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.
