RT Journal Article
T1 Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
A1 Diouf, Ibrahima
A1 Rodríguez De Fonseca, María Belén
A1 Deme, Abdoulaye
A1 Caminade, Cyril
A1 Morse, Andrew
A1 Cisse, Moustapha
A1 Sy, Ibrahima
A1 Dia, Ibrahima
A1 Ermert, Volker
A1 Ndione, Jacques-André
A1 Gaye, Amadou
AB 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.
PB MDPI
SN 1660-4601
YR 2017
FD 2017-09-25
LK https://hdl.handle.net/20.500.14352/19152
UL https://hdl.handle.net/20.500.14352/19152
LA eng
NO Unión Europea. FP7
NO Ministerio de Ciencia e Innovación (MICINN)/CSIC
NO The Farr Institute for Health Informatics Research
DS Docta Complutense
RD 9 abr 2025