes and environmental covariates. The model predictions show three distinct spatial hotspots, ranging from the high-probability regions to the medium- and low-probability regions. Regions along Lake Kariba, the Kafue River, and the Luangwa Rivers, as well as along the Mumbwa, Chibombo, Kapiri Mposhi, and Mpika districts were predicted to be suitable habitats for both species. The rainfall and temperature extremes were the most contributing variables in the predictive models.
Vector-borne diseases (VBDs) pose a major threat to human and animal health, with more than 80% of the global population being at risk of acquiring at least one major VBD. Being profoundly affected by the ongoing climate change and anthropogenic disturbances, modelling approaches become an essential tool to assess and compare multiple scenarios (past, present and future), and further the geographic risk of transmission of VBDs. Ecological niche modelling (ENM) is rapidly becoming the gold-standard method for this task. The purpose of this overview is to provide an insight of the use of ENM to assess the geographic risk of transmission of VBDs. We have summarised some fundamental concepts and common approaches to ENM of VBDS, and then focused with a critical view on a number of crucial issues which are often disregarded when modelling the niches of VBDs. Furthermore, we have briefly presented what we consider the most relevant uses of ENM when dealing with VBDs. Niche modelling of VBDs is far from being simple, and there is still a long way to improve. Therefore, this overview is expected to be a useful benchmark for niche modelling of VBDs in future research.
Introduction: Culex quinquefasciatus is a mosquito species of significant public health importance due to its ability to transmit multiple pathogens that can cause mosquito-borne diseases, such as West Nile fever and St. Louis encephalitis. In Harris County, Texas, Cx. quinquefasciatus is a common vector species and is subjected to insecticide-based management by the Harris County Public Health Department. However, insecticide resistance in mosquitoes has increased rapidly worldwide and raises concerns about maintaining the effectiveness of vector control approaches. This concern is highly relevant in Texas, with its humid subtropical climate along the Gulf Coast that provides suitable habitat for Cx. quinquefasciatus and other mosquito species that are known disease vectors. Therefore, there is an urgent and ongoing need to monitor the effectiveness of current vector control programs. Methods: In this study, we evaluated the impact of vector control approaches by estimating the effective population size of Cx. quinquefasciatus in Harris County. We applied Approximate Bayesian Computation to microsatellite data to estimate effective population size. We collected Cx. quinquefasciatus samples from two mosquito control operation areas; 415 and 802, during routine vector monitoring in 2016 and 2017. No county mosquito control operations were applied at area 415 in 2016 and 2017, whereas extensive adulticide spraying operations were in effect at area 802 during the summer of 2016. We collected data for eighteen microsatellite markers for 713 and 723 mosquitoes at eight timepoints from 2016 to 2017 in areas 415 and 802, respectively. We also investigated the impact of Hurricane Harvey’s landfall in the Houston area in August of 2017 on Cx. quinquefasciatus population fluctuation. Results: We found that the bottleneck scenario was the most probable historical scenario describing the impact of the winter season at area 415 and area 802, with the highest posterior probability of 0.9167 and 0.4966, respectively. We also detected an expansion event following Hurricane Harvey at area 802, showing a 3.03-fold increase in 2017. Discussion: Although we did not detect significant effects of vector control interventions, we found considerable influences of the winter season and a major hurricane on the effective population size of Cx. quinquefasciatus. The fluctuations in effective population size in both areas showed a significant seasonal pattern. Additionally, the significant population expansion following Hurricane Harvey in 2017 supports the necessity for post-hurricane vector-control interventions.
Context Climate change and global warming have been hypothesized to influence the increased prevalence of obesity worldwide. However, the evidence is scarce. Objective We aimed to investigate how outside temperature might affect adipose tissue physiology and metabolic traits. Methods The expression of genes involved in thermogenesis/browning and adipogenesis were evaluated (through quantitative polymerase chain reaction) in the subcutaneous adipose tissue (SAT) from 1083 individuals recruited in 5 different regions of Spain (3 in the North and 2 in the South). Plasma biochemical variables and adiponectin (enzyme-linked immunosorbent assay) were collected through standardized protocols. Mean environmental outdoor temperatures were obtained from the National Agency of Meteorology. Univariate, multivariate, and artificial intelligence analyses (Boruta algorithm) were performed. Results The SAT expression of genes associated with browning (UCP1, PRDM16, and CIDEA) and ADIPOQ were significantly and negatively associated with minimum, average, and maximum temperatures. The latter temperatures were also negatively associated with the expression of genes involved in adipogenesis (FASN, SLC2A4, and PLIN1). Decreased SAT expression of UCP1 and ADIPOQ messenger RNA and circulating adiponectin were observed with increasing temperatures in all individuals as a whole and within participants with obesity in univariate, multivariate, and artificial intelligence analyses. The differences remained statistically significant in individuals without type 2 diabetes and in samples collected during winter. Conclusion Decreased adipose tissue expression of genes involved in browning and adiponectin with increased environmental temperatures were observed. Given the North-South gradient of obesity prevalence in these same regions, the present observations could have implications for the relationship of the obesity pandemic with global warming.
BACKGROUND: Malaria remains a public health challenge across several African and South-East Asia Region countries, including India, despite making gains in malaria-related morbidity and mortality. Poor climatic and socioeconomic factors are known to increase population vulnerability to malaria. However, there is scant literature from India exploring this link using large population-based data. OBJECTIVES: This study aims to study the role of climatic and socioeconomic factors in determining population vulnerability to malaria in India. MATERIALS AND METHODS: We used logistic regression models on a nationally representative sample of 91,207 households, obtained from the National Sample Survey Organization (69(th) round), to study the determinants of household vulnerability. RESULTS: Households that resided in high (odds ratio [OR]: 1.876, P < 0.01) and moderately high (OR: 3.427, P < 0.01), compared to low climatically vulnerable states were at greater odds of suffering from malaria. Among households that faced the problem of mosquitoes/flies compared to the reference group, the urban households were at higher risk of suffering from malaria (OR: 8.318, P < 0.01) compared to rural households (OR: 2.951, P < 0.01). Households from the lower income quintiles, caste, poor physical condition of their houses, poor garbage management, and water stagnation around the source of drinking water, strongly predicted malaria vulnerability. CONCLUSION: Household's vulnerability to malaria differed according to state climatic vulnerability level and socioeconomic factors. More efforts by integrating local endemicity, epidemiological, and entomological information about malaria transmission must be considered while designing malaria mitigation strategies for better prevention and treatment outcomes.
BACKGROUND: The distributions of ticks and tick-borne pathogens are thought to have changed rapidly over the last two decades, with their ranges expanding into new regions. This expansion has been driven by a range of environmental and socio-economic factors, including climate change. Spatial modelling is being increasingly used to track the current and future distributions of ticks and tick-borne pathogens and to assess the associated disease risk. However, such analysis is dependent on high-resolution occurrence data for each species. To facilitate such analysis, in this review we have compiled georeferenced tick locations in the Western Palearctic, with a resolution accuracy under 10 km, that were reported between 2015 and 2021 METHODS: The PubMed and Web of Science databases were searched for peer-reviewed papers documenting the distribution of ticks that were published between 2015 and 2021, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The papers were then screened and excluded in accordance with the PRISMA flow chart. Coordinate-referenced tick locations along with information on identification and collection methods were extracted from each eligible publication. Spatial analysis was conducted using R software (version 4.1.2). RESULTS: From the 1491 papers identified during the initial search, 124 met the inclusion criteria, and from these, 2267 coordinate-referenced tick records from 33 tick species were included in the final dataset. Over 30% of articles did not record the tick location adequately to meet inclusion criteria, only providing a location name or general location. Among the tick records, Ixodes ricinus had the highest representation (55%), followed by Dermacentor reticulatus (22.1%) and Ixodes frontalis (4.8%). The majority of ticks were collected from vegetation, with only 19.1% collected from hosts. CONCLUSIONS: The data presented provides a collection of recent high-resolution, coordinate-referenced tick locations for use in spatial analyses, which in turn can be used in combination with previously collated datasets to analyse the changes in tick distribution and research in the Western Palearctic. In the future it is recommended that, where data privacy rules allow, high-resolution methods are routinely used by researchers to geolocate tick samples and ensure their work can be used to its full potential.
The community structure of sand flies indicates the level of adaptation of vector species in a region, and in the context of vector management and control, this information allows for identifying the potential risks of pathogen transmission. This study aimed to analyze sand fly diversity and spatial-temporal distribution in an endemic area of cutaneous leishmaniasis. The study was carried out in the Carrizales hamlet (Caldas), between September 2019 and October 2021. The monthly distribution of sand fly species was evaluated through collections with CDC traps. Shannon and evenness indices were calculated and used to compare species frequencies at each house. The association between climatic variables and the frequency of sand flies was evaluated using Spearman’s correlation. A total of 6,265 females and 1,958 males belonging to 23 species were found. Low diversity and evenness were observed, with the dominance of Nyssomyia yuilli yuilli (Young & Porter). Ecological and diversity indices did not reveal differences between the houses. The sand fly community was composed of 3 dominant species, Ny. yuilli yuilli, Psychodopygus ayrozai (Barretto & Coutinho), and Ps. panamensis (Shannon), representing 75.8% of the total catches. No statistical association was found between the absolute frequency of sand flies, rainfall, and temperature. The results show one dominant species, this fact has epidemiological relevance since density influences parasite-vector contact. The high densities of sand flies recorded in peri- and intradomiciliary areas highlight the necessity of periodic monitoring of vector populations and control activities to reduce the risk of Leishmania transmission in this endemic area.
Anopheles mosquitoes are the vectors of human malaria, a disease responsible for a significant burden of global disease and over half a million deaths in 2020. Here, methods using a time series of cost-free Earth Observation (EO) data, 45,844 in situ mosquito monitoring captures, and the cloud processing platform Google Earth Engine are developed to identify the biogeographical variables driving the abundance and distribution of three malaria vectors-Anopheles gambiae s.l., An. funestus, and An. paludis-in two highly endemic areas in the Democratic Republic of the Congo. EO-derived topographical and time series land surface temperature and rainfall data sets are analysed using Random Forests (RFs) to identify their relative importance in relation to the abundance of the three mosquito species, and they show how spatial and temporal distributions vary by site, by mosquito species, and by month. The observed relationships differed between species and study areas, with the overall number of biogeographical variables identified as important in relation to species abundance, being 30 for An. gambiae s.l. and An. funestus and 26 for An. paludis. Results indicate rainfall and land surface temperature to consistently be the variables of highest importance, with higher rainfall resulting in greater mosquito abundance through the creation of pools acting as mosquito larval habitats; however, proportional coverage of forest and grassland, as well as proximity to forests, are also consistently identified as important. Predictive application of the RF models generated monthly abundance maps for each species, identifying both spatial and temporal hot-spots of high abundance and, by proxy, increased malaria infection risk. Results indicate greater temporal variability in An. gambiae s.l. and An. paludis abundances in response to seasonal rainfall, whereas An. funestus is generally more temporally stable, with maximum predicted abundances of 122 for An. gambiae s.l., 283 for An. funestus, and 120 for An. paludis. Model validation produced R-2 values of 0.717 for An. gambiae s.l., 0.861 for An. funestus, and 0.448 for An. paludis. Monthly abundance values were extracted for 248,089 individual buildings, demonstrating how species abundance, and therefore biting pressure, varies spatially and seasonally on a building-to-building basis. These methods advance previous broader regional mosquito mapping and can provide a crucial tool for designing bespoke control programs and for improving the targeting of resource-constrained disease control activities to reduce malaria transmission and subsequent mortality in endemic regions, in line with the WHO’s ‘High Burden to High Impact’ initiative. The developed method was designed to be widely applicable to other areas, where suitable in situ mosquito monitoring data are available. Training materials were also made freely available in multiple languages, enabling wider uptake and implementation of the methods by users without requiring prior expertise in EO.
Global travel and climate change have drastically increased the number of countries with endemic or epidemic dengue. The largest dengue outbreak in Taiwan, with 43,419 cases and 228 deaths, occurred in 2015. Practical and cost-effective tools for early prediction of clinical outcomes in dengue patients, especially the elderly, are limited. This study identified the clinical profile and prognostic indicators of critical outcomes in dengue patients on the basis of clinical parameters and comorbidities. A retrospective cross-sectional study was conducted in a tertiary hospital from 1 July 2015 to 30 November 2015. Patients diagnosed with dengue were enrolled, and the initial clinical presentations, diagnostic laboratory data, details of the underlying comorbidities, and initial management recommendations based on 2009 World Health Organization (WHO) guidelines were used to evaluate prognostic indicators of critical outcomes in dengue patients. Dengue patients from another regional hospital were used to evaluate accuracy. A group B (4 points) classification, temperature < 38.5 °C (1 point), lower diastolic blood pressure (1 point), prolonged activated partial thromboplastin time (aPTT) (2 points), and elevated liver enzymes (1 point) were included in the scoring system. The area under the receiver operating characteristic curve of the clinical model was 0.933 (95% confidence interval [CI]: 0.905-0.960). The tool had good predictive value and clinical applicability for identifying patients with critical outcomes.
Prior research has shown that climate literacy is sparse among low- and middle-income countries. Additionally, no standardized questionnaire exists for researchers to measure climate literacy among general populations, particularly with regards to climate change effects on vector-borne diseases (VBDs). We developed a comprehensive literacy scale to assess current knowledge, attitudes, and behaviors towards climate change and VBD dynamics among women enrolled in the Caribbean Consortium for Research in Environmental and Occupational Health (CCREOH) cohort in Suriname. Items were generated by our research team and reviewed by a group of six external climate and health experts. After the expert review, a total of 31 climate change and 21 infectious disease items were retained. We estimated our sample size at a 10:1 ratio of participants to items for each scale. In total, 301 women were surveyed. We validated our scales through exploratory (n = 180) and confirmatory factor analyses (n = 121). An exploratory factor analysis for our general Climate Change Scale provided a four-construct solution of 11 items. Our chi-squared value (X(2) = 74.32; p = 0.136) indicated that four factors were sufficient. A confirmatory factor analysis reinforced our findings, providing a good model fit (X(2) = 39.03; p = 0.23; RMSEA = 0.015). Our Infectious Disease Scale gave a four-construct solution of nine items (X(2) = 153.86; p = 0.094). A confirmatory factor analysis confirmed these results, with a chi-squared value of 19.16 (p = 0.575) and an RMSEA of 0.00. This research is vitally important for furthering climate and health education, especially with increases in VBDs spread by Aedes mosquitoes in the Caribbean, South America, and parts of the southern United States.
In recent decades, the presence of flaviviruses of concern for human health in Europe has drastically increased,exacerbated by the effects of climate change – which has allowed the vectors of these viruses to expand into new territories. Co-circulation of West Nile virus (WNV), Usutu virus (USUV), and tick-borne encephalitis virus (TBEV) represents a threat to the European continent, and this is further complicated by the difficulty of obtaining an early and discriminating diagnosis of infection. Moreover, the possibility of introducing non-endemic pathogens, such as Japanese encephalitis virus (JEV), further complicates accurate diagnosis. Current flavivirus diagnosis is based mainly on RT-PCR and detection of virus-specific antibodies. Yet, both techniques suffer from limitations, and the development of new assays that can provide an early, rapid, low-cost, and discriminating diagnosis of viral infection is warranted. In the pursuit of ideal diagnostic assays, flavivirus non-structural protein 1 (NS1) serves as an excellent target for developing diagnostic assays based on both the antigen itself and the antibodies produced against it. This review describes the potential of such NS1-based diagnostic methods, focusing on the application of flaviviruses that co-circulate in Europe.
Dengue is the world’s fastest-growing vector borne disease and has significant epidemic potential in suitable climates. Recent disease models incorporating climate change scenarios predict geographic expansion across the globe, including parts of the United States and Europe. It will be increasingly important in the next decade for dermatologists to become familiar with dengue, as it commonly manifests with rashes, which can be used to aid diagnosis. In this review, we discuss dengue for general dermatologists, specifically focusing on its cutaneous manifestations, epidemiology, diagnosis, treatment, and prevention. As dengue continues to spread in both endemic and new locations, dermatologists may have a larger role in the timely diagnosis and management of this disease.
Aedes mosquitoes are some of the most important and globally expansive vectors of disease. Public health efforts are largely focused on prevention of human-vector contact. A range of entomological indices are used to measure risk of disease, though with conflicting results (i.e. larval or adult abundance does not always predict risk of disease). There is a growing interest in the development and use of biomarkers for exposure to mosquito saliva, including for Aedes spp, as a proxy for disease risk. In this study, we conduct a comprehensive geostatistical analysis of exposure to Aedes mosquito bites among a pediatric cohort in a peri‑urban setting endemic to dengue, Zika, and chikungunya viruses. We use demographic, household, and environmental variables (the flooding index (NFI), land type, and proximity to a river) in a Bayesian geostatistical model to predict areas of exposure to Aedes aegypti bites. We found that hotspots of exposure to Ae. aegypti salivary gland extract (SGE) were relatively small (< 500 m and sometimes < 250 m) and stable across the two-year study period. Age was negatively associated with antibody responses to Ae. aegypti SGE. Those living in agricultural settings had lower antibody responses than those living in urban settings, whereas those living near recent surface water accumulation were more likely to have higher antibody responses. Finally, we incorporated measures of larval and adult density in our geostatistical models and found that they did not show associations with antibody responses to Ae. aegypti SGE after controlling for other covariates in the model. Our results indicate that targeted house- or neighborhood-focused interventions may be appropriate for vector control in this setting. Further, demographic and environmental factors more capably predicted exposure to Ae. aegypti mosquitoes than commonly used entomological indices.
Dengue is a vector borne disease caused by virus serotypes DENV-1, DENV-2, DENV-3, and DENV-4, representing a significant public health concern in the Region of the Americas (2,997,097 cases in 2023). This study explores the relationship between dengue incidence and climate changes in the city of São Paulo-Brazil. During the first semester of 2023, Brazil reported the highest number of dengue cases in Americas’ Region. Our data reveals a correlation between the high temperature and rainfall season persistence and the extension of dengue incidence into the winter season. The findings highlight the importance of understanding the relationship between climate change and disease transmission patterns to develop effective strategies for prevention and control.
Dengue is a major public health problem in Myanmar. The country aims to reduce morbidity by 50% and mortality by 90% by 2025 based on 2015 data. To support efforts to reach these goals it is important to have a detailed picture of the epidemiology of dengue, its relationship to meteorological factors and ideally to predict ahead of time numbers of cases to plan resource allocations and control efforts. Health facility-level data on numbers of dengue cases from 2012 to 2017 were obtained from the Vector Borne Disease Control Unit, Department of Public Health, Myanmar. A detailed analysis of routine dengue and dengue hemorrhagic fever (DHF) incidence was conducted to examine the spatial and temporal epidemiology. Incidence was compared to climate data over the same period. Dengue was found to be widespread across the country with an increase in spatial extent over time. The temporal pattern of dengue cases and fatalities was episodic with annual outbreaks and no clear longitudinal trend. There were 127,912 reported cases and 632 deaths from 2012 and 2017 with peaks in 2013, 2015 and 2017. The case fatality rate was around 0.5% throughout. The peak season of dengue cases was from May to August in the wet season but in 2014 peak dengue season continued until November. The strength of correlation of dengue incidence with different climate factors (total rainfall, maximum, mean and minimum temperature and absolute humidity) varied between different States and Regions. Monthly incidence was forecasted 1 month ahead using the Auto Regressive Integrated Moving Average (ARIMA) method at country and subnational levels. With further development and validation, this may be a simple way to quickly generate short-term predictions at subnational scales with sufficient certainty to use for intervention planning.
Nepal is an endemic country for dengue infection with rolling of every 3 year’s clear cyclic outbreaks with exponential growth since 2019 outbreak and the virus gearing towards the non-foci temperate hill regions. However, the information regarding circulating serotype and genotype is not frequent. This research discusses on the clinical features, diagnosis, epidemiology, circulating serotype and genotype among 61 dengue suspected cases from different hospitals of Nepal during the window period 2017-2018 between the two outbreaks of 2016 and 2019. E-gene sequences from PCR positive samples were subjected to phylogenetic analysis under time to most recent common ancestor tree using Markov Chain Monte Carlo (MCMC) and BEAST v2.5.1. Both evolution and genotypes were determined based on the phylogenetic tree. Serotyping by Real-time PCR and Nested PCR showed the co-circulation of all the 3 serotypes of dengue in the year 2017 and only DENV-2 in 2018. Genotype V for DENV-1 and Cosmopolitan Genotype IVa for DENV-2 were detected. The detected Genotype V of DENV-1 in Terai was found close to Indian genotype while Cosmopolitan IVa of DENV-2 found spreading to geographically safe hilly region (now gripped to 9 districts) was close to South-East Asia. The genetic drift of DENV-2 is probably due to climate change and rapid viral evolution which could be a representative model for high altitude shift of the infection. Further, the increased primary infection indicates dengue venturing to new populations. Platelets count together with Aspartate transaminase and Aalanine transaminase could serve as important clinical markers to support clinical diagnosis. The study will support future dengue virology and epidemiology in Nepal.
The incidence of vector-borne diseases is rising as deforestation, climate change, and globalization bring humans in contact with arthropods that can transmit pathogens. In particular, incidence of American Cutaneous Leishmaniasis (ACL), a disease caused by parasites transmitted by sandflies, is increasing as previously intact habitats are cleared for agriculture and urban areas, potentially bringing people into contact with vectors and reservoir hosts. Previous evidence has identified dozens of sandfly species that have been infected with and/or transmit Leishmania parasites. However, there is an incomplete understanding of which sandfly species transmit the parasite, complicating efforts to limit disease spread. Here, we apply machine learning models (boosted regression trees) to leverage biological and geographical traits of known sandfly vectors to predict potential vectors. Additionally, we generate trait profiles of confirmed vectors and identify important factors in transmission. Our model performed well with an average out of sample accuracy of 86%. The models predict that synanthropic sandflies living in areas with greater canopy height, less human modification, and within an optimal range of rainfall are more likely to be Leishmania vectors. We also observed that generalist sandflies that are able to inhabit many different ecoregions are more likely to transmit the parasites. Our results suggest that Psychodopygus amazonensis and Nyssomia antunesi are unidentified potential vectors, and should be the focus of sampling and research efforts. Overall, we found that our machine learning approach provides valuable information for Leishmania surveillance and management in an otherwise complex and data sparse system.
A major trade-off of land-use change is the potential for increased risk of infectious diseases, a.o. through impacting disease vector life-cycles. Evaluating the public health implications of land-use conversions requires spatially detailed modelling linking land-use to vector ecology. Here, we estimate the impact of deforestation for oil palm cultivation on the number of life-cycle completions of Aedes albopictus via its impact on local microclimates. We apply a recently developed mechanistic phenology model to a fine-scaled (50-m resolution) microclimate dataset that includes daily temperature, rainfall and evaporation. Results of this combined model indicate that the conversion from lowland rainforest to plantations increases suitability for A. albopictus development by 10.8%, moderated to 4.7% with oil palm growth to maturity. Deforestation followed by typical plantation planting-maturation-clearance-replanting cycles is predicted to create pulses of high development suitability. Our results highlight the need to explore sustainable land-use scenarios that resolve conflicts between agricultural and human health objectives.
Lyme borreliosis, the most common vector-borne disease in Europe and North America, is attracting growing concern due to its expanding geographic range. The growth in incidence and geographic spread is largely attributed to climate and land-use changes that support the tick vector and thereby increase disease risk. Despite a wide range of symptoms displayed by Lyme borreliosis patients, the demographic patterns in clinical manifestations and seasonal case timing have not been thoroughly investigated and may result from differences in exposure, immunity and pathogenesis. We analysed 25 years of surveillance data from Norway, supplemented by population demography data, using a Bayesian modelling framework. The analyses aimed to detect differences in case seasonality and clinical manifestations of Lyme borreliosis across age and sex differentiated patient groups. The results showed a bimodal pattern of incidence over age, where children (0-9 years) had the highest incidence, young adults (20-29 years) had low incidence and older adults had a second incidence peak in the ages 70-79 years. Youth (0-19 years) presented with a higher proportion of neuroborreliosis cases and a lower proportion of arthritic manifestations compared to adults (20+ years). Adult males had a higher overall incidence than adult females and a higher proportion of arthritis cases. The seasonal timing of Lyme borreliosis consistently occurred around 4.4 weeks earlier in youth compared to adults, regardless of clinical manifestation. All demographic groups exhibited a shift towards an earlier seasonal timing over the 25-year study period, which appeared unrelated to changes in population demographics. However, the disproportionate incidence of Lyme borreliosis in seniors requires increased public awareness and knowledge about this high-risk group as the population continues to age concurrently with disease emergence. Our findings highlight the importance of considering patient demographics when analysing the emergence and seasonal patterns of vector-borne diseases using long-term surveillance data.
Dengue virus (DENV) is an enveloped, single-stranded RNA virus, a member of the Flaviviridae family (which causes Dengue fever), and an arthropod-transmitted human viral infection. Bangladesh is well known for having some of Asia’s most vulnerable Dengue outbreaks, with climate change, its location, and it’s dense population serving as the main contributors. For speculation about DENV outbreak characteristics, it is crucial to determine how meteorological factors correlate with the number of cases. This study used five time series models to observe the trend and forecast Dengue cases. Current data-based research has also applied four statistical models to test the relationship between Dengue-positive cases and meteorological parameters. Datasets were used from NASA for meteorological parameters, and daily DENV cases were obtained from the Directorate General of Health Service (DGHS) open-access websites. During the study period, the mean of DENV cases was 882.26 ± 3993.18, ranging between a minimum of 0 to a maximum of 52,636 daily confirmed cases. The Spearman’s rank correlation coefficient between climatic variables and Dengue incidence indicated that no substantial relationship exists between daily Dengue cases and wind speed, temperature, and surface pressure (Spearman’s rho; r = -0.007, p > 0.05; r = 0.085, p > 0.05; and r = -0.086, p > 0.05, respectively). Still, a significant relationship exists between daily Dengue cases and dew point, relative humidity, and rainfall (r = 0.158, p < 0.05; r = 0.175, p < 0.05; and r = 0.138, p < 0.05, respectively). Using the ARIMAX and GA models, the relationship for Dengue cases with wind speed is -666.50 [95% CI: -1711.86 to 378.86] and -953.05 [-2403.46 to 497.36], respectively. A similar negative relation between Dengue cases and wind speed was also determined in the GLM model (IRR = 0.98). Dew point and surface pressure also represented a negative correlation in both ARIMAX and GA models, respectively, but the GLM model showed a positive association. Additionally, temperature and relative humidity showed a positive correlation with Dengue cases (105.71 and 57.39, respectively, in the ARIMAX, 633.86, and 200.03 in the GA model). In contrast, both temperature and relative humidity showed negative relation with Dengue cases in the GLM model. In the Poisson regression model, windspeed has a substantial significant negative connection with Dengue cases in all seasons. Temperature and rainfall are significantly and positively associated with Dengue cases in all seasons. The association between meteorological factors and recent outbreak data is the first study where we are aware of the use of maximum time series models in Bangladesh. Taking comprehensive measures against DENV outbreaks in the future can be possible through these findings, which can help fellow researchers and policymakers.
Tropical and potentially fatal malaria is brought on by the parasite Plasmodium spp which spreads through infected female anopheles mosquitoes within the human populations. In Ghana, malaria is endemic and perennial, with distinct seasonal fluctuations in the northern part. Children aged below five years are among the population most vulnerable to malaria in Ghana. This study’s goal is to establish how suspected malaria cases, tested malaria cases, and climatic conditions impact confirmed malaria cases in children under five years in the Sunyani Municipality, Bono Region, Ghana. The dependent variable, monthly number of confirmed cases of malaria in children under five years, was modelled with the independent variables, monthly number of tested cases of malaria in children under five years, mean monthly relative humidity, mean monthly rainfall, and mean monthly temperature, in the Sunyani Municipality. We employed multiple linear regression after data transformation, exploratory data analysis, and correlation analysis. Results show that tested malaria cases and climatic factors significantly influence confirmed malaria cases in children under 5 years. About 41.8% of variations in confirmed malaria cases among children under 5 years is attributed to climatic factors and the number of tested cases. Moreover, results show that increase in tested cases and rainfall leads to more confirmed malaria cases among children under 5 years, while increase in temperature reduces malaria infections. To reduce the incidence of malaria in children under five years, the government and its stakeholders should encourage parent to let their children sleep in treated mosquito nets, distil stagnant waters during raining seasons, spray bushes with antimosquito insecticides, and destroy all breeding grounds of mosquitoes at all times. We proposed that all malaria cases should be laboratory tested and properly confirmed.
BACKGROUND: Effective mass drug administration (MDA) is the cornerstone in the elimination of lymphatic filariasis (LF) and a critical component in combatting all neglected tropical diseases for which preventative chemotherapy is recommended (PC-NTDs). Despite its importance, MDA coverage, however defined, is rarely investigated systematically across time and geography. Most commonly, investigations into coverage react to unsatisfactory outcomes and tend to focus on a single year and health district. Such investigations omit more macro-level influences including sociological, environmental, and programmatic factors. The USAID NTD database contains measures of performance from thousands of district-level LF MDA campaigns across 14 years and 10 West African countries. Specifically, performance was measured as an MDA’s epidemiological coverage, calculated as persons treated divided by persons at risk. This analysis aims to explain MDA coverage across time and geography in West Africa using sociological, environmental, and programmatic factors. METHODOLOGY: The analysis links epidemiological coverage data from 3,880 LF MDAs with contextual, non-NTD data via location (each MDA was specific to a health district) and time (MDA month, year). Contextual data included rainfall, temperature, violence or social unrest, COVID-19, the 2014 Ebola outbreak, road access/isolation, population density, observance of Ramadan, and the number of previously completed MDAs. PRINCIPAL FINDINGS: We fit a hierarchical linear regression model with coverage as the dependent variable and performed sensitivity analyses to confirm the selection of the explanatory factors. Above average rainfall, COVID-19, Ebola, violence and social unrest were all significantly associated with lower coverage. Years of prior experience in a district and above average temperature were significantly associated with higher coverage. CONCLUSIONS/SIGNIFICANCE: These generalized and context-focused findings supplement current literature on coverage dynamics and MDA performance. Findings may be used to quantify typically anecdotal considerations in MDA planning. The model and methodology are offered as a tool for further investigation.
