Incorporating the effects of humidity in a mechanistic model of Anopheles gambiae mosquito population dynamics in the Sahel region of Africa

Livestock keeping, mosquitoes and community viewpoints: a mixed methods assessment of relationships between livestock management, malaria vector biting risk and community perspectives in rural Tanzania

BACKGROUND

Livestock keeping is one of the potential factors related to malaria transmission. To date, the impact of livestock keeping on malaria transmission remains inconclusive, as some studies suggest a zooprophylactic effect while others indicate a zoopotentiation effect. This study assessed the impact of livestock management on malaria transmission risks in rural Tanzania. Additionally, the study explored the knowledge and perceptions of residents about the relationships between livestock keeping and malaria transmission risks in a selected village.

METHODS

In a longitudinal entomological study in Minepa village, South Eastern Tanzania, 40 households were randomly selected (20 with livestock, 20 without). Weekly mosquito collection was performed from January to April 2023. Indoor and outdoor collections used CDC-Light traps, Prokopack aspirators, human-baited double-net traps, and resting buckets. A subsample of mosquitoes was analysed using PCR and ELISA for mosquito species identification and blood meal detection. Livestock's impact on mosquito density was assessed using negative binomial GLMMs. Additionally, in-depth interviews explored community knowledge and perceptions of the relationship between livestock keeping and malaria transmission risks.

RESULTS

A total of 48,677 female Anopheles mosquitoes were collected. Out of these, 89% were Anopheles gambiae sensu lato (s.l.) while other species were Anopheles funestus s.l., Anopheles pharoensis, Anopheles coustani, and Anopheles squamosus. The findings revealed a statistically significant increase in the overall number of An. gambiae s.l. outdoors (RR = 1.181, 95%CI 1.050-1.862, p = 0.043). Also, there was an increase of the mean number of An. funestus s.l. mosquitoes collected in households with livestock indoors (RR = 2.866, 95%CI: 1.471-5.582, p = 0.002) and outdoors (RR = 1.579,95%CI 1.080-2.865, p = 0.023). The human blood index of Anopheles arabiensis mosquitoes from houses with livestock was less than those without livestock (OR = 0.149, 95%CI 0.110-0.178, p < 0.001). The majority of participants in the in-depth interviews reported a perceived high density of mosquitoes in houses with livestock compared to houses without livestock.

CONCLUSION

Despite the potential for zooprophylaxis, this study indicates a higher malaria transmission risk in livestock-keeping communities. It is crucial to prioritize and implement targeted interventions to control vector populations within these communities. Furthermore, it is important to enhance community education and awareness regarding covariates such as livestock that influence malaria transmission.

Future malaria environmental suitability in Africa is sensitive to hydrology

Changes in climate shift the geographic locations that are suitable for malaria transmission because of the thermal constraints on vector Anopheles mosquitos and Plasmodium spp. malaria parasites and the lack of availability of surface water for vector breeding. Previous Africa-wide assessments have tended to solely represent surface water using precipitation, ignoring many important hydrological processes. Here, we applied a validated and weighted ensemble of global hydrological and climate models to estimate present and future areas of hydroclimatic suitability for malaria transmission. With explicit surface water representation, we predict a net decrease in areas suitable for malaria transmission from 2025 onward, greater sensitivity to future greenhouse gas emissions, and different, more complex, malaria transmission patterns. Areas of malaria transmission that are projected to change are smaller than those estimated by precipitation-based estimates but are associated with greater changes in transmission season lengths.

The Impact of Climatic Factors on Temporal Mosquito Distribution and Population Dynamics in an Area Targeted for Sterile Insect Technique Pilot Trials

It is widely accepted that climate affects the mosquito life history traits; however, its precise role in determining mosquito distribution and population dynamics is not fully understood. This study aimed to investigate the influence of various climatic factors on the temporal distribution of Anopheles arabiensis populations in Mamfene, South Africa between 2014 and 2019. Time series analysis, wavelet analysis, cross-correlation analysis, and regression model combined with the autoregressive integrated moving average (ARIMA) model were utilized to assess the relationship between climatic factors and An. arabiensis population density. In total 3826 adult An. arabiensis collected was used for the analysis. ARIMA (0, 1, 2) (0, 0, 1)12 models closely described the trends observed in An. arabiensis population density and distribution. The wavelet coherence and time-lagged correlation analysis showed positive correlations between An. arabiensis population density and temperature (r = 0.537 ), humidity (r = 0.495) and rainfall (r = 0.298) whilst wind showed negative correlations (r = -0.466). The regression model showed that temperature (p = 0.00119), rainfall (p = 0.0436), and humidity (p = 0.0441) as significant predictors for forecasting An. arabiensis abundance. The extended ARIMA model (AIC = 102.08) was a better fit for predicting An. arabiensis abundance compared to the basic model. Anopheles arabiensis still remains the predominant malaria vector in the study area and climate variables were found to have varying effects on the distribution and abundance of An. arabiensis. This necessitates other complementary vector control strategies such as the Sterile Insect Technique (SIT) which involves releasing sterile males into the environment to reduce mosquito populations. This requires timely mosquito and climate information to precisely target releases and enhance the effectiveness of the program, consequently reducing the malaria risk.

Malaria positivity rate trend analysis at water resources development project of Wonji Sugar Estate Oromia, Ethiopia

Evidence on the trends of the proportion of malaria infections detected by routine passive case detection at health facilities is important for public health decision making especially in areas moving towards elimination. The objective was to assess nine years of trends on clinical malaria infections detected at health facility and its associated climate factors, in the water resource development set up of Wonji sugar estate, Oromia, Ethiopia. Retrospective data were collected from malaria-suspected patient recording logbook at Wonji sugar factory's primary hospital. Monthly average meteorological data were obtained from the estate meteorological station. Data were collected from April through June 2018 and January 2022. The data were analyzed using Stata version 16.0 software for Chi-square and regression analysis. Over the last nine years, 34,388 cases were legible for analysis with complete data. Of these, 11.75% (4039/34388) were positive for clinical malaria. Plasmodium vivax test positivity was the highest proportion (8.2%, n = 2820) followed by Plasmodium falciparum (3.48%, n = 1197) and mixed infections (P. falciparum and P. vivax, 0.06%, n = 21). The odds of being positive for malaria was highest in males (AOR = 1.46; 95%CI = 1.36-1.52; P < 0.001) compared to females and in older individuals of above 15 years old (AOR = 4.55, 95%CI = 4.01-5.17, P < 0.001) followed by school-aged children (5-15 years old) (AOR = 2.16; 95%CI = 1.88-2.49, P < 0.001). There was no significant variation in the proportion of malaria-positive cases in the dry and wet seasons (P = 0.059). Malaria test positivity rates were associated with average monthly rainfall (AdjIRR = 1.00; 95%CI = 1.00-1.001, P < 0.001) while negatively associated with average monthly minim temperature (adjIRR = 0.94; 95%CI = 0.94-0.95; P < 0.001) and average monthly relative humidity (adjIRR = 0.99, 95%CI = 0.99-1.00, P = 0.023). There was year-round malaria transmission, adults especially males and school children frequently tested malaria positive. Hence, alternative vector management tools like larval source management have to be deployed besides ITNs and IRS in such water development areas to achieve the malaria elimination goal.

Spatial and temporal analyses of the influences of meteorological and environmental factors on Aedes albopictus (Diptera: Culicidae) population dynamics during the peak abundance period at a city scale

Aedes albopictus (Diptera: Culicidae) is a major vector of multiple diseases. While vaccines have been developed, preventing these Aedes-borne diseases continues to primarily depend on monitoring and controlling the vector population. Despite increasing research on the impacts of various factors on Ae. albopictus population dynamics, there is still no consensus on how meteorological or environmental factors affect vector distribution. In this study, the relationships between mosquito abundance and meteorological and environmental indicators were examined at the town level based on data collected from July to September, the peak abundance period of 2019 in Shanghai. In addition to performing Poisson regression, we employed the geographically weighted Poisson regression model to account for spatial dependency and heterogeneity. The result showed that the environmental factors (notably human population density, the Normalized Difference Vegetation Index (NDVI), socioeconomic deprivation, and road density) had more significant impacts than the meteorological variables in accounting for the spatial variation of mosquito abundance at a city scale. The dominant environmental variable differed in urban and rural places. Furthermore, our findings indicated that deprived townships are more susceptible to higher vector densities compared to non-deprived townships. Therefore, it is crucial not only to allocate more resources but also to increase attention towards controlling the vectors responsible for their transmission in these townships.

