Abstract
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.
Highlights
Malaria is a vector-borne disease, which requires three essential factors for its existence and transmission: Plasmodium parasites, mosquito vectors, and human hosts [1,2]
We aim to characterize the spatio-temporal variability of simulated malaria parameters, such as [61] that used in the seasonal climate forecast from the ENSEMBLES Project [62] to drive the Liverpool Malaria Model (LMM)
Due to its northernmost position, the station of Saint-Louis located in the River Valley of Senegal (Vallée du Fleuve Sénégal), is characterized by a very dry climate compared to the southern part of Senegal
Summary
Malaria is a vector-borne disease, which requires three essential factors for its existence and transmission: Plasmodium parasites, mosquito vectors, and human hosts [1,2]. There are three specific species of Plasmodium affecting humans, which are Plasmodium falciparum (the most common and lethal parasite species in sub-Saharan Africa), Plasmodium vivax, and Plasmodium ovale, the common species to humans and gorillas. Plasmodium knowlesi, like Plasmodium malariae, generally affects primates and gorillas, with some examples of transmission to humans in Malaysia [3]. There are various species of anopheles vectors which are competent for malaria transmission. The main malaria vectors in Africa and Senegal are Anopheles gambiae, Anopheles funestus, Anopheles arabiensis, and Anopheles melas to a lesser extent [4,5,6]
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