In an increasingly interconnected society, preventing epidemics has become a major challenge. Numerous infectious diseases spread between individuals by a vector, creating bipartite networks of infection with the characteristics of complex networks. In the case of dengue, a mosquito-borne disease, these infection networks include a vector-the Aedes aegypti mosquito-which has expanded its endemic area due to climate change. In this scenario, innovative approaches are essential to help public agents in the fight against the disease. Using an agent-based model, we investigated the network morphology of a dengue endemic region considering four different serotypes and a small population. The degree, betweenness, and closeness distributions are evaluated for the bipartite networks, considering the interactions up to the second order for each serotype. We observed scale-free features and heavy tails in the degree distribution and betweenness and quantified the decay of the degree distribution with a q-Gaussian fit function. The simulation results indicate that the spread of dengue is primarily driven by human-to-human and human-to-mosquito interaction, reinforcing the importance of controlling the vector to prevent episodes of epidemic outbreaks.
Mosquito-borne diseases are a public health concern. Pharmacists are often a patient’s first stop for health information and may be asked questions regarding transmission, symptoms, and treatment of mosquito borne viruses (MBVs). The objective of this paper is to review transmission, geographic location, symptoms, diagnosis and treatment of MBVs. We discuss the following viruses with cases in the US in recent years: Dengue, West Nile, Chikungunya, LaCrosse Encephalitis, Eastern Equine Encephalitis Virus, and Zika. Prevention, including vaccines, and the impact of climate change are also discussed.
In America, the presence of Rhipicephalus sanguineus sensu stricto and Rhipicephalus linnaei has been confirmed. Both species are found in sympatry in the southern United States, northern Mexico, southern Brazil, and Argentina. The objective of this work is to evaluate the projection of the potential distribution of the ecological niche of Rhipicephalus sanguineus sensu lato in two climate change scenarios in Mexico and the border with Central America and the United States. Initially, a database of personal collections of the authors, GBIF, Institute of Epidemiological Diagnosis and Reference, and scientific articles was built. The ENMs were projected for the current period and two future scenarios: RCP and SSP used for the kuenm R package, the ecological niche of R. sanguineus s.l. It is distributed throughout the Mexico and Texas (United States), along with the border areas between Central America, Mexico, and the United States. Finally, it is observed that the ecological niche of R. sanguineus s.l. in the current period coincides in three degrees with the routes of human migration. Based on this information, and mainly on the flow of migrants from Central America to the United States, the risk of a greater gene flow in this area increases, so the risk relating to this border is a latent point that must be analyzed.
The amount and distribution of precipitation can determine dengue risk by affecting mosquito breeding; however, previous studies failed to incorporate this bivariate characteristic to examine dengue fever transmission. In the present research, nationwide data on daily dengue cases in China between January 2005 and December 2020 were obtained, and the top 12 cities accounting for 78% of total cases were selected for analysis. Precipitation patterns were quantified by weekly precipitation and precipitation concentration degree (PCD). On the basis of the combinations of both parameters, the exposure-response relationships of precipitation with dengue risk were established using generalised additive models, and the high-dengue-risk thresholds of precipitation patterns were further identified. Dengue burden was assessed by calculating attributable dengue cases. For the same amount of precipitation, the dispersed precipitation in the pre-summer rainy season leads to a higher dengue risk in autumn. The weekly precipitation of 100-150 mm and PCD of 0.2-0.4 constitute the highest risk scenario, and the average frequency of precipitation associated with dengue risk in 2013-2020 is 1.6 times higher than that in 2005-2012. A total of 3093 attributable dengue cases are identified. From 2005 to 2020, the amount of dispersed precipitation increased in southern and southwestern China and posed high dengue risks in central China. This study has improved the understanding of the health impacts of irregular rainfall under climate change. Our approach to identifying thresholds provides information for early warning systems and helps reduce the risk of dengue transmission in the long run.
Dengue transmission poses significant challenges for public health authorities worldwide due to its susceptibility to various factors, including environmental and climate variability, affecting its incidence and geographic spread. This study focuses on Costa Rica, a country characterized by diverse microclimates nearby, where dengue has been endemic since its introduction in 1993. Using wavelet coherence and clustering analysis, we performed a time-series analysis to uncover the intricate connections between climate, local environmental factors, and dengue occurrences. The findings indicate that multiannual dengue frequency (3 yr) is correlated with the Oceanic Niño Index and the Tropical North Atlantic Index. This association is particularly prominent in cantons located along the North and South Pacific Coast, as well as in the Central cantons of the country. Furthermore, the time series of these climate indices exhibit a leading phase of approximately nine months ahead of dengue cases. Additionally, the clustering analysis uncovers non-contiguous groups of cantons that exhibit similar correlation patterns, irrespective of their proximity or adjacency. This highlights the significance of climate factors in influencing dengue dynamics across diverse regions, regardless of spatial closeness or distance between them. On the other hand, the annual dengue frequency was correlated with local environmental indices. A persistent correlation between dengue cases and local environmental variables is observed over time in the North Pacific and the Central Region of the country’s Northwest, with environmental factors leading by less than three months. These findings contribute to understanding dengue transmission’s spatial and temporal dynamics in Costa Rica, highlighting the importance of climate and local environmental factors in dengue surveillance and control efforts.
A new database of the Entomological Inoculation Rate (EIR) was used to directly link the risk of infectious mosquito bites to climate in Sub-Saharan Africa. Applying a statistical mixed model framework to high-quality monthly EIR measurements collected from field campaigns in Sub-Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission was within a temperature window of 15°C-40°C and was sustained if average temperature was well above 15°C or below 40°C. Monthly maximum rainfall for seasonal malaria transmission did not exceed 600 in west Central Africa, and 400 mm in the Sahel, Guinea Savannah, and East Africa. Based on a multi-regression model approach, rainfall and temperature seasonality were found to be significantly associated with malaria seasonality in all parts of Sub-Saharan Africa except in west Central Africa. Topography was found to have significant influence on which climate variable is an important determinant of malaria seasonality in East Africa. Seasonal malaria transmission onset lags behind rainfall only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year-round. High-quality EIR measurements can usefully supplement established metrics for seasonal malaria. The study’s outcome is important for the improvement and validation of weather-driven dynamical mathematical malaria models that directly simulate EIR. Our results can contribute to the development of fit-for-purpose weather-driven malaria models to support health decision-making in the fight to control or eliminate malaria in Sub-Saharan Africa.
This study was performed in order to understand the effect of climatological variables on the malaria situation in the north-east region of India, which is prolonged by the disease. Time-series analysis of major climate parameters like rainfall, maximum temperature, minimum temperature, mean temperature, relative humidity, and soil moisture distributions is carried out, and their correlation with the malaria incidence is quantified state-wise, which is the unique part of the study. The correlation analysis reveals that malaria is significantly related with the maximum temperature and soil moisture in three out of eight states in NE India. To assess the climate variability, the inter-dependency between the meteorological parameters is obtained and the state wise correlation matrix for all states are reported. The analysis shows that maximum and mean temperature has highest positive correlation whereas minimum temperature and relative humidity has negative correlation. The climate-malaria relation is being carried out in the study region using the regression analysis and the results revealed that the regional climate has the most impact for the malaria incidence in the state of Arunachal Pradesh, Meghalaya, Tripura and Nagaland and in other states the impact is moderate. Analysis of variance modelling in the regions also indicates the degree of the fitment of both the data sets with the regression model and it is observed that the relation is also significant in the same 4 states. As a case study the impact of large scale oscillations like El Niño-Southern Oscillation on the malaria load is also assessed which can be a good indicator in the prediction of the climate and in turn the malaria incidences over the region.
Temperature, precipitation, relative humidity (RH), and Normalized Different Vegetation Index (NDVI), influence malaria transmission dynamics. However, an understanding of interactions between socioeconomic indicators, environmental factors and malaria incidence can help design interventions to alleviate the high burden of malaria infections on vulnerable populations. Our study thus aimed to investigate the socioeconomic and climatological factors influencing spatial and temporal variability of malaria infections in Mozambique. METHODS: We used monthly malaria cases from 2016 to 2018 at the district level. We developed an hierarchical spatial-temporal model in a Bayesian framework. Monthly malaria cases were assumed to follow a negative binomial distribution. We used integrated nested Laplace approximation (INLA) in R for Bayesian inference and distributed lag nonlinear modeling (DLNM) framework to explore exposure-response relationships between climate variables and risk of malaria infection in Mozambique, while adjusting for socioeconomic factors. RESULTS: A total of 19,948,295 malaria cases were reported between 2016 and 2018 in Mozambique. Malaria risk increased with higher monthly mean temperatures between 20 and 29°C, at mean temperature of 25°C, the risk of malaria was 3.45 times higher (RR 3.45 [95%CI: 2.37-5.03]). Malaria risk was greatest for NDVI above 0.22. The risk of malaria was 1.34 times higher (1.34 [1.01-1.79]) at monthly RH of 55%. Malaria risk reduced by 26.1%, for total monthly precipitation of 480 mm (0.739 [95%CI: 0.61-0.90]) at lag 2 months, while for lower total monthly precipitation of 10 mm, the risk of malaria was 1.87 times higher (1.87 [1.30-2.69]). After adjusting for climate variables, having lower level of education significantly increased malaria risk (1.034 [1.014-1.054]) and having electricity (0.979 [0.967-0.992]) and sharing toilet facilities (0.957 [0.924-0.991]) significantly reduced malaria risk. CONCLUSION: Our current study identified lag patterns and association between climate variables and malaria incidence in Mozambique. Extremes in climate variables were associated with an increased risk of malaria transmission, peaks in transmission were varied. Our findings provide insights for designing early warning, prevention, and control strategies to minimize seasonal malaria surges and associated infections in Mozambique a region where Malaria causes substantial burden from illness and deaths.
Leishmaniosis (or leishmaniasis) is a neglected parasitosis most commonly transmitted by the sandfly bite. Changes in temperature, precipitation, and humidity can greatly affect the vectors and reservoir hosts. This study aimed to determine the association between temperature, air humidity, and weather conditions with the incidence of leishmaniasis in Montenegro during a seven-decade period (1945-2014) and to statistically compare and correlate the obtained data. In the studied period, there were 165 registered cases of leishmaniosis, 96.4%, in the coastal and central region of Montenegro, with an average incidence rate of 0.45/100.000. The visceral form of leishmaniosis predominated (99% of the cases), with only one case of cutaneous disease. Climate factors (average temperature, air humidity, and precipitation) had an impact on the occurrence of leishmaniosis in Montenegro. Air temperature elevated by 1 °C in all regions of Montenegro was significantly correlated with an increased incidence of leishmaniosis, by 0.150 (0.013 to 0.287; p < 0.05). In order to improve prevention and control of this disease, it is also necessary to investigate other factors with a possible impact on the number of cases of this neglected parasitosis.
Climate change is most strongly felt in the polar regions of the world, with significant impacts on the species that live there. The arrival of parasites and pathogens from more temperate areas may become a significant problem for these populations, but current observations of parasite presence often lack a historical reference of prior absence. Observations in the high Arctic of the seabird tick Ixodes uriae suggested that this species expanded poleward in the last two decades in relation to climate change. As this tick can have a direct impact on the breeding success of its seabird hosts and vectors several pathogens, including Lyme disease spirochaetes, understanding its invasion dynamics is essential for predicting its impact on polar seabird populations. Here, we use population genetic data and host serology to test the hypothesis that I. uriae recently expanded into Svalbard. Both black-legged kittiwakes (Rissa tridactyla) and thick-billed murres (Uria lomvia) were sampled for ticks and blood in Kongsfjorden, Spitsbergen. Ticks were genotyped using microsatellite markers and population genetic analyses were performed using data from 14 reference populations from across the tick’s northern distribution. In contrast to predictions, the Spitsbergen population showed high genetic diversity and significant differentiation from reference populations, suggesting long-term isolation. Host serology also demonstrated a high exposure rate to Lyme disease spirochaetes (Bbsl). Targeted PCR and sequencing confirmed the presence of Borrelia garinii in a Spitsbergen tick, demonstrating the presence of Lyme disease bacteria in the high Arctic for the first time. Taken together, results contradict the notion that I. uriae has recently expanded into the high Arctic. Rather, this tick has likely been present for some time, maintaining relatively high population sizes and an endemic transmission cycle of Bbsl. Close future observations of population infestation/infection rates will now be necessary to relate epidemiological changes to ongoing climate modifications.
BACKGROUND: Climate change and globalization contribute to the expansion of mosquito vectors and their associated pathogens. Long spared, temperate regions have had to deal with the emergence of arboviruses traditionally confined to tropical regions. Chikungunya virus (CHIKV) was reported for the first time in Europe in 2007, causing a localized outbreak in Italy, which then recurred repeatedly over the years in other European localities. This raises the question of climate effects, particularly temperature, on the dynamics of vector-borne viruses. The objective of this study is to improve the understanding of the molecular mechanisms set up in the vector in response to temperature. METHODS: We combine three complementary approaches by examining Aedes albopictus mosquito gene expression (transcriptomics), bacterial flora (metagenomics) and CHIKV evolutionary dynamics (genomics) induced by viral infection and temperature changes. RESULTS: We show that temperature alters profoundly mosquito gene expression, bacterial microbiome and viral population diversity. We observe that (i) CHIKV infection upregulated most genes (mainly in immune and stress-related pathways) at 20°C but not at 28°C, (ii) CHIKV infection significantly increased the abundance of Enterobacteriaceae Serratia marcescens at 28°C and (iii) CHIKV evolutionary dynamics were different according to temperature. CONCLUSION: The substantial changes detected in the vectorial system (the vector and its bacterial microbiota, and the arbovirus) lead to temperature-specific adjustments to reach the ultimate goal of arbovirus transmission; at 20°C and 28°C, the Asian tiger mosquito Ae. albopictus was able to transmit CHIKV at the same efficiency. Therefore, CHIKV is likely to continue its expansion in the northern regions and could become a public health problem in more countries than those already affected in Europe.
Malaria is still a disease of massive burden in Africa, also influenced by climate change. The fluctuations and trends of the temperature and precipitation are well-known determinant factors influencing the disease’s vectors and incidence rates. This study provides a concise account of malaria trends. It describes the association between average temperature and malaria incidence rates (IR) in nine sub-Saharan African countries: Nigeria, Ethiopia, South Africa, Kenya, Uganda, Ghana, Mozambique, Zambia and Zimbabwe. The incidence of malaria can vary both in areas where the disease is already present, and in regions where it is present in low numbers or absent. The increased vulnerability to the disease under increasing average temperatures and humidity is due to the new optimal level for vector breeding in areas where vector populations and transmission are low, and populations are sensitive due to low acquired immunity. METHODS: A second source trend analysis was carried out of malaria cases and incidence rates (the number of new malaria cases per 1000 population at risk per year) with data from the World Health Organization (WHO) and average annual mean temperature from 2000 to 2018 from the World Bank’s Climate Change Knowledge Portal (CCKP). Additionally, descriptive epidemiological methods were used to describe the development and trends in the selected countries. Furthermore, MS Excel was chosen for data analysis and visualization. RESULTS: Findings obtained from this article align with the recent literature, highlighting a declining trend (20-80%) of malaria IR (incidence rate) from 2000 to 2018. However, malaria IR varies considerably, with high values in Uganda, Mozambique, Nigeria and Zambia, moderate values in Ghana, Zimbabwe, and Kenya, and low values in South Africa and Ethiopia in 2018. Evidence suggests varying IRs after average temperature fluctuations in several countries (e.g., Zimbabwe, Ethiopia). Also, an inverse temperature-IR relationship occurs, the sharp decrease of IR during 2012-2014 and 2000-2003, respectively, occurred with increasing average temperatures in Ghana and Nigeria. The decreasing trends and fluctuations, partly accompanying the temperature, should result from the intervention programmes and rainfall variability. The vulnerability and changing climate could arrest the recent trends of falling IR. CONCLUSION: Thus, malaria is still a crucial public health issue in sub-Saharan Africa, although a robust decreasing IR occurred in most studied countries.
A review of the extant literature reveals the extent to which the spread of communicable diseases will be significantly impacted by climate change. Specific research into how this will likely be observed in the countries of the Gulf Cooperation Council (GCC) is, however, greatly lacking. This report summarises the unique public health challenges faced by the GCC countries in the coming century, and outlines the need for greater investment in public health research and disease surveillance to better forecast the imminent epidemiological landscape. Significant data gaps currently exist regarding vector occurrence, spatial climate measures, and communicable disease case counts in the GCC – presenting an immediate research priority for the region. We outline policy work necessary to strengthen public health interventions, and to facilitate evidence-driven mitigation strategies. Such research will require a transdisciplinary approach, utilising existing cross-border public health initiatives, to ensure that such investigations are well-targeted and effectively communicated.
BACKGROUND: Cutaneous leishmaniasis (CL) is a vector-borne disease prevalent in Algeria since 2000. The disease has significant impacts on affected communities, including morbidity and social stigma. OBJECTIVE: Investigate the association between environmental factors and the incidence of CL in the province of Ghardaïa and assess the predictive capacity of these factors for disease occurrence. DESIGN: Retrospective SETTING: The study area included both urban and rural communities. METHODS: We analyzed a dataset on CL in the province of Ghardaïa, Algeria, spanning from 2000 to 2020. The dataset included climatic variables such as temperature, average humidity, wind speed, rainfall, and the normalized difference vegetation index (NDVI). Using generalized additive models, we examined the relationships and interactions between these variables to predict the emergence of CL in the study area. MAIN OUTCOME MEASURES: The identification of the most significant environmental factors associated with the incidence and the predicted incidence rates of CL in the province of Ghardaïa, Algeria. SAMPLE SIZE AND CHARACTERISTICS: 252 monthly observations of both climatic and epidemiological variables. RESULTS: Relative humidity and wind speed were the primary climatic factors influencing the occurrence of CL epidemics in Ghardaïa, Algeria. Additionally, NDVI was a significant environmental factor associated with CL incidence. Surprisingly, temperature did not show a strong effect on CL occurrence, while rainfall was not statistically significant. The final fitted model predictions were highly correlated with real cases. CONCLUSION: This study provides a better understanding of the long-term trend in how environmental and climatic factors contribute to the emergence of CL. Our results can inform the development of effective early warning systems for preventing the transmission and emergence of vector-borne diseases. LIMITATIONS: Incorporating additional reservoir statistics such as rodent density and a human development index in the region could improve our understanding of disease transmission.
Badung district has recorded the highest dengue fever (DF) in Bali Province. This research presents the distribution of DF in Badung district and analyses its association with climate and visitors. The monthly data of DF, climate and number of visitors during January 2013 to December 2017 were analysed using Poisson Regression. A total of 10,689 new DF cases were notified from January 2013 to December 2017. DF in 2016 was recorded as the heaviest incidence. Monthly DF cases have positive association with average temperature (0.59 (95% CI: 0.56-.62)), precipitation (5.7 x 10(-4) (95% CI: 3.8 x 10(-4) – 7.6 x 10(-4))), humidity (.014 (95% CI: 0.003-.025)) and local visitors (7.40 x 10(-6) 95% CI: 5.88 x 10(-6) : 8.91 x 10(-6)). Negative association was shown between DF cases with foreign visitors (-2.18 x 10(-6) (95% CI: -2.50 x 10(-6) : -1.87 x 10(-6))). This study underlines the urgency to integrate climate and tourism for DF surveillance.
Future distribution of dengue risk is usually predicted based on predicted climate changes using general circulation models (GCMs). However, it is difficult to validate the GCM results and assess the uncertainty of the predictions. The observed changes in climate may be very different from the GCM results. We aim to utilize trends in observed climate dynamics to predict future risks of Aedes albopictus in China. METHODS: We collected Ae. albopictus surveillance data and observed climate records from 80 meteorological stations from 1970 to 2021. We analyzed the trends in climate change in China and made predictions on future climate for the years 2050 and 2080 based on trend analyses. We analyzed the relationship between climatic variables and the prevalence of Ae. albopictus in different months/seasons. We built a classification tree model (based on the average of 999 runs of classification and regression tree analyses) to predict the monthly/seasonal Ae. albopictus distribution based on the average climate from 1970 to 2000 and assessed the contributions of different climatic variables to the Ae. albopictus distribution. Using these models, we projected the future distributions of Ae. albopictus for 2050 and 2080. RESULTS: The study included Ae. albopictus surveillance from 259 sites in China found that winter to early spring (November-February) temperatures were strongly correlated with Ae. albopictus prevalence (prediction accuracy ranges 93.0-98.8%)-the higher the temperature the higher the prevalence, while precipitation in summer (June-September) was important predictor for Ae. albopictus prevalence. The machine learning tree models predicted the current prevalence of Ae. albopictus with high levels of agreement (accuracy > 90% and Kappa agreement > 80% for all 12 months). Overall, winter temperature contributed the most to Ae. albopictus distribution, followed by summer precipitation. An increase in temperature was observed from 1970 to 2021 in most places in China, and annual change rates varied substantially from -0.22 ºC/year to 0.58 ºC/year among sites, with the largest increase in temperature occurring from February to April (an annual increase of 1.4-4.7 ºC in monthly mean, 0.6-4.0 ºC in monthly minimum, and 1.3-4.3 ºC in monthly maximum temperature) and the smallest in November and December. Temperature increases were lower in the tropics/subtropics (1.5-2.3 ºC from February-April) compared to the high-latitude areas (2.6-4.6 ºC from February-April). The projected temperatures in 2050 and 2080 by this study were approximately 1-1.5 °C higher than those projected by GCMs. The estimated current Ae. albopictus risk distribution had a northern boundary of north-central China and the southern edge of northeastern China, with a risk period of June-September. The projected future Ae. albopictus risks in 2050 and 2080 cover nearly all of China, with an expanded risk period of April-October. The current at-risk population was estimated to be 960 million and the future at-risk population was projected to be 1.2 billion. CONCLUSIONS: The magnitude of climate change in China is likely to surpass GCM predictions. Future dengue risks will expand to cover nearly all of China if current climate trends continue.
Chikungunya virus is widespread throughout the tropics, where it causes recurrent outbreaks of chikungunya fever. In recent years, outbreaks have afflicted populations in East and Central Africa, South America and Southeast Asia. The virus is transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Chikungunya fever is characterized by severe arthralgia and myalgia that can persist for years and have considerable detrimental effects on health, quality of life and economic productivity. The effects of climate change as well as increased globalization of commerce and travel have led to growth of the habitat of Aedes mosquitoes. As a result, increasing numbers of people will be at risk of chikungunya fever in the coming years. In the absence of specific antiviral treatments and with vaccines still in development, surveillance and vector control are essential to suppress re-emergence and epidemics.
Rationale for review Chikungunya outbreaks continue to occur, with changing epidemiology. Awareness about chikungunya is low both among the at-risk travellers and healthcare professionals, which can result in underdiagnosis and underreporting. This review aims to improve awareness among healthcare professionals regarding the risks of chikungunya for travellers. Key findings Chikungunya virus transmission to humans occurs mainly via daytime-active mosquitoes, Aedes aegypti and Aedes albopictus. The areas where these mosquitoes live is continuously expanding, partly due to climate changes. Chikungunya is characterized by an acute onset of fever with joint pain. These symptoms generally resolve within 1-3 weeks, but at least one-third of the patients suffer from debilitating rheumatologic symptoms for months to years. Large outbreaks in changing regions of the world since the turn of the 21st century (e.g. Caribbean, La Reunion; currently Brazil, India) have resulted in growing numbers of travellers importing chikungunya, mainly to Europe and North America. Viremic travellers with chikungunya infection have seeded chikungunya clusters (France, United States of America) and outbreaks (Italy in 2007 and 2017) in non-endemic countries where Ae. albopictus mosquitoes are present. Community preventive measures are important to prevent disease transmission by mosquitoes. Individual preventive options are limited to personal protection measures against mosquito bites, particularly the daytime-active mosquitos that transmit the chikungunya virus. Candidate vaccines are on the horizon and regulatory authorities will need to assess environmental and host risk factors for persistent sequelae, such as obesity, age (over 40 years) and history of arthritis or inflammatory rheumatologic disease to determine which populations should be targeted for these chikungunya vaccines. Conclusions/recommendations Travellers planning to visit destinations with active CHIKV circulation should be advised about the risk for chikungunya, prevention strategies, the disease manifestations, possible chronic rheumatologic sequelae and, if symptomatic, seek medical evaluation and report potential exposures.
BACKGROUND: Plasmodium vivax malaria is one of the major infectious diseases of public health concern in Nouakchott, the capital city of Mauritania and the biggest urban setting in the Sahara. The assessment of the current trends in malaria epidemiology is primordial in understanding the dynamics of its transmission and developing an effective control strategy. METHODS: A 6 year (2015-2020) prospective study was carried out in Nouakchott. Febrile outpatients with a clinical suspicion of malaria presenting spontaneously at Teyarett Health Centre or the paediatric department of Mother and Children Hospital Centre were screened for malaria using a rapid diagnostic test, microscopic examination of Giemsa-stained blood films, and nested polymerase chain reaction. Data were analysed using Microsoft Excel and GraphPad Prism and InStat software. RESULTS: Of 1760 febrile patients included in this study, 274 (15.5%) were malaria-positive by rapid diagnostic test, 256 (14.5%) were malaria-positive by microscopy, and 291 (16.5%) were malaria-positive by PCR. Plasmodium vivax accounted for 216 of 291 (74.2%) PCR-positive patients; 47 (16.1%) and 28 (9.6%) had P. falciparum monoinfection or P. vivax-P. falciparum mixed infection, respectively. During the study period, the annual prevalence of malaria declined from 29.2% in 2015 to 13.2% in 2019 and 2.1% in 2020 (P < 0.05). Malaria transmission was essentially seasonal, with a peak occurring soon after the rainy season (October-November), and P. vivax infections, but not P. falciparum infections, occurred at low levels during the rest of the year. The most affected subset of patient population was adult male white and black Moors. The decline in malaria prevalence was correlated with decreasing annual rainfall (r = 0.85; P = 0.03) and was also associated with better management of the potable water supply system. A large majority of included patients did not possess or did not use bed nets. CONCLUSIONS: Control interventions based on prevention, diagnosis, and treatment should be reinforced in Nouakchott, and P. vivax-specific control measures, including chloroquine and 8-aminoquinolines (primaquine, tafenoquine) for treatment, should be considered to further improve the efficacy of interventions and aim for malaria elimination.
Ongoing climate change has accelerated the outbreak and expansion of climate-sensitive infectious diseases such as dengue fever. Dengue fever will remain a threat until safe and effective vaccines and antiviral drugs have been developed, distributed, and administered on a global scale. By predicting the spatiotemporal distribution of dengue fever outbreaks, we can effectively implement dengue fever prevention and control. Our study aims to predict the spatiotemporal distribution of dengue fever outbreaks in Chinese Taiwan using a U-Net based encoder – decoder model with daily datasets of sea-surface temperature, rainfall, and shortwave radiation from Remote Sensing (RS) instruments and dengue fever case notification data. Although the prediction accuracy of the proposed model was low and the overlapping areas between the ground truth and pixelwise prediction were few, some of the pixels were located nearby the ground truth, suggesting that the application of RS data and deep learning may help to predict the spatiotemporal distribution of dengue fever outbreaks. With further improvements, the deep learning model might effectively learn a small amount of training data for a specific task.
The Arctic is warming at four times the global rate, changing the diversity, activity and distribution of vectors and associated pathogens. While the Arctic is not often considered a hotbed of vector-borne diseases, Jamestown Canyon virus (JCV) and Snowshoe Hare virus (SSHV) are mosquito-borne zoonotic viruses of the California serogroup endemic to the Canadian North. The viruses are maintained by transovarial transmission in vectors and circulate among vertebrate hosts, both of which are not well characterized in Arctic regions. While most human infections are subclinical or mild, serious cases occur, and both JCV and SSHV have recently been identified as leading causes of arbovirus-associated neurological diseases in North America. Consequently, both viruses are currently recognised as neglected and emerging viruses of public health concern. This review aims to summarise previous findings in the region regarding the enzootic transmission cycle of both viruses. We identify key gaps and approaches needed to critically evaluate, detect, and model the effects of climate change on these uniquely northern viruses. Based on limited data, we predict that (1) these northern adapted viruses will increase their range northwards, but not lose range at their southern limits, (2) undergo more rapid amplification and amplified transmission in endemic regions for longer vector-biting seasons, (3) take advantage of northward shifts of hosts and vectors, and (4) increase bite rates following an increase in the availability of breeding sites, along with phenological synchrony between the reproduction cycle of theorized reservoirs (such as caribou calving) and mosquito emergence.
