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Int. J. Environ. Res. Public Health 2017, 14(10), 1119; doi:10.3390/ijerph14101119

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

1
Laboratoire de Physique de l’Atmosphère et de l’Océan-Siméon Fongang, Ecole Supérieure Polytechnique de l’Université Cheikh Anta Diop (UCAD), BP 5085, Dakar-Fann, Dakar 10700, Senegal
2
Department of Geophysics and Meteorology, Universidad Complutense de, Plaza de las Ciencias s/n, Madrid 28040, Spain
3
Instituto de Geociencias IGEO, CSIC-UCM, Agencia Estatal del Consejo Superior de Investigaciones Científicas, Madrid 28040, Spain
4
Unité de Formation et de Recherche de Sciences Appliquées et de Technologie, Université Gaston Berger de Saint-Louis, BP 234, Saint-Louis 32000, Senegal
5
Department of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Water House Building, Liverpool L693GL, UK
6
National Institute for Health Research [M1] (NIHR), Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool L69 3GL, UK
7
Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Roxby Building, Liverpool L69 7ZT, UK
8
Programme National de Lutte contre le Paludisme (PNLP), BP 25 270 Dakar-Fann, Dakar 10700, Senegal
9
Centre de Suivi Ecologique, BP 15532, Fann Résidense, Dakar 10700, Senegal
10
Institut Pasteur de Dakar (IPD), Unité d’Entomologie Médicale, 36 Av. Pasteur, BP 220 Dakar, Dakar 12900, Senegal
11
Institute of Geophysics and Meteorology, University of Cologne, Kerpenerstr. 13, D-50923 Cologne, Germany
*
Author to whom correspondence should be addressed.
Received: 4 July 2017 / Revised: 14 September 2017 / Accepted: 18 September 2017 / Published: 25 September 2017
(This article belongs to the Special Issue Malaria Epidemiology and Control: Current Situation and Perspectives)
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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. View Full-Text
Keywords: climate; malaria; observations; simulations; stations; Senegal; model climate; malaria; observations; simulations; stations; Senegal; model
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Diouf, I.; Rodriguez-Fonseca, B.; Deme, A.; Caminade, C.; Morse, A.P.; Cisse, M.; Sy, I.; Dia, I.; Ermert, V.; Ndione, J.-A.; Gaye, A.T. Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal. Int. J. Environ. Res. Public Health 2017, 14, 1119.

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