The current landscape of software tools for the climate-sensitive infectious disease modelling community

Climate-sensitive infectious disease modelling is crucial for public health planning and is underpinned by a complex network of software tools. We identified only 37 tools that incorporated both climate inputs and epidemiological information to produce an output of disease risk in one package, were transparently described and validated, were named (for future searching and versioning), and were accessible (ie, the code was published during the past 10 years or was available on a repository, web platform, or other user interface). We noted disproportionate representation of developers based at North American and European institutions. Most tools (n=30 [81%]) focused on vector-borne diseases, and more than half (n=16 [53%]) of these tools focused on malaria. Few tools (n=4 [11%]) focused on food-borne, respiratory, or water-borne diseases. The under-representation of tools for estimating outbreaks of directly transmitted diseases represents a major knowledge gap. Just over half (n=20 [54%]) of the tools assessed were described as operationalised, with many freely available online.

Understanding the spatial non-stationarity in the relationships between malaria incidence and environmental risk factors using Geographically Weighted Random Forest: A case study in Rwanda

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R²: 0.944) and Tripura (RMSE: 0.0451; R²: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.

Malaria hotspots and climate change trends in the hyper-endemic malaria settings of Mizoram along the India-Bangladesh borders

India has made tremendous progress in reducing malaria mortality and morbidity in the last decade. Mizoram State in North-East India is one of the few malaria-endemic regions where malaria transmission has continued to remain high. As Mizoram shares international borders with Bangladesh and Myanmar, malaria control in this region is critical for malaria elimination efforts in all the three countries. For identifying hotspots for targeted intervention, malaria data from 385 public health sub-centers across Mizoram were analyzed in the Geographic Information System. Almost all the sub-centers reporting high Annual Parasite Index (> 10) are located in Mizoram's districts that border Bangladesh. Getis-Ord G_i* statistic shows most of the sub-centers located along the Bangladesh border in the Lawngtlai and Lunglei districts to be the malaria hotspots. The hotspots also extended into the Mamit and Siaha districts, especially along the borders of Lawngtlai and Lunglei. Analysis of terrain, climatic, and land use/land cover datasets obtained from the Global Modelling and Assimilation Office and satellite images show Mizoram's western part (Lawngtlai, Lunglei, and Mamit districts) to experience similar topographic and climatic conditions as the bordering Rangamati district in the Chittagong division of Bangladesh. Climatic trends in this region from 1981 to 2021, estimated by the Mann-Kendall test and Sen's slope estimates, show an increasing trend in minimum temperature, relative humidity, rainfall, and the associated shift of climatic pattern (temperate to tropical monsoon) could facilitate malaria transmission. The quasi-Poisson regression model estimates a strong association (p < 0.001) between total malaria cases, temperature range, and elevation. The Kruskal-Wallis H test shows a statistically significant association between malaria cases and forest classes (p < 0.001). A regional coordination and strategic plan are required to eliminate malaria from this hyper-endemic malaria region of North-East India.

Impact of aerial humidity on seasonal malaria: an ecological study in Zambia

Background

Seasonal patterns of malaria cases in many parts of Africa are generally associated with rainfall, yet in the dry seasons, malaria transmission declines but does not always cease. It is important to understand what conditions support these periodic cases. Aerial moisture is thought to be important for mosquito survival and ability to forage, but its role during the dry seasons has not been well studied. During the dry season aerial moisture is minimal, but intermittent periods may arise from the transpiration of peri-domestic trees or from some other sources in the environment. These periods may provide conditions to sustain pockets of mosquitoes that become active and forage, thereby transmitting malaria. In this work, humidity along with other ecological variables that may impact malaria transmission have been examined.

Methods

Negative binomial regression models were used to explore the association between peri-domestic tree humidity and local malaria incidence. This was done using sensitive temperature and humidity loggers in the rural Southern Province of Zambia over three consecutive years. Additional variables including rainfall, temperature and elevation were also explored.

Results

A negative binomial model with no lag was found to best fit the malaria cases for the full year in the evaluated sites of the Southern Province of Zambia. Local tree and granary night-time humidity and temperature were found to be associated with local health centre-reported incidence of malaria, while rainfall and elevation did not significantly contribute to this model. A no lag and one week lag model for the dry season alone also showed a significant effect of humidity, but not temperature, elevation, or rainfall.

Conclusion

The study has shown that throughout the dry season, periodic conditions of sustained humidity occur that may permit foraging by resting mosquitoes, and these periods are associated with increased incidence of malaria cases. These results shed a light on conditions that impact the survival of the common malaria vector species, Anopheles arabiensis, in arid seasons and suggests how they emerge to forage when conditions permit.

Malaria in Senegal: Recent and Future Changes Based on Bias-Corrected CMIP6 Simulations

Malaria is a constant reminder of the climate change impacts on health. Many studies have investigated the influence of climatic parameters on aspects of malaria transmission. Climate conditions can modulate malaria transmission through increased temperature, which reduces the duration of the parasite’s reproductive cycle inside the mosquito. The rainfall intensity and frequency modulate the mosquito population’s development intensity. In this study, the Liverpool Malaria Model (LMM) was used to simulate the spatiotemporal variation of malaria incidence in Senegal. The simulations were based on the WATCH Forcing Data applied to ERA-Interim data (WFDEI) used as a point of reference, and the biased-corrected CMIP6 model data, separating historical simulations and future projections for three Shared Socio-economic Pathways scenarios (SSP126, SSP245, and SSP585). Our results highlight a strong increase in temperatures, especially within eastern Senegal under the SSP245 but more notably for the SSP585 scenario. The ability of the LMM model to simulate the seasonality of malaria incidence was assessed for the historical simulations. The model revealed a period of high malaria transmission between September and November with a maximum reached in October, and malaria results for historical and future trends revealed how malaria transmission will change. Results indicate a decrease in malaria incidence in certain regions of the country for the far future and the extreme scenario. This study is important for the planning, prioritization, and implementation of malaria control activities in Senegal.

Effect of the Rural and Urban Microclimate on Mosquito Richness and Abundance in Yucatan State, Mexico

The objective of this study was to analyze the effect of the rural and urban microclimate on the presence of mosquitoes. Temperature (T) and relative humidity (RH; indoors and outdoors), as well as mosquito richness and abundance were measured in two sites (urban and rural) of the Yucatan State, Mexico. Species richness was higher in the urban site, whereas mosquito abundance was higher in the rural site. The microclimates of urban and rural housing differently affect mosquito richness and abundance. Mosquito richness and abundance were higher outdoors than indoors in the urban site, but they were higher indoors than outdoors in the rural site. For the urban site, analysis of the relation of T and RH with the registered parameters revealed that species richness increased with increasing indoor RH, and that mosquito abundance increased with increasing indoor T and RH. In the rural site, species richness was not affected, but abundance increased with increasing T and RH (indoors as well as outdoors). Results are discussed in the context of the management of mosquito transmitted diseases. No IRB approval was necessary since no ethical implications were identified to be reviewed by the ethical committee for the research of the ECOSUR Institution.

Ecohydrology 2.0

This paper aims at a definition of the domain of ecohydrology, a relatively new discipline borne out of an intrusion-as advertised by this Topical Collection of the Rendiconti Lincei-of hydrology and geomorphology into ecology (or vice-versa, depending on the reader's background). The study of hydrologic controls on the biota proves, in our view, significantly broader than envisioned by its original focus that was centered on the critical zone where much of the action of soil, climate and vegetation interactions takes place. In this review of related topics and contributions, we propose a reasoned broadening of perspective, in particular by firmly centering ecohydrology on the fluvial catchment as its fundamental control volume. A substantial unity of materials and methods suggests that our advocacy may be considered legitimate.

Exploring agricultural land-use and childhood malaria associations in sub-Saharan Africa

Agriculture in Africa is rapidly expanding but with this comes potential disbenefits for the environment and human health. Here, we retrospectively assess whether childhood malaria in sub-Saharan Africa varies across differing agricultural land uses after controlling for socio-economic and environmental confounders. Using a multi-model inference hierarchical modelling framework, we found that rainfed cropland was associated with increased malaria in rural (OR 1.10, CI 1.03-1.18) but not urban areas, while irrigated or post flooding cropland was associated with malaria in urban (OR 1.09, CI 1.00-1.18) but not rural areas. In contrast, although malaria was associated with complete forest cover (OR 1.35, CI 1.24-1.47), the presence of natural vegetation in agricultural lands potentially reduces the odds of malaria depending on rural-urban context. In contrast, no associations with malaria were observed for natural vegetation interspersed with cropland (veg-dominant mosaic). Agricultural expansion through rainfed or irrigated cropland may increase childhood malaria in rural or urban contexts in sub-Saharan Africa but retaining some natural vegetation within croplands could help mitigate this risk and provide environmental co-benefits.

