Modelling hotspots of the two dominant Rift Valley fever vectors (Aedes vexans and Culex poicilipes) in Barkedji, Senegal

BACKGROUND: Climatic and environmental variables were used successfully by using models to predict Rift Valley fever (RVF) virus outbreaks in East Africa. However, these models are not replicable in the West African context due to a likely difference of the dynamic of the virus emergence. For these reasons specific models mainly oriented to the risk mapping have been developed. Hence, the areas of high vector pressure or virus activity are commonly predicted. However, the factors impacting their occurrence are poorly investigated and still unknown. In this study, we examine the impact of climate and environmental factors on the likelihood of occurrence of the two main vectors of RVF in West Africa (Aedes vexans and Culex poicilipes) hotspots. METHODS: We used generalized linear mixed models taking into account spatial autocorrelation, in order to overcome the default threshold for areas with high mosquito abundance identified by these models. Getis’ Gi*(d) index was used to define local adult mosquito abundance clusters (hotspot). RESULTS: For Culex poicilipes, a decrease of the minimum temperature promotes the occurrence of hotspots, whereas, for Aedes vexans, the likelihood of hotspot occurrence is negatively correlated with relative humidity, maximum and minimum temperatures. However, for the two vectors, proximity to ponds would increase the risk of being in an hotspot area. CONCLUSIONS: These results may be useful in the improvement of RVF monitoring and vector control management in the Barkedji area. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13071-016-1399-3) contains supplementary material, which is available to authorized users

Variabilité intra-saisonnière de la pluviométrie et émergence de la fièvre de la vallée du Rift dans la vallée du fleuve Sénégal : nouvelles considérations

L’analyse de la variabilité intra-saisonnière de la pluviométrie à Barkédji, Ross-Béthio et Ranérou, localités ayant enregistré des événements de Fièvre de la Vallée du Rift (FVR) respectivement durant les saisons des pluies 1993, 1994, 1999 et 2002 permet de voir que toutes ces saisons des pluies se terminent par un pic pluviométrique précédé d’une pause assez longue, ayant entraîné par endroits l’assèchement des mares avant leur remise en eau. Ces fortes pluies survenues en fin de saison permettent le maintien à un niveau élevé des populations de Culex mais aussi, et surtout, l’éclosion des œufs des Aedes. Grâce à la transmission transovarienne, le virus se retrouve dans un environnement doublement favorable avec des possibilités de dispersion et d’amplification. La combinaison de tous ces facteurs laisse à penser que l’émergence de la FVR en fin de saison des pluies est à mettre en relation avec le rythme pluviométrique à ce moment de l’année

Impact of future climate change on malaria in West Africa

Understanding the regional impact of future climate change is one of the major global challenges of this century. This study investigated possible effects of climate change on malaria in West Africa in the near future (2006–2035) and the far future (2036–2065) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), compared to an observed evaluation period (1981–2010). Projected rainfall and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4). The malaria model used is the Liverpool malaria model (LMM), a dynamical malaria model driven by daily time series of rainfall and temperature obtained from the CORDEX data. Our results highlight the unimodal shape of the malaria prevalence distribution, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall. Projections showed that the mean annual malaria prevalence would decrease in both climatological periods under both RCPs but with a larger magnitude of decreasing under the RCP8.5. We found that the mean malaria prevalence for the reference period is greater than the projected prevalence for 6 of the 8 downscaled GCMs. The study enhances understanding of how malaria is impacted under RCP4.5 and RCP8.5 emission scenarios. These results indicate that the southern area of West Africa is at most risk of epidemics, and the malaria control programs need extra effort and help to make the best use of available resources by stakeholders.The CORDEX project.http://link.springer.com/journal/704am2023Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH)UP Centre for Sustainable Malaria Control (UP CSMC

Impact of future climate change on malaria in West Africa

Understanding the regional impact of future climate change is one of the major global challenges of this century. This study investigated possible effects of climate change on malaria in West Africa in the near future (2006–2035) and the far future (2036–2065) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), compared to an observed evaluation period (1981–2010). Projected rainfall and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4). The malaria model used is the Liverpool malaria model (LMM), a dynamical malaria model driven by daily time series of rainfall and temperature obtained from the CORDEX data. Our results highlight the unimodal shape of the malaria prevalence distribution, and the seasonal malaria transmission contrast is closely linked to the latitudinal variation of the rainfall. Projections showed that the mean annual malaria prevalence would decrease in both climatological periods under both RCPs but with a larger magnitude of decreasing under the RCP8.5. We found that the mean malaria prevalence for the reference period is greater than the projected prevalence for 6 of the 8 downscaled GCMs. The study enhances understanding of how malaria is impacted under RCP4.5 and RCP8.5 emission scenarios. These results indicate that the southern area of West Africa is at most risk of epidemics, and the malaria control programs need extra effort and help to make the best use of available resources by stakeholders.The CORDEX project.http://link.springer.com/journal/704am2023Geography, Geoinformatics and MeteorologySchool of Health Systems and Public Health (SHSPH)UP Centre for Sustainable Malaria Control (UP CSMC

Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal

The analysis of the spatial and temporal variability of climate parameters is crucial to study the impact of climate-sensitive vector-borne diseases such as malaria. The use of malaria models is an alternative way of producing potential malaria historical data for Senegal due to the lack of reliable observations for malaria outbreaks over a long time period. Consequently, here we use the Liverpool Malaria Model (LMM), driven by different climatic datasets, in order to study and validate simulated malaria parameters over Senegal. The findings confirm that the risk of malaria transmission is mainly linked to climate variables such as rainfall and temperature as well as specific landscape characteristics. For the whole of Senegal, a lag of two months is generally observed between the peak of rainfall in August and the maximum number of reported malaria cases in October. The malaria transmission season usually takes place from September to November, corresponding to the second peak of temperature occurring in October. Observed malaria data from the Programme National de Lutte contre le Paludisme (PNLP, National Malaria control Programme in Senegal) and outputs from the meteorological data used in this study were compared. The malaria model outputs present some consistencies with observed malaria dynamics over Senegal, and further allow the exploration of simulations performed with reanalysis data sets over a longer time period. The simulated malaria risk significantly decreased during the 1970s and 1980s over Senegal. This result is consistent with the observed decrease of malaria vectors and malaria cases reported by field entomologists and clinicians in the literature. The main differences between model outputs and observations regard amplitude, but can be related not only to reanalysis deficiencies but also to other environmental and socio-economic factors that are not included in this mechanistic malaria model framework. The present study can be considered as a validation of the reliability of reanalysis to be used as inputs for the calculation of malaria parameters in the Sahel using dynamical malaria models