On the climate-health issue, studies have already attempted to understand the influence of climate change on the transmission of malaria. Extreme weather events such as floods, droughts, or heat waves can alter the course and distribution of malaria. This study aims to understand the impact of future climate change on malaria transmission using, for the first time in Senegal, the ICTP’s community-based vector-borne disease model, TRIeste (VECTRI). This biological model is a dynamic mathematical model for the study of malaria transmission that considers the impact of climate and population variability. A new approach for VECTRI input parameters was also used. A bias correction technique, the cumulative distribution function transform (CDF-t) method, was applied to climate simulations to remove systematic biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) that could alter impact predictions. Beforehand, we use reference data for validation such as CPC global unified gauge-based analysis of daily precipitation (CPC for Climate Prediction Center), ERA5-land reanalysis, Climate Hazards InfraRed Precipitation with Station data (CHIRPS), and African Rainfall Climatology 2.0 (ARC2). The results were analyzed for two CMIP5 scenarios for the different time periods: assessment: 1983-2005; near future: 2006-2028; medium term: 2030-2052; and far future: 2077-2099). The validation results show that the models reproduce the annual cycle well. Except for the IPSL-CM5B model, which gives a peak in August, all the other models (ACCESS1-3, CanESM2, CSIRO, CMCC-CM, CMCC-CMS, CNRM-CM5, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, inmcm4, and IPSL-CM5B) agree with the validation data on a maximum peak in September with a period of strong transmission in August-October. With spatial variation, the CMIP5 model simulations show more of a difference in the number of malaria cases between the south and the north. Malaria transmission is much higher in the south than in the north. However, the results predicted by the models on the occurrence of malaria by 2100 show differences between the RCP8.5 scenario, considered a high emission scenario, and the RCP4.5 scenario, considered an intermediate mitigation scenario. The CanESM2, CMCC-CM, CMCC-CMS, inmcm4, and IPSL-CM5B models predict decreases with the RCP4.5 scenario. However, ACCESS1-3, CSIRO, NRCM-CM5, GFDL-CM3, GFDL-ESM2G, and GFDL-ESM2M predict increases in malaria under all scenarios (RCP4.5 and RCP8.5). The projected decrease in malaria in the future with these models is much more visible in the RCP8.5 scenario. The results of this study are of paramount importance in the climate-health field. These results will assist in decision-making and will allow for the establishment of preventive surveillance systems for local climate-sensitive diseases, including malaria, in the targeted regions of Senegal.
Due to the rapid geographic spread of the Aedes mosquito and the increase in dengue incidence, dengue fever has been an increasing concern for public health authorities in tropical and subtropical countries worldwide. Significant challenges such as climate change, the burden on health systems, and the rise of insecticide resistance highlight the need to introduce new and cost-effective tools for developing public health interventions. Various and locally adapted statistical methods for developing climate-based early warning systems have increasingly been an area of interest and research worldwide. Costa Rica, a country with microclimates and endemic circulation of the dengue virus (DENV) since 1993, provides ideal conditions for developing projection models with the potential to help guide public health efforts and interventions to control and monitor future dengue outbreaks. Climate information was incorporated to model and forecast the dengue cases and relative risks using a Bayesian spatio-temporal model, from 2000 to 2021, in 32 Costa Rican municipalities. This approach is capable of analyzing the spatio-temporal behavior of dengue and also producing reliable predictions.
We describe clinical and laboratory findings of 3 autochthonous cases of dengue in the Paris Region, France, during September-October 2023. Increasing trends in cases, global warming, and growth of international travel mean that such infections likely will increase during warm seasons in France, requiring stronger arbovirus surveillance networks.
Scrub typhus, also known as Tsutsugamushi disease, is a climate-sensitive vector-borne disease that poses a growing public health threat. However, studies on the association between scrub typhus epidemics and meteorological factors in South Korea need to be complemented. Therefore, we aimed to analyze the association among ambient temperature, precipitation, and the incidence of scrub typhus in South Korea. First, we obtained data on the weekly number of scrub typhus cases and concurrent meteorological variables at the city-county level (Si-Gun) in South Korea between 2001 and 2019. Subsequently, a two-stage meta-regression analysis was conducted. In the first stage, we conducted time-series regression analyses using a distributed lag nonlinear model (DLNM) to investigate the association between temperature, precipitation, and scrub typhus incidence at each location. In the second stage, we employed a multivariate meta-regression model to combine the association estimates from all municipalities, considering regional indicators, such as mite species distribution, Normalized Difference Vegetation Index (NDVI), and urban-rural classification. Weekly mean temperature and weekly total precipitation exhibited a reversed U-shaped nonlinear association with the incidence of scrub typhus. The overall cumulative association with scrub typhus incidence peaked at 18.7 C° (with RRs of 9.73, 95% CI: 5.54-17.10) of ambient temperature (reference 9.7 C°) and 162.0 mm (with RRs of 1.87, 95% CI: 1.02-3.83) of precipitation (reference 2.8 mm), respectively. These findings suggest that meteorological factors contribute to scrub typhus epidemics by interacting with vectors, reservoir hosts, and human behaviors. This information serves as a reference for future public health policies and epidemiological research aimed at controlling scrub typhus infections.
In Bangladesh, particularly in Dhaka city, dengue fever is a major factor in serious sickness and hospitalization. The weather influences the temporal and geographical spread of the vector-borne disease dengue in Dhaka. As a result, rainfall and ambient temperature are considered macro factors influencing dengue since they have a direct impact on Aedes aegypti population density, which changes seasonally dependent on these critical variables. This study aimed to clarify the relationship between climatic variables and the incidence of dengue disease. METHODS: A total of 2253 dengue and climate data were used for this study. Maximum and minimum temperature (°C), humidity (grams of water vapor per kilogram of air g.kg(-1)), rainfall (mm), sunshine hour (in (average) hours per day), and wind speed (knots (kt)) in Dhaka were considered as the independent variables for this study which trigger the dengue incidence in Dhaka city, Bangladesh. Missing values were imputed using multiple imputation techniques. Descriptive and correlation analyses were performed for each variable and stationary tests were observed using Dicky Fuller test. However, initially, the Poisson model, zero-inflated regression model, and negative binomial model were fitted for this problem. Finally, the negative binomial model is considered the final model for this study based on minimum AIC values. RESULTS: The mean of maximum and minimum temperature, wind speed, sunshine hour, and rainfall showed some fluctuations over the years. However, a mean number of dengue cases reported a higher incidence in recent years. Maximum and minimum temperature, humidity, and wind speed were positively correlated with dengue cases. However, rainfall and sunshine hours were negatively associated with dengue cases. The findings showed that factors such as maximum temperature, minimum temperature, humidity, and windspeed are crucial in the transmission cycles of dengue disease. On the other hand, dengue cases decreased with higher levels of rainfall. CONCLUSION: The findings of this study will be helpful for policymakers to develop a climate-based warning system in Bangladesh.
Dengue fever remains a significant global health concern, imposing a substantial burden on public health systems worldwide. Recent studies have suggested that climate change, specifically the increase in surface temperatures associated with global warming, may impact the transmission dynamics of dengue. This study aimed to assess the relationship between annual surface temperature changes from 1961 to 2019 and the burden of dengue in 185 countries. The dengue burden was evaluated for 2019 using disability-adjusted life years (DALYs) and the annual rate of change (ARC) in DALY rates assessed from 1990 to 2019. A cross-sectional and ecological analysis was conducted using two publicly available datasets. Regression coefficients (β) and 95% confidence intervals (CI) were used to examine the relationship between annual surface temperature changes and the burden of dengue. The results revealed a significant negative relationship between mean surface temperatures and DALY rates in 2019 (β = -16.9, 95% CI -26.9 to -6.8). Similarly, a significant negative relationship was observed between the temperature variable and the ARC (β = -0.99, 95% CI -1.66 to -0.32). These findings suggest that as temperatures continue to rise, the burden of dengue may globally decrease. The ecology of the vector and variations in seasons, precipitation patterns, and humidity levels may partially contribute to this phenomenon. Our study contributes to the expanding body of evidence regarding the potential implications of climate change for dengue dynamics. It emphasizes the critical importance of addressing climate change as a determinant of global health outcomes.
Dengue fever is a vector-borne disease affecting millions yearly, mostly in tropical and subtropical countries. Driven mainly by social and environmental factors, dengue incidence and geographical expansion have increased in recent decades. Therefore, understanding how climate variables drive dengue outbreaks is challenging and a problem of interest for decision-makers that could aid in improving surveillance and resource allocation. Here, we explore the effect of climate variables on relative dengue risk in 32 cantons of interest for public health authorities in Costa Rica. Relative dengue risk is forecast using a Generalized Additive Model for location, scale, and shape and a Random Forest approach. Models use a training period from 2000 to 2020 and predicted climatic variables obtained with a vector auto-regressive model. Results show reliable projections, and climate variables predictions allow for a prospective instead of a retrospective study.
In the United States (US), tick-borne diseases (TBDs) have more than doubled in the past fifteen years and are a major contributor to the overall burden of vector-borne diseases. The most common TBDs in the US-Lyme disease, rickettsioses (including Rocky Mountain spotted fever), and anaplasmosis-have gradually shifted in recent years, resulting in increased morbidity and mortality. In this systematic review, we examined climate change and other environmental factors that have influenced the epidemiology of these TBDs in the US while highlighting the opportunities for a One Health approach to mitigating their impact. We searched Medline Plus, PUBMED, and Google Scholar for studies focused on these three TBDs in the US from January 2018 to August 2023. Data selection and extraction were completed using Covidence, and the risk of bias was assessed with the ROBINS-I tool. The review included 84 papers covering multiple states across the US. We found that climate, seasonality and temporality, and land use are important environmental factors that impact the epidemiology and patterns of TBDs. The emerging trends, influenced by environmental factors, emphasize the need for region-specific research to aid in the prediction and prevention of TBDs.
West Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence. OBJECTIVES: Our goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans. METHODS: We developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R2 = 0.61. RESULTS: Our model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels < 23.3 mm/month as having incidence levels over 11 times greater than those of counties that are wetter. Among the climate predictors, winter precipitation, fall precipitation, and winter temperature were the three most important predictive variables. DISCUSSION: We consider which aspects of the WNV transmission cycle climate conditions may benefit the most and argued that dry and cold winters are climate conditions optimal for the mosquito species key to amplifying WNV transmission. Our statistical model may be useful in projecting shifts in WNV risk in response to climate change. https://doi.org/10.1289/EHP10986.
Arboviruses cause millions of infections each year; however, only limited options are available for treatment and pharmacological prevention. Mosquitoes are among the most important vectors for the transmission of several pathogens to humans. Despite advances, the sampling, viral detection, and control methods for these insects remain ineffective. Challenges arise with the increase in mosquito populations due to climate change, insecticide resistance, and human interference affecting natural habitats, which contribute to the increasing difficulty in controlling the spread of arboviruses. Therefore, prioritizing arbovirus surveillance is essential for effective epidemic preparedness. In this review, we offer a concise historical account of the discovery and monitoring of arboviruses in mosquitoes, from mosquito capture to viral detection. We then analyzed the advantages and limitations of these traditional methods. Furthermore, we investigated the potential of emerging technologies to address these limitations, including the implementation of next-generation sequencing, paper-based devices, spectroscopic detectors, and synthetic biosensors. We also provide perspectives on recurring issues and areas of interest such as insect-specific viruses.
Mosquitoes, sandflies, and ticks are hematophagous arthropods that pose a huge threat to public and veterinary health. They are capable of serving as vectors of disease agents that can and have caused explosive epidemics affecting millions of people and animals. Several factors like climate change, urbanization, and international travel contribute substantially to the persistence and dispersal of these vectors from their established areas to newly invaded areas. Once established in their new home, they can serve as vectors for disease transmission or increase the risk of disease emergence. Turkiye (formerly Turkey) is vulnerable to climate change and has experienced upward trends in annual temperatures and rising sea levels, and greater fluctuations in precipitation rates. It is a potential hotspot for important vector species because the climate in various regions is conducive for several insect and acari species and serves as a conduit for refugees and immigrants fleeing areas troubled with armed conflicts and natural disasters, which have increased substantially in recent years. These people may serve as carriers of the vectors or be infected by disease agents that require arthropod vectors for transmission. Although it cannot be supposed that every arthropod species is a competent vector, this review aims to (1) illustrate the factors that contribute to the persistence and dispersal of arthropod vectors, (2) determine the status of the established arthropod vector species in Turkiye and their capability of serving as vectors of disease agents, and (3) assess the role of newly-introduced arthropod vectors into Turkiye and how they were introduced into the country. We also provide information on important disease incidence (if there’s any) and control measures applied by public health officials from different provinces.
Flaviviruses are a diverse group of enveloped RNA viruses that cause significant clinical manifestations in the pregnancy and postpartum periods. This review highlights the epidemiology, pathophysiology, clinical features, diagnosis, and prevention of the key arthropod-borne flaviviruses of concern in pregnancy and the neonatal period-Zika, Dengue, Japanese encephalitis, West Nile, and Yellow fever viruses. Increased disease severity during pregnancy, risk of congenital malformations, and manifestations of postnatal infection vary widely amongst this virus family and may be quite marked. Laboratory confirmation of infection is complex, especially due to the reliance on serology for which flavivirus cross-reactivity challenges diagnostic specificity. As such, a thorough clinical history including relevant geographic exposures and prior vaccinations is paramount for accurate diagnosis. Novel vaccines are eagerly anticipated to ameliorate the impact of these flaviviruses, particularly neuroinvasive disease manifestations and congenital infection, with consideration of vaccine safety in pregnant women and children pivotal. Moving forward, the geographical spread of flaviviruses, as for other zoonoses, will be heavily influenced by climate change due to the potential expansion of vector and reservoir host habitats. Ongoing ‘One Health’ engagement across the human-animal-environment interface is critical to detect and responding to emergent flavivirus epidemics.
BACKGROUND: The potential reappearance and/or expansion of vector-borne diseases is one of the terrifying issues awaiting humanity in the context of climate change. The presence of competent Anopheles vectors, as well as suitable environmental circumstances, may result in the re-emergence of autochthonous Malaria, after years of absence. In Morocco, international travel and migration movements from Malaria-endemic areas have recently increased the number of imported cases, raising awareness of Malaria’s possible reintroduction. Using machine learning we developed model predictions, under current and future (2050) climate, for the prospective distribution of Anopheles claviger, Anopheles labranchiae, Anopheles multicolor, and Anopheles sergentii implicated or incriminated in Malaria transmission. RESULTS: All modelled species are expected to find suitable habitats and have the potential to become established in the northern and central parts of the country, under present-day conditions. Distinct changes in the distributions of the four mosquitoes are to be expected under climate change. Even under the most optimistic scenario, all investigated species are likely to acquire new habitats that are now unsuitable, placing further populations in danger. We also observed a northward and altitudinal shift in their distribution towards higher altitudes. CONCLUSION: Climate change is expected to expand the potential range of malaria vectors in Morocco. Our maps and predictions offer a way to intelligently focus efforts on surveillance and control programmes. To reduce the threat of human infection, it is crucial for public health authorities, entomological surveillance teams, and control initiatives to collaborate and intensify their actions, continuously monitoring areas at risk. © 2023 Society of Chemical Industry.
BACKGROUND: A better understanding of vector distribution and malaria transmission dynamics at a local scale is essential for implementing and evaluating effectiveness of vector control strategies. Through the data gathered in the framework of a cluster randomized controlled trial (CRT) evaluating the In2Care (Wageningen, Netherlands) Eave Tubes strategy, the distribution of the Anopheles vector, their biting behaviour and malaria transmission dynamics were investigated in Gbêkê region, central Côte d’Ivoire. METHODS: From May 2017 to April 2019, adult mosquitoes were collected monthly using human landing catches (HLC) in twenty villages in Gbêkê region. Mosquito species wereidentified morphologically. Monthly entomological inoculation rates (EIR) were estimated by combining the HLC data with mosquito sporozoite infection rates measured in a subset of Anopheles vectors using PCR. Finally, biting rate and EIR fluctuations were fit to local rainfall data to investigate the seasonal determinants of mosquito abundance and malaria transmission in this region. RESULTS: Overall, Anopheles gambiae, Anopheles funestus, and Anopheles nili were the three vector complexes found infected in the Gbêkê region, but there was a variation in Anopheles vector composition between villages. Anopheles gambiae was the predominant malaria vector responsible for 84.8% of Plasmodium parasite transmission in the area. An unprotected individual living in Gbêkê region received an average of 260 [222-298], 43.5 [35.8-51.29] and 3.02 [1.96-4] infected bites per year from An. gambiae, An. funestus and An. nili, respectively. Vector abundance and malaria transmission dynamics varied significantly between seasons and the highest biting rate and EIRs occurred in the months of heavy rainfall. However, mosquitoes infected with malaria parasites remained present in the dry season, despite the low density of mosquito populations. CONCLUSION: These results demonstrate that the intensity of malaria transmission is extremely high in Gbêkê region, especially during the rainy season. The study highlights the risk factors of transmission that could negatively impact current interventions that target indoor control, as well as the urgent need for additional vector control tools to target the population of malaria vectors in Gbêkê region and reduce the burden of the disease.
West Nile viral infection causes severe neuroinvasive disease in less than 1% of infected humans. There are no targeted therapeutics for this serious and potentially fatal disease, and to date no vaccine has been approved for humans. With climate change expected to result in rising incidence of West Nile and other related vector-borne viral infections, there is an increasing need to identify those at risk for serious disease and potential leads for therapeutic and vaccine development. Genetic variation, particularly in genes whose products are either directly or indirectly connected to immune response to infections, is a critical avenue of investigation to identify those at higher risk of clinically apparent West Nile infection. Given the small percent of infections that progress to severe disease and the relatively low numbers of reported infections, it is challenging to conduct well-powered studies to identify genetic factors associated with more severe outcomes. In this chapter, we outline several approaches with the objective to take full advantage of all available data in order to identify genetic factors which lead to increased risk of severe West Nile neuroinvasive disease. These methods are generalizable to other conditions with limited cohort size and rare outcomes.
Simple Summary Dirofilariasis is caused by Dirofilaria spp. worm infections, transmitted by mosquitoes, and affects humans and animals worldwide. Often, infected animals show symptoms relating to the cardiopulmonary system (heart and lung) and subcutaneous tissue (eye and skin). This study assessed the current published data on the distribution and prevalence of dirofilariasis across Sri Lanka and India. This analysis found that almost all cases of human dirofilariasis reported in Sri Lanka and India are presented as subcutaneous infections, with the eye being the most commonly affected organ. Both heartworm and subcutaneous infections are found in the dog populations in India. However, only subcutaneous infections have so far been reported in Sri Lanka, and the rationale behind this geographical distribution of infection patterns of dirofilariasis remains unknown and warrants further research. There was a low infection rate in the pet and working dog populations in India and Sri Lanka, but this may change due to climate change and emerging anti-parasitic drug resistance. It was identified in this study that some regions within India and Sri Lanka have not yet been surveyed for dirofilariasis, and future studies need to target these unsurveyed areas to better understand the geographical and species distribution of dirofilariasis in these two countries. Dirofilariasis is an emerging vector-borne tropical disease of public health importance that mainly affects humans and dogs. Dirofilaria immitis and D. repens are the two well-documented dirofilariasis-causing filarioid helminths of both medical and veterinary concerns in India and Sri Lanka. This systematic review and meta-analysis aimed to describe and summarize the current evidence of dirofilariasis prevalence and distribution in India and Sri Lanka. Interestingly, D. repens is reported to circulate in both dogs (prevalence of 35.8% (95% CI: 11.23-60.69)) and humans (97% of published case reports) in India and Sri Lanka, but D. immitis is reported to be present in the dog populations in India (prevalence of 9.7% (95% CI: 8.5-11.0%)), and so far, it has not been reported in Sri Lanka. This peculiar distribution of D. immitis and D. repens in the two neighbouring countries could be due to the interaction between the two parasite species, which could affect the pattern of infection of the two worm species in dogs and thus influence the geographical distribution of these two filarial worms. In medical and veterinary practice, histopathology was the most commonly used diagnostic technique (31.3%; 95% CI 2.5-60.2%). The low specificity of histopathology to speciate the various Dirofilaria spp. may lead to misdiagnosis. It was identified in this study that several regions of India and Sri Lanka have not yet been surveyed for dirofilariasis. This limits our understanding of the geographical distribution and interspecies interactions of the two parasites within these countries. Parasite distribution, disease prevalence, and interspecies interactions between the vectors and the host should be targeted for future research.
Dengue fever is a virulent disease spreading over 100 tropical and subtropical countries in Africa, the Americas, and Asia. This arboviral disease affects around 400 million people globally, severely distressing the healthcare systems. The unavailability of a specific drug and ready-to-use vaccine makes the situation worse. Hence, policymakers must rely on early warning systems to control intervention-related decisions. Forecasts routinely provide critical information for dangerous epidemic events. However, the available forecasting models (e.g., weather-driven mechanistic, statistical time series, and machine learning models) lack a clear understanding of different components to improve prediction accuracy and often provide unstable and unreliable forecasts. This study proposes an ensemble wavelet neural network with exogenous factor(s) (XEWNet) model that can produce reliable estimates for dengue outbreak prediction for three geographical regions, namely San Juan, Iquitos, and Ahmedabad. The proposed XEWNet model is flexible and can easily incorporate exogenous climate variable(s) confirmed by statistical causality tests in its scalable framework. The proposed model is an integrated approach that uses wavelet transformation into an ensemble neural network framework that helps in generating more reliable long-term forecasts. The proposed XEWNet allows complex non-linear relationships between the dengue incidence cases and rainfall; however, mathematically interpretable, fast in execution, and easily comprehensible. The proposal’s competitiveness is measured using computational experiments based on various statistical metrics and several statistical comparison tests. In comparison with statistical, machine learning, and deep learning methods, our proposed XEWNet performs better in 75% of the cases for short-term and long-term forecasting of dengue incidence.
The Aedes aegypti mosquito is the main vector of dengue and is a synanthropic insect and due to its anthropophilic nature, it has specific reproductive needs. In addition to that, it also needs tropical regions that provide climate-prone conditions that favor vector development. In this article, we propose the cross-correlation analysis between the climatic variables air temperature, relative humidity, weekly average precipitation and dengue cases in the period from 2017 to early 2021 in the municipality of Alagoinhas, Bahia, Brazil. To do so, we apply the trend-free cross-correlation, [Formula: see text], being a generalization of the fluctuation analysis without trend, where we calculate the cross correlation between time series to establish the influence of these variables on the occurrence of dengue disease. The results obtained here were a moderate correlation between relative humidity and the incidence of dengue cases, and a low correlation for relative air temperature and precipitation. However, the predominant factor in the incidence of dengue cases in the city of Alagoinhas is relative humidity and not air temperature and precipitation.
Dengue fever, transmitted by Aedes mosquitoes, is a significant public health concern in tropical and subtropical regions. With the end of the COVID-19 pandemic and the reopening of the borders, dengue fever remains a threat to mainland China, Zhejiang province of China is facing a huge risk of importing the dengue virus. This study aims to analyze and predict the current and future potential risk regions for Aedes vectors distribution and dengue prevalence in Zhejiang province of China. METHOD: We collected occurrence records of DENV and DENV vectors globally from 2010 to 2022, along with historical and future climate data and human population density data. In order to predict the probability of DENV distribution in Zhejiang province of China under future conditions, the ecological niche of Ae. aegypti and Ae. albopictus was first performed with historical climate data based on MaxEnt. Then, predicted results along with a set of bioclimatic variables, elevation and human population density were included in MaxEnt model to analyze the risk region of DENV in Zhejiang province. Finally, the established model was utilized to predict the spatial pattern of DENV risk in the current and future scenarios in Zhejiang province of China. RESULTS: Our findings indicated that approximately 89.2% (90,805.6 KM(2)) of Zhejiang province of China is under risk, within about 8.0% (8,144 KM(2)) classified as high risk area for DENV prevalence. Ae. albopictus were identified as the primary factor influencing the distribution of DENV. Future predictions suggest that sustainable and “green” development pathways may increase the risk of DENV prevalence in Zhejiang province of China. Conversely, Fossil-fueled development pathways may reduce the risk due to the unsuitable environment for vectors. CONCLUSIONS: The implications of this research highlight the need for effective vector control measures, community engagement, health education, and environmental initiatives to mitigate the potential spread of dengue fever in high-risk regions of Zhejiang province of China.
BACKGROUND: Understanding coupled human-environment factors which promote Aedes aegypti abundance is critical to preventing the spread of Zika, chikungunya, yellow fever and dengue viruses. High temperatures and aridity theoretically make arid lands inhospitable for Ae. aegypti mosquitoes, yet their populations are well established in many desert cities. METHODS: We investigated associations between socioeconomic and built environment factors and Ae. aegypti abundance in Maricopa County, Arizona, home to Phoenix metropolitan area. Maricopa County Environmental Services conducts weekly mosquito surveillance with CO(2)-baited Encephalitis Vector Survey or BG-Sentinel traps at > 850 locations throughout the county. Counts of adult female Ae. aegypti from 2014 to 2017 were joined with US Census data, precipitation and temperature data, and 2015 land cover from high-resolution (1 m) aerial images from the National Agricultural Imagery Program. RESULTS: From 139,729 trap-nights, 107,116 Ae. aegypti females were captured. Counts were significantly positively associated with higher socioeconomic status. This association was partially explained by higher densities of non-native landscaping in wealthier neighborhoods; a 1% increase in the density of tree cover around the trap was associated with a ~ 7% higher count of Ae. aegypti (95% CI: 6-9%). CONCLUSIONS: Many models predict that climate change will drive aridification in some heavily populated regions, including those where Ae. aegypti are widespread. City climate change adaptation plans often include green spaces and vegetation cover to increase resilience to extreme heat, but these may unintentionally create hospitable microclimates for Ae. aegypti. This possible outcome should be addressed to reduce the potential for outbreaks of Aedes-borne diseases in desert cities.
Dengue is among the fastest-spreading vector-borne infectious disease, with outbreaks often overwhelm the health system and result in huge morbidity and mortality in its endemic populations in the absence of an efficient warning system. A large number of prediction models are currently in use globally. As such, this study aimed to systematically review the published literature that used quantitative models to predict dengue outbreaks and provide insights about the current practices. A systematic search was undertaken, using the Ovid MEDLINE, EMBASE, Scopus and Web of Science databases for published citations, without time or geographical restrictions. Study selection, data extraction and management process were devised in accordance with the ‘Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies’ (‘CHARMS’) framework. A total of 99 models were included in the review from 64 studies. Most models sourced climate (94.7%) and climate change (77.8%) data from agency reports and only 59.6% of the models adjusted for reporting time lag. All included models used climate predictors; 70.7% of them were built with only climate factors. Climate factors were used in combination with climate change factors (13.4%), both climate change and demographic factors (3.1%), vector factors (6.3%), and demographic factors (5.2%). Machine learning techniques were used for 39.4% of the models. Of these, random forest (15.4%), neural networks (23.1%) and ensemble models (10.3%) were notable. Among the statistical (60.6%) models, linear regression (18.3%), Poisson regression (18.3%), generalized additive models (16.7%) and time series/autoregressive models (26.7%) were notable. Around 20.2% of the models reported no validation at all and only 5.2% reported external validation. The reporting of methodology and model performance measures were inadequate in many of the existing prediction models. This review collates plausible predictors and methodological approaches, which will contribute to robust modelling in diverse settings and populations.
BACKGROUND: In recent years, the state of Mato Grosso has presented one of the highest dengue incidence rates in Brazil. The meeting of the Amazon, Cerrado and Pantanal biomes results in a large variation of rainfall and temperature across different regions of the state. In addition, Mato Grosso has been undergoing intense urban growth since the 1970s, mainly due to the colonization of the Mid-North and North regions. We analyzed factors involved in dengue incidence in Mato Grosso from 2008 to 2019. METHODS: The Moran Global Index was used to assess spatial autocorrelation of dengue incidence using explanatory variables such as temperature, precipitation, deforestation, population density and municipal development index. Areas at risk of dengue were grouped by the Local Moran Indicator. RESULTS: We noticed that areas at risk of dengue expanded from the Mid-North region to the North; the same pattern occurred from the Southeast to the Northeast; the South region remained at low-risk levels. The increase in incidence was influenced by precipitation, deforestation and the municipal development index. CONCLUSIONS: The identification of risk areas for dengue in space and time enables public health authorities to focus their control and prevention efforts, reducing infestation and the potential impact of dengue in the human population.
INTRODUCTION/OBJECTIVE: West Nile virus (WNV) is one of the most widely distributed flaviviruses worldwide. It is considered an endemic and emerging pathogen in different areas of the Europe and Mediterranean countries (MR). Mosquitoes of the genus Culex spp. are the main vectors, and birds its main vertebrate hosts. It can occasionally infect mammals, including humans. Different environmental factors can influence its distribution and transmission through its effects on vector or host populations. Our objective was to determine environmental factors associated with changes in vector distribution and WNV transmission in Europe and MR. MATERIAL & METHODS: Systematic peer review of articles published between 2000 and 2020. We selected studies on WNV, and its vectors carried out in Europe and MR. The search included terms referring to climatic and environmental factors. RESULTS: We included 65 studies, of which 21 (32%) were conducted in Italy. Culex spp. was studied in 26 papers (40%), humans in 19 papers (29%) and host animals (mainly horses) in 16 papers (25%), whereas bird reservoirs were addressed in 5 studies (8%). A significant positive relationship was observed between changes in temperature and precipitation patterns and the epidemiology of WNV, although contrasting results were found among studies. Other factors positively related to WNV dynamics were the normalized difference vegetation index (NDVI] and expansion of anthropized habitats. CONCLUSION: The epidemiology of WNV seems to be related to climatic factors that are changing globally due to ongoing climate change. Unfortunately, the complete zoonotic cycle was not analyzed in most papers, making it difficult to determine the independent impact of environment on the different components of the transmission cycle. Given the current expansion and endemicity of WNV in the area, it is important to adopt holistic approaches to understand WNV epidemiology and to improve WNV surveillance and control.