The neglected role of relative humidity in the interannual variability of urban malaria in Indian cities

The rapid pace of urbanization makes it imperative that we better understand the influence of climate forcing on urban malaria transmission. Despite extensive study of temperature effects in vector-borne infections in general, consideration of relative humidity remains limited. With process-based dynamical models informed by almost two decades of monthly surveillance data, we address the role of relative humidity in the interannual variability of epidemic malaria in two semi-arid cities of India. We show a strong and significant effect of humidity during the pre-transmission season on malaria burden in coastal Surat and more arid inland Ahmedabad. Simulations of the climate-driven transmission model with the MLE (Maximum Likelihood Estimates) of the parameters retrospectively capture the observed variability of disease incidence, and also prospectively predict that of 'out-of-fit' cases in more recent years, with high accuracy. Our findings indicate that relative humidity is a critical factor in the spread of urban malaria and potentially other vector-borne epidemics, and that climate change and lack of hydrological planning in cities might jeopardize malaria elimination efforts.

Aedes albopictus life table: environment, food, and age dependence survivorship and reproduction in a tropical area

Background

Environmental conditions affect the biology of mosquito vectors. Aedes albopictus is a major vector of many important diseases including dengue, Zika, and chikungunya in China. Understanding the development, fecundity, and survivorship of Ae. albopictus mosquitoes in different environmental conditions is beneficial for the implementation of effective vector control measures.

Methods

Aedes albopictus larval and adult life-table experiments were conducted under natural conditions in indoor, half-shaded, and fully shaded settings, simulating the three major habitat types in Hainan Province, a tropical island in the South China Sea. Temperature, humidity, and light intensity were recorded daily. Larval rearing used habitat water and tap water, with and without additional artificial food. Development time, survivorship, pupation rate, and adult emergence rates were monitored. Adult mosquito survivorship and fecundity were monitored daily and reproductive rates were determined, and age-dependent survivorship and reproduction were analyzed.

Results

The pupation time and male and female emergence times were significantly shorter in indoor conditions than in shaded and half-shaded conditions for both tap water with added food and habitat water with added food groups. For habitat water with added food, the shaded environment had the lowest pupation rate among the settings. For tap water with added food group, the shaded environment had the lowest pupation rate. The mean survival time of females was 27.3 ± 0.8 days in the indoor condition, which was significantly longer than that in the half-shaded (18.4 ± 0.6 days) and shaded (13.8 ± 1.2 days) conditions. Adult mortality was age-dependent, and the rate of change in mortality with age was not significantly different among different environmental conditions. The mean net replacement rate (R₀) of female mosquitoes showed no significant difference among the three conditions, whereas the per capita intrinsic growth rate (r) in the shaded condition was 42.0% and 20.4% higher than that in the indoor and half-shaded conditions, respectively. Female daily egg mass was also age-dependent in all the settings, decaying exponentially with age.

Conclusions

Our results imply that half-shaded conditions are likely the best natural condition for adult emergence and female reproduction, and food supply is crucial for larval development and pupation. The results provide new avenues for integrated mosquito management in indoor and outdoor areas, especially in half-shaded areas.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-05081-x.

How will mosquitoes adapt to climate warming?

The potential for adaptive evolution to enable species persistence under a changing climate is one of the most important questions for understanding impacts of future climate change. Climate adaptation may be particularly likely for short-lived ectotherms, including many pest, pathogen, and vector species. For these taxa, estimating climate adaptive potential is critical for accurate predictive modeling and public health preparedness. Here, we demonstrate how a simple theoretical framework used in conservation biology-evolutionary rescue models-can be used to investigate the potential for climate adaptation in these taxa, using mosquito thermal adaptation as a focal case. Synthesizing current evidence, we find that short mosquito generation times, high population growth rates, and strong temperature-imposed selection favor thermal adaptation. However, knowledge gaps about the extent of phenotypic and genotypic variation in thermal tolerance within mosquito populations, the environmental sensitivity of selection, and the role of phenotypic plasticity constrain our ability to make more precise estimates. We describe how common garden and selection experiments can be used to fill these data gaps. Lastly, we investigate the consequences of mosquito climate adaptation on disease transmission using Aedes aegypti-transmitted dengue virus in Northern Brazil as a case study. The approach outlined here can be applied to any disease vector or pest species and type of environmental change.

Remote sensing of environmental risk factors for malaria in different geographic contexts

BACKGROUND

Despite global intervention efforts, malaria remains a major public health concern in many parts of the world. Understanding geographic variation in malaria patterns and their environmental determinants can support targeting of malaria control and development of elimination strategies.

METHODS

We used remotely sensed environmental data to analyze the influences of environmental risk factors on malaria cases caused by Plasmodium falciparum and Plasmodium vivax from 2014 to 2017 in two geographic settings in Ethiopia. Geospatial datasets were derived from multiple sources and characterized climate, vegetation, land use, topography, and surface water. All data were summarized annually at the sub-district (kebele) level for each of the two study areas. We analyzed the associations between environmental data and malaria cases with Boosted Regression Tree (BRT) models.

RESULTS

We found considerable spatial variation in malaria occurrence. Spectral indices related to land cover greenness (NDVI) and moisture (NDWI) showed negative associations with malaria, as the highest malaria rates were found in landscapes with low vegetation cover and moisture during the months that follow the rainy season. Climatic factors, including precipitation and land surface temperature, had positive associations with malaria. Settlement structure also played an important role, with different effects in the two study areas. Variables related to surface water, such as irrigated agriculture, wetlands, seasonally flooded waterbodies, and height above nearest drainage did not have strong influences on malaria.

CONCLUSION

We found different relationships between malaria and environmental conditions in two geographically distinctive areas. These results emphasize that studies of malaria-environmental relationships and predictive models of malaria occurrence should be context specific to account for such differences.

Ten years of monitoring malaria trend and factors associated with malaria test positivity rates in Lower Moshi

Background

High altitude settings in Eastern Africa have been reported to experience increased malaria burden due to vector habitat expansion. This study explored possible associations between malaria test positivity rates and its predictors including malaria control measures and meteorological factors at a high-altitude, low malaria transmission setting, south of Mount Kilimanjaro.

Methods

Malaria cases reported at the Tanganyika Plantation Company (TPC) hospital’s malaria registers, meteorological data recorded at TPC sugar factory and data on bed nets distributed in Lower Moshi from 2009 to 2018 were studied. Correlation between bed nets distributed and malaria test positivity rates were explored by using Pearson correlation analysis and the associations between malaria test positivity rates and demographic and meteorological variables were determined by logistic regression and negative binomial regression analyses, respectively.

Results

Malaria cases reported at TPC hospital ranged between 0.48 and 2.26% per year and increased slightly at the introduction of malaria rapid diagnostic tests. The risk of testing positive for malaria were significantly highest among individuals aged between 6 and 15 years (OR = 1.65; 1.65 CI = 1.28–2.13; p = 0.001) and 16–30 years (OR = 1.49; CI = 1.17–1.89; p = 0.001) and when adjusted for age, the risk were significantly higher among male individuals when compared to female individuals (OR = 1.54; 1.00–1.31; p = 0.044). Malaria test positivity rates were positively associated with average monthly minimum temperatures and negatively associated with average monthly maximum temperatures (incidence rate ratio (IRR) = 1.37, 95% confidence interval (CI) = 1.05–1.78, p = 0.019 and IRR = 0.72, 95% CI = 0.58–0.91, p = 0.005, respectively). When analysed with one month lag for predictor variables, malaria test positivity rates were still significantly associated with average monthly minimum and maximum temperatures (IRR = 1.67, 95% CI = 1.28–2.19, p = 0.001 and IRR = 0.68, 95% CI = 0.54–0.85, p = 0.001, respectively). Average monthly rainfall and relative humidity with or without a one month lag was not associated with malaria test positivity rates in the adjusted models. Explopring possible associations between distribution of long-lasting insecticidal nets, (LLINs) and malaria test positivity rates showed no apparent correlation between numbers of LLINs distributed in a particular year and malaria test positivity rates.

Conclusion

In Lower Moshi, the risk of being tested positive for malaria was highest for older children and male individuals. Higher minimum and lower maximum temperatures were the strongest climatic predictors for malaria test positivity rates. In areas with extensive irrigation activity as in Lower Moshi, vector abundance and thus malaria transmission may be less dependent on rainfall patterns and humidity. Mass distribution of LLINs did not have an effect in this area with already very low malaria transmission.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03730-1.

Ecological drivers of genetic connectivity for African malaria vectors Anopheles gambiae and An. arabiensis

Anopheles gambiae and An. arabiensis are major malaria vectors in sub-Saharan Africa. Knowledge of how geographical factors drive the dispersal and gene flow of malaria vectors can help in combatting insecticide resistance spread and planning new vector control interventions. Here, we used a landscape genetics approach to investigate population relatedness and genetic connectivity of An. gambiae and An. arabiensis across Kenya and determined the changes in mosquito population genetic diversity after 20 years of intensive malaria control efforts. We found a significant reduction in genetic diversity in An. gambiae, but not in An. arabiensis as compared to prior to the 20-year period in western Kenya. Significant population structure among populations was found for both species. The most important ecological driver for dispersal and gene flow of An. gambiae and An. arabiensis was tree cover and cropland, respectively. These findings highlight that human induced environmental modifications may enhance genetic connectivity of malaria vectors.

Copula geoadditive modelling of anaemia and malaria in young children in Kenya, Malawi, Tanzania and Uganda

BACKGROUND

Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity and mortality. This study aimed to explore the complex relationship between anaemia and malaria in young children across the districts or counties of four contiguous sub-Saharan African countries, namely Kenya, Malawi, Tanzania and Uganda, while accounting for the effects of socio-economic, demographic and environmental factors. Geospatial maps were constructed to visualise the relationship between the two responses across the districts of the countries.

METHODS

A joint bivariate copula regression model was used, which estimates the correlation between the two responses conditional on the linear, non-linear and spatial effects of the explanatory variables considered. The copula framework allows the dependency structure between the responses to be isolated from their marginal distributions. The association between the two responses was set to vary according to the district of residence across the four countries.

RESULTS

The study revealed a positive association between anaemia and malaria throughout the districts, the strength of which varied across the districts of the four countries. Due to this heterogeneous association between anaemia and malaria, we further considered the joint probability of each combination of outcome of anaemia and malaria to further reveal more about the relationship between the responses. A considerable number of districts had a high joint probability of a child being anaemic but not having malaria. This might suggest the existence of other significant drivers of childhood anaemia in these districts.

CONCLUSIONS

This study presents an alternative technique to joint modelling of anaemia and malaria in young children which assists in understanding more about their relationship compared to techniques of multivariate modelling. The approach used in this study can aid in visualising the relationship through mapping of their correlation and joint probabilities. These maps produced can then help policy makers target the correct set of interventions, or prevent the use of incorrect interventions, particularly for childhood anaemia, the causes of which are multiple and complex.

Cyclic epidemics and extreme outbreaks induced by hydro-climatic variability and memory

A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme per se, rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.

The readily transformable Impatiens walleriana efficiently attracts nectar feeding with Aedes and Culex mosquitoes in simulated outdoor garden settings in Mississippi and Florida

Mosquitoes rely upon plant nectars for their energy needs, a trait that has the potential to allow nectar to serve as a platform for producing and delivering toxins to nuisance and/or vector mosquito species. Impatiens walleriana (Order: Ericales, Family: Balsaminaceae) is a readily transformable and widely planted nectar plant that has been previously shown to attract mosquito nectar-feeding. However, those feeding studies were only conducted indoors and did not test if variable environmental conditions will affect nectar feeding. In this study, we tested incidence of nectar feeding from the extrafloral nectaries of I. walleriana with the mosquitoes Aedes aegypti, Ae. albopictus and Culex quinquefasciatus (Order: Diptera, Family: Culicidae) in simulated, outdoor garden settings in Mississippi and Florida. I. walleriana and other common garden plants (in a 1:4 ratio) were placed into a mesh-lined 4'x7' mesocosm along with 50 mosquitoes. To track nectar feeding, the nectar of I. walleriana was tagged with red dye and mosquitoes were analyzed for red dye fluorescence after feeding. Fluorescence analysis demonstrated that 81.9% of male and 86.6% of female mosquitoes fed on the nectar of I. walleriana within 24 h. This suggests that mosquitoes may readily feed on impatiens nectar in outdoor garden settings at temperate and semi-tropical sites, even when alternate common garden plants are available. This attraction capacity is essential for the further consideration of I. walleriana for development as a transgenic, mosquitocidal nectar plant.

Climate Variability and Malaria over West Africa

Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1-2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.

Linking Mosquito Ecology, Traits, Behavior, and Disease Transmission

Mosquitoes are considered to be the deadliest animals on Earth because the diseases they transmit claim at least a million human lives every year globally. Here, we discuss the scales at which the effects of ecological factors cascade to influence epidemiologically relevant behaviors of adult mosquitoes. In particular, we focused our review on the environmental conditions (coarse-scale variables) that shape the life-history traits of larvae and adult mosquitoes (fine-scale traits), and how these factors and their association, in turn, modulate adult behaviors to influence mosquito-borne disease transmission. Finally, we explore the integration of physical, physiological, and behavioral information into predictive models with epidemiological applications.

Shift in Potential Malaria Transmission Areas in India, Using the Fuzzy-Based Climate Suitability Malaria Transmission (FCSMT) Model under Changing Climatic Conditions

The future implications of climate change on malaria transmission at the global level have already been reported, however such evidences are scarce and limited in India. Here our study aims to assess, identify and map the potential effects of climate change on Plasmodium vivax (Pv) and Plasmodium falciparum (Pf) malaria transmission in India. A Fuzzy-based Climate Suitability Malaria Transmission (FCSMT) model under the GIS environment was generated using Temperature and Relative Humidity data, extracted from CORDEX South Asia for Baseline (1976-2005) and RCP 4.5 scenario for future projection by the 2030s (2021-2040). National malaria data were used at the model analysis stage. Model outcomes suggest that climate change may significantly increase the spatial spread of Pv and Pf malaria with a numerical increase in the transmission window's (TW) months, and a shift in the months of transmission. Some areas of the western Himalayan states are likely to have new foci of Pv malaria transmission. Interior parts of some southern and eastern states are likely to become more suitable for Pf malaria transmission. Study has also identified the regions with a reduction in transmission months by the 2030s, leading to unstable malaria, and having the potential for malaria outbreaks.

Prevalence of afebrile malaria and development of risk-scores for gradation of villages: A study from a hot-spot in Odisha

Introduction

Malaria is a public health emergency in India and Odisha. The national malaria elimination programme aims to expedite early identification, treatment and follow-up of malaria cases in hot-spots through a robust health system, besides focusing on efficient vector control. This study, a result of mass screening conducted in a hot-spot in Odisha, aimed to assess prevalence, identify and estimate the risks and develop a management tool for malaria elimination.

Methods

Through a cross-sectional study and using WHO recommended Rapid Diagnostic Test (RDT), 13221 individuals were screened. Information about age, gender, education and health practices were collected along with blood sample (5 μl) for malaria testing. Altitude, forestation, availability of a village health worker and distance from secondary health center were captured using panel technique. A multi-level poisson regression model was used to analyze association between risk factors and prevalence of malaria, and to estimate risk scores.

Results

The prevalence of malaria was 5.8% and afebrile malaria accounted for 79 percent of all confirmed cases. Higher proportion of Pv infections were afebrile (81%). We found the prevalence to be 1.38 (1.1664–1.6457) times higher in villages where the Accredited Social Health Activist (ASHA) didn’t stay; the risk increased by 1.38 (1.0428–1.8272) and 1.92 (1.4428–2.5764) times in mid- and high-altitude tertiles. With regard to forest coverage, villages falling under mid- and highest-tertiles were 2.01 times (1.6194–2.5129) and 2.03 times (1.5477–2.6809), respectively, more likely affected by malaria. Similarly, villages of mid tertile and lowest tertile of education had 1.73 times (1.3392–2.2586) and 2.50 times (2.009–3.1244) higher prevalence of malaria.

Conclusion

Presence of ASHA worker in villages, altitude, forestation, and education emerged as principal predictors of malaria infection in the study area. An easy-to-use risk-scoring system for ranking villages based on these risk factors could facilitate resource prioritization for malaria elimination.