BACKGROUND: Aedes (Stegomyia)-borne diseases are an expanding global threat, but gaps in surveillance make comprehensive and comparable risk assessments challenging. Geostatistical models combine data from multiple locations and use links with environmental and socioeconomic factors to make predictive risk maps. Here we systematically review past approaches to map risk for different Aedes-borne arboviruses from local to global scales, identifying differences and similarities in the data types, covariates, and modelling approaches used. METHODS: We searched on-line databases for predictive risk mapping studies for dengue, Zika, chikungunya, and yellow fever with no geographical or date restrictions. We included studies that needed to parameterise or fit their model to real-world epidemiological data and make predictions to new spatial locations of some measure of population-level risk of viral transmission (e.g. incidence, occurrence, suitability, etc.). RESULTS: We found a growing number of arbovirus risk mapping studies across all endemic regions and arboviral diseases, with a total of 176 papers published 2002-2022 with the largest increases shortly following major epidemics. Three dominant use cases emerged: (i) global maps to identify limits of transmission, estimate burden and assess impacts of future global change, (ii) regional models used to predict the spread of major epidemics between countries and (iii) national and sub-national models that use local datasets to better understand transmission dynamics to improve outbreak detection and response. Temperature and rainfall were the most popular choice of covariates (included in 50% and 40% of studies respectively) but variables such as human mobility are increasingly being included. Surprisingly, few studies (22%, 31/144) robustly tested combinations of covariates from different domains (e.g. climatic, sociodemographic, ecological, etc.) and only 49% of studies assessed predictive performance via out-of-sample validation procedures. CONCLUSIONS: Here we show that approaches to map risk for different arboviruses have diversified in response to changing use cases, epidemiology and data availability. We identify key differences in mapping approaches between different arboviral diseases, discuss future research needs and outline specific recommendations for future arbovirus mapping.
Currently, West-Nile virus (WNV) is spreading worldwide to colder regions due to climate change. Human mortality and morbidity are prevalent and steadily increasing, associated with costs to public health systems. Therefore, the question of the impact of scientific engagement arises. What trends, barriers, and incentives for research related to global burdens are important in this context? To answer these questions, this study provides detailed insights into the publication patterns of WNV research and interprets them using several parameters, such as absolute and relative publication indices and socioeconomic and epidemiological characteristics. It is shown that national interests combined with regional outbreaks significantly influence publication intensity. Thus, a correlation between national publication volume and the number of WNV cases was observed. In contrast to most life science topics, the scientific interest in WNV significantly decreased after 2006. The USA, as the main actor in WNV research, is at the centre of international networking. Recently, European countries are also getting involved according to their new-emerging outbreaks. The results demonstrate national interest in research activities with a lack of globally focused approaches that are urgently needed to better understand and assess the distribution and characteristics of WNV.
Worldwide, mosquito monitoring and control programs consume large amounts of resources in the effort to minimise mosquito-borne disease incidence. On-site larval monitoring is highly effective but time consuming. A number of mechanistic models of mosquito development have been developed to reduce the reliance on larval monitoring, but none for Ross River virus, the most commonly occurring mosquito-borne disease in Australia. This research modifies existing mechanistic models for malaria vectors and applies it to a wetland field site in Southwest, Western Australia. Environmental monitoring data were applied to an enzyme kinetic model of larval mosquito development to simulate timing of adult emergence and relative population abundance of three mosquito vectors of the Ross River virus for the period of 2018-2020. The model results were compared with field measured adult mosquitoes trapped using carbon dioxide light traps. The model showed different patterns of emergence for the three mosquito species, capturing inter-seasonal and inter-year variation, and correlated well with field adult trapping data. The model provides a useful tool to investigate the effects of different weather and environmental variables on larval and adult mosquito development and can be used to investigate the possible effects of changes to short-term and long-term sea level and climate changes.
Pathogen strain diversity is an important driver of the trajectory of epidemics. The role of bioclimatic factors on the spatial distribution of dengue virus (DENV) serotypes has, however, not been previously studied. Hence, we developed municipality-scale environmental suitability maps for the four dengue virus serotypes using maximum entropy modeling. We fit climatic variables to municipality presence records from 2012 to 2020 in Mexico. Bioclimatic variables were explored for their environmental suitability to different DENV serotypes, and the different distributions were visualized using three cutoff probabilities representing 90%, 95%, and 99% sensitivity. Municipality-level results were then mapped in ArcGIS. The overall accuracy for the predictive models was 0.69, 0.68, 0.75, and 0.72 for DENV-1, DENV-2, DENV-3, and DENV-4, respectively. Important predictors of all DENV serotypes were the growing degree days for December, January, and February, which are an indicator of higher temperatures and the precipitation of the wettest month. The minimum temperature of the coldest month between -5 & DEG;C and 20 & DEG;C was found to be suitable for DENV-1 and DENV-2 serotypes. Respectively, above 700-900 mm of rainfall, the suitability for DENV-1 and DENV-2 begins to decline, while higher humidity still favors DENV-3 and DENV-4. The sensitivity concerning the suitability map was developed for Mexico. DENV-1, DENV-2, DENV-3, and DENV-4 serotypes will be found more commonly in the municipalities classified as suitable based on their respective sensitivity of 91%, 90%, 89%, and 85% in Mexico. As the microclimates continue to change, specific bioclimatic indices may be used to monitor potential changes in DENV serotype distribution. The suitability for DENV-1 and DENV-2 is expected to increase in areas with lower minimum temperature ranges, while DENV-3 and DENV-4 will likely increase in areas that experience higher humidity. Ongoing surveillance of municipalities with predicted suitability of 89% and 85% should be expanded to account for the accurate DENV serotype prevalence and association between bioclimatic parameters.
Mosquito-borne diseases have been spreading across Europe over the past two decades, with climate change contributing to this spread. Temperature and precipitation are key factors in a mosquito’s life cycle, and are greatly affected by climate change. Using a machine learning framework, Earth Observation data, and future climate projections of temperature and precipitation, this work studies three different cases (Veneto region in Italy, Upper Rhine Valley in Germany and Pancevo, Serbia) and focuses on (i) evaluating the impact of climate factors on mosquito abundance and (ii) long-term forecasting of mosquito abundance based on EURO-CORDEX future climate projections under different Representative Concentration Pathways (RCPs) scenarios. The study shows that increases in precipitation and temperature are directly linked to increased mosquito abundance, with temperature being the main driving factor. Additionally, as the climatic conditions become more extreme, meaning higher variance, the mosquito abundance increases. Moreover, we show that in the upcoming decades mosquito abundance is expected to increase. In the worst-case scenario (RCP8.5) Serbia will face a 10% increase, Italy around a 40% increase, and Germany will reach almost a 200% increase by 2100, relative to the decade 2010-2020. However, in terms of absolute numbers both in Italy and Germany, the expected increase is similar. An interesting finding is that either strong (RCP2.6) or moderate mitigation actions (RCP4.5) against greenhouse gas concentration lead to similar levels of future mosquito abundance, as opposed to no mitigation action at all (RCP8.5), which is projected to lead to high mosquito abundance for all cases studied.
Dengue is the fastest emerging arboviral disease in the world, imposing a substantial health and economic burden in the tropics and subtropics. The mosquito, Aedes aegypti, is the primary vector of dengue in the Philippines. We examined the genetic structure of Ae. aegypti populations collected from the Philippine major islands (Luzon, Visayas and Mindanao), each with highland (Baguio city, Cebu city mountains and Maramag, Bukidnon, respectively) and lowland sites (Quezon city; Liloan, Cebu and Cagayan de Oro [CDO] city, respectively) during the wet (2017-2018 and 2018-2019) and dry seasons (2018 and 2019). Mosquitoes (n = 1800) were reared from field-collected eggs and immatures, and were analyzed using 12 microsatellite loci. Generalized linear model analyses revealed yearly variations between highlands and lowlands in the major islands as supported by Bayesian clustering analyses on: 1) stronger selection (inbreeding coefficient, F(IS) = 0.52) in 2017-2018 than in 2018-2019 (F(IS) = 0.32) as influenced by rainfall, 2) the number of non-neutral loci indicating selection, and 3) differences of effective population size although at p = 0.05. Across sites except Baguio and CDO cities: 1) F(IS) varied seasonally as influenced by relative humidity (RH), and 2) the number of non-neutral loci varied as influenced by RH and rainfall indicating selection. Human-mediated activities and not isolation by distance influenced genetic differentiations of mosquito populations within (F(ST) = 0.04) the major islands and across sites (global F(ST) = 0.16). Gene flow (Nm) and potential first generation migrants among populations were observed between lowlands and highlands within and across major islands. Our results suggest that dengue control strategies in the epidemic wet season are to be changed into whole year-round approach, and water pipelines are to be installed in rural mountains to prevent the potential breeding sites of mosquitoes.
BACKGROUND: The objective of this study was to evaluate the spatial and temporal patterns of disease prevalence clusters of dengue (DENV), chikungunya (CHIKV) and Zika (ZIKV) virus and how socio-economic and climatic variables simultaneously influence the risk and rate of occurrence of infection in Mexico. METHODS: To determine the spatiotemporal clustering and the effect of climatic and socio-economic covariates on the rate of occurrence of disease and risk in Mexico, we applied correlation methods, seasonal and trend decomposition using locally estimated scatterplot smoothing, hotspot analysis and conditional autoregressive Bayesian models. RESULTS: We found cases of the disease are decreasing and a significant association between DENV, CHIKV and ZIKV cases and climatic and socio-economic variables. An increment of cases was identified in the northeastern, central west and southeastern regions of Mexico. Climatic and socio-economic covariates were significantly associated with the rate of occurrence and risk of the three arboviral disease cases. CONCLUSION: The association of climatic and socio-economic factors is predominant in the northeastern, central west and southeastern regions of Mexico. DENV, CHIKV and ZIKV cases showed an increased risk in several states in these regions and need urgent attention to allocate public health resources to the most vulnerable regions in Mexico.
Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011-2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011-2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.
Aedes aegypti, the primary vector for dengue, chikungunya and zika, breeds mainly in stored/stagnant water and thrives in contexts of rapid urbanization in tropical countries. Some have warned that climate change, in conjunction with urbanization, could drive the proliferation of Aedes aegypti mosquitoes. In Colombia dengue has been endemic since the 1990s and the country had the highest number of cases of zika virus in the world after Brazil. Studies have found that domestic stored water contributes to high percentages of the total Ae. aegypti pupal population in Colombian urban sectors. In particular, neighborhoods where water service provision is intermittent are vulnerable to mosquito-borne diseases as water is stored inside households. This article draws on archival work, interviews, and entomological literature to reflect on the ways in which rapid urbanization in the context of armed conflict, infrastructural inequality, the absence of formal jobs, and specific water laws and regulations produce water and Aedes aegypti in the city. It offers an initial attempt to theorize water with larvae by focusing on two interrelated processes. First, the historical and geographic processes that underlie the production of stored water, which despite being treated can become a place of fertility where mosquitoes can flourish. Secondly, the processes by which water, mosquitoes, pathogens, and human bodies become interrelated. This entails thinking about some homes in Barranquilla as socioecological assemblages that are dynamically produced, socially and materially.
OBJECTIVE: To examine the sequence of environmental and entomological events prior to a substantial increase in Ross River virus (RRV) and Barmah Forest virus (BFV) notifications with a view to informing future public health response. METHODS: Rainfall, tidal, mosquito and human arboviral notification data were analysed to determine the temporality of events. RESULTS: Following two extremely dry years, there was a substantial increase in the abundance of mosquitoes along coastal New South Wales (NSW) two weeks after a significant rainfall event and high tides in February 2020. Subsequently, RRV and BFV notifications in north east NSW began to increase eight and nine weeks respectively after the high rainfall, with RRV notifications peaking 12 weeks after the high rainfall. CONCLUSIONS: Mosquito bite avoidance messaging should be instigated within two weeks of high summer rainfall, especially after an extended dry period. IMPLICATIONS FOR PUBLIC HEALTH: Intense summertime rain events, which are expected to increase in frequency in south-east Australia with climate change, can lead to significant increases in arboviral disease. These events need to be recognised by public health practitioners to facilitate timely public health response. This has taken on added importance since the emergence of Japanese encephalitis virus in southeastern Australia in 2022.
It is unclear whether West Nile virus (WNV) circulates endemically in Portugal. Despite the country’s adequate climate for transmission, Portugal has only reported four human WNV infections so far. We performed a review of WNV-related data (1966-2020), explored mosquito (2016-2019) and land type distributions (1992-2019), and used climate data (1981-2019) to estimate WNV transmission suitability in Portugal. Serological and molecular evidence of WNV circulation from animals and vectors was largely restricted to the south. Land type and climate-driven transmission suitability distributions, but not the distribution of WNV-capable vectors, were compatible with the North-South divide present in serological and molecular evidence of WNV circulation. Our study offers a comprehensive, data-informed perspective and review on the past epidemiology, surveillance and climate-driven transmission suitability of WNV in Portugal, highlighting the south as a subregion of importance. Given the recent WNV outbreaks across Europe, our results support a timely change towards local, active surveillance.
West Nile virus (WNV) is a member of the Japanese encephalitis serocomplex, which was first described in 1937 as neurotropic virus in Uganda in 1937. Subsequently, WNV was identified in the rest of the old-world and from 1999 in North America. Birds are the primary hosts, and WNV is maintained in a bird-mosquito-bird cycle, with pigs as amplifying hosts and humans and horses as incidental hosts. WNV transmission is warranted by mosquitoes, usually of the Culex spp., with a tendency to spill over when mosquitoes’ populations build up. Other types of transmissions have been described in endemic areas, as trough transplanted organs and transfused blood, placenta, maternal milk, and in some occupational settings. WNV infections in North America and Europe are generally reported during the summer and autumn. Extreme climate phenomena and soil degradation are important events which contribute to expansion of mosquito population and consequently to the increasing number of infections. Draught plays a pivotal role as it makes foul water standing in city drains and catch basins richer of organic material. The relationship between global warming and WNV in climate areas is depicted by investigations on 16,298 WNV cases observed in the United States during the period 2001-2005 that showed that a 5°C increase in mean maximum weekly temperature was associated with a 32-50% higher incidence of WNV infection. In Europe, during the 2022 season, an increase of WNV cases was observed in Mediterranean countries where 1,041 cases were reported based on ECDC data. This outbreak can be associated to the climate characteristics reported during this period and to the introduction of a new WNV-1 lineage. In conclusion, current climate change is causing an increase of mosquito circulation that supports the widest spread of some vector-borne virus including WNV diffusion in previously non-permissible areas. This warrant public health measures to control vectors circulation to reduce WNV and to screen blood and organ donations.
Dengue fever (DF) is a major public health concern in the Pakistan, and has been a significant cause of hospitalizations and deaths among males and females of all ages. Dengue viruses and their mosquito vectors are sensitive to their environment and temperature, rainfall and time of day have well-defined roles in the transmission cycle. Therefore changes in these conditions may contribute to increasing incidence. The present study was planned to investigate the impact of meteorological factors (rain fall, temperature and humidity) and vector indices ((container index (CI) and Breteau index (BI)) on the DF cases reported from three large and populated cities, Lahore (LHR), Faisalabad (FSD) and Rawalpindi (RWP), Punjab, Pakistan during 2017-2018. Dengue fever cases were recorded by visiting the study stations and cross-checked with data from the Punjab Information Technology Board (PITB), Lahore. Metrological data of FSD, LHR and RWP were obtained from the Pakistan Metrological Department (PMD). Most of the DF cases were reported after 62.5-106.5 mm rainfall, 22.1-30.25 degrees C temperature, and 53.5-73.5% relative humidity (r.h.) from FSD, LHR and RWP. CI and BI were significantly correlated (P < 0.01) with mean DF cases reported (BI: 0.824**, 0.000 and CI: 0.706**, 0.000). The r.h. at 5 pm also significantly correlated (P < 0.05) with BI (0.247*, 0.036) and CI (0.266*, 0.024). Maximum DF spread and cases were reported during May and September in FSD, October and November in LHR, and October in RWP during 2017 and 2018. Non availability of specific medicine and vaccine of dengue fever and dengue hemorrhagic fever, these indexes could be helpful in control programs to identify areas at high risk for dengue transmission and its significance can be used to halt the outbreak of dengue.
There are limited data on why the 2016 Zika outbreak in Miami-Dade County, Florida was confined to certain neighborhoods. In this research, Aedes aegypti, the primary vector of Zika virus, are studied to examine neighborhood-level differences in their population dynamics and underlying processes. Weekly mosquito data were acquired from the Miami-Dade County Mosquito Control Division from 2016 to 2020 from 172 traps deployed around Miami-Dade County. Using random forest, a machine learning method, predictive models of spatiotemporal dynamics of Ae. aegypti in response to meteorological conditions and neighborhood-specific socio-demographic and physical characteristics, such as land-use and land-cover type and income level, were created. The study area was divided into two groups: areas affected by local transmission of Zika during the 2016 outbreak and unaffected areas. Ae. aegypti populations in areas affected by Zika were more strongly influenced by 14- and 21-day lagged weather conditions. In the unaffected areas, mosquito populations were more strongly influenced by land-use and day-of-collection weather conditions. There are neighborhood-scale differences in Ae. aegypti population dynamics. These differences in turn influence vector-borne disease diffusion in a region. These results have implications for vector control experts to lead neighborhood-specific vector control strategies and for epidemiologists to guide vector-borne disease risk preparations, especially for containing the spread of vector-borne disease in response to ongoing climate change.
Tick-borne encephalitis (TBE) is endemic in several European countries, and its incidence has recently increased. Various factors may explain this phenomenon: social factors (changes in human behavior, duration and type of leisure activities and increased tourism in European high-risk areas), ecological factors (e.g., effects of climate change on the tick population and reservoir animals), and technological factors (improved diagnostics, increased medical awareness). Furthermore, the real burden of TBE is not completely known, as the performance of surveillance systems is suboptimal and cases of disease are under-reported in several areas. Given the potentially severe clinical course of the disease, the absence of any antiviral therapy, and the impossibility of interrupting the transmission of the virus in nature, vaccination is the mainstay of prevention and control. TBE vaccines are effective (protective effect of approximately 95% after completion of the basic vaccination-three doses) and well tolerated. However, their uptake in endemic areas is suboptimal. In the main endemic countries where vaccination is included in the national/regional immunization program (with reimbursed vaccination programs), this decision was driven by a cost-effectiveness assessment (CEA), which is a helpful tool in the decision-making process. All CEA studies conducted have demonstrated the cost-effectiveness of TBE vaccination. Unfortunately, CEA is still lacking in many endemic countries, including Italy. In the future, it will be necessary to fill this gap in order to introduce an effective vaccination strategy in endemic areas. Finally, raising awareness of TBE, its consequences and the benefit of vaccination is critical in order to increase vaccination coverage and reduce the burden of the disease.
Dengue is a re-emerging neglected disease of major public health importance. This review highlights important considerations for dengue disease in Africa, including epidemiology and underestimation of disease burden in African countries, issues with malaria misdiagnosis and co-infections, and potential evidence of genetic protection from severe dengue disease in populations of African descent. The findings indicate that dengue virus prevalence in African countries and populations may be more widespread than reported data suggests, and that the Aedes mosquito vectors appear to be increasing in dissemination and number. Changes in climate, population, and plastic pollution are expected to worsen the dengue situation in Africa. Dengue misdiagnosis is also a problem in Africa, especially due to the typical non-specific clinical presentation of dengue leading to misdiagnosis as malaria. Finally, research suggests that a protective genetic component against severe dengue exists in African descent populations, but further studies should be conducted to strengthen this association in various populations, taking into consideration socioeconomic factors that may contribute to these findings. The main takeaway is that Africa should not be overlooked when it comes to dengue, and more attention and resources should be devoted to this disease in Africa.
The annual death statistics due to vector-borne diseases transmitted by Aedes mosquitoes cause a still growing concern for the public health in the affected regions. An improved understanding of how climatic and population changes impact the spread of Aedes aegypti will help estimate the future populations exposure and vulnerability, and is essential to the improvement of public health preparedness. We apply an empirically well-investigated process-based mathematical model based on the life cycle of the mosquito to assess how climate scenarios (Representative Concentration Pathways (RCP)) and population scenarios (Shared Socioeconomic Pathways (SSP)) will affect the growth and potential distribution of this mosquito in China. Our results show that the risk area is predicted to expand considerably, increasing up to 21.46% and 24.75% of China’s land area in 2050 and 2070, respectively, and the new added area lies mainly in the east and center of China. The population in the risk area grows substantially up to 2050 and then drops down steadily. However, these predicted changes vary noticeably among different combinations between RCPs and SSPs with the RCP2.6*SSP4 yielding the most favorable scenario in 2070, representing approximately 14.11% of China’s land area and 113 cities at risk, which is slightly lower compared to 2019. Our results further reveal that there is a significant trade-off between climatic and human population impacts on the spreading of Aedes aegypti, possibly leading to an overestimation (underestimation) in sparsely (densely) populated areas if the populations impact on the mosquito’s life history is unaccounted for. These results suggest that both climate and population changes are crucial factors in the formation of the populations exposure to Aedes-borne virus transmission in China, however, a reduced population growth rate may slow down the spread of this mosquito by effectively counteracting the climate warming impacts.
With global warming and socioeconomic developments, there is a tendency toward the emergence and spread of mountain-type zoonotic visceral leishmaniasis (MT-ZVL) in China. Timely identification of the transmission risk and spread of MT-ZVL is, therefore, of great significance for effectively interrupting the spread of MT-ZVL and eliminating the disease. In this study, 26 environmental variables-namely, climatic, geographical, and 2 socioeconomic indicators were collected from regions where MT-ZVL patients were detected during the period from 2019 to 2021, to create 10 ecological niche models. The performance of these ecological niche models was evaluated using the area under the receiver-operating characteristic curve (AUC) and true skill statistic (TSS), and ensemble models were created to predict the transmission risk of MT-ZVL in China. All ten ecological niche models were effective at predicting the transmission risk of MT-ZVL in China, and there were significant differences in the mean AUC (H = 33.311, p < 0.05) and TSS values among these ten models (H = 26.344, p < 0.05). The random forest, maximum entropy, generalized boosted, and multivariate adaptive regression splines showed high performance at predicting the transmission risk of MT-ZVL (AUC > 0.95, TSS > 0.85). Ensemble models predicted a transmission risk of MT-ZVL in the provinces of Shanxi, Shaanxi, Henan, Gansu, Sichuan, and Hebei, which was centered in Shanxi Province and presented high spatial clustering characteristics. Multiple ensemble ecological niche models created based on climatic and environmental variables are effective at predicting the transmission risk of MT-ZVL in China. This risk is centered in Shanxi Province and tends towards gradual radiation dispersion to surrounding regions. Our results provide insights into MT-ZVL surveillance in regions at high risk of MT-ZVL.
Ticks are able to transmit the highest number of pathogen species of any blood-feeding arthropod and represent a growing threat to public health and agricultural systems worldwide. While there are numerous and varied causes and effects of changes to tick-borne disease (re)emergence, three primary challenges to tick control were identified in this review from a U.S. borders perspective. (1) Climate change is implicated in current and future alterations to geographic ranges and population densities of tick species, pathogens they can transmit, and their host and reservoir species, as highlighted by Ixodes scapularis and its expansion across southern Canada. (2) Modern technological advances have created an increasingly interconnected world, contributing to an increase in invasive tick species introductions through the increased speed and frequency of trade and travel. The introduction of the invasive Haemaphysalis longicornis in the eastern U.S. exemplifies the challenges with control in a highly interconnected world. (3) Lastly, while not a new challenge, differences in disease surveillance, control, and management strategies in bordering countries remains a critical challenge in managing ticks and tick-borne diseases. International inter-agency collaborations along the U.S.-Mexico border have been critical in control and mitigation of cattle fever ticks (Rhipicephalus spp.) and highlight the need for continued collaboration and research into integrated tick management strategies. These case studies were used to identify challenges and opportunities for tick control and mitigation efforts through a One Health framework.
Tick-borne diseases are a major health problem worldwide and could become even more important in Europe in the future. Due to changing climatic conditions, ticks are assumed to be able to expand their ranges in Europe towards higher latitudes and altitudes, which could result in an increased occurrence of tick-borne diseases.There is a great interest to identify potential (new) areas of distribution of vector species in order to assess the future infection risk with vector-borne diseases, improve surveillance, to develop more targeted monitoring program, and, if required, control measures.Based on an ecological niche modelling approach we project the climatic suitability for the three tick species Ixodes ricinus, Dermacentor reticulatus and Dermacentor marginatus under current and future climatic conditions in Europe. These common tick species also feed on humans and livestock and are vector competent for a number of pathogens.For niche modelling, we used a comprehensive occurrence data set based on several databases and publications and six bioclimatic variables in a maximum entropy approach. For projections, we used the most recent IPCC data on current and future climatic conditions including four different scenarios of socio-economic developments.Our models clearly support the assumption that the three tick species will benefit from climate change with projected range expansions towards north-eastern Europe and wide areas in central Europe with projected potential co-occurrence.A higher tick biodiversity and locally higher abundances might increase the risk of tick-borne diseases, although other factors such as pathogen prevalence and host abundances are also important.
Crimean-Congo haemorrhagic fever (CCHF) is a zoonotic disease caused by the Crimean-Congo hemorrhagic fever virus (CCHFV). Ticks of the genus Hyalomma are the main vectors and represent a reservoir for the virus. CCHF is maintained in nature in an endemic vertebrate-tick-vertebrate cycle. The disease is prevalent in wide geographical areas including Asia, Africa, South-Eastern Europe and the Middle East. It is of great importance for the public health given its occasionally high case/fatality ratio of CCHFV in humans. Climate change and the detection of possible CCHFV vectors in Central Europe suggest that the establishment of the transmission in Central Europe may be possible in future. We have developed a compartment-based nonlinear Ordinary Differential Equation (ODE) system to model the disease transmission cycle including blood sucking ticks, livestock and human. Sensitivity analysis of the basic reproduction number R0 shows that decreasing the tick survival time is an efficient method to control the disease. The model supports us in understanding the influence of different model parameters on the spread of CCHFV. Tick-to-tick transmission through co-feeding and the CCHFV circulation through transstadial and transovarial transmission are important factors to sustain the disease cycle. The proposed model dynamics are calibrated through an empirical multi-country analysis and multidimensional plot reveals that the disease-parameter sets of different countries burdened with CCHF are different. This information may help decision makers to select efficient control strategies.
Chagas disease, caused by the protozoa Trypanosoma cruzi, is an important yet neglected disease that represents a severe public health problem in the Americas. Although the alteration of natural habitats and climate change can favor the establishment of new transmission cycles for T. cruzi, the compound effect of human-modified landscapes and current climate change on the transmission dynamics of T. cruzi has until now received little attention. A better understanding of the relationship between these factors and T. cruzi presence is an important step towards finding ways to mitigate the future impact of this disease on human communities. Here, we assess how wild and domestic cycles of T. cruzi transmission are related to human-modified landscapes and climate conditions (LUCC-CC). Using a Bayesian datamining framework, we measured the correlations among the presence of T. cruzi transmission cycles (sylvatic, rural, and urban) and historical land use, land cover, and climate for the period 1985 to 2012. We then estimated the potential range changes of T. cruzi transmission cycles under future land-use and -cover change and climate change scenarios for 2050 and 2070 time-horizons, with respect to “green” (RCP 2.6), “business-as-usual” (RCP 4.5), and “worst-case” (RCP 8.5) scenarios, and four general circulation models. Our results show how sylvatic and domestic transmission cycles could have historically interacted through the potential exchange of wild triatomines (insect vectors of T. cruzi) and mammals carrying T. cruzi, due to the proximity of human settlements (urban and rural) to natural habitats. However, T. cruzi transmission cycles in recent times (i.e., 2011) have undergone a domiciliation process where several triatomines have colonized and adapted to human dwellings and domestic species (e.g., dogs and cats) that can be the main blood sources for these triatomines. Accordingly, Chagas disease could become an emerging health problem in urban areas. Projecting potential future range shifts of T. cruzi transmission cycles under LUCC-CC scenarios we found for RCP 2.6 no expansion of favourable conditions for the presence of T. cruzi transmission cycles. However, for RCP 4.5 and 8.5, a significant range expansion of T. cruzi could be expected. We conclude that if sustainable goals are reached by appropriate changes in socio-economic and development policies we can expect no increase in suitable habitats for T. cruzi transmission cycles.
Background: Dengue hemorrhagic fever (DHF) was one of infectious diseases that is still a concern in Indonesia, especially the Manado city. This study aimed to analyze the variability of temperature, rainfall, humidity, and the incidence of DHF in Manado city 2011-2020. Method: This ecological research used secondary data obtained from the Health Office and the Central Bureau of Statistics of the Manado City. The variables studied were air temperature, humidity, rainfall and the incidence of DHF in Manado city 2011-2020. Result: in 2016 there were 567 cases of dengue fever and the highest was in the Malalayang sub-district and the lowest was in the Ranomut sub-district. In 2017 there was a significant decrease to 139 cases, the highest in Malalayang sub-district with 32 cases, and the lowest in Bunaken sub-district with 1 case. In 2018, there was an increase in cases by 294 cases, the highest was in the Malalayang sub-district. The air temperature continues to fluctuate where based on the trendline it is found that the air temperature tends to increase. Whereas, the humidity tends to decrease. The rainfall in the city of Manado continues to fluctuate, where based on the trendline it is found that rainfall tends to decrease. Mosquitoes are cold-blooded animals and their metabolic processes or life cycles depend on the environment’s temperature. The DHF cases continue to fluctuate (up and down) where based on the trendline it is found that DHF cases tend to decrease Conclusion: In the period 2011-2020 in Manado City, air temperature tends to increase but rainfall, humidity, and cases of DHF tend to decrease.
The increasing incidence of arboviral diseases in tropical endemic areas and their emergence in new temperate countries is one of the most important challenges that Public Health agencies are currently facing. Because mosquitoes are poikilotherms, shifts in temperature influence physiological functions besides egg viability. These traits impact not only vector density, but also their interaction with their hosts and arboviruses. As such the relationship among mosquitoes, arboviral diseases and temperature is complex. Here, we summarize current knowledge on the thermal biology of Aedes invasive mosquitoes, highlighting differences among species. We also emphasize the need to expand knowledge on the variability in thermal sensitivity across populations within a species, especially in light of climate change that encompasses increase not only in mean environmental temperature but also in the frequency of hot and cold snaps. Finally, we suggest a novel experimental approach to investigate the molecular architecture of thermal adaptation in mosquitoes.