Effects of Socio-Environmental Factors on Malaria Infection in Pakistan: A Bayesian Spatial Analysis

The role of socio-environmental factors in shaping malaria dynamics is complex and inconsistent. Effects of socio-environmental factors on malaria in Pakistan at district level were examined. Annual malaria cases data were obtained from Directorate of Malaria Control Program, Pakistan. Meteorological data were supplied by Pakistan Meteorological Department. A major limitation was the use of yearly, rather than monthly/weekly malaria data in this study. Population data, socio-economic data and education score data were downloaded from internet. Bayesian conditional autoregressive model was used to find the statistical association of socio-environmental factors with malaria in Pakistan. From 136/146 districts in Pakistan, >750,000 confirmed malaria cases were included, over a three years' period (2013-2015). Socioeconomic status ((posterior mean value -3.965, (2.5% quintile, -6.297%), (97.5% quintile, -1.754%)) and human population density (-7.41 × 10-4, -0.001406%, -1.05 × 10-4 %) were inversely related, while minimum temperature (0.1398, 0.05275%, 0.2145%) was directly proportional to malaria in Pakistan during the study period. Spatial random effect maps presented that moderate relative risk (RR, 0.75 to 1.24) and high RR (1.25 to 1.99) clusters were scattered throughout the country, outnumbering the ones' with low RR (0.23 to 0.74). Socio-environmental variables influence annual malaria incidence in Pakistan and needs further evaluation.

Predicting the direct and indirect impacts of climate change on malaria in coastal Kenya

Background

The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. This study investigates the combined, i.e. direct and indirect, impacts of climate change on the dynamics of malaria through modifications in: (i) the sporogonic cycle of Plasmodium induced by air temperature increase, and (ii) the life cycle of Anopheles vector triggered by changes in natural breeding habitat arising from the altered moisture dynamics resulting from acclimation responses of vegetation under climate change. The study is performed for a rural region in Kilifi county, Kenya.

Methods and findings

We use a stochastic lattice-based malaria (SLIM) model to make predictions of changes in Anopheles vector abundance, the life cycle of Plasmodium parasites, and thus malaria transmission under projected climate change in the study region. SLIM incorporates a nonlinear temperature-dependence of malaria parasite development to estimate the extrinsic incubation period of Plasmodium. It is also linked with a spatially distributed eco-hydrologic modeling framework to capture the impacts of climate change on soil moisture dynamics, which served as a key determinant for the formation and persistence of mosquito larval habitats on the land surface. Malaria incidence data collected from 2008 to 2013 is used for SLIM model validation. Projections of climate change and human population for the region are used to run the models for prediction scenarios.

Under elevated atmospheric CO₂ concentration ([CO₂]) only, modeled results reveal wetter soil moisture in the root zone due to the suppression of transpiration from vegetation acclimation, which increases the abundance of Anopheles vectors and the risk of malaria. When air temperature increases are also considered along with elevated [CO₂], the life cycle of Anopheles vector and the extrinsic incubation period of Plasmodium parasites are shortened nonlinearly. However, the reduction of soil moisture resulting from higher evapotranspiration due to air temperature increase also reduces the larval habitats of the vector. Our findings show the complicated role of vegetation acclimation under elevated [CO₂] on malaria dynamics and indicate an indirect but ignored impact of air temperature increase on malaria transmission through reduction in larval habitats and vector density.

Conclusions

Vegetation acclimation triggered by elevated [CO₂] under climate change increases the risk of malaria. In addition, air temperature increase under climate change has opposing effects on mosquito larval habitats and the life cycles of both Anopheles vectors and Plasmodium parasites. The indirect impacts of temperature change on soil moisture dynamics are significant and should be weighed together with the direct effects of temperature change on the life cycles of mosquitoes and parasites for future malaria prediction and control.

Impact of Weekly Climatic Variables on Weekly Malaria Incidence throughout Thailand: A Country-Based Six-Year Retrospective Study

Purpose. This study aimed to evaluate climatic data, including mean temperature, relative humidity, and rainfall, and their association with malaria incidence throughout Thailand from 2012 to 2017. The correlation of climatic parameters including temperature, relative humidity, and rainfall in each province and the weekly malaria incidence was analyzed using Spearman's rank correlation. The results showed that the mean temperature correlated with malaria incidence (p value < 0.05) in 44 provinces in Thailand. These correlations were frequently found in the western and southern parts of Thailand. Relative humidity correlated with malaria incidence (p value < 0.05) in 35 provinces. These correlations were frequently found in the northern and northeastern parts of Thailand. Rainfall correlated with malaria incidence (p value < 0.05) in 38 provinces. These correlations were frequently found in the northern parts and some western parts of Thailand. The impacts of the mean temperature, relative humidity, and rainfall were observed frequently in specific provinces, including Chiang Mai, Chiang Rai, Trat, Kanchanaburi, Ubonratchathani, and Si Sa Ket. This is the first study to report areas where climatic data are associated with malaria incidence throughout Thailand from 2012 to 2017. These results can map out the climatic change process over time and across the country, which is the foundation for effective early warning systems for malaria, public health awareness campaigns, and the adoption of proper adaption measures that will help in malaria detection, diagnosis, and treatment.

Prevention of malaria transmission around reservoirs: an observational and modelling study on the effect of wind direction and village location

BACKGROUND

Many large dams are constructed annually in Africa, with associated reservoirs that might exacerbate the risk of malaria in new villages built nearby. We aimed to investigate the heterogeneous risk of malaria around reservoirs related to the impact of wind direction on malaria transmission.

METHODS

Between June 15, 2012, and April 22, 2015, we obtained field data on climate and hydrological conditions, and monitored Anopheles mosquito populations around the Koka reservoir in Ethiopia using in-situ weather stations, mosquito light traps, and larval dipping. The field data were used to calibrate a field-tested, spatially explicit mechanistic malaria transmission model, the Hydrology, Entomology, and Malaria Transmission Simulator (HYDREMATS), to investigate the effect of relative wind direction on malaria transmission and associated mechanisms. We combined our simulation results and observational data to assess the association between village location around a reservoir and risk of malaria.

FINDINGS

HYDREMATS simulations showed that wind blowing from a village towards a reservoir increases the size of malaria vector populations, whereas wind blowing from a reservoir towards a village decreases the size of malaria vector populations, which was consistent with our field data. Larval mortality is low in locations with village-to-reservoir wind due to weak surface waves, and this wind direction creates conditions that enable mosquitoes to identify village locations more easily than in conditions caused by reservoir-to-village wind, which increases the size of malaria vector populations, and thus malaria transmission. Among the wind conditions investigated (0·5-5 m/s), the effect of CO₂ attraction on the size of the Anopheles population was largest at wind speeds of 0·5 m/s and 1 m/s, decreasing with higher wind speed. At a wind speed of 5 m/s, the effect of CO₂ attraction was negligible, whereas the effect of waves was strongest. The effect of advection on Anopheles population size was negligible at all wind speeds and wind directions.

INTERPRETATION

The effect of wind on malaria transmission around reservoirs can be substantial. The transmission of malaria can be minimised if the location of villages situated near a reservoir is carefully considered. For areas in which the environmental conditions surrounding a resevoir are equal, villages should be located downwind of reservoirs to reduce the incidence of malaria, although further research will be required in locations where wind direction changes in different seasons.

FUNDING

US National Science Foundation, and Cooperative Agreement between the Masdar Institute of Science and Technology, Abu Dhabi, UAE, and the Massachusetts Institute of Technology, Cambridge, MA, USA.

Influence of Climatic Factors on Malaria Epidemic in Gulu District, Northern Uganda: A 10-Year Retrospective Study

Background

Globally, 15 countries, mainly in Sub-Saharan Africa, account for 80% of malaria cases and 78% of malaria related deaths. In Uganda, malaria is endemic and the mortality and morbidity due to malaria cause significant negative impact on the economy. In Gulu district, malaria is the leading killer disease among children <5 years. In 2015, the high intensity of malaria infection in Northern Uganda revealed a possible link between malaria and rainfall. However, available information on the influence of climatic factors on malaria are scarce, conflicting, and highly contextualized and therefore one cannot reference such information to malaria control policy in Northern Uganda, thus the need for this study.

Methods and Results

During the 10 year's retrospective study period a total of 2,304,537 people suffered from malaria in Gulu district. Malaria infection was generally stable with biannual peaks during the months of June-July and September-October but showed a declining trend after introduction of indoor residual spraying. Analysis of the departure of mean monthly malaria cases from the long-term mean monthly malaria cases revealed biannual seasonal outbreaks before and during the first year of introduction of indoor residual spraying. However, there were two major malaria epidemics in 2015 following discontinuation of indoor residual spraying in the late 2014. Children <5 years of age were disproportionally affected by malaria and accounted for 47.6% of the total malaria cases. Both rainfall (P=0.04) and relative humidity (P=0.003) had significant positive correlations with malaria. Meanwhile, maximum temperature had significant negative correlation with malaria (P=0.02) but minimum temperature had no correlation with malaria (P=0.29).

Conclusion

Malaria in Gulu disproportionately affects children under 5 years and shows seasonality with a generally stable trend influenced by rainfall and relative humidity. However, indoor residual spraying is a very promising method to achieve a sustained malaria control in this population.