Chikungunya fever, an acute infectious disease caused by Chikungunya virus (CHIKV), is transmitted by Aedes aegypti mosquitoes, with fever, rash, and joint pain as the main features. 1952, the first outbreak of Chikungunya fever was in Tanzania, Africa, and the virus was isolated in 1953. The epidemic has expanded from Africa to South Asia, the Indian Ocean islands and the Americas, and is now present in more than 100 countries and territories worldwide, causing approximately 1 million infections worldwide each year. In addition, fatal cases have been reported, making CHIKV a relevant public health disease. The evolution of the virus, globalization, and climate change may have contributed to the spread of CHIKV. 2005-2006 saw the most severe outbreak on Reunion Island, affecting nearly 35% of the population. Since 2005, cases of Chikungunya fever have spread mainly in tropical and subtropical regions, eventually reaching the Americas through the Caribbean island. Today, CHIKV is widely spread worldwide and is a global public health problem. In addition, the lack of a preventive vaccine and approved antiviral treatment makes CHIKV a major global health threat. In this review, we discuss the current knowledge on the pathogenesis of CHIKV, focusing on the atypical disease manifestations. We also provide an updated review of the current development of CHIKV vaccines. Overall, these aspects represent some of the most recent advances in our understanding of CHIKV pathogenesis and also provide important insights into the current development of CHIKV and potential CHIKV vaccines for current development and clinical trials.
BACKGROUND: Meteorological factors can affect the emergence of scrub typhus for a period lasting days to weeks after their occurrence. Furthermore, the relationship between meteorological factors and scrub typhus is complicated because of lagged and non-linear patterns. Investigating the lagged correlation patterns between meteorological variables and scrub typhus may promote an understanding of this association and be beneficial for preventing disease outbreaks. METHODS: We extracted data on scrub typhus cases in rural areas of Panzhihua in Southwest China every week from 2008 to 2017 from the China Information System for Disease Control and Prevention. The distributed lag non-linear model (DLNM) was used to study the temporal lagged correlation between weekly meteorological factors and weekly scrub typhus. RESULTS: There were obvious lagged associations between some weather factors (rainfall, relative humidity, and air temperature) and scrub typhus with the same overall effect trend, an inverse-U shape; moreover, different meteorological factors had different significant delayed contributions compared with reference values in many cases. In addition, at the same lag time, the relative risk increased with the increase of exposure level for all weather variables when presenting a positive association. CONCLUSIONS: The results found that different meteorological factors have different patterns and magnitudes for the lagged correlation between weather factors and scrub typhus. The lag shape and association for meteorological information is applicable for developing an early warning system for scrub typhus.
Over the last two decades there has been an increase in outbreaks of arboviral diseases, being Spain at high risk for disease emergence. This paper reviews the current evidence regarding the transmissibility, disease epidemiology, control strategies and mosquito-borne disease drivers and maintaining factors in Spain. There is risk of autochthonous cases and outbreaks in Spain due to recent transmission occurrence. Recently, there has been an expansion of Aedes Albopticus, a vector for Dengue, Zika and Chikungunya; and Cullex spp., vector for West Nile Virus, already endemic in Spain. Their establishment has been facilitated by climate and environmental drivers. If climate change projections are to be met, an increase in disease transmission is to be expected, as well as the re-establishment of other vectors such as Aedes Aegypti. Our review supports the need to understand the threat of these emerging diseases and implement preventive strategies in order to minimise their impact.
Anopheles stephensi is an invasive Asian malaria vector that initially emerged in Africa in 2012 and was reported in Sudan in 2019. We investigated the distribution and population structure of An. stephensi throughout Sudan by using sequencing and molecular tools. We confirmed the presence of An. stephensi in eight border-states, identifying both natural and human-made breeding sites. Our analysis revealed the presence of 20 haplotypes with different distributions per state. This study revealed a countrywide spread of An. stephensi in Sudan, with confirmed presence in borders states with Chad, Egypt, Eritrea, Ethiopia, Libya, Republic of Central Africa, and South Sudan. Detection of An. stephensi at points of entry with these countries, particularly Chad, Libya, and South Sudan, indicates the rapid previously undetected spread of this invasive vector. Our phylogenetic and haplotype analysis suggested local establishment and evolutionary adaptation of the vector to different ecological and environmental conditions in Sudan. Urgent engagement of the global community is essential to control and prevent further spread into Africa.
BACKGROUND: The influence of rising global temperatures on malaria dynamics and distribution remains controversial, especially in central highland regions. We aimed to address this subject by studying the spatiotemporal heterogeneity of malaria and the effect of climate change on malaria transmission over 27 years in Hainan, an island province in China. METHODS: For this longitudinal cohort study, we used a decades-long dataset of malaria incidence reports from Hainan, China, to investigate the pattern of malaria transmission in Hainan relative to temperature and the incidence at increasing altitudes. Climatic data were obtained from the local meteorological stations in Hainan during 1984-2010 and the WorldClim dataset. A temperature-dependent R(0) model and negative binomial generalised linear model were used to decipher the relationship between climate factors and malaria incidence in the tropical region. FINDINGS: Over the past few decades, the annual peak incidence has appeared earlier in the central highland regions but later in low-altitude regions in Hainan, China. Results from the temperature-dependent model showed that these long-term changes of incidence peak timing are linked to rising temperatures (of about 1·5°C). Further, a 1°C increase corresponds to a change in cases of malaria from -5·6% (95% CI -4·5 to -6·6) to -9·2% (95% CI -7·6 to -10·9) from the northern plain regions to the central highland regions during the rainy season. In the dry season, the change in cases would be 4·6% (95% CI 3·7 to 5·5) to 11·9% (95% CI 9·8 to 14·2) from low-altitude areas to high-altitude areas. INTERPRETATION: Our study empirically supports the idea that increasing temperatures can generate opposing effects on malaria dynamics for lowland and highland regions. This should be further investigated and incorporated into future modelling, disease burden calculations, and malaria control, with attention for central highland regions under climate change. FUNDING: Scientific and Technological Innovation 2030: Major Project of New Generation Artificial Intelligence, National Natural Science Foundation of China, Beijing Natural Science Foundation, National Key Research and Development Program of China, Young Elite Scientist Sponsorship Program by CAST, Research on Key Technologies of Plague Prevention and Control in Inner Mongolia Autonomous Region, and Beijing Advanced Innovation Program for Land Surface Science.
Dengue is a harmful tropical disease that causes death to many people. Currently, the dengue vaccine development is still at an early stage, and only intervention methods exist after dengue cases increase. Thus, previously, two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system. Successfully, an Autoregressive Distributed Lag (ADL) Model was developed using three factors: the epidemiological, entomological, and environmental with an accuracy of 85%; but a higher percentage is required in minimizing the error for the model to be useful. Hence, this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90% accuracy to be embedded in an early warning computer system using the Internet of Things (IoT) approach. Eighty-one weeks of time series data of the three factors were used in six forecasting models, which were Autoregressive Distributed Lag (ADL), Hierarchical Forecasting (Bottom-up and Optimal combination) and three Machine Learning methods: (Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest). Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance. The findings indicated Random Forest outperformed the other models with an accuracy of 95% when including all three factors. But practically, collecting mosquito related data (the entomological factor) was very costly and time consuming. Thus, it was removed from the model, and the accuracy dropped to 92% but still high enough to be of practical use, i.e., beyond 90%. However, the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall. Hence, further analysis was conducted in determining the location of the rain gauges. This has led to the recommendation that the rain gauges should be located about 3-4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system. Therefore, this early warning system can save lives, and prevention is better than cure.
An increase in the annual daily temperature is documented and predicted to occur in the coming decades. Climate change has a direct effect and adverse impact on human health, as well as on multiple ecosystems and their species. The purpose of this paper is to review the effect of climate change on neglected tropical diseases including leishmaniasis, schistosomiasis, and lymphatic filariasis in the Eastern Mediterranean Region (EMR). A list of engine web searches was done; 280 full-text records were assessed for eligibility. Only 48 original records were included within the final selection for the review study. Most research results show an alteration of neglected diseases related to climate change influencing specifically the Eastern Mediterranean Region, in addition to the expectation of more effects at the level of vectors and reservoir whether its vector transmission route or its egg hatching and replication or even the survival of adult worms in the coming years. At the same time, not all articles related to the region interpret the direct or indirect effect of climate variations on these specific diseases. Although few studies were found describing some of climate change effects on neglected tropical diseases in the region, still, the region lacks research funding, technical, and mathematical model expertise regarding the direct effect of climate change on the ecosystems of these neglected tropical diseases.
Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements.
The rapid pace of urbanization makes it imperative that we better understand the influence of climate forcing on urban malaria transmission. Despite extensive study of temperature effects in vector-borne infections in general, consideration of relative humidity remains limited. With process-based dynamical models informed by almost two decades of monthly surveillance data, we address the role of relative humidity in the interannual variability of epidemic malaria in two semi-arid cities of India. We show a strong and significant effect of humidity during the pre-transmission season on malaria burden in coastal Surat and more arid inland Ahmedabad. Simulations of the climate-driven transmission model with the MLE (Maximum Likelihood Estimates) of the parameters retrospectively capture the observed variability of disease incidence, and also prospectively predict that of ‘out-of-fit’ cases in more recent years, with high accuracy. Our findings indicate that relative humidity is a critical factor in the spread of urban malaria and potentially other vector-borne epidemics, and that climate change and lack of hydrological planning in cities might jeopardize malaria elimination efforts.
PURPOSE OF REVIEW: This review seeks to identify factors contributing to the changing epidemiology of Chagas disease in the United States of America (US). By showcasing screening programs for Chagas disease that currently exist in endemic and non-endemic settings, we make recommendations for expanding access to Chagas disease diagnosis and care in the US. RECENT FINDINGS: Several factors including but not limited to increasing migration, climate change, rapid population growth, growing urbanization, changing transportation patterns, and rising poverty are thought to contribute to changes in the epidemiology of Chagas disease in the US. Outlined are some examples of successful screening programs for Chagas disease in other countries as well as in some areas of the US, notably those which focus on screening high-risk populations and are linked to affordable and effective treatment options. SUMMARY: Given concerns that Chagas disease prevalence and even risk of transmission may be increasing in the US, there is a need for improving detection and treatment of the disease. There are many successful screening programs in place that can be replicated and/or expanded upon in the US. Specifically, we propose integrating Chagas disease into relevant clinical guidelines, particularly in cardiology and obstetrics/gynecology, and using advocacy as a tool to raise awareness of Chagas disease.
Tick-borne diseases are among the challenges associated with warming climate. Many studies predict, and already note, expansion of ticks’ habitats to the north, bringing previously non-endemic diseases, such as borreliosis and encephalitis, to the new areas. In addition, higher temperatures accelerate phases of ticks’ development in areas where ticks have established populations. Earlier works have shown that meteorological parameters, such as temperature and humidity influence ticks’ survival and define their areas of habitat. Here, we study the link between climatic parameters and tick-related hospital visits as well as borreliosis incidence rates focusing on European Russia. We have used yearly incidence rates of borreliosis spanning a period of 20 years (1997-2016) and weekly tick-related hospital visits spanning two years (2018-2019). We identify regions in Russia characterized by similar dynamics of incidence rates and dominating tick species. For each cluster, we find a set of climatic parameters that are significantly correlated with the incidence rates, though a linear regression approach using exclusively climatic parameters to incidence prediction was less than 50% effective. On a weekly timescale, we find correlations of different climatic parameters with hospital visits. Finally, we trained two long short-term memory neural network models to project the tick-related hospital visits until the end of the century, under the RCP8.5 climate scenario, and present our findings in the evolution of the tick season length for different regions in Russia. Our results show that the regions with an expected increase in both tick season length and borreliosis incidence rates are located in the southern forested areas of European Russia. Oppositely, our projections suggest no prolongation of the tick season length in the northern areas with already established tick population.
BACKGROUND AND AIM: Dengue hemorrhagic fever (DHF) is an infectious disease caused by the dengue virus (DENV) and is transmitted through the bite of the Aedes aegypti and Aedes albopictus mosquitoes. This study aims to analyze the relationship between the incidence of DHF which can be influenced by climatic factors in the same month (non-time lag), climatic factors with a lag of 1 month (time lag 1), climatic factors with a lag of 2 months (time lag 2), population density, and vector density. METHODS: The study design used is an ecological study. The data is sourced from the South Jakarta City Administration of Health, the South Jakarta City Administration of Central Statistics, and the Meteorology, Climatology and Geophysics Agency. Data were analyzed using correlation test. RESULTS: The results showed that the incidence of DHF was related to non-time lag rainfall, time lag 1, and time lag 2, air temperature time lag 2, air humidity non-time lag, time lag 1, and time lag 2, population density, and numbers of mosquito’s larvae free index (ABJ). CONCLUSIONS: DHF is still a disease that needs to be watched out for in the South Jakarta Administrative City, requiring the government and the people of the South Jakarta Administration to continue to increase efforts to prevent and control DHF.
Dirofilaria repens is a nematode affecting domestic and wild canids, transmitted by several species of mosquitoes of different genera. It usually causes a non-pathogenic subcutaneous infection in dogs and is the principal agent of human dirofilariasis in the Old World. The geographic distribution of D. repens is changing rapidly, and several factors contribute to the spread of the infection to non-endemic areas. A mathematical model for transmission of Dirofilaria spp. was built, using a system of ordinary differential equations that consider the interactions between reservoirs, vectors, and humans. The transmission simulations of D. repens were carried out considering a projection in time, with intervals of 15 and 100 years. For the dynamics of the vector, seasonal variations were presented as series with quarter periodicity during the year. The results of the simulations highlight the peak of contagions in the reservoir and in humans, a product of the action of the vector when it remains active throughout the year. A 300% infection increase in the reservoir was observed during the first decade and remains present in the population with a representative number of cases. When the vector maintains its density and infectivity during the year, the incidence of the infection in humans increases. Accumulated cases amount to 45 per 100,000 inhabitants, which corresponds to a cumulative incidence of 0.05%, in 85 years. This indicates that early prevention of infection in canids would significantly reduce the disease, also reducing the number of accumulated cases of human dirofilariasis by D. repens. The interaction between the simulations generated by the model highlights the sensitivity of the epidemiological curve to the periodicity of seasonality, reaffirming the hypothesis of the probability of movement of the zoonotic disease to non-endemic areas, due to climate change.
Malaria is a vector-borne disease, likely to be affected by climate change. In this study, general circulation model (GCM)-based scenarios were used for projecting future climate patterns and malaria incidence by artificial neural networks (ANN) in Zahedan district, Iran. Daily malaria incidence data of Zahedan district from 2000 to 2019 were inquired. The gamma test was used to select the appropriate combination of parameters for nonlinear modeling. The future climate pattern projections were obtained from HadGEM2-ES. The output was downscaled using LARS-WG stochastic weather generator under two Representative Concentration Pathway (RCP2.6 and RCP8.5) scenarios. The effect of climate change on malaria transmission for 2021-2060 was simulated by ANN. The designed model indicated that the future climate in Zahedan district will be warmer, more humid, and with more precipitation. Assessment of the potential impact of climate change on the incidence of malaria by ANN showed the number of malaria cases in Zahedan under both scenarios (RCP2.6 and RCP 8.5). It should be noted that due to the lack of daily malaria data before 2013, monthly data from 2000 were used only for initial analysis; and in preprocessing and simulation analyses, the daily malaria data from 2013 to 2019 were used. Therefore, if proper interventions are not implemented, malaria will continue to be a health issue in this region.
BACKGROUND: Weather fluctuation affects the incidence of malaria through a network of causuative pathays. Globally, human activities have ultered weather conditions over time, and consequently the number of malaria cases. This study aimed at determining the influence of humidity, temperature and rainfall on malaria incidence in an inland (Muyuka) and a coastal (Tiko) settings for a period of seven years (2011-2017) as well as predict the number of malaria cases two years after (2018 and 2019). METHODS: Malaria data for Muyuka Health District (MHD) and Tiko Health District (THD) were obtained from the Regional Delegation of Public Health and Tiko District Health service respectively. Climate data for MHD was obtained from the Regional Delegation of Transport while that of THD was gotten from Cameroon Development Coorporation. Spearman rank correlation was used to investigate the relationship between number of malaria cases and the weather variables and the simple seasonal model was used to forecast the number of malaria cases for 2018 and 2019. RESULTS: The mean monthly rainfall, temperature and relative humidity for MHD were 200.38 mm, 27.05(0)C, 82.35% and THD were 207.36 mm, 27.57 °C and 84.32% respectively, with a total number of malaria cases of 56,745 and 40,160. In MHD, mean yearly humidity strongly correlated negatively with number of malaria cases (r = - 0.811, p = 0.027) but in THD, a moderate negative yearly correlation was observed (r = - 0.595, p = 0.159). In THD, the mean seasonal temperature moderately correlated (r = 0.599, p = 0.024) positively with the number of malaria cases, whereas MHD had a very weak negative correlation (r = - 0.174, p = 0.551). Likewise mean seasonal rainfall in THD moderately correlated (r = - 0.559, p = 0.038) negatively with malaria cases, contrary to MHD which showed a very weak positive correlation (r = 0.425, p = 0.130). The simple seasonal model predicted 6,842 malaria cases in Muyuka, for 2018 and same number for 2019, while 3167 cases were observed in 2018 and 2848 in 2019. Also 6,738 cases of malaria were predicted for MHD in 2018 likewise 2019, but 7327 cases were observed in 2018 and 21,735 cases in 2019. CONCLUSION: Humidity is the principal climatic variable that negatively influences malaria cases in MHD, while higher seasonal temperatures and lower seasonal rain fall significantly increase malaria cases in THD.
BACKGROUND: Dengue fever is the most common arboviral disease in humans, with an estimated 50-100 million annual infections worldwide. Dengue fever cases have increased substantially in the past four decades, driven largely by anthropogenic factors including climate change. More than half the population of Peru is at risk of dengue infection and due to its geography, Peru is also particularly sensitive to the effects of El Niño Southern Oscillation (ENSO). Determining the effect of ENSO on the risk for dengue outbreaks is of particular public health relevance and may also be applicable to other Aedes-vectored viruses. METHODS: We conducted a time-series analysis at the level of the district-month, using surveillance data collected from January 2000 to September 2018 from all districts with a mean elevation suitable to survival of the mosquito vector (<2,500m), and ENSO and weather data from publicly-available datasets maintained by national and international agencies. We took a Bayesian hierarchical modeling approach to address correlation in space, and B-splines with four knots per year to address correlation in time. We furthermore conducted subgroup analyses by season and natural region. RESULTS: We detected a positive and significant effect of temperature (°C, RR 1.14, 95% CI 1.13, 1.15, adjusted for precipitation) and ENSO (ICEN index: RR 1.17, 95% CI 1.15, 1.20; ONI index: RR 1.04, 95% CI 1.02, 1.07) on outbreak risk, but no evidence of a strong effect for precipitation after adjustment for temperature. Both natural region and season were found to be significant effect modifiers of the ENSO-dengue effect, with the effect of ENSO being stronger in the summer and the Selva Alta and Costa regions, compared with winter and Selva Baja and Sierra regions. CONCLUSIONS: Our results provide strong evidence that temperature and ENSO have significant effects on dengue outbreaks in Peru, however these results interact with region and season, and are stronger for local ENSO impacts than remote ENSO impacts. These findings support optimization of a dengue early warning system based on local weather and climate monitoring, including where and when to deploy such a system and parameterization of ENSO events, and provide high-precision effect estimates for future climate and dengue modeling efforts.
Malaysia Mosquito Autocidal Trap (MyMAT) is a green technology Aedes mosquito trap that does not use harmful chemical substances. This study aimed to evaluate the efficiency of MyMAT in reducing dengue cases and relating the cases to rainfall. An experimental field study was conducted for 42 weeks at Pangsapuri Nilam Sari, Shah Alam, Selangor. A total of 624 MyMAT was allocated at four blocks: inside each apartment and outside at the corridors in each level. Mosquito and rainfall data were collected weekly using MyMAT and a mobile rain gauge, respectively. The dengue cases data was retrieved from the e-dengue system obtained from the Malaysia Ministry of Health. The findings showed that MyMAT could catch 97% of Aedes mosquitoes and reduced dengue cases on average of 78%, indicating MyMAT is a reliable Aedes mosquito trap. Interestingly the findings also revealed a significant relationship between dengue cases, the number of Aedes mosquitoes, and rainfall. This week notified dengue cases increased when last two weeks mosquitoes increased due to previous two weeks rainfall increment. Thus indicating an indirect but significant relationship between this week notified dengue cases with the last four weeks rainfall. These relationships can be used in establishing a dengue outbreak forecasting model, which can act as an early warning system.
Aim: Climate and weather conditions play a crucial role in the dynamics and distribution of ticks and tick-borne diseases. In this study, we explored the influence of a heavy rainfall (flood) occurrence on the seasonal activity and density of host-seeking Haemaphysalis tick vectors in Wayanad district, Kerala, India. Methodology: Wayanad district in Kerala state was selected as the study area. Ticks were collected from December 2017 to May 2019, monthly for five consecutive days by dragging method. Tick density was analyzed with climate data obtained from the meteorological station. Results: The total number of ticks collected post-flood decreased to 59% in Kurichiyad (site 1) and 63% in Muthanga (site 2), and the seasonal nymphal peak density was shifted. A seasonal peak of tick activity was normally observed from December to February. This peak occurrence was missing after flood in the study areas created with waterlogging and vegetation overgrowth. Interpretation: The present study revealed the effect of flood events in the study sites with significant differences in the abundance of five Haemaphysalis tick species during pre and post-flood periods and forest and wildlife habitats. This difference in the changing climatic conditions and increasing annual flood seasons in the Western Ghats may shift this region’s ticks questing activity and tick-borne disease ecology.
It has great importance to study the potential effects of climate change on Plasmodium vivax malaria in Greece because the country can be the origin of the spread of vivax malaria to the northern areas. The potential lengths of the transmission seasons of Plasmodium vivax malaria were forecasted for 2041-2060 and 2061-2080 and were combined. The potential ranges were predicted by Climate Envelope Modelling Method. The models show moderate areal increase and altitudinal shift in the malaria-endemic areas in Greece in the future. The length of the transmission season is predicted to increase by 1 to 2 months, mainly in the mid-elevation regions and the Aegean Archipelago. The combined factors also predict the decrease of vivax malaria-free area in Greece. It can be concluded that rather the elongation of the transmission season will lead to an increase of the malaria risk in Greece than the increase in the suitability values.
BACKGROUND: Symptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined. METHODS: We analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of <72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach. RESULTS: We included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84-0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV >90%). CONCLUSION: Supervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account-this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.
The Aedes aegypti mosquito is the main vector for several diseases of global importance, such as dengue and yellow fever. This species was first identified on Madeira Island in 2005, and between 2012 and 2013 was responsible for an outbreak of dengue that affected several thousand people. However, the potential distribution of the species on the island remains poorly investigated. Here we assess the suitability of current and future climatic conditions to the species on the island and complement this assessment with estimates of the suitability of land use and human settlement conditions. We used four modelling algorithms (boosted regression trees, generalized additive models, generalized linear models and random forest) and data on the distribution of the species worldwide and across the island. For both climatic and non-climatic factors, suitability estimates predicted the current distribution of the species with good accuracy (mean area under the Receiver Operating Characteristic curve = 0.88 ±0.06, mean true skill statistic = 0.72 ±0.1). Minimum temperature of coldest month was the most influential climatic predictor, while human population density, residential housing density and public spaces were the most influential predictors describing land use and human settlement conditions. Suitable areas under current climates are predicted to occur mainly in the warmer and densely inhabited coastal areas of the southern part of the island, where the species is already established. By mid-century (2041-2060), the extent of climatically suitable areas is expected to increase, mainly towards higher altitudes and in the eastern part of the island. Our work shows that ongoing efforts to monitor and prevent the spread of Ae. aegypti on Madeira Island will have to increasingly consider the effects of climate change.
Although previous research frequently indicates that climate factors impact dengue transmission, the results are inconsistent. Therefore, this systematic review and meta-analysis highlights and address the complex global health problems towards the human-environment interface and the inter-relationship between these variables. For this purpose, four online electronic databases were searched to conduct a systematic assessment of published studies reporting the association between dengue cases and climate between 2010 and 2022. The meta-analysis was conducted using random effects to assess correlation, publication bias and heterogeneity. The final assessment included eight studies for both systematic review and meta-analysis. A total of four meta-analyses were conducted to evaluate the correlation of dengue cases with climate variables, namely precipitation, temperature, minimum temperature and relative humidity. The highest correlation is observed for precipitation between 83 mm and 15 mm (r = 0.38, 95% CI = 0.31, 0.45), relative humidity between 60.5% and 88.7% (r = 0.30, 95% CI = 0.23, 0.37), minimum temperature between 6.5 °C and 21.4 °C (r = 0.28, 95% CI = 0.05, 0.48) and mean temperature between 21.0 °C and 29.8 °C (r = 0.07, 95% CI = -0.1, 0.24). Thus, the influence of climate variables on the magnitude of dengue cases in terms of their distribution, frequency, and prevailing variables was established and conceptualised. The results of this meta-analysis enable multidisciplinary collaboration to improve dengue surveillance, epidemiology, and prevention programmes.
Extrinsic environmental factors influence the spatiotemporal dynamics of many organisms, including insects that transmit the pathogens responsible for vector-borne diseases (VBDs). Temperature is an especially important constraint on the fitness of a wide variety of ectothermic insects. A mechanistic understanding of how temperature impacts traits of ectotherms, and thus the distribution of ectotherms and vector-borne infections, is key to predicting the consequences of climate change on transmission of VBDs like malaria. However, the response of transmission to temperature and other drivers is complex, as thermal traits of ectotherms are typically nonlinear, and they interact to determine transmission constraints. In this study, we assess and compare the effect of temperature on the transmission of two malaria parasites, Plasmodium falciparum and Plasmodium vivax, by two malaria vector species, Anopheles gambiae and Anopheles stephensi. We model the nonlinear responses of temperature dependent mosquito and parasite traits (mosquito development rate, bite rate, fecundity, proportion of eggs surviving to adulthood, vector competence, mortality rate, and parasite development rate) and incorporate these traits into a suitability metric based on a model for the basic reproductive number across temperatures. Our model predicts that the optimum temperature for transmission suitability is similar for the four mosquito-parasite combinations assessed in this study, but may differ at the thermal limits. More specifically, we found significant differences in the upper thermal limit between parasites spread by the same mosquito (A. stephensi) and between mosquitoes carrying P. falciparum. In contrast, at the lower thermal limit the significant differences were primarily between the mosquito species that both carried the same pathogen (e.g., A. stephensi and A. gambiae both with P. falciparum). Using prevalence data, we show that the transmission suitability metric S(T) calculated from our mechanistic model is consistent with observed P. falciparum prevalence in Africa and Asia but is equivocal for P. vivax prevalence in Asia, and inconsistent with P. vivax prevalence in Africa. We mapped risk to illustrate the number of months various areas in Africa and Asia predicted to be suitable for malaria transmission based on this suitability metric. This mapping provides spatially explicit predictions for suitability and transmission risk.
BACKGROUND: Dengue is endemic in the Philippines. Aedes aegypti is the primary vector. This study aimed to determine the hatching behavior and viability of Ae. aegypti first-generation (F1) eggs when exposed to temperature and photoperiod regimes under laboratory conditions. METHODS: Parental eggs were collected from selected highland and lowland sites in the Philippine big islands (Luzon, Visayas, and Mindanao) during the wet (2017-2018) and dry (2018) seasons. F1 egg cohorts were exposed separately in environmental chambers at 18, 25, and 38 °C with respective photoperiods for 6 weeks. Phenotypes (percent pharate larvae [PPL], hatch rates [HRs], and reproductive outputs [ROs]) were determined. RESULTS: Results of multivariate analyses of variance (MANOVA) between seasons showed significant main effects of temperature, season, and big island on all phenotypes across all sites. Significant interaction effects between seasons on all phenotypes across sites were shown between or among (1) season and big island, (2) season and temperature, (3) big island and temperature, (4) season, big island, and temperature, (5) big island, altitude, and temperature, and (6) season, big island, altitude, and temperature. Factors associated with the big islands might include their ecology, available breeding sites, and day lengths due to latitudinal differences, although they were not measured in the field. MANOVA results within each season on all phenotypes across sites showed (1) significant main effects of big island and temperature, and (2) significant interaction effects between big island and temperature within the wet season and (3) between temperature and photoperiod within the dry season. PPL were highest at 18 °C and were formed even at 38 °C in both seasons. Pharate larvae might play an adaptive role in global warming, expanded distribution to highlands, and preponderance to transmit human diseases. HRs in both seasons were highest at 25 °C and lowest at 38 °C. ROs were highest at 25 °C in the wet season and at 18 °C in the dry season. CONCLUSIONS: Temperature and latitude of Philippine big islands influenced the development-related phenotypes of Ae. aegypti in both seasons. The two seasons influenced the phenotypes and their interaction effects with big island and/or temperature and/or altitude. Recommendations include year-round enhanced 4S control strategies for mosquito vectors and water pipeline installation in rural highlands.
INTRODUCTION: Dengue fever disease is affected by many scoioeconomic and enviromental factors throughout endemic areas globally. These factors contribute to increase the incidence of endemic dengue endemic in Jeddah, Saudi Arabia. OBJECTIVES: This study aimed to investigate the distribution and spatial patterns of dengue fever cases in Jeddah, and to determine if there is an association between dengue fever and the following environmental factors: temperature, humidity, land cover, climate, rainfall, epicenter of reproduction, and socioeconomic factors. METHODS: A descriptive and analytical cross-sectional study was conducted in Jeddah in 2020. The study included all reported suspected and confirmed dengue cases. The sample size was 1458 cases. Data were obtained from the Dengue Active Surveillance System and the confirmed cases were geo-distributed in areas by QGIS. All significant variables were included in the logistic regression table. RESULTS: The majority (61.9 %) were suspected cases and 38.1 % confirmed cases. The majority of the cases were male. The highest spatial distribution was in the middle of Jeddah and the lowest in the south. The highest temporal distribution for confirmed cases was in June, and for suspected cases in December. Age, gender, occupation, and area were all significantly associated with the dengue reported cases. Most all the enviromental factors were not statistically significant. CONCLUSION: The study showed three clusters of dengue fever and infection concentrated in the middle and east of Jeddah. The lack of investigation in the environmental factors regarding the dengue distribution and its impact on the population area has to be taken seriously and dengue intervention programs should be implemented to reduce the endemic dengue in Jeddah.
A model for the Aedes aegypti lifecycle is developed that takes into account temperature-dependent maturation and death rates for several life stages, wet and dry egg oviposition with flooding, as well as three classes of larval habitat with different temperature profiles: outdoor (subject to external temperature fluctuations, human-inhabited), indoor (temperature moderated, human-inhabited, interior), and enclosed (temperature moderated, human free, exterior). An equilibrium analysis shows that the temperature range of outdoor viable equilibrium populations aligns closely with reported risk levels. Temperature patterns for El Paso, Texas; New York, New York; New Orleans, Louisiana; Orlando, Florida; and Miami, Florida, are considered. In four of these locations (all but New York), enclosed habitats can support mosquito populations even if all outdoor and indoor habitats are removed. In two locations (El Paso and New York) the model shows that in spite of the disappearance of adult mosquitoes during colder temperatures, populations reach seasonal steady state due to the survival of eggs. The results have implications for both vector and disease control.
BACKGROUND: Plasmodium vivax was endemic in northern Europe until the early twentieth century. Considering climate change and the recent emergence of other vector borne diseases in Europe, historical insight into the relationship between malaria and environmental factors in northern Europe is needed. This article describes malaria epidemiology in late-nineteenth century Denmark. METHODS: We described the seasonality and spatial patterns of malaria, and the relationship of the disease with environmental factors such as soil types, clay content and elevation for the period 1862-1914. We studied demographic and seasonal patterns and malaria mortality in the high-morbidity period of 1862-1880. Finally, we studied the relationship between malaria seasonality and temperature and precipitation using a Spearman correlation test. RESULTS: We found that the highest incidence occurred in eastern Denmark. Lolland-Falster medical region experienced the highest incidence (14.5 cases per 1000 pop.) and Bornholm medical region experienced the lowest incidence (0.57 cases per 1000 pop.). Areas with high malaria incidence also had high soil clay content, high agricultural production, and Lolland-Falster furthermore has a low elevation. Malaria incidence typically peaked in May and was associated with high temperatures in July and August of the previous year but not with precipitation. The case fatality rate was 0.17%, and the disease affected both sexes and all age groups except for infants. In 1873, a large epidemic occurred following flooding from a storm surge in November 1872. CONCLUSIONS: Malaria gradually declined in Denmark during our study period and had essentially disappeared by 1900. The high adult and low child morbidity in 1862-1880 indicates that malaria was not highly endemic in this period, as malaria is most frequent among children in highly endemic areas today. The association of high malaria incidence in spring with warmer temperatures in the previous summer suggests that transmission took place in the previous summers. The close geographical connection between malaria and soil types, agricultural production and elevation suggests that these factors are detrimental to sustain endemic malaria. Our findings of a close connection between malaria and environmental factors such as climate and geography provides insights to address potential reintroduction of malaria in temperate climates.
Zika virus is a mosquito-borne flavivirus known to cause severe birth defects and neuroimmunological disorders. We have previously demonstrated that mosquito transmission of Zika virus decreases with temperature. While transmission was optimized at 29°C, it was limited at cool temperatures (<22°C) due to poor virus establishment in the mosquitoes. Temperature is one of the strongest drivers of vector-borne disease transmission due to its profound effect on ectothermic mosquito vectors, viruses, and their interaction. Although there is substantial evidence of temperature effects on arbovirus replication and dissemination inside mosquitoes, little is known about whether temperature affects virus replication directly or indirectly through mosquito physiology. In order to determine the mechanisms behind temperature-induced changes in Zika virus transmission potential, we investigated different steps of the virus replication cycle in mosquito cells (C6/36) at optimal (28°C) and cool (20°C) temperatures. We found that the cool temperature did not alter Zika virus entry or translation, but it affected genome replication and reduced the amount of double-stranded RNA replication intermediates. If replication complexes were first formed at 28°C and the cells were subsequently shifted to 20°C, the late steps in the virus replication cycle were efficiently completed. These data suggest that cool temperature decreases the efficiency of Zika virus genome replication in mosquito cells. This phenotype was observed in the Asian lineage of Zika virus, while the African lineage Zika virus was less restricted at 20°C. IMPORTANCE With half of the human population at risk, arboviral diseases represent a substantial global health burden. Zika virus, previously known to cause sporadic infections in humans, emerged in the Americas in 2015 and quickly spread worldwide. There was an urgent need to better understand the disease pathogenesis and develop therapeutics and vaccines, as well as to understand, predict, and control virus transmission. In order to efficiently predict the seasonality and geography for Zika virus transmission, we need a deeper understanding of the host-pathogen interactions and how they can be altered by environmental factors such as temperature. Identifying the step in the virus replication cycle that is inhibited under cool conditions can have implications in modeling the temperature suitability for arbovirus transmission as global environmental patterns change. Understanding the link between pathogen replication and environmental conditions can potentially be exploited to develop new vector control strategies in the future.
Aedes albopictus is a competent vector of several arboviruses that has spread throughout the United States over the last three decades. With the emergence of Zika virus in the Americas in 2015-2016 and an increased need to understand the current distributions of Ae. albopictus in the US, we initiated surveillance efforts to determine the abundance of invasive Aedes species in Iowa. Here, we describe surveillance efforts from 2016 to 2020 in which we detect stable and persistent populations of Aedes albopictus in three Iowa counties. Based on temporal patterns in abundance and genetic analysis of mitochondrial DNA haplotypes between years, our data support that Ae. albopictus are overwintering and have likely become established in the state. The localization of Ae. albopictus predominantly in areas of urbanization, and noticeable absence in rural areas, suggests that these ecological factors may contribute to overwintering success. Together, these data document the establishment of Ae. albopictus in Iowa and their expansion into the Upper Midwest, where freezing winter temperatures were previously believed to limit their spread. With impending climate change, our study provides evidence for the further expansion of Ae. albopictus into temperate regions of the United States resulting in increased risks for vector-borne disease transmission.
Ticks pose an emerging threat of infectious pathogen transmission in the United States in part due to expanding suitable habitat ranges in the wake of climate change. Active and passive tick surveillance can inform maps of tick distributions to warn the public of their risk of exposure to ticks. In Colorado, widespread active surveillance programs have difficulty due to the state’s diverse terrain. However, combining multiple citizen science techniques can create a more accurate representation of tick distribution than any passive surveillance dataset alone. Our study uses county-level tick distribution data from Northern Arizona University, the Colorado Department of Public Health and the Environment, and veterinary surveillance in addition to literature data to assess the distribution of the Rocky Mountain wood tick, Dermacentor andersoni, and the American dog tick, Dermacentor variabilis. We found that D. andersoni for the most part inhabits counties at higher elevations than D. variabilis in Colorado.
BACKGROUND: In malaria endemic countries, seasonal malaria chemoprevention (SMC) interventions are performed during the high malaria transmission in accordance with epidemiological surveillance data. In this study we propose a predictive approach for tailoring the timing and number of cycles of SMC in all health districts of Mali based on sub-national epidemiological surveillance and rainfall data. Our primary objective was to select the best of two approaches for predicting the onset of the high transmission season at the operational scale. Our secondary objective was to evaluate the number of malaria cases, hospitalisations and deaths in children under 5 years of age that would be prevented annually and the additional cost that would be incurred using the best approach. METHODS: For each of the 75 health districts of Mali over the study period (2014-2019), we determined (1) the onset of the rainy season period based on weekly rainfall data; (ii) the onset and duration of the high transmission season using change point analysis of weekly incidence data; and (iii) the lag between the onset of the rainy season and the onset of the high transmission. Two approaches for predicting the onset of the high transmission season in 2019 were evaluated. RESULTS: In the study period (2014-2019), the onset of the rainy season ranged from week (W) 17 (W17; April) to W34 (August). The onset of the high transmission season ranged from W25 (June) to W40 (September). The lag between these two events ranged from 5 to 12 weeks. The duration of the high transmission season ranged from 3 to 6 months. The best of the two approaches predicted the onset of the high transmission season in 2019 to be in June in two districts, in July in 46 districts, in August in 21 districts and in September in six districts. Using our proposed approach would prevent 43,819 cases, 1943 hospitalisations and 70 deaths in children under 5 years of age annually for a minimal additional cost. Our analysis shows that the number of cycles of SMC should be changed in 36 health districts. CONCLUSION: Adapting the timing of SMC interventions using our proposed approach could improve the prevention of malaria cases and decrease hospitalisations and deaths. Future studies should be conducted to validate this approach.
INTRODUCTION: Despite the implementation of control strategies at the national scale, the malaria burden remains high in Mali, with more than 2.8 million cases reported in 2019. In this context, a new approach is needed, which accounts for the spatio-temporal variability of malaria transmission at the local scale. This study aimed to describe the spatio-temporal variability of malaria incidence and the associated meteorological and environmental factors in the health district of Kati, Mali. METHODS: Daily malaria cases were collected from the consultation records of the 35 health areas of Kati’s health district, for the period 2015-2019. Data on rainfall, relative humidity, temperature, wind speed, the normalized difference vegetation index, air pressure, and land use-land cover were extracted from open-access remote sensing sources, while data on the Niger River’s height and flow were obtained from the National Department of Hydraulics. To reduce the dimension and account for collinearity, strongly correlated meteorological and environmental variables were combined into synthetic indicators (SI), using a principal component analysis. A generalized additive model was built to determine the lag and the relationship between the main SIs and malaria incidence. The transmission periods were determined using a change-point analysis. High-risk clusters (hotspots) were detected using the SatScan method and were ranked according to risk level, using a classification and regression tree analysis. RESULTS: The peak of the malaria incidence generally occurred in October. Peak incidence decreased from 60 cases per 1000 person-weeks in 2015, to 27 cases per 1000 person-weeks in 2019. The relationship between the first SI (river flow and height, relative humidity, and rainfall) and malaria incidence was positive and almost linear. A non-linear relationship was found between the second SI (air pressure and temperature) and malaria incidence. Two transmission periods were determined per year: a low transmission period from January to July-corresponding to a persisting transmission during the dry season-and a high transmission period from July to December. The spatial distribution of malaria hotspots varied according to the transmission period. DISCUSSION: Our study confirmed the important variability of malaria incidence and found malaria transmission to be associated with several meteorological and environmental factors in the Kati district. The persistence of malaria during the dry season and the spatio-temporal variability of malaria hotspots reinforce the need for innovative and targeted strategies.
Malaria is one of the most widespread communicable diseases in the southeast regions of Iran, particularly the Chabahar County. Although the outbreak of this disease is a climate-related phenomenon, a comprehensive analysis of the malaria-climate relationship has not yet been investigated in Iran. The aims of this study are as follows: a) analyzing the seasonal characteristics of the various species of the infection; b) differentiating between number of patients during El Niño and La Niña and also during the wet and dry years. The monthly malaria statistics collected from twelve health centers were firstly averaged into seasonal scale and then composited with the corresponding data of the ground-based meteorological records, Southern Oscillation Index (SOI), and the satellite-based rainfall data. The proper statistical tests were used to detect differences in the number of patients between El Niño and La Niña and also between the adopted wet and dry episodes. Infection rate from the highest to the lowest was associated with summer, autumn, spring, and winter, respectively. Plasmodium falciparum, P. vivax, and the other species were responsible for 22%, 75%, and 3% of the sickness, respectively. The outbreak of P. falciparum/P. vivax occurs during autumn/summer. Due to the malaria eradication programs in urban areas, infection statistics collected from the rural areas were found to be more climate-related than that of urban regions. For rural/urban areas, the infection statistics exhibited a significant decline/increase during El Niño episodes. In autumn, spring, and winter, the patient number has significantly increased/decreased during the dry/wet years, respectively. These relationships were, however, reversed in summer.
INTRODUCTION: Malaria is a life-threatening acute febrile illness which is affecting the lives of millions globally. Its distribution is characterized by spatial, temporal, and spatiotemporal heterogeneity. Detection of the space-time distribution and mapping high-risk areas is useful to target hot spots for effective intervention. METHODS: Time series cross sectional study was conducted using weekly malaria surveillance data obtained from Amhara Public Health Institute. Poisson model was fitted to determine the purely spatial, temporal, and space-time clusters using SaTScan™ 9.6 software. Spearman correlation, bivariate, and multivariable negative binomial regressions were used to analyze the relation of the climatic factors to count of malaria incidence. RESULT: Jabitenan, Quarit, Sekela, Bure, and Wonberma were high rate spatial cluster of malaria incidence hierarchically. Spatiotemporal clusters were detected. A temporal scan statistic identified 1 risk period from 1 July 2013 to 30 June 2015. The adjusted incidence rate ratio showed that monthly average temperature and monthly average rainfall were independent predictors for malaria incidence at all lag-months. Monthly average relative humidity was significant at 2 months lag. CONCLUSION: Malaria incidence had spatial, temporal, spatiotemporal variability in West Gojjam zone. Mean monthly temperature and rainfall were directly and negatively associated to count of malaria incidence respectively. Considering these space-time variations and risk factors (temperature and rainfall) would be useful for the prevention and control and ultimately achieve elimination.
The disease dengue is associated with both mesoscale and synoptic scale meteorology. However, previous studies for south-east Asia have found a very limited association between synoptic variables and the reported number of dengue cases. Hence there is an urgent need to establish a more clear association with dengue incidence rates and the most relevant meteorological variables in order to institute an early warning system.& nbsp;This article develops a rigorous Bayesian modelling framework to identify the most important covariates and their lagged effects for constructing an early warning system for the Central Region of Malaysia where the case rates have increased substantially in the recent past. Our modelling includes multiple synoptic scale Nin tilde o indices, which are related to the phenomenon of El Nin tilde o Southern Oscillation (ENSO), along with other relevant mesoscale environmental measurements and an unobserved variable derived from reanalysis data. An empirically well validated hierarchical Bayesian spatio-temporal is used to build a probabilistic early warning system for detecting an upcoming dengue epidemic.& nbsp;Our study finds a 46.87% increase in dengue cases due to one degree increase in the central equatorial Pacific sea surface temperature with a lag time of six weeks. We discover the existence of a mild association with relative risk 0.9774 (CI: 0.9602, 0.9947) between the rate of cases and a distant lagged cooling effect in the region of coastal South America related to a phenomenon called El Nin tilde o Modoki. The Bayesian model also establishes that the synoptic meteorological drivers can enhance short-term early detection of dengue outbreaks and these can also potentially be used to provide longer-term forecasts.
BACKGROUND: The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico. METHODS: We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters. RESULTS: DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence. CONCLUSIONS: Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.
Dengue disease has become a major public health problem. Accurate and precise identification, prediction and mapping of high-risk areas are crucial elements of an effective and efficient early warning system in countering the spread of dengue disease. In this paper, we present the fusion area-cell spatiotemporal generalized geoadditive-Gaussian Markov random field (FGG-GMRF) framework for joint estimation of an area-cell model, involving temporally varying coefficients, spatially and temporally structured and unstructured random effects, and spatiotemporal interaction of the random effects. The spatiotemporal Gaussian field is applied to determine the unobserved relative risk at cell level. It is transformed to a Gaussian Markov random field using the finite element method and the linear stochastic partial differential equation approach to solve the “big n” problem. Sub-area relative risk estimates are obtained as block averages of the cell outcomes within each sub-area boundary. The FGG-GMRF model is estimated by applying Bayesian Integrated Nested Laplace Approximation. In the application to Bandung city, Indonesia, we combine low-resolution area level (district) spatiotemporal data on population at risk and incidence and high-resolution cell level data on weather variables to obtain predictions of relative risk at subdistrict level. The predicted dengue relative risk at subdistrict level suggests significant fine-scale heterogeneities which are not apparent when examining the area level. The relative risk varies considerably across subdistricts and time, with the latter showing an increase in the period January-July and a decrease in the period August-December.
BACKGROUND & OBJECTIVES: Weather and climate are directly linked to human health including the distribution and occurrence of vector-borne diseases which are of significant concern for public health. METHODS: In this review, studies on spatiotemporal distribution of dengue, Barmah Forest Virus (BFV) and Ross River Virus (RRV) in Australia and malaria in Papua New Guinea (PNG) under the influence of climate change and/ or human society conducted in the past two decades were analysed and summarised. Environmental factors such as temperature, rainfall, relative humidity and tides were the main contributors from climate. RESULTS: The Socio-Economic Indexes for Areas (SEIFA) index (a product from the Australian Bureau of Statistics that ranks areas in Australia according to relative socio-economic advantage and disadvantage) was important in evaluating contribution from human society. INTERPRETATION & CONCLUSION: For future studies, more emphasis on evaluation of impact of the El Niño-Southern Oscillation (ENSO) and human society on spatio-temporal distribution of vector borne diseases is recommended to highlight importance of the environmental factors in spreading mosquito-borne diseases in Australia and PNG.
Historically, visceral leishmaniasis (VL) in Italy was constrained to Mediterranean areas. However, in the last 20 years, sand fly vectors and human cases of VL have been detected in northern Italy, traditionally classified as a cold area unsuitable for sand fly survival. We aim to study the spatio-temporal pattern and climatic determinants of VL incidence in Italy. National Hospital Discharge Register records were used to identify incident cases of VL between 2009 and 2016. Incident rates were computed for each year (N = 8) and for each province (N = 110). Data on mean temperature and cumulative precipitation were obtained from the ERA5-Land re-analysis. Age- and sex-standardized incidence rates were modeled with Bayesian spatial and spatio-temporal conditional autoregressive Poisson models in relation to the meteo-climatic parameters. Statistical inference was based on Monte Carlo−Markov chains. We identified 1123 VL cases (incidence rate: 2.4 cases/1,000,000 person-years). The highest incidence rates were observed in southern Italy, even though some areas of northern Italy experienced high incidence rates. Overall, in the spatial analysis, VL incidence rates were positively associated with average air temperatures (β for 1 °C increase in average mean average temperature: 0.14; 95% credible intervals (CrI): 0.01, 0.27) and inversely associated with average precipitation (β for 20 mm increase in average summer cumulative precipitation: −0.28, 95% CrI: −0.42, −0.13). In the spatio-temporal analysis, no association between VL cases and season-year specific temperature and precipitation anomalies was detected. Our findings indicate that VL is endemic in the whole Italian peninsula and that climatic factors, such as air temperature and precipitation, might play a relevant role in shaping the geographical distribution of VL cases. These results support that climate change might affect leishmaniasis distribution in the future.
BACKGROUND: Cutaneous leishmaniasis (CL) is a vector-borne parasitic diseases of public health importance that is prevalent in the West Bank but not in the Gaza Strip. The disease caused by parasitic protozoans from the genus Leishmania and it is transmitted by infected phlebotomine sand flies. The aim of our study is to investigate the eco-epidemiological parameters and spatiotemporal projections of CL in Palestine over a 30-years period from 1990 through 2020 and to explore future projections until 2060. METHODOLOGY/PRINCIPAL FINDINGS: This long-term descriptive epidemiological study includes investigation of demographic characteristics of reported patients by the Palestinian Ministry of Health (PMoH). Moreover, we explored spatiotemporal distribution of CL including future projection based on climate change scenarios. The number of CL patients reported during this period was 5855 cases, and the average annual incidence rate (AAIR) was 18.5 cases/105 population. The male to female ratio was 1.25:1. Patients-age ranged from 2 months to 89 years (mean = 22.5, std 18.67, and the median was 18 years). More than 65% of the cases came from three governates in the West Bank; Jenin 29% (1617 cases), Jericho 25% (1403), and Tubas 12% (658) with no cases reported in the Gaza Strip. Seasonal occurrence of CL starts to increase in December and peaked during March and April of the following year. Current distribution of CL indicate that Jericho, Tubas, Jenin and Nablus have the most suitable climatic settings for the sandfly vectors. Future projections until 2060 suggest an increasing incidence from northwest of Jenin down to the southwest of Ramallah, disappearance of the foci in Jericho and Tubas throughout the Jordan Vally, and possible emergence of new foci in Gaza Strip. CONCLUSIONS/SIGNIFICANCE: The future projection of CL in Palestine until 2060 show a tendency of increasing incidence in the north western parts of the West Bank, disappearance from Jericho and Tubas throughout the Jordan Vally, and emergence of new CL endemic foci in the Gaza Strip. These results should be considered to implement effective control and surveillance systems to counteract spatial expansion of CL vectors.
Plague persists in the plague natural foci today. Although previous studies have found climate drives plague dynamics, quantitative analysis on animal plague risk under climate change remains understudied. Here, we analyzed plague dynamics in the Tibetan Plateau (TP) which is a climate-sensitive area and one of the most severe animal plague areas in China to disentangle variations in marmot plague enzootic foci, diffusion patterns, and their possible links with climate and anthropogenic factors. Specifically, we developed a time-sharing ecological niche modelling framework to identify finer potential plague territories and their temporal epidemic trends. Models were conducted by assembling animal records and multi-source ecophysiological variables with actual ecological effects (both climatic predictors and landscape factors) and driven by matching plague strains to periods corresponding to meteorological datasets. The models identified abundant animal plague territories over the TP and suggested the spatial patterns varied spatiotemporal dimension across the years, undergoing repeated spreading and contractions. Plague risk increased in the 1980s and 2000s, with the risk area increasing by 17.7 and 55.5 thousand km(2), respectively. The 1990s and 2010s were decades of decreased risk, with reductions of 71.9 and 39.5 thousand km(2), respectively. Further factor analysis showed that intrinsic conditions (i.e., elevation, soil, and geochemical landscape) provided fundamental niches. In contrast, climatic conditions, especially precipitation, led to niche differentiation and resulted in varied spatial patterns. Additionally, while increased human interference may temporarily reduce plague risks, there is a strong possibility of recurrence. This study reshaped the plague distribution at multiple time scales in the TP and revealed multifactorial synergistic effects on the spreading and contraction of plague foci, confirming that TP plague is increasingly sensitive to climate change. These findings may facilitate groups to take measures to combat the plague threats and prevent potential future human plague from occurring.
Solar geoengineering is often framed as a stopgap measure to decrease the magnitude, impacts, and injustice of climate change. However, the benefits or costs of geoengineering for human health are largely unknown. We project how geoengineering could impact malaria risk by comparing current transmission suitability and populations-at-risk under moderate and high greenhouse gas emissions scenarios (Representative Concentration Pathways 4.5 and 8.5) with and without geoengineering. We show that if geoengineering deployment cools the tropics, it could help protect high elevation populations in eastern Africa from malaria encroachment, but could increase transmission in lowland sub-Saharan Africa and southern Asia. Compared to extreme warming, we find that by 2070, geoengineering would nullify a projected reduction of nearly one billion people at risk of malaria. Our results indicate that geoengineering strategies designed to offset warming are not guaranteed to unilaterally improve health outcomes, and could produce regional trade-offs among Global South countries that are often excluded from geoengineering conversations.
Malaria is a major public health problem especially in Africa where 94% of global malaria cases occur. Malaria prevalence and mortalities are disproportionately higher among children. In 2019, children accounted for 67% of malaria deaths globally. Recently, climatic factors have been acknowledged to influence the number and severity of malaria cases. Plasmodium falciparum-the most deadly malaria parasite, accounts for more than 95% of malaria infections among children in Ghana. Using the 2017 Ghana Demographic Health Survey data, we examined the local variation in the prevalence and climatic determinants of child malaria. The findings showed that climatic factors such as temperature, rainfall aridity and Enhanced Vegetation Index are significantly and positively associated with Plasmodium falciparum malaria prevalence among children in Ghana. However, there are local variations in how these climatic factors affect child malaria prevalence. Plasmodium falciparum malaria prevalence was highest among children in the south western, north western and northern Ghana.
BACKGROUND: As Indonesia aims for malaria elimination by 2030, provisional malaria epidemiology and risk factors evaluation are important in pursue of this national goal. Therefore, this study aimed to understand the risk factor of malaria in Northern Sumatera. METHODS: Malaria cases from 2019 to 2020 were obtained from the Indonesian Ministry of Health Electronic Database. Climatic variables were provided by the Center for Meteorology and Geophysics Medan branch office. Multivariable logistic regression was undertaken to understand the risk factors of imported malaria. A zero-inflated Poisson multivariable regression model was used to study the climatic drivers of indigenous malaria. RESULTS: A total of 2208 (indigenous: 76.0% [1679] and imported: 17.8% [392]) were reported during the study period. Risk factors of imported malaria were: ages 19-30 (adjusted odds ratio [AOR] = 3.31; 95% confidence interval [CI] 1.67, 2.56), 31-45 (AOR = 5.69; 95% CI 2.65, 12.20), and > 45 years (AOR = 5.11; 95% CI 2.41, 10.84). Military personnel and forest workers and miners were 1,154 times (AOR = 197.03; 95% CI 145.93, 9,131.56) and 44 times (AOR = 44.16; 95% CI 4.08, 477,93) more likely to be imported cases as compared to those working as employees and traders. Indigenous Plasmodium falciparum increased by 12.1% (95% CrI 5.1%, 20.1%) for 1% increase in relative humidity and by 21.0% (95% CrI 9.0%, 36.2%) for 1 °C increase in maximum temperature. Plasmodium vivax decreased by 0.8% (95% CrI 0.2%, 1.3%) and 16.7% (95% CrI 13.7%, 19.9%) for one meter and 1 °C increase of altitude and minimum temperature. Indigenous hotspot was reported by Kota Tanjung Balai city and Asahan regency, respectively. Imported malaria hotspots were reported in Batu Bara, Kota Tebing Tinggi, Serdang Bedagai and Simalungun. CONCLUSION: Both indigenous and imported malaria is limited to a few regencies and cities in Northern Sumatera. The control measures should focus on these risk factors to achieve elimination in Indonesia.
Understanding the response of dengue fever to climate change remains a global public health concern. A rich array of mathematical models have been proposed to help estimate future population exposure and vulnerability. While these models have proved helpful in modeling mosquito distribution and/or revealing dengue transmission mechanism, they have rarely been incorporated into distribution estimates, particularly at large spatial and temporal scales, to evaluate dengue response to long-term environmental change. Here, we develop a novel mechanistic phenology model that explicitly describes the dengue epidemic process completion (DPEC) ac-cording to empirically derived responses to environmental conditions. Further, we apply this model to Aedes albopictus and dengue transmission in mainland China. We validate the model with recorded indigenous dengue cases, and reveal the power of model prediction. Results suggest that future temperature rise promotes geographic expansion of mosquitoes and dengue fever, respectively around 3-15% and 4-10% increment in the area by 2080, compared to nowadays. Results also indicate a more extended season (1-2 months increment) and stronger intensity (up to 4 DEPC increment) of dengue transmission by 2080. Most importantly, our model discloses a weak correlation between the spreading pattern of dengue and Aedes albopictus. Using the spatial expansion trend of mosquito to infer the risk of dengue to the human population is likely to bring about strong bias in spreading direction and/or overestimate dengue distribution. Our study paves a way to provide a useful tool and precise information for predicting dengue dynamics. It also helps design control strategies to prevent arbovirus outbreaks worldwide in areas colonized by Aedes mosquitoes.
The Zika virus (ZIKV) epidemic, which was followed by an unprecedented outbreak of congenital microcephaly, emerged in Brazil unevenly, with apparent pockets of susceptibility. The present study aimed to detect high-risk areas for ZIKV infection and microcephaly in Goiania, a large city of 1.5 million inhabitants in Central-West Brazil. Using geocoded surveillance data from the Brazilian Information System for Notifiable Diseases (SINAN) and from the Public Health Event Registry (RESP-microcefalia), we analyzed the spatiotemporal distribution and socioeconomic indicators of laboratory confirmed (RT-PCR and/or anti-ZIKV IgM ELISA) symptomatic ZIKV infections among pregnant women and clinically confirmed microcephaly in neonates, from 2016 to 2020. We investigated temporal patterns by estimating the risk of symptomatic maternal ZIKV infections and microcephaly per 1000 live births per month. We examined the spatial distribution of maternal ZIKV infections and microcephaly cases across the 63 subdistricts of Goiania by manually plotting the geographical coordinates. We used spatial scan statistics estimated by discrete Poisson models to detect high clusters of maternal ZIKV infection and microcephaly and compared the distributions by socioeconomic indicators measured at the subdistrict level. In total, 382 lab-confirmed cases of maternal ZIKV infections, and 31 cases of microcephaly were registered in the city of Goiania. More than 90% of maternal cases were reported between 2016 and 2017. The highest incidence of ZIKV cases among pregnant women occurred between February and April 2016. A similar pattern was observed in the following year, although with a lower number of cases, indicating seasonality for ZIKV infection, during the local rainy season. Most congenital microcephaly cases occurred with a time-lag of 6 to 7 months after the peak of maternal ZIKV infection. The highest estimated incidence of maternal ZIKV infections and microcephaly were 39.3 and 2.5 cases per 1000 livebirths, respectively. Districts with better socioeconomic indicators and with higher proportions of self-identified white inhabitants were associated with lower risks of maternal ZIKV infection. Overall, the findings indicate heterogeneity in the spatiotemporal patterns of maternal ZIKV infections and microcephaly, which were correlated with seasonality and included a high-risk geographic cluster. Our findings identified geographically and socio-economically underprivileged groups that would benefit from targeted interventions to reduce exposure to vector-borne infections. Author summaryThe first wave of Zika virus (ZIKV) epidemic and its Congenital Zika Syndrome, has vanished. However, the consequences have remained for the affected children and families ever since.In Brazil, the first cases of microcephaly, detected in the end of 2015 in the Northeast region, especially in coastal cities, quickly spread to other regions and cities in countryside of Brazil. Understanding the temporal and spatial dynamics of cases distribution is essential to identify areas of greater risk and enable preparedness for a future wave of cases.In this study, we analyzed the spatiotemporal distribution of cases of ZIKV infection in pregnant women and cases of microcephaly in newborns by district, over a five-year period, in a large city in Midwest Brazil. Additionally, cases of microcephaly were correlated with the socioeconomic and structural conditions at the local level.Our findings indicate heterogeneity in the spatiotemporal patterns of maternal ZIKV infections and microcephaly, which were correlated with seasonality and included a persistent high-risk geographic location (cluster) in the city of Goiania. We could identify geographically and socio-economically underprivileged groups, with higher risk for ZIKV infection, that would benefit from targeted interventions to reduce exposure to new vector borne infections.