Timing malaria transmission with mosquito fluctuations

Temporal variations in the activity of arthropod vectors can dramatically affect the epidemiology and evolution of vector‐borne pathogens. Here, we explore the “Hawking hypothesis”, which states that these pathogens may evolve the ability to time investment in transmission to match the activity of their vectors. First, we use a theoretical model to identify the conditions promoting the evolution of time‐varying transmission strategies in pathogens. Second, we experimentally test the “Hawking hypothesis” by monitoring the within‐host dynamics of Plasmodium relictum throughout the acute and the chronic phases of the bird infection. We detect a periodic increase of parasitemia and mosquito infection in the late afternoon that coincides with an increase in the biting activity of its natural vector. We also detect a positive effect of mosquito bites on Plasmodium replication in the birds both in the acute and in the chronic phases of the infection. This study highlights that Plasmodium parasites use two different strategies to increase the match between transmission potential and vector availability. We discuss the adaptive nature of these unconditional and plastic transmission strategies with respect to the time scale and the predictability of the fluctuations in the activity of the vector.

Weather variables and the El Niño Southern Oscillation may drive the epidemics of dengue in Guangdong Province, China

OBJECTIVE

To investigate the periodicity of dengue and the relationship between weather variables, El Niño Southern Oscillation (ENSO) and dengue incidence in Guangdong Province, China.

METHODS

Guangdong monthly dengue incidence and weather data and El Niño index information for 1988 to 2015 were collected. Wavelet analysis was used to investigate the periodicity of dengue, and the coherence and time-lag phases between dengue and weather variables and ENSO. The Generalized Additive Model (GAM) approach was further employed to explore the dose-response relationship of those variables on dengue. Finally, random forest analysis was applied to measure the relative importance of the climate predictors.

RESULTS

Dengue in Guangdong has a dominant annual periodicity over the period 1988-2015. Mean minimum temperature, total precipitation, and mean relative humidity are positively related to dengue incidence for 2, 3, and 4months lag, respectively. ENSO in the previous 12months may have driven the dengue epidemics in 1995, 2002, 2006 and 2010 in Guangdong. GAM analysis indicates an approximate linear association for the temperature-dengue relationship, approximate logarithm curve for the humidity-dengue relationship, and an inverted U-shape association for the precipitation-dengue (the threshold of precipitation is 348mm per month) and ENSO-dengue relationships (the threshold of ENSO index is 0.6°C). The monthly mean minimum temperature in the previous two months was identified as the most important climate variable associated with dengue epidemics in Guangdong Province.

CONCLUSION

Our study suggests weather factors and ENSO are important predictors of dengue incidence. These findings provide useful evidence for early warning systems to help to respond to the global expansion of dengue fever.

Environmental Determinants of Malaria Transmission Around the Koka Reservoir in Ethiopia

New dam construction is known to exacerbate malaria transmission in Africa as the vectors of malaria-Anopheles mosquitoes-use bodies of water as breeding sites. Precise environmental mechanisms of how reservoirs exacerbate malaria transmission are yet to be identified. Understanding of these mechanisms should lead to a better assessment of the impacts of dam construction and to new prevention strategies. Combining extensive multiyear field surveys around the Koka Reservoir in Ethiopia and rigorous model development and simulation studies, environmental mechanisms of malaria transmission around the reservoir were examined. Most comprehensive and detailed malaria transmission model, Hydrology, Entomology, and Malaria Transmission Simulator, was applied to a village adjacent to the reservoir. Significant contributions to the dynamics of malaria transmission are shaped by wind profile, marginal pools, temperature, and shoreline locations. Wind speed and wind direction influence Anopheles populations and malaria transmission during the major and secondary mosquito seasons. During the secondary mosquito season, a noticeable influence was also attributed to marginal pools. Temperature was found to play an important role, not so much in Anopheles population dynamics, but in malaria transmission dynamics. Change in shoreline locations drives malaria transmission dynamics, with closer shoreline locations to the village making malaria transmission more likely. Identified environmental mechanisms help in predicting malaria transmission seasons and in developing village relocation strategies upon dam construction to minimize the risk of malaria.

Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia

Background

Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors.

Methods

The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored.

Results

The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions.

Conclusions

A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.

Temporal dynamic of malaria in a suburban area along the Niger River

Background

Even if rainfall and temperature are factors classically associated to malaria, little is known about other meteorological factors, their variability and combinations related to malaria, in association with river height variations. Furthermore, in suburban area, urbanization and growing population density should be assessed in relation to these environmental factors. The aim of this study was to assess the impact of combined environmental, meteorological and hydrological factors on malaria incidence through time in the context of urbanization.

Methods

Population observational data were prospectively collected. Clinical malaria was defined as the presence of parasites in addition to clinical symptoms. Meteorological and hydrological factors were measured daily. For each factors variation indices were estimated. Urbanization was yearly estimated assessing satellite imaging and field investigations. Principal component analysis was used for dimension reduction and factors combination. Lags between malaria incidences and the main components were assessed by cross-correlation functions. Generalized additive model was used to assess relative impact of different environmental components, taking into account lags, and modelling non-linear relationships. Change-point analysis was used to determine transmission periods within years.

Results

Malaria incidences were dominated by annual periodicity and varied through time without modification of the dynamic, with no impact of the urbanization. The main meteorological factor associated with malaria was a combination of evaporation, humidity and rainfall, with a lag of 3 months. The relationship between combined temperature factors showed a linear impact until reaching high temperatures limiting malaria incidence, with a lag 3.25 months. Height and variation of the river were related to malaria incidence (respectively 6 week lag and no lag).

Conclusions

The study emphasizes no decreasing trend of malaria incidence despite accurate access to care and control strategies in accordance to international recommendations. Furthermore, no decreasing trend was showed despite the urbanization of the area. Malaria transmission remain increase 3 months after the beginning of the dry season. Addition to evaporation versus humidity/rainfall, nonlinear relationship for temperature and river height and variations have to be taken into account when implementing malaria control programmes.

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India

Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover (p < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024-1.042)], maximum humidity [IRR-1.016 (1.013-1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231-1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974-0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

Background

Mozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication.

Methods

Time series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model.

Results

Between 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1)₅₂, and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately.

Conclusion

The Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-017-2205-6) contains supplementary material, which is available to authorized users.

Mathematical assessment of the role of temperature and rainfall on mosquito population dynamics

A new stage-structured model for the population dynamics of the mosquito (a major vector for numerous vector-borne diseases), which takes the form of a deterministic system of non-autonomous nonlinear differential equations, is designed and used to study the effect of variability in temperature and rainfall on mosquito abundance in a community. Two functional forms of eggs oviposition rate, namely the Verhulst-Pearl logistic and Maynard-Smith-Slatkin functions, are used. Rigorous analysis of the autonomous version of the model shows that, for any of the oviposition functions considered, the trivial equilibrium of the model is locally- and globally-asymptotically stable if a certain vectorial threshold quantity is less than unity. Conditions for the existence and global asymptotic stability of the non-trivial equilibrium solutions of the model are also derived. The model is shown to undergo a Hopf bifurcation under certain conditions (and that increased density-dependent competition in larval mortality reduces the likelihood of such bifurcation). The analyses reveal that the Maynard-Smith-Slatkin oviposition function sustains more oscillations than the Verhulst-Pearl logistic function (hence, it is more suited, from ecological viewpoint, for modeling the egg oviposition process). The non-autonomous model is shown to have a globally-asymptotically stable trivial periodic solution, for each of the oviposition functions, when the associated reproduction threshold is less than unity. Furthermore, this model, in the absence of density-dependent mortality rate for larvae, has a unique and globally-asymptotically stable periodic solution under certain conditions. Numerical simulations of the non-autonomous model, using mosquito surveillance and weather data from the Peel region of Ontario, Canada, show a peak mosquito abundance for temperature and rainfall values in the range [Formula: see text]C and [15-35] mm, respectively. These ranges are recorded in the Peel region between July and August (hence, this study suggests that anti-mosquito control effects should be intensified during this period).

Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis

BACKGROUND

Malaria continues to be one of the most devastating diseases in the world, killing more humans than any other infectious disease. Malaria parasites are entirely dependent on Anopheles mosquitoes for transmission. For this reason, vector population dynamics is a crucial determinant of malaria risk. Consequently, it is important to understand the biology of malaria vector mosquitoes in the study of malaria transmission. Temperature and precipitation also play a significant role in both aquatic and adult stages of the Anopheles.

METHODS

In this study, a climate-based, ordinary-differential-equation model is developed to analyse how temperature and the availability of water affect mosquito population size. In the model, the influence of ambient temperature on the development and the mortality rate of Anopheles arabiensis is considered over a region in KwaZulu-Natal Province, South Africa. In particular, the model is used to examine the impact of climatic factors on the gonotrophic cycle and the dynamics of mosquito population over the study region.