Increasing temperatures due to climate change have contributed to a northward range expansion of Ixodes scapularis ticks in Canada. These ticks harbour pathogens of public and animal health significance, including Borrelia burgdorferi and Anaplasma phagocytophilum, which cause Lyme disease and anaplasmosis, respectively, in humans, dogs and horses, and Borrelia miyamotoi, which causes a flu-like relapsing fever in humans. To address the risks associated with these vector-borne zoonotic diseases, continuous tick surveillance is advised. This study examined spatial patterns of B. burgdorferi, B. miyamotoi and A. phagocytophilum from ticks submitted through a national study on ticks of companion animals. From 1 April 2019 to 31 March 2020, we received a total of 1541 eligible submissions from 94 veterinary clinics across Canada. Individual and pooled samples of a maximum of either 5 I. scapularis, I. pacificus or I. angustus samples from the same animal and of the same life stage were screened using real-time PCR targeting genes 23S rRNA for Borrelia spp. and msp2 for A. phagocytophilum. Confirmatory testing was conducted on all 23S rRNA positive samples using a duplex assay for ospA and flaB to differentiate B. burgdorferi and B. miyamotoi, respectively. Prevalence estimates were highest (>20%) for B. burgdorferi in southwestern Manitoba, eastern Ontario, southwestern Quebec, New Brunswick and Nova Scotia. Estimates of B. miyamotoi and A. phagocytophilum were much lower (<5%), except for higher A. phagocytophilum (>5%) estimates for southern Manitoba, eastern Ontario and Prince Edward Island. Findings from this study, combined with other surveillance approaches, can be used to guide veterinary and public health approaches for ticks and tick-borne diseases.
Under the Global Program to Eliminate Lymphatic Filariasis (LF) American Samoa conducted seven rounds of mass drug administration (MDA) between 2000 and 2006. Subsequently, the territory passed the WHO recommended school-based transmission assessment survey (TAS) in 2011/2012 (TAS-1) and 2015 (TAS-2) but failed in 2016, when both TAS-3 and a community survey found LF antigen prevalence above what it had been in previous surveys. This study aimed to identify potential environmental drivers of LF to refine future surveillance efforts to detect re-emergence and recurrence. Data on five LF infection markers: antigen, Wb123, Bm14 and Bm33 antibodies and microfilaraemia, were obtained from a population-wide serosurvey conducted in American Samoa in 2016. Spatially explicit data on environmental factors were derived from freely available sources. Separate multivariable Poisson regression models were developed for each infection marker to assess and quantify the associations between LF infection markers and environmental variables. Rangeland, tree cover and urban cover were consistently associated with a higher seroprevalence of LF-infection markers, but to varying magnitudes between landcover classes. High slope gradient, population density and crop cover had a negative association with the seroprevalence of LF infection markers. No association between rainfall and LF infection markers was detected, potentially due to the limited variation in rainfall across the island. This study demonstrated that seroprevalence of LF infection markers were more consistently associated with topographical environmental variables, such as gradient of the slope, rather than climatic variables, such as rainfall. These results provide the initial groundwork to support the detection of areas where LF transmission is more likely to occur, and inform LF elimination efforts through better understanding of the environmental drivers.
Vector-borne diseases can usually be examined with a vector-host model like the SIRUV model. This, however, depends on parameters that contain detailed information about the mosquito population that we usually do not know. For this reason, in this article, we reduce the SIRUV model to an SIR model with a time-dependent and periodic transmission rate beta(t). Since the living conditions of the mosquitos depend on the local weather conditions, meteorological data sets flow into the model in order to achieve a more realistic behavior. The developed SIR model is adapted to existing data sets of hospitalized dengue cases in Jakarta (Indonesia) and Colombo (Sri Lanka) using numerical optimization based on Pontryagin’s maximum principle. A previous data analysis shows that the results of this parameter fit are within a realistic range and thus allow further investigations. Based on this, various simulations are carried out and the prediction quality of the model is examined.
BACKGROUND: Limited evidence is available about the association between tropical cyclones and dengue incidence. This study aimed to examine the effects of tropical cyclones on the incidence of dengue and to explore the vulnerable populations in Guangzhou, China. METHODS: Weekly dengue case data, tropical cyclone and meteorological data during the tropical cyclones season (June to October) from 2015 to 2019 were collected for the study. A quasi-Poisson generalized linear model combined with a distributed lag non-linear model was conducted to quantify the association between tropical cyclones and dengue, controlling for meteorological factors, seasonality, and long-term trend. Proportion of dengue cases attributable to tropical cyclone exposure was calculated. The effect difference by sex and age groups was calculated to identify vulnerable populations. The tropical cyclones were classified into two levels to compare the effects of different grades of tropical cyclones on the dengue incidence. RESULTS: Tropical cyclones were associated with an increased number of dengue cases with the maximum risk ratio of 1.41 (95% confidence interval 1.17-1.69) in lag 0 week and cumulative risk ratio of 2.13 (95% confidence interval 1.28-3.56) in lag 0-4 weeks. The attributable fraction was 6.31% (95% empirical confidence interval 1.96-10.16%). Men and the elderly were more vulnerable to the effects of tropical cyclones than the others. The effects of typhoons were stronger than those of tropical storms among various subpopulations. CONCLUSIONS: Our findings indicate that tropical cyclones may increase the incidence of dengue within a 4-week lag in Guangzhou, China, and the effects were more pronounced in men and the elderly. Precautionary measures should be taken with a focus on the identified vulnerable populations to control the transmission of dengue associated with tropical cyclones.
Lyme borreliosis is the leading tick-related illness in Europe, caused by Borrelia Burgdorferi s.l. Lower Saxony, Germany, including its capital, Hanover, has a higher proportion of infected ticks than central European countries, justifying a research focus on the potential human consequences. The current knowledge gap on human incident infections, particularly in Western Germany, demands serological insights, especially regarding a potentially changing climate-related tick abundance and activity. We determined the immunoglobulin G (IgG) and immunoglobulin M (IgM) serostatuses for 8009 German National Cohort (NAKO) participants from Hanover, examined in 2014-2018. We used an enzyme-linked immunosorbent assay (ELISA) as the screening and a line immunoblot as confirmation for the Borrelia Burgdorferi s.l. antibodies. We weighted the seropositivity proportions to estimate general population seropositivity and estimated the force of infection (FOI). Using logistic regression, we investigated risk factors for seropositivity. Seropositivity was 3.0% (IgG) and 2.1% (IgM). The FOI varied with age, sharply increasing in participants aged ≥40 years. We confirmed advancing age and male sex as risk factors. We reported reduced odds for seropositivity with increasing body mass index and depressive symptomatology, respectively, pointing to an impact of lifestyle-related behaviors. The local proportion of seropositive individuals is comparable to previous estimates for northern Germany, indicating a steady seroprevalence.
The Flavivirus genus is made up of viruses that are either mosquito-borne or tick-borne and other viruses transmitted by unknown vectors. Flaviviruses present a significant threat to global health and infect up to 400 million of people annually. As the climate continues to change throughout the world, these viruses have become prominent infections, with increasing number of infections being detected beyond tropical borders. These include dengue virus (DENV), West Nile virus (WNV), Japanese encephalitis virus (JEV), and Zika virus (ZIKV). Several highly conserved epitopes of flaviviruses had been identified and reported to interact with antibodies, which lead to cross-reactivity results. The major interest of this review paper is mainly focused on the serological cross-reactivity between DENV serotypes, ZIKV, WNV, and JEV. Direct and molecular techniques are required in the diagnosis of Flavivirus-associated human disease. In this review, the serological assays such as neutralization tests, enzyme-linked immunosorbent assay, hemagglutination-inhibition test, Western blot test, and immunofluorescence test will be discussed. Serological assays that have been developed are able to detect different immunoglobulin isotypes (IgM, IgG, and IgA); however, it is challenging when interpreting the serological results due to the broad antigenic cross-reactivity of antibodies to these viruses. However, the neutralization tests are still considered as the gold standard to differentiate these flaviviruses.
It is important to study the recent malaria incidence trends in urban areas resulting from rapid urbanization that can lead to changes in environmental conditions for malaria. This retrospective study assessed trends in malaria patients, their distribution according to parasite species, patient demographics, and weather data for the past 8 years at a malaria clinic in the National Institute of Malaria Research, New Delhi, India. We overlaid the effects of environmental factors such as rainfall, relative humidity, and temperature on malaria incidence. The malaria data were digitized for a period spanning 2012 to 2019, during which 36,892 patients with fever attended the clinic. Of these, 865 (2.3%) were diagnosed with malaria microscopically. Plasmodium vivax was predominant (96.2%), and very few patients were of Plasmodium falciparum (3.5%) or mixed infections (0.3%). The patients with malaria were within a 10-km radius of the clinic. Males (70.9%) were more commonly affected than females (29.1%). Of the total malaria patients, a majority (∼78%) belonged to the > 15-year age group. A total of 593 malaria patients (68.6%) received primaquine. These patients were most commonly diagnosed in April through October. Furthermore, there was a lag of 1 month between the rainfall peak and the malaria case peak. The peak in malaria cases corresponded to a mean temperature of 25 to 30°C and a relative humidity of 60% to 80%. This analysis will be useful for policymakers in evaluating current interventions and in accelerating malaria control further in urban areas of India.
Background: Plasmodium ovale is a neglected malarial parasite that can form latent hypnozoites in the human liver. Over the last decade, molecular surveillance studies of non-falciparum malaria in Africa have highlighted that P. ovale is circulating below the radar, including areas where Plasmodium falciparum is in decline. To eliminate malaria where P. ovale is endemic, a better understanding of its epidemiology, asymptomatic carriage, and transmission biology is needed. Methods: We performed a pilot study on P. ovale transmission as part of an ongoing study of human-to-mosquito transmission of P. falciparum from asymptomatic carriers. To characterize the malaria asymptomatic reservoir, cross-sectional qPCR surveys were conducted in Bagamoyo, Tanzania, over three transmission seasons. Positive individuals were enrolled in transmission studies of P. falciparum using direct skin feeding assays (DFAs) with Anopheles gambiae s.s. (IFAKARA strain) mosquitoes. For a subset of participants who screened positive for P. ovale on the day of DFA, we incubated blood-fed mosquitoes for 14 days to assess sporozoite development. Results: Molecular surveillance of asymptomatic individuals revealed a P. ovale prevalence of 11% (300/2718), compared to 29% (780/2718) for P. falciparum. Prevalence for P. ovale was highest at the beginning of the long rainy season (15.5%, 128/826) in contrast to P. falciparum, which peaked later in both the long and short rainy seasons. Considering that these early-season P. ovale infections were low-density mono-infections (127/128), we speculate many were due to hypnozoite-induced relapse. Six of eight P. ovale-infected asymptomatic individuals who underwent DFAs successfully transmitted P. ovale parasites to A. gambiae. Conclusions: Plasmodium ovale is circulating at 4-15% prevalence among asymptomatic individuals in coastal Tanzania, largely invisible to field diagnostics. A different seasonal peak from co-endemic P. falciparum, the capacity to relapse, and efficient transmission to Anopheles vectors likely contribute to its persistence amid control efforts focused on P. falciparum.
Dengue fever (DF) is associated with significant morbidity across the tropics and sub-tropics. Here, we used a temperature-based model of the extrinsic incubation period (EIP) and a temperature and humidity-based model for adult mosquito survival to explore the relationship between seasonal climate variability and DF in Brazil from 2014 to 2019. We found that municipalities with higher mosquito survival probabilities and shorter EIPs were more likely to be associated with DF case reports, but with significant intra-annual variability. A 0.012 or above probability of Aedes aegypti surviving the EIP was associated with a greater than 50% probability of DF being reported in the municipality. We extrapolated these results to the Americas using climate data over the last decade (2010-2019) to map the seasonal change in the range of areas suitable for dengue virus transmission and the magnitude of the population living in those areas. Areas near the Equator exhibited high suitability throughout the year whereas suitability in the subtropics and temperate regions varied seasonally, especially moving poleward. Strengthening our understanding of DF seasonality is essential to mitigating risks, particularly as the Americas experience the impacts of climate change.
BACKGROUND: In North African countries, zoonotic cutaneous leishmaniasis (ZCL) is a seasonal disease linked to Phlebotomus papatasi, Scopoli, 1786, the primary proven vector of L. major dynamics. Even if the disease is of public health importance, studies of P. papatasi seasonal dynamics are often local and dispersed in space and time. Therefore, a detailed picture of the biology and behavior of the vector linked with climatic factors and the framework of ZCL outbreaks is still lacking at the North African countries’ level. Our study aims to fill this gap via a systematic review and meta-analysis of the seasonal incidence of ZCL and the activity of P. papatasi in North African countries. We address the relationship between the seasonal number of declared ZCL cases, the seasonal dynamic of P. papatasi, and climatic variables at the North African region scale. METHODS: We selected 585 publications, dissertations, and archives data published from 1990 to July 2022. The monthly incidence data of ZCL were extracted from 15 documents and those on the seasonal dynamic of P. papatasi from 11 publications from four North African countries. RESULTS: Our analysis disclosed that for most studied sites, the highest ZCL incidence is recorded from October to February (the hibernal season of the vector), while the P. papatasi density peaks primarily during the hot season of June to September. Overall, at the North African region scale, two to four months laps are present before the apparition of the scars reminiscent of infection by L. major. CONCLUSIONS: Such analysis is of interest to regional decision-makers for planning control of ZCL in North African countries. They can also be a rationale on which future field studies combining ZCL disease incidence, vector activity, and climatic data can be built.
This study analyzes the risks to public health and life quality in the conditions of permafrost degradation caused by the ongoing climate change in the Russian Arctic. There are more than 200 Siberian anthrax cattle burial grounds in the Russian permafrost regions. Permafrost degradation poses the risks of thawing of frozen carcasses of the infected animals and propagation of infectious diseases. Permafrost degradation leads to infiltration of toxic waste in the environment. Such waste contains mercury, which migrates into the rivers and forms methylmercury (MeHg) in fish. Other risks associated with permafrost degradation include damage to the existing social infrastructure (housing, health-care facilities, roads, etc.). Various risks to public well-being that emerge because of permafrost degradation were addressed in this study. Relative hazard indices were developed and calculated to characterize the probability of outbreaks of Siberian anthrax in the future. These indices linked the rates of permafrost degradation and the number of Siberian anthrax cattle burials to the potential hazard of re-emergence of Siberian anthrax among local populations in 70 municipal districts under the ongoing warming. The expected damage to public housing, health-care facilities, and motorways was assessed. Accessibility of health care in various regions of the Russian Arctic was analyzed. The economic costs associated with various scenarios of possible destruction of residential buildings, health-care facilities, and roads built on permafrost were estimated.
The study sought to review the works of literature on agent-based modeling and the influence of climatic and environmental factors on disease outbreak, transmission, and surveillance. Thus, drawing the influence of environmental variables such as vegetation index, households, mosquito habitats, breeding sites, and climatic variables including precipitation or rainfall, temperature, wind speed, and relative humidity on dengue disease modeling using the agent-based model in an African context and globally was the aim of the study. A search strategy was developed and used to search for relevant articles from four databases, namely, PubMed, Scopus, Research4Life, and Google Scholar. Inclusion criteria were developed, and 20 articles met the criteria and have been included in the review. From the reviewed works of literature, the study observed that climatic and environmental factors may influence the arbovirus disease outbreak, transmission, and surveillance. Thus, there is a call for further research on the area. To benefit from arbovirus modeling, it is crucial to consider the influence of climatic and environmental factors, especially in Africa, where there are limited studies exploring this phenomenon.
BACKGROUND: Malaria remains one of the most devastating diseases globally, and the control of mosquitoes as the vector is mainly dependent on chemical insecticides. Elevated temperatures associated with future warmer climates could affect mosquitoes’ metabolic enzyme expression and increase insecticide resistance, making vector control difficult. Understanding how mosquito rearing temperatures influence their susceptibility to insecticide and expression of metabolic enzymes could aid in the development of novel tools and strategies to control mosquitoes in a future warmer climate. This study evaluated the effects of temperature on the susceptibility of Anopheles gambiae sensu lato (s.l.) mosquitoes to pyrethroids and their expression of metabolic enzymes. METHODS: Anopheles gambiae s.l. eggs obtained from laboratory-established colonies were reared under eight temperature regimes (25, 28, 30, 32, 34, 36, 38, and 40 °C). Upon adult emergence, 3- to 5-day-old female non-blood-fed mosquitoes were used for susceptibility tests following the World Health Organization (WHO) bioassay protocol. Batches of 20-25 mosquitoes from each temperature regime (25-34 °C) were exposed to two pyrethroid insecticides (0.75% permethrin and 0.05% deltamethrin). In addition, the levels of four metabolic enzymes (α-esterase, β-esterase, glutathione S-transferase [GST], and mixed-function oxidase [MFO]) were examined in mosquitoes that were not exposed and those that were exposed to pyrethroids. RESULTS: Mortality in An. gambiae s.l. mosquitoes exposed to deltamethrin and permethrin decreased at temperatures above 28 °C. In addition, mosquitoes reared at higher temperatures were more resistant and had more elevated enzyme levels than those raised at low temperatures. Overall, mosquitoes that survived after being exposed to pyrethroids had higher levels of metabolic enzymes than those that were not exposed to pyrethroids. CONCLUSIONS: This study provides evidence that elevated temperatures decreased An. gambiae s.l. mosquitoes’ susceptibility to pyrethroids and increased the expression of metabolic enzymes. This evidence suggests that elevated temperatures projected in a future warmer climate could increase mosquitoes’ resistance to insecticides and complicate malaria vector control measures. This study therefore provides vital information, and suggests useful areas of future research, on the effects of temperature variability on mosquitoes that could guide vector control measures in a future warmer climate.
The spread of malaria is related to climate change because temperature and rainfall are key parameters of climate change. Fluctuations in temperature affect the spread of malaria by lowering or speeding up its rate of transmission. The amount of rainfall also affects the transmission of malaria by offering a lot of sites suitable for mosquitoes to breed in. However, a high amount of rainfall does not have a great effect. Because of the high malaria incidence and the death rates in African regions, by using malaria incidence data, temperature data and rainfall data collected in 1901-2015, we construct and analyze climate networks to show how climate relates to the transmission of malaria in African countries. Malaria networks show a positive correlation with temperature and rainfall networks, except for the 1981-2015 period, in which the malaria network shows a negative correlation with rainfall.
Arbovirus infections, such as dengue, zika, chikungunya, and yellow fever, are a major public health problem worldwide. As the main vectors, mosquitoes have been classified by the Center for Disease Control and Prevention as one of the deadliest animals alive. In this ecological study, we analyzed the population dynamics of important genera and species of mosquito vectors. Mosquito immatures were collected using ovitraps and at natural breeding sites: bamboos and bromeliads. Adult mosquitoes were captured using CDC traps with CO(2), Shannon traps, and manual suction tubes. Collections took place during the rainy and dry seasons from 2019 to 2020 in the Serra dos Órgãos National Park, Rio de Janeiro state, Brazil. The highest number of species was recorded in the ovitraps, followed by CDC and bromeliads. The breeding site with the lowest diversity was bamboo, though it showed the highest level of evenness compared to the other breeding sites. The medically important genera reported were Haemagogus spp., Aedes spp., Culex spp., and Wyeomyia spp. Culicid eggs increased in the rainy season, with a peak in November 2019 and January and February 2020, and lower abundance in the dry season, from September to October 2019. Mosquito eggs had a strong positive correlation (ρ = 0.755) with temperature and a moderate positive correlation (ρ = 0.625) with rainfall. This study shows how environmental variables can influence the ecology of disease-vector mosquitoes, which are critical in the maintenance of arbovirus circulation in a threatened biome within the most densely populated region of Brazil.
BACKGROUND: The 2017-2018 yellow fever virus (YFV) outbreak in southeastern Brazil marked a reemergence of YFV in urban states that had been YFV-free for nearly a century. Unlike earlier urban YFV transmission, this epidemic was driven by forest mosquitoes. The objective of this study was to evaluate environmental drivers of this outbreak. METHODOLOGY/PRINCIPAL FINDINGS: Using surveillance data from the Brazilian Ministry of Health on human and non-human primate (NHP) cases of YFV, we traced the spatiotemporal progression of the outbreak. We then assessed the epidemic timing in relation to drought using a monthly Standardized Precipitation Evapotranspiration Index (SPEI) and evaluated demographic risk factors for rural or outdoor exposure amongst YFV cases. Finally, we developed a mechanistic framework to map the relationship between drought and YFV. Both human and NHP cases were first identified in a hot, dry, rural area in northern Minas Gerais before spreading southeast into the more cool, wet urban states. Outbreaks coincided with drought in all four southeastern states of Brazil and an extreme drought in Minas Gerais. Confirmed YFV cases had an increased odds of being male (OR 2.6; 95% CI 2.2-3.0), working age (OR: 1.8; 95% CI: 1.5-2.1), and reporting any recent travel (OR: 2.8; 95% CI: 2.3-3.3). Based on this data as well as mosquito and non-human primate biology, we created the “Mono-DrY” mechanistic framework showing how an unusual drought in this region could have amplified YFV transmission at the rural-urban interface and sparked the spread of this epidemic. CONCLUSIONS/SIGNIFICANCE: The 2017-2018 YFV epidemic in Brazil originated in hot, dry rural areas of Minas Gerais before expanding south into urban centers. An unusually severe drought in this region may have created environmental pressures that sparked the reemergence of YFV in Brazil’s southeastern cities.
BACKGROUND: Lyme borreliosis is the most prevalent vector-borne disease in Europe, and numbers might increase due to climate change. However, borreliosis is not notifiable in North Rhine-Westphalia (NRW), Germany. Hence, little is known about the current human seroprevalence in NRW. However, the proportion of Borrelia burgdorferi sensu lato-infected ticks has increased in a NRW nature reserve. The literature suggests increasing age and male sex as risk factors for seropositivity, whereas the influence of socioeconomic status is controversial. Thus, we aimed to determine regional seropositivity for Borrelia burgdorferi sensu lato (B. burgdorferi s.l.) and its risk factors in the Rhineland Study population in Bonn, NRW, and to compare it with previous surveys to evaluate potential effects of climate change. METHODS: We assessed seropositivity in 2865 Rhineland Study participants by determining immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies for B. burgdorferi s.l. using a two-step algorithm combining enzyme-linked immunosorbent assay tests and line immunoblots. We calculated the odds of being classified as IgG or IgM positive as a function of age, sex, and educational level using binomial logistic regression models. We applied varying seropositivity classifications and weights considering age, sex and education to compensate for differences between the sample and regional population characteristics. RESULTS: IgG antibodies for B. burgdorferi s.l. were present in 2.4% and IgM antibodies in 0.6% of the participants (weighted: 2.2% [IgG], 0.6% [IgM]). The likelihood of IgG seropositivity increased by 3.0% (95% confidence interval [CI] 1.5-5.2%) per 1 year increase in age. Men had 1.65 times the odds for IgG seropositivity as women (95% CI 1.01-2.73), and highly educated participants had 1.83 times the odds (95% CI 1.10-3.14) as participants with an intermediate level of education. We found no statistically significant link between age, sex, or education and IgM seropositivity. Our weighted and age-standardized IgG seroprevalence was comparable to the preceding serosurvey German Health Interview and Examination Survey for Adults (DEGS) for NRW. CONCLUSIONS: We confirmed that increasing age and male sex are associated with increased odds for IgG seropositivity and provide evidence for increased seropositivity in the highly educated group. B. burgdorferi s.l. seropositivity remained constant over the past decade in this regional German population.
The burden of arbovirus diseases in Brazil has increased within the past decade due to the emergence of chikungunya and Zika and endemic circulation of all four dengue serotypes. Changes in temperature and rainfall patterns may alter conditions to favor vector-host transmission and allow for cyclic re-emergence of disease. We sought to determine the impact of climate conditions on arbovirus co-circulation in Rio de Janeiro, Brazil. We assessed the spatial and temporal distributions of chikungunya, dengue, and Zika cases from Brazil’s national notifiable disease information system (SINAN) and created autoregressive integrated moving average models (ARIMA) to predict arbovirus incidence accounting for the lagged effect of temperature and rainfall. Each year, we estimate that the combined arboviruses were associated with an average of 8429 to 10,047 lost Disability-Adjusted Life Years (DALYs). After controlling for temperature and precipitation, our model predicted a three cycle pattern where large arbovirus outbreaks appear to be primed by a smaller scale surge and followed by a lull of cases. These dynamic arbovirus patterns in Rio de Janeiro support a mechanism of susceptibility enhancement until the theoretical threshold of population immunity allows for temporary cross protection among certain arboviruses. This suspected synergy presents a major public health challenge due to overlapping locations and seasonality of arbovirus diseases, which may perpetuate disease burden and overwhelm the health system.
BACKGROUND: In metropolitan Tokyo in 2014, Japan experienced its first domestic dengue outbreak since 1945. The objective of the present study was to quantitatively assess the future risk of dengue in Japan using climate change scenarios in a high-resolution geospatial environment by building on a solid theory as a baseline in consideration of future adaptation strategies. METHODS: Using climate change scenarios of the Model for Interdisciplinary Research on Climate version 6 (MIROC6), representative concentration pathway (RCP) 2.6, 4.5, and 8.5, we computed the daily average temperature and embedded this in the effective reproduction number of dengue, R(T), to calculate the extinction probability and interepidemic period across Japan. RESULTS: In June and October, the R(T) with daily average temperature T, was <1 as in 2022; however, an elevation in temperature increased the number of days with R(T) >1 during these months under RCP8.5. The time period with a risk of dengue transmission gradually extended to late spring (April-May) and autumn (October-November). Under the RCP8.5 scenario in 2100, the possibility of no dengue-free months was revealed in part of southernmost Okinawa Prefecture, and the epidemic risk extended to the entire part of northernmost Hokkaido Prefecture. CONCLUSION: Each locality in Japan must formulate action plans in response to the presented scenarios. Our geographic analysis can help local governments to develop adaptation policies that include mosquito breeding site elimination, distribution of adulticides and larvicides, and elevated situation awareness to prevent transmission via bites from Aedes vectors.
Ixodes scapularis is a vector of tick-borne diseases. Climate change is frequently invoked as an important cause of geographic expansions of tick-borne diseases. Environmental variables such as temperature and precipitation have an important impact on the geographical distribution of disease vectors. We used the maximum entropy model to project the potential geographic distribution and future trends of I. scapularis. The main climatic variables affecting the distribution of potential suitable areas were screened by the jackknife method. Arc Map 10.5 was used to visualize the projection results to better present the distribution of potential suitable areas. Under climate change scenarios, the potential suitable area of I. scapularis is dynamically changing. The largest suitable area of I. scapularis is under SSP3-7.0 from 2081 to 2100, while the smallest is under SSP5-8.5 from 2081 to 2100, even smaller than the current suitable area. Precipitation in May and September are the main contributing factors affecting the potential suitable areas of I. scapularis. With the opportunity to spread to more potential suitable areas, it is critical to strengthen surveillance to prevent the possible invasion of I. scapularis.
Flood hazard maps display areas inundated by water bodies after extreme rainfall events occur, helping governments focus on performing protection works there. However, rainfall also causes small disasters at other moments, which are not included in these maps, such as traffic impedance and water-borne diseases, both inside and outside the mapped floodplain. Their mitigation would help build resilience and reduce inequality. Unfortunately, small disasters are overlooked in traditional risk management for being tolerable, mild, and scattered. Though they do occur with high frequency, it is challenging to get data sources to describe them accurately. Therefore, efforts must be made to base analyses on available on-site reports. The objectives of this paper are to relate small disasters to rainfall parameters and neighborhood attributes, and to prioritize neighborhoods for intervention in Cali, Colombia. Contributions provided here are useful to prioritize areas in other cities, and to follow better data gathering practices.
Oropouche virus (OROV) is an emerging vector-borne arbovirus with high epidemic potential, causing illness in more than 500,000 people. Primarily contracted through its midge and mosquito vectors, OROV remains prevalent in its wild, non-human primate and sloth reservoir hosts as well. This virus is spreading across Latin America; however, the majority of cases occur in Brazil. The aim of this research is to document OROV’s presence in Brazil using the One Health approach and geospatial techniques. A scoping review of the literature (2000 to 2021) was conducted to collect reports of this disease in humans and animal species. Data were then geocoded by first and second subnational levels and species to map OROV’s spread. In total, 14 of 27 states reported OROV presence across 67 municipalities (second subnational level). However, most of the cases were in the northern region, within the tropical and subtropical moist broadleaf forests biome. OROV was identified in humans, four vector species, four genera of non-human primates, one sloth species, and others. Utilizing One Health was important to understand the distribution of OROV across several species and to suggest possible environmental, socioeconomic, and demographic drivers of the virus’s presence. As deforestation, climate change, and migration rates increase, further study into the spillover potential of this disease is needed.