RESULTS

The results fairly accurately quantify the seasonality of the population of An. arabiensis over the region and also demonstrate the influence of climatic factors on the vector population dynamics. The model simulates the population dynamics of both immature and adult An. arabiensis. The simulated larval density produces a curve which is similar to observed data obtained from another study.

CONCLUSION

The model is efficiently developed to predict An. arabiensis population dynamics, and to assess the efficiency of various control strategies. In addition, the model framework is built to accommodate human population dynamics with the ability to predict malaria incidence in future.

Remote Sensing-Driven Climatic/Environmental Variables for Modelling Malaria Transmission in Sub-Saharan Africa

Malaria is a serious public health threat in Sub-Saharan Africa (SSA), and its transmission risk varies geographically. Modelling its geographic characteristics is essential for identifying the spatial and temporal risk of malaria transmission. Remote sensing (RS) has been serving as an important tool in providing and assessing a variety of potential climatic/environmental malaria transmission variables in diverse areas. This review focuses on the utilization of RS-driven climatic/environmental variables in determining malaria transmission in SSA. A systematic search on Google Scholar and the Institute for Scientific Information (ISI) Web of Knowledge(SM) databases (PubMed, Web of Science and ScienceDirect) was carried out. We identified thirty-five peer-reviewed articles that studied the relationship between remotely-sensed climatic variable(s) and malaria epidemiological data in the SSA sub-regions. The relationship between malaria disease and different climatic/environmental proxies was examined using different statistical methods. Across the SSA sub-region, the normalized difference vegetation index (NDVI) derived from either the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) or Moderate-resolution Imaging Spectrometer (MODIS) satellite sensors was most frequently returned as a statistically-significant variable to model both spatial and temporal malaria transmission. Furthermore, generalized linear models (linear regression, logistic regression and Poisson regression) were the most frequently-employed methods of statistical analysis in determining malaria transmission predictors in East, Southern and West Africa. By contrast, multivariate analysis was used in Central Africa. We stress that the utilization of RS in determining reliable malaria transmission predictors and climatic/environmental monitoring variables would require a tailored approach that will have cognizance of the geographical/climatic setting, the stage of malaria elimination continuum, the characteristics of the RS variables and the analytical approach, which in turn, would support the channeling of intervention resources sustainably.

Large-Volume Gravid Traps Enhance Collection of Culex Vectors

Gravid mosquito collections were compared among several large-volume (infusion volume ≥35 liters) gravid trap designs and the small-volume (infusion volume  =  6 liters) Centers for Disease Control and Prevention (CDC) gravid trap used routinely by vector control districts for vector and pathogen surveillance. The numbers of gravid Culex quinquefasciatus, Cx. tarsalis, and Cx. stigmatosoma collected by large gravid traps were greater than by the CDC gravid trap during nearly all overnight trials. Large-volume gravid traps collected on average 6.6-fold more adult female Culex mosquitoes compared to small-volume CDC gravid traps across 3 seasons during the 3 years of the studies. The differences in gravid mosquito collections between large-versus small-volume gravid traps were greatest during spring, when 8- to 56-fold more Culex individuals were collected using large-volume gravid traps. The proportion of gravid females in collections did not differ appreciably among the more effective trap designs tested. Important determinants of gravid trap performance were infusion container size and type as well as infusion volume, which determined the distance between the suction trap and the infusion surface. Of lesser importance for gravid trap performance were the number of suction traps, method of suction trap mounting, and infusion concentration. Fermentation of infusions between 1 and 4 wk weakly affected total mosquito collections, with Cx. stigmatosoma collections moderately enhanced by comparatively young and organically enriched infusions. A suction trap mounted above 100 liters of organic infusion housed in a 121-liter black plastic container collected the most gravid mosquitoes over the greatest range of experimental conditions, and a 35-liter infusion with side-mounted suction traps was a promising lesser-volume alternative design.

The Role of Climatic and Density Dependent Factors in Shaping Mosquito Population Dynamics: The Case of Culex pipiens in Northwestern Italy

Culex pipiens mosquito is a species widely spread across Europe and represents a competent vector for many arboviruses such as West Nile virus (WNV), which has been recently circulating in many European countries, causing hundreds of human cases. In order to identify the main determinants of the high heterogeneity in Cx. pipiens abundance observed in Piedmont region (Northwestern Italy) among different seasons, we developed a density-dependent stochastic model that takes explicitly into account the role played by temperature, which affects both developmental and mortality rates of different life stages. The model was calibrated with a Markov chain Monte Carlo approach exploring the likelihood of recorded capture data gathered in the study area from 2000 to 2011; in this way, we disentangled the role played by different seasonal eco-climatic factors in shaping the vector abundance. Illustrative simulations have been performed to forecast likely changes if temperature or density–dependent inputs would change. Our analysis suggests that inter-seasonal differences in the mosquito dynamics are largely driven by different temporal patterns of temperature and seasonal-specific larval carrying capacities. Specifically, high temperatures during early spring hasten the onset of the breeding season and increase population abundance in that period, while, high temperatures during the summer can decrease population size by increasing adult mortality. Higher densities of adult mosquitoes are associated with higher larval carrying capacities, which are positively correlated with spring precipitations. Finally, an increase in larval carrying capacity is expected to proportionally increase adult mosquito abundance.

Where have all the mosquito nets gone? Spatial modelling reveals mosquito net distributions across Tanzania do not target optimal Anopheles mosquito habitats

Background

Malaria remains the deadliest vector-borne disease despite long-term, costly control efforts. The United Republic of Tanzania has implemented countrywide anti-malarial interventions over more than a decade, including national insecticide-treated net (ITN) rollouts and subsequent monitoring. While previous analyses have compared spatial variation in malaria endemicity with ITN distributions, no study has yet compared Anopheles habitat suitability to determine proper allocation of ITNs. This study assesses where mosquitoes were most likely to thrive before implementation of large-scale ITN interventions in Tanzania and determine if ITN distributions successfully targeted those areas.

Methods

Using Maxent, a species distribution model was constructed relating anopheline mosquito occurrences for 1999–2003 to high resolution environmental observations. A 2011–2012 layer of mosquito net ownership was created using georeferenced data across Tanzania from the Demographic and Health Surveys. The baseline mosquito habitat suitability was compared to subsequent ITN ownership using (1) the average ITN numbers per house and (2) the proportion of households with ≥1 net to test whether national ITN ownership targets have been met and have tracked malaria risk.

Results

Elevation, land cover, and human population distribution outperformed variants of temperature and Normalized Difference Vegetation Index (NDVI) in anopheline distribution models. The spatial distribution of ITN ownership across Tanzania was near-random spatially (Moran’s I = 0.07). Householders reported owning 2.488 ITNs on average and 93.41 % of households had ≥1 ITN. Mosquito habitat suitability was statistically unrelated to reported ITN ownership and very weakly to the proportion of households with ≥1 ITN (R² = 0.051). Proportional ITN ownership/household varied relative to mosquito habitat suitability (Levene’s test F = 3.0037). Quantile regression was used to assess trends in ITN ownership among households with the highest and lowest 10 % of ITN ownership. ITN ownership declined significantly toward areas with the highest vector habitat suitability among households with lowest ITN ownership (t = −3.38). In areas with lowest habitat suitability, ITN ownership was consistently higher.

Conclusions

Insecticide-treated net ownership is critical for malaria control. While Tanzania-wide efforts to distribute ITNs has reduced malaria impacts, gaps and variance in ITN ownership are unexpectedly large in areas where malaria risk is highest. Supplemental ITN distributions targeting prime Anopheles habitats are likely to have disproportionate human health benefits.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0841-x) contains supplementary material, which is available to authorized users.

Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival

Climate change and global warming are emerging as important threats to human health, particularly through the potential increase in vector- and water-borne diseases. Environmental variables are known to affect substantially the population dynamics and abundance of the poikilothermic vectors of disease, but the exact extent of this sensitivity is not well established. Focusing on malaria and its main vector in Africa, Anopheles gambiae sensu stricto, we present a set of novel mathematical models of climate-driven mosquito population dynamics motivated by experimental data suggesting that in An. gambiae, mortality is temperature and age dependent. We compared the performance of these models to that of a "standard" model ignoring age dependence. We used a longitudinal dataset of vector abundance over 36 months in sub-Saharan Africa for comparison between models that incorporate age dependence and one that does not, and observe that age-dependent models consistently fitted the data better than the reference model. This highlights that including age dependence in the vector component of mosquito-borne disease models may be important to predict more reliably disease transmission dynamics. Further data and studies are needed to enable improved fitting, leading to more accurate and informative model predictions for the An. gambiae malaria vector as well as for other disease vectors.

Interspecific Larval Competition Differentially Impacts Adult Survival in Dengue Vectors

Mosquitoes often experience intraspecific and interspecific competition among larvae attributable to high densities and nutrient limitation, especially container mosquitoes including Aedes aegypti (L.) and Aedes albopictus (Skuse). Density-dependent effects on larvae impact adult production and adult traits that influence transmission of arboviruses. To improve our understanding of the mechanisms by which density-dependence influences transmission and identify species-specific traits, we tested the hypotheses: 1) Competitive asymmetry in favor of Ae. albopictus over Ae. aegypti translates to altered adult female survival, and 2) Ae. aegypti adult females are more resistant to life-shortening effects of low-humidity conditions than Ae. albopictus. We gauged the relative impact of inter- and intraspecific larval competition on adult survival in high- and low-humidity regimes (77 and 44% relative humidity, respectively). For Ae. albopictus, intraspecific but not interspecific competition usually reduced adult survival under both humidity regimes. For Ae. aegypti, both intraspecific and interspecific competition reduced adult survival. Ae. albopictus adult survival was minimally influenced by interspecific competition with Ae. aegypti, consistent with observations that Ae. albopictus is the superior competitor. A species comparison indicated that Ae. aegypti exhibited a survival advantage relative to Ae. albopictus under both low- and high-humidity conditions. However, similar survival of these Aedes species was observed in some cases depending on conditions experienced in both the aquatic and terrestrial environments. These results demonstrate plasticity in survival rates of dengue and chikungunya vectors and the significance of considering the influence of biological interactions during the immature stages and abiotic conditions during the adult stage.

A generic weather-driven model to predict mosquito population dynamics applied to species of Anopheles, Culex and Aedes genera of southern France

An accurate understanding and prediction of mosquito population dynamics are needed to identify areas where there is a high risk of mosquito-borne disease spread and persistence. Simulation tools are relevant for supporting decision-makers in the surveillance of vector populations, as models of vector population dynamics provide predictions of the greatest risk periods for vector abundance, which can be particularly helpful in areas with a highly variable environment. We present a generic weather-driven model of mosquito population dynamics, which was applied to one species of each of the genera Anopheles, Culex, and Aedes, located in the same area and thus affected by similar weather conditions. The predicted population dynamics of Anopheles hyrcanus, Culex pipiens, and Aedes caspius were not similar. An. hyrcanus was abundant in late summer. Cx. pipiens was less abundant but throughout the summer. The abundance of both species showed a single large peak with few variations between years. The population dynamics of Ae. caspius showed large intra- and inter-annual variations due to pulsed egg hatching. Predictions of the model were compared to longitudinal data on host-seeking adult females. Data were previously obtained using CDC-light traps baited with carbon dioxide dry ice in 2005 at two sites (Marais du Viguerat and Tour Carbonnière) in a favourable temperate wetland of southern France (Camargue). The observed and predicted periods of maximal abundance for An. hyrcanus and Cx. pipiens tallied very well. Pearson's coefficients for these two species were over 75% for both species. The model also reproduced the major trends in the intra-annual fluctuations of Ae. caspius population dynamics, with peaks occurring in early summer and following the autumn rainfall events. Few individuals of this species were trapped so the comparison of predicted and observed dynamics was not relevant. A global sensitivity analysis of the species-specific models enabled us to identify the parameters most influencing the maximal abundance of mosquitoes. These key parameters were almost similar between species, but not with the same contributions. The emergence of adult mosquitoes was identified as a key process in the population dynamics of all of the three species considered here. Parameters associated with adult emergence therefore need to be precisely known to achieve accurate predictions. Our model is a flexible and efficient tool that predicts mosquito abundance based on local environmental factors. It is useful to and already used by a mosquito surveillance manager in France.

The Importance of Habitat in the Ecology of Decomposition on Rabbit Carcasses in Malaysia: Implications in Forensic Entomology

The stages of decomposition and the faunal succession on rabbit carcasses in three different habitats, namely jungle, rural, and highland areas, were studied. Three New Zealand White rabbit (Oryctolagus cuniculus) carcasses weighing ∼2 kg were sampled daily until the decomposition process was completed. Representative specimens of adult flies, larvae, pupa, and mites were collected from the carcasses and processed in the laboratory. There were differences in decomposition rate and faunal succession between the carcasses. The fastest rate of decomposition was recorded in rural area, and the slowest rate of decomposition was recorded in highland area. The carcasses exhibited the same pattern of colonization by adult flies, but the dominant species of larvae and adult flies on each carcass in specific habitats were different. The primary species of flies recorded in jungle were Chrysomya megacephala F., Chrysomya rufifacies (Macquart), Chrysomya chani Kurahashi, Chrysomya villenuevi Patton, Chrysomya nigripes Aubertin, Chrysomya pinguis (Walker), Hemipyrellia ligurriens (Wiedemann), Hemipyrellia tagaliana (Bigot), Hypopyiopsis fumipennis (Walker), Hypopygiopsis violacea (Macquart), and Hydrotaea spinigera Stein represented by both adults and larvae. Musca domestica L., Atherigona sp., Lioproctia pattoni (Senior-White), Lioproctia saprianovae Pape & Bänziger, and Seniorwhitea princeps (Wiedemann) were represented by adults only. The biodiversity of flies in the rural area were C. megacephala, C. rufifacies, H. ligurriens, Fannia canicularis L., Hydrotaea chalcogaster (Wiedemann), and Hyd. spinigera represented by both adults and larvae, meanwhile M. domestica, Atherigona sp., Boettcherisca peregrina (Robineau-Desvoidy), Parasarcophaga taenionota Wiedemann, Parasarcophaga scopariiformis Senior-White, and S. princeps were represented by adults only. The species of flies collected in the highland area were Lucilia porphyrina (Walker), C. megacephala, C. rufifacies, C. villenuevi, C. pinguis, H. ligurriens, Hyd. spinigera, Hyd. chalcogaster, F. canicularis, and Boettcherisca highlandica Kurahashi & Tan represented by both adults and larvae, whereas C. nigripes, Chrysomya thanomthini Kurahashi & Tumrasvin, M. domestica, Atherigona sp., Parasarcophaga albiceps Meigen, P. taenionota, Sepsidae, Phoridae, and Millichidae were represented by adults only. Faunal succession followed the sequence of dominant flies, i.e., Calliphoridae, Sarcophagidae, Muscidae, Sepsidae, and lastly Stratiomyidae for jungle, or Sepsidae for rural and highland studies. Mites, from suborders Mesostigmata, Prostigmata, Astigmatina, and Oribatida, were also recovered throughout decomposition, which could be used for future implementation in forensic investigations. The data obtained from this study could provide more accurate indicators for local forensic scientists in solving criminal cases especially on the determination of time and primary location of death.

Statistical modeling of the abundance of vectors of West African Rift Valley fever in Barkédji, Senegal

Rift Valley fever is an emerging mosquito-borne disease that represents a threat to human and animal health. The exophilic and exophagic behavior of the two main vector in West Africa (Aedes vexans and Culex poicilipes), adverse events post-vaccination, and lack of treatment, render ineffective the disease control. Therefore it is essential to develop an information system that facilitates decision-making and the implementation of adaptation strategies. In East Africa, RVF outbreaks are linked with abnormally high rainfall, and can be predicted up to 5 months in advance by modeling approaches using climatic and environmental parameters. However, the application of these models in West Africa remains unsatisfactory due to a lack of data for animal and human cases and differences in the dynamics of the disease emergence and the vector species involved in transmission. Models have been proposed for West Africa but they were restricted to rainfall impact analysis without a spatial dimension. In this study, we developed a mixed Bayesian statistical model to evaluate the effects of climatic and ecological determinants on the spatiotemporal dynamics of the two main vectors. Adult mosquito abundance data were generated from July to December every fortnight in 2005-2006 at 79 sites, including temporary ponds, bare soils, shrubby savannah, wooded savannah, steppes, and villages in the Barkédji area. The results demonstrate the importance of environmental factors and weather conditions for predicting mosquito abundance. The rainfall and minimum temperature were positively correlated with the abundance of Cx. poicilipes, whereas the maximum temperature had negative effects. The rainfall was negatively correlated with the abundance of Ae. vexans. After combining land cover classes, weather conditions, and vector abundance, our model was used to predict the areas and periods with the highest risks of vector pressure. This information could support decision-making to improve RVF surveillance activities and to implement better intervention strategies.