Global changes in response to human encroachment into natural habitats and carbon emissions are driving the biodiversity extinction crisis and increasing disease emergence risk. Host distributions are one critical component to identify areas at risk of viral spillover, and bats act as reservoirs of diverse viruses. We developed a reproducible ecological niche modelling pipeline for bat hosts of SARS-like viruses (subgenus Sarbecovirus), given that several closely related viruses have been discovered and sarbecovirus-host interactions have gained attention since SARS-CoV-2 emergence. We assessed sampling biases and modelled current distributions of bats based on climate and landscape relationships and project future scenarios for host hotspots. The most important predictors of species distributions were temperature seasonality and cave availability. We identified concentrated host hotspots in Myanmar and projected range contractions for most species by 2100. Our projections indicate hotspots will shift east in Southeast Asia in locations greater than 2°C hotter in a fossil-fuelled development future. Hotspot shifts have implications for conservation and public health, as loss of population connectivity can lead to local extinctions, and remaining hotspots may concentrate near human populations.
ABSTRACT: Ticks represent important vectors and reservoirs of pathogens, causing a number of diseases in humans and animals, and significant damage to livestock every year. Modern research into protection against ticks and tick-borne diseases focuses mainly on the feeding stage, i.e. the period when ticks take their blood meal from their hosts during which pathogens are transmitted. Physiological functions in ticks, such as food intake, saliva production, reproduction, development, and others are under control of neuropeptides and peptide hormones which may be involved in pathogen transmission that cause Lyme borreliosis or tick-borne encephalitis. According to current knowledge, ticks are not reservoirs or vectors for the spread of COVID-19 disease. The search for new vaccination methods to protect against ticks and their transmissible pathogens is a challenge for current science in view of global changes, including the increasing migration of the human population. HIGHLIGHTS: • Tick-borne diseases have an increasing incidence due to climate change and increased human migration• To date, there is no evidence of transmission of coronavirus COVID-19 by tick as a vector• To date, there are only a few modern, effective, and actively- used vaccines against ticks or tick-borne diseases• Neuropeptides and their receptors expressed in ticks may be potentially used for vaccine design.
Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.
Malaria occurrence is highly related to the geographical distribution of Anopheles dirus (An. dirus) in the South-East Asia Region and Western Pacific Region (SEAR/WPR). Future climate change has been shown to alter the geographical distribution of malaria vectors. However, few studies have investigated the impact of climate change on the potential distribution of An. dirus in the SEAR/WPR. We considered future climate and land-use data under two climate change scenarios for Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and population data from five Shared Socioeconomic Pathways (SSPs), by using three machine learning models, namely, Random Forest (RF), Boosted Regression Trees (BRT), and Maximum entropy (Maxent) to project the geographical distribution of An. Dirus and to estimate the exposed population. A pseudo-absence dataset was generated based on the relationships between model performance and the distance from the pseudo-absence point to the occurrence point in order to improve model accuracy for projection of the Environmentally Suitable Area (ESA) and exposed human population. The results show that the pseudo-absence data corresponding to the distance of 250 km are appropriate for modeling. The RF method ultimately proved to have the highest accuracy. The predicted ESA of An. dirus would mainly be distributed across Myanmar, Thailand, the southern and eastern part of India, Vietnam, the northern part of Cambodia, and the southern part of Laos. The future ESA is estimated to be reduced under the RCP 4.5 climate change scenario. In the 2070s under RCP 8.5, the reduction of ESA is even greater, especially in Thailand (loss of 35.49 10,000 square kilometers), Myanmar (26.24), Vietnam (17.52), and India (15), which may prevent around 282.6 million people from the risk of malaria under the SSP3 scenarios in the SEAR/WPR. Our predicted areas and potential impact groups for An. dirus under future climate change may provide new insights into regional malaria transmission mechanisms and deployment of malaria control measures based on local conditions in the SEAR/WPR’s.
OBJECTIVES: Malaria is a vector-borne disease that remains a serious public health problem due to its climatic sensitivity. Accurate prediction of malaria re-emergence is very important in taking corresponding effective measures. This study aims to investigate the impact of climatic factors on the re-emergence of malaria in mainland China. DESIGN: A modelling study. SETTING AND PARTICIPANTS: Monthly malaria cases for four Plasmodium species (P. falciparum, P. malariae, P. vivax and other Plasmodium) and monthly climate data were collected for 31 provinces; malaria cases from 2004 to 2016 were obtained from the Chinese centre for disease control and prevention and climate parameters from China meteorological data service centre. We conducted analyses at the aggregate level, and there was no involvement of confidential information. PRIMARY AND SECONDARY OUTCOME MEASURES: The long short-term memory sequence-to-sequence (LSTMSeq2Seq) deep neural network model was used to predict the re-emergence of malaria cases from 2004 to 2016, based on the influence of climatic factors. We trained and tested the extreme gradient boosting (XGBoost), gated recurrent unit, LSTM, LSTMSeq2Seq models using monthly malaria cases and corresponding meteorological data in 31 provinces of China. Then we compared the predictive performance of models using root mean squared error (RMSE) and mean absolute error evaluation measures. RESULTS: The proposed LSTMSeq2Seq model reduced the mean RMSE of the predictions by 19.05% to 33.93%, 18.4% to 33.59%, 17.6% to 26.67% and 13.28% to 21.34%, for P. falciparum, P. vivax, P. malariae, and other plasmodia, respectively, as compared with other candidate models. The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%. CONCLUSIONS: The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. Therefore, the LSTMSeq2Seq model can be effectively applied in the malaria re-emergence prediction.
The risk of the mosquito-borne diseases malaria, dengue fever and Zika virus is expected to shift both temporally and spatially under climate change. As climate change projections continue to improve, our ability to predict these shifts is also enhanced. This paper predicts transmission suitability for these mosquito-borne diseases, which are three of the most significant, using the most up-to-date climate change projections. Using a mechanistic methodology, areas that are newly suitable and those where people are most at risk of transmission under the best- and worst-case climate change scenarios have been identified. The results show that although transmission suitability is expected to decrease overall for malaria, some areas will become newly suitable, putting naïve populations at risk. In contrast, transmission suitability for dengue fever and Zika virus is expected to increase both in duration and geographical extent. Although transmission suitability is expected to increase in temperate zones for a few months of the year, suitability remains focused in the tropics. The highest transmission suitability in tropical regions is likely to exacerbate the intense existing vulnerability of these populations, especially children, to the multiple consequences of climate change, and their severe lack of resources and agency to cope with these impacts and pressures. As these changes in transmission suitability are amplified under the worst-case climate change scenario, this paper makes the case in support of enhanced and more urgent efforts to mitigate climate change than has been achieved to date. By presenting consistent data on the climate-driven spread of multiple mosquito-borne diseases, our work provides more holistic information to underpin prevention and control planning and decision making at national and regional levels.
Death rate of dengue fever is low, because dengue fever become severe illness only when second infection happened. However, global warming is getting severe recently, which make the infection distribution of dengue fever different. Common method of previous studies used climate factors combined with social or geographic factors to predict dengue fever. However, recent study did not use combination of these three factors into dengue fever prediction. We proposed a method that combines these three factors with data of Taiwanese dengue fever and uses the secondary area divided by the population as the granularity. Random Forest (RF) and XGBoost (XGB) are used for prediction model of weekly dengue fever infection area. Experimental results showed that the Receiver Operator Characteristic (ROC)/Area Under the Curve (AUC) of RF and XGB are both higher than 93%, and the Recall rate is higher than 80%. With the result, government can determine which area should do disinfection process to reduce the infection rate of dengue infection. Because of accurate prediction and disinfection process, the personnel cost can be reduced and it can prevent waste of medical recourse.
BACKGROUND: Aedes aegypti and Ae. albopictus mosquitoes are major vectors for several human diseases of global importance, such as dengue and yellow fever. Their life cycles and hosted arboviruses are climate sensitive and thus expected to be impacted by climate change. Most studies investigating climate change impacts on Aedes at global or continental scales focused on their future global distribution changes, whereas a single study focused on its effects on Ae. aegypti densities regionally. OBJECTIVES: A process-based approach was used to model densities of Ae. aegypti and Ae. albopictus and their potential evolution with climate change using a panel of nine CMIP6 climate models and climate scenarios ranging from strong to low mitigation measures at the Southeast Asian scale and for the next 80 y. METHODS: The process-based model described, through a system of ordinary differential equations, the variations of mosquito densities in 10 compartments, corresponding to 10 different stages of mosquito life cycle, in response to temperature and precipitation variations. Local field data were used to validate model outputs. RESULTS: We show that both species densities will globally increase due to future temperature increases. In Southeast Asia by the end of the century, Ae. aegypti densities are expected to increase from 25% with climate mitigation measures to 46% without; Ae. albopictus densities are expected to increase from 13%-21%, respectively. However, we find spatially contrasted responses at the seasonal scales with a significant decrease in Ae. albopictus densities in lowlands during summer in the future. DISCUSSION: These results contrast with previous results, which brings new insight on the future impacts of climate change on Aedes densities. Major sources of uncertainties, such as mosquito model parametrization and climate model uncertainties, were addressed to explore the limits of such modeling. https://doi.org/10.1289/EHP11068.
The recent increase in the global incidence of dengue fever resulted in over 2.7 million cases in Latin America and many cases in Southeast Asia and has warranted the development and application of early warning systems (EWS) for futuristic outbreak prediction. EWS pertaining to dengue outbreaks is imperative; given the fact that dengue is linked to environmental factors owing to its dominance in the tropics. Prediction is an integral part of EWS, which is dependent on several factors, in particular, climate, geography, and environmental factors. In this study, we explore the role of increased susceptibility to a DENV serotype and climate variability in developing novel predictive models by analyzing RT-PCR and DENV-IgM confirmed cases in Singapore and Honduras, which reported high dengue incidence in 2019 and 2020, respectively. A random-sampling-based susceptible-infected-removed (SIR) model was used to obtain estimates of the susceptible fraction for modeling the dengue epidemic, in addition to the Bayesian Markov Chain Monte Carlo (MCMC) technique that was used to fit the model to Singapore and Honduras case report data from 2012 to 2020. Regression techniques were used to implement climate variability in two methods: a climate-based model, based on individual climate variables, and a seasonal model, based on trigonometrically varying transmission rates. The seasonal model accounted for 98.5% and 92.8% of the variance in case count in the 2020 Singapore and 2019 Honduras outbreaks, respectively. The climate model accounted for 75.3% and 68.3% of the variance in Singapore and Honduras outbreaks respectively, besides accounting for 75.4% of the variance in the major 2013 Singapore outbreak, 71.5% of the variance in the 2019 Singapore outbreak, and over 70% of the variance in 2015 and 2016 Honduras outbreaks. The seasonal model accounted for 14.2% and 83.1% of the variance in the 2013 and 2019 Singapore outbreaks, respectively, in addition to 91% and 59.5% of the variance in the 2015 and 2016 Honduras outbreaks, respectively. Autocorrelation lag tests showed that the climate model exhibited better prediction dynamics for Singapore outbreaks during the dry season from May to August and in the rainy season from June to October in Honduras. After incorporation of susceptible fractions, the seasonal model exhibited higher accuracy in predicting outbreaks of higher case magnitude, including those of the 2019-2020 dengue epidemic, in comparison to the climate model, which was more accurate in outbreaks of smaller magnitude. Such modeling studies could be further performed in various outbreaks, such as the ongoing COVID-19 pandemic to understand the outbreak dynamics and predict the occurrence of future outbreaks.
Dengue fever is a severe disease spread by Aedes mosquito-borne dengue viruses (DENVs) in tropical areas such as Bangladesh. Since its breakout in the 1960s, dengue fever has been endemic in Bangladesh, with the highest concentration of infections in the capital, Dhaka. This study aims to develop a machine learning model that can use relevant information about the factors that cause Dengue outbreaks within a geographic region. To predict dengue cases in 11 different districts of Bangladesh, we created a DengueBD dataset and employed two machine learning algorithms, Multiple Linear Regression (MLR) and Support Vector Regression (SVR). This research also explores the correlation among environmental factors like temperature, rainfall, and humidity with the rise and decline trend of Dengue cases in different cities of Bangladesh. The entire dataset was divided into an 80:20 ratio, with 80 percent used for training and 20% used for testing. The research findings imply that, for both the MLR with 67% accuracy along with Mean Absolute Error (MAE) of 4.57 and SVR models with 75% accuracy along with Mean Absolute Error (MAE) of 4.95, the number of dengue cases reduces throughout the winter season in the country and increases mainly during the rainy season in the next ten months, from August 2021 to May 2022. Importantly, Dhaka, Bangladesh’s capital, will see the maximum number of dengue patients during this period. Overall, the results of this data-driven analysis show that machine learning algorithms have enormous potential for predicting dengue epidemics.
BACKGROUND: Peromyscopsylla hesperomys and Orchopeas sexdentatus are regarded to be representative plague vectors in the United States. The incidence of plague is rising globally, possibly due to climate change and environmental damage. Environmental factors such as temperature and precipitation have a significant impact on the temporal and spatial distribution of plague vectors. METHODS: Maximum entropy models (MaxEnt) were utilized to predict the distributions of these two fleas and their trends into the future. The main environmental factors influencing the distribution of these two fleas were analyzed. A risk assessment system was constructed to calculate the invasion risk values of the species. RESULTS: Temperature has a significant effect on the distribution of the potentially suitable areas for P. hesperomys and O. sexdentatus. They have the potential to survive in suitable areas of China in the future. The risk assessment system indicated that the risk level for the invasion of these two species into China was moderate. CONCLUSION: In order to achieve early detection, early interception, and early management, China should perfect its monitoring infrastructure and develop scientific prevention and control strategies to prevent the invasion of foreign flea vectors.
The geographic boundaries of arboviruses continue to expand, posing a major health threat to millions of people around the world. This expansion is related to the availability of effective vectors and suitable habitats. Armigeres subalbatus (Coquillett, 1898), a common and neglected species, is of increasing interest given its potential vector capacity for Zika virus. However, potential distribution patterns and the underlying driving factors of Ar. subalbatus remain unknown. In the current study, detailed maps of their potential distributions were developed under both the current as well as future climate change scenarios (SSP126 and SSP585) based on CMIP6 data, employing the MaxEnt model. The results showed that the distribution of the Ar. subalbatus was mainly affected by temperature. Mean diurnal range was the strongest predictor in shaping the distribution of Ar. subalbatus, with an 85.2% contribution rate. By the 2050s and 2070s, Ar. subalbatus will have a broader potential distribution across China. There are two suitable expansion types under climate change in the 2050s and 2070s. The first type is continuous distribution expansion, and the second type is sporadic distribution expansion. Our comprehensive analysis of Ar. subalbatus’s suitable distribution areas shifts under climate change and provides useful and insightful information for developing management strategies for future arboviruses.
The distribution of disease vectors such as mosquitoes is changing. Climate change, invasions and vector control strategies all alter the distribution and abundance of mosquitoes. When disease vectors undergo a range shift, so do disease burdens. Predicting such shifts is a priority to adequately prepare for disease control. Accurate predictions of distributional changes depend on how factors such as temperature and competition affect mosquito life-history traits, particularly body size and reproduction. Direct estimates of both body size and reproduction in mosquitoes are logistically challenging and time-consuming, so the field has long relied upon linear (isometric) conversions between wing length (a convenient proxy of size) and reproductive output. These linear transformations underlie most models projecting species’ distributions and competitive interactions between native and invasive disease vectors. Using a series of meta-analyses, we show that the relationship between wing length and fecundity are nonlinear (hyperallometric) for most mosquito species. We show that whilst most models ignore reproductive hyperallometry (with respect to wing length), doing so introduces systematic biases into estimates of population growth. In particular, failing to account for reproductive hyperallometry overestimates the effects of temperature and underestimates the effects of competition. Assuming isometry also increases the potential to misestimate the efficacy of vector control strategies by underestimating the contribution of larger females in population replenishment. Finally, failing to account for reproductive hyperallometry and variation in body size can lead to qualitative errors via the counter-intuitive effects of Jensen’s inequality. For example, if mean sizes decrease, but variance increases, then reproductive outputs may actually increase. We suggest that future disease vector models incorporate hyperallometric relationships to more accurately predict changes in mosquito distribution in response to global change.
BACKGROUND: Rodent infestation is a global biological problem. Rodents are widely distributed worldwide, cause harm to agriculture, forestry, and animal husbandry production and spread a variety of natural focal diseases. In this study, 10 ecological niche models were combined into an ensemble model to assess the distribution of suitable habitats for Rhombomys opimus and to predict the impact of future climate change on the distribution of R. opimus under low, medium and high socioeconomic pathway scenarios of CMIP6. RESULTS: In general, with the exception of extreme climates (2090-SSP585), the current and potential future ranges of R. opimus habitat are maintained at approximately 220 × 10(4) km(2) . In combination with human footprint data, the potential distribution area of R. opimus was found to coincide with areas with a moderate human footprint. In addition, this distribution area will gradually shift to higher-latitude regions, and the suitable habitat area of R. opimus will gradually shrink in China, Iran, Afghanistan, and Turkmenistan while increasing in Mongolia and Kazakhstan. CONCLUSIONS: These results help identify the impact of climate change on the potential distribution of R. opimus and provide supportive information for the development of management strategies to protect against future ecological and human health risks. © 2022 Society of Chemical Industry.
Southern Brazil concentrates a considerable number of cases of cutaneous leishmaniasis reported since 1980, and Paraná is the state that most records CL cases in the region. The main sand fly species incriminated as vectors of Leishmania (Viannia) braziliensis (Vianna,1911) are Migonemyia (Migonemyia) migonei (França, 1920), Nyssomyia (Nyssomyia) neivai (Pinto, 1926) and Nyssomyia (Nyssomyia) whitmani (Antunes & Coutinho, 1936). In this study, we evaluated areas with climatic suitability for the distribution of these vectors and correlated these data with CL incidence in the state. The occurrence points of Mg. migonei, Ny. neivai, and Ny. whitmani were extracted from a literature review and field data. For CL analysis in the state of Paraná, data were obtained from the Informatics Department of the Unified Health System of Brazil (DATASUS), covering the period from 2001 to 2019. The layers of bioclimatic variables from the WorldClim database were used in the study. Species distribution modeling was developed using the MaxEnt Software version 3.4.4. ArcGIS software version 10.5 was used to develop suitability maps and the graphical representation of disease incidence. The AUC values were acceptable for all models (> 0,8). Bioclimatic variables BIO13 and BIO14 were the most influential in the distribution of Mg. migonei, while BIO19 and BIO6 were the variables that most influenced the distribution of Ny. neivai, and Ny. whitmani was most influenced by variables BIO5 and BIO9. During 19 years, 4992 cases of CL were reported in the state by 286 municipalities (71,6%). Northern Paraná showed the highest number of areas with very high and high climatic suitability for the occurrence of these species, coinciding with the highest number of CL cases. The modeling tools allowed analyzing the association between climatic variables and the geographical distribution of CL in the state. Moreover, they provided a better understanding of the climatic conditions related to the distribution of different species, favoring the monitoring of risk areas, the implementation of preventive measures, risk awareness, early and accurate diagnosis, and consequent timely treatment.
Future projections of malaria transmission is made for Odisha, a highly endemic region of India, through numerical simulations using the VECTRI dynamical model. The model is forced with bias-corrected temperature and rainfall from a global climate model (CCSM4) for the baseline period 1975-2005 and for the projection periods 2020s, 2050s, and 2080s under RCP8.5 emission scenario. The temperature, rainfall, mosquito density and entomological inoculation rate (EIR), generated from the VECTRI model are evaluated with the observation and analyzed further to estimate the future malaria transmission over Odisha on a spatio-temporal scale owing to climate change. Our results reveal that the malaria transmission in Odisha as a whole during summer and winter monsoon seasons may decrease in future due to the climate change except in few districts with the high elevations and dense forest regions such as Kandhamal, Koraput, Raygada and Mayurbhanj districts where an increase in malaria transmission is found. Compared to the baseline period, mosquito density shows decrease in most districts of the south, southwest, central, north and northwest regions of Odisha in 2030s, 2050s and 2080s. An overall decrease in malaria transmission of 20-40% (reduction in EIR) is seen during the monsoon season (June-Sept) over Odisha with the increased surface temperature of 3.5-4 °C and with the increased rainfall of 20-35% by the end of the century with respect to the baseline period. Furthermore, malaria transmission is likely to reduce in future over most of the Odisha regions with the increase in future warm and cold nights temperatures.
Dipetalogaster maxima is a primary vector of Chagas disease in the Cape region of Baja California Sur, Mexico. The geographic distribution of D. maxima is limited to this small region of the Baja California Peninsula in Mexico. Our study aimed to construct the ecological niche models (ENMs) of this understudied vector species and the parasite responsible for Chagas disease (Trypanosoma cruzi). We modelled the ecological niches of both species under current and future climate change projections in 2050 using four Representative Concentration Pathways (RCPs): RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. We also assessed the human population at risk of exposure to D. maxima bites, the hypothesis of ecological niche equivalency and similarity between D. maxima and T. cruzi, and finally the abundance centroid hypothesis. The ENM predicted a higher overlap between both species in the Western and Southern coastal regions of the Baja California Peninsula. The climate change scenarios predicted a Northern shift in the ecological niche of both species. Our findings suggested that the highly tourist destination of Los Cabos is a high-risk zone for Chagas disease circulation. Overall, the study provides valuable data to vector surveillance and control programs.
In the last two years we have experienced the effects of the COVID-19 pandemic in our lives and hospitals. Pandemics are part of the history of humanity and we can be certain that in the future new pandemics will appear. In fact, due to the growth in the human population, increased travel and global warming, it is to be expected that new pandemic pathogens will arise more frequently than before. Additionally, decreased barriers between animals and humans which will give rise to spillover events which will result in the introduction of new zoonotic pathogens in humans. In each of the parts of this series we will, in a short format, highlight a potential pandemic pathogen and describe its characteristics, history and potential for global pandemics. This part of the series is devoted to the Zika virus (ZIKV). We describe the history of ZIKV, the clinical picture and finally, we conclude with a discussion about the pandemic risk of ZIKV infection.
PURPOSE OF REVIEW: Leishmaniasis is a leading cause of parasitic death, with incidence rising from decreased resources to administer insecticide and anti-leishmanial treatments due to the COVID-19 pandemic. Leishmaniasis is nonendemic in the United States (U.S.), but enzootic canine populations and potentially competent vectors warrant monitoring of autochthonous disease as a fluctuating climate facilitates vector expansion. Recent studies concerning sand fly distribution and vector capacity were assessed for implications of autochthonous transmission within the U.S. RECENT FINDINGS: Climate change and insecticide resistance provide challenges in sand fly control. While most Leishmania-infected dogs in the U.S. were infected via vertical transmission or were imported, autochthonous vector-borne cases were reported. Autochthonous vector-borne human cases have been reported in four states. Further vaccine research could contribute to infection control. SUMMARY: Both cutaneous and visceral leishmaniasis cases in the U.S. are increasingly reported. Prevention measures including vector control and responsible animal breeding are critical to halt this zoonotic disease.
The presence, abundance, and distribution of sandflies are strongly influenced by climate and environmental changes. This study aimed to describe the sandfly fauna in an intense transmission area for visceral leishmaniasis and to evaluate the association between the abundance of Lutzomyia longipalpis sensu lato (Lutz & Neiva 1912) (Diptera: Psychodidae) and climatic variables. Captures were carried out 2 yr (July 2017 to June 2019) with automatic light traps in 16 sites of the urban area of Campo Grande, Mato Grosso do Sul state. The temperature (°C), relative humidity (%), precipitation (mm3), and wind speed (km/h) were obtained by a public domain database. The Wilcoxon test compared the absolute frequencies of the species by sex. The association between climatic variables and the absolute frequency of Lu. longipalpis s.l. was assessed using the Spearman’s correlation coefficient. A total of 1,572 sandflies into four species were captured. Lutzomyia longipalpis s.l. was the most abundant species and presented a significant correlation with the average temperature, humidity, and wind speed in different periods. Lutzomyia longipalpis s.l. was captured in all months, showing its plasticity in diverse weather conditions. We emphasize the importance of regular monitoring of vectors and human and canine cases, providing data for surveillance and control actions to continue to be carried out in the municipality.
After several pandemics over the last two millennia, the wildlife reservoirs of plague (Yersinia pestis) now persist around the world, including in the western United States. Routine surveillance in this region has generated comprehensive records of human cases and animal seroprevalence, creating a unique opportunity to test how plague reservoirs are responding to environmental change. Here, we test whether animal and human data suggest that plague reservoirs and spillover risk have shifted since 1950. To do so, we develop a new method for detecting the impact of climate change on infectious disease distributions, capable of disentangling long-term trends (signal) and interannual variation in both weather and sampling (noise). We find that plague foci are associated with high-elevation rodent communities, and soil biochemistry may play a key role in the geography of long-term persistence. In addition, we find that human cases are concentrated only in a small subset of endemic areas, and that spillover events are driven by higher rodent species richness (the amplification hypothesis) and climatic anomalies (the trophic cascade hypothesis). Using our detection model, we find that due to the changing climate, rodent communities at high elevations have become more conducive to the establishment of plague reservoirs-with suitability increasing up to 40% in some places-and that spillover risk to humans at mid-elevations has increased as well, although more gradually. These results highlight opportunities for deeper investigation of plague ecology, the value of integrative surveillance for infectious disease geography, and the need for further research into ongoing climate change impacts.
The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.
We proposed in this study a deterministic mathematical model of malaria transmission with climate variation factor. In the first place, fundamental properties of the model, such as positivity of solution and boundedness of the biological feasibility of the model, were proved whenever all initial data of the states were nonnegative. The next-generation matrix method is used to compute a basic reproduction number with respect to the disease-free equilibrium point. The Jacobian matrix and the Lyapunov function are used to check the local and global stability of disease-free equilibriums. If the basic reproduction number is less than one, the model’s disease-free equilibrium points are both locally and globally asymptotically stable; otherwise, an endemic equilibrium occurs. The results of the sensitivity analysis of the basic reproduction numbers were obtained, and its biological interpretation was provided. The existence of bifurcation was discussed, and the model exhibits forward and backward bifurcations with respect to the first and second basic reproduction numbers, respectively. Secondly, using the maximum principle of Pontryagin, the optimal malaria reduction strategies are described with three control measures, namely, treated bed nets, infected human treatment, and indoor residual spraying. Finally, based on numerical simulations of the optimality system, the combination of treatment and indoor spraying is the most efficient and least expensive strategy for malaria eradication.
(1) Background: This paper will present an elaboration of the risk assessment methodology by Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ), Eurac Research and United Nations University Institute for Environment and Human Security (UNU-EHS) for the assessment of dengue. (2) Methods: We validate the risk assessment model by best-fitting it with the number of dengue cases per province using the least-square fitting method. Seven out of thirty-four provinces in Indonesia were chosen (North Sumatra, Jakarta Capital, West Java, Central Java, East Java, Bali and East Kalimantan). (3) Results: A risk assessment based on the number of dengue cases showed an increased risk in 2010, 2015 and 2016 in which the effects of El Nino and La Nina extreme climates occurred. North Sumatra, Bali, and West Java were more influenced by the vulnerability component, in line with their risk analysis that tends to be lower than the other provinces in 2010, 2015 and 2016 when El Nino and La Nina occurred. (4) Conclusion: Based on data from the last ten years, in Jakarta Capital, Central Java, East Java and East Kalimantan, dengue risks were mainly influenced by the climatic hazard component while North Sumatra, Bali and West Java were more influenced by the vulnerability component.
Climate variability is a key factor in driving malaria outbreaks. As shown in previous studies, climate-driven malaria modeling provides a better understanding of malaria transmission dynamics, generating malaria-related parameters validated as a reliable benchmark to assess the impact of climate on malaria. In this framework, the present study uses climate observations and reanalysis products to evaluate the predictability of malaria incidence in West Africa. Sea surface temperatures (SSTs) are shown as a skillful predictor of malaria incidence, which is derived from climate-driven simulations with the Liverpool Malaria Model (LMM). Using the SST-based Statistical Seasonal Forecast model (S4CAST) tool, we find robust modes of anomalous SST variability associated with skillful predictability of malaria incidence Accordingly, significant SST anomalies in the tropical Pacific and Atlantic Ocean basins are related to a significant response of malaria incidence over West Africa. For the Mediterranean Sea, warm SST anomalies are responsible for increased surface air temperatures and precipitation over West Africa, resulting in higher malaria incidence; conversely, cold SST anomalies are responsible for decreased surface air temperatures and precipitation over West Africa, resulting in lower malaria incidence.. Our results put forward the key role of SST variability as a source of predictability of malaria incidence, being of paramount interest to decision-makers who plan public health measures against malaria in West Africa. Accordingly, SST anomalies could be used operationally to forecast malaria risk over West Africa for early warning systems.
Where ticks are found, tick-borne diseases can present a threat to human and animal health. The aetiology of many of these important diseases, including Lyme disease, bovine babesiosis, tick-borne fever and louping ill, have been known for decades whilst others have only recently been documented in the United Kingdom (UK). Further threats such as the importation of exotic ticks through human activity or bird migration, combined with changes to either the habitat or climate could increase the risk of tick-borne disease persistence and transmission. Prevention of tick-borne diseases for the human population and animals (both livestock and companion) is dependent on a thorough understanding of where and when pathogen transmission occurs. This information can only be gained through surveillance that seeks to identify where tick populations are distributed, which pathogens are present within those populations, and the periods of the year when ticks are active. To achieve this, a variety of approaches can be applied to enhance knowledge utilising a diverse range of stakeholders (public health professionals and veterinarians through to citizen scientists). Without this information, the application of mitigation strategies to reduce pathogen transmission and impact is compromised and the ability to monitor the effects of climate change or landscape modification on the risk of tick-borne disease is more challenging. However, as with many public and animal health interventions, there needs to be a cost-benefit assessment on the most appropriate intervention applied. This review will assess the challenges of tick-borne diseases in the UK and argue for a cross-disciplinary approach to their surveillance and control.
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.
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.
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: 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.
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: 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: 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: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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: 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.
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.
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: 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.
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.
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.
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 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 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 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.
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.
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.
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.
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.
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.
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.
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, 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.
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.
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.
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.
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: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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: 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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: 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.
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.
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.
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.
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.
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.
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.
