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...

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Main Authors: Ibrahima Diouf, Belen Rodriguez-Fonseca, Abdoulaye Deme, Cyril Caminade, Andrew P. Morse, Moustapha Cisse, Ibrahima Sy, Ibrahima Dia, Volker Ermert, Jacques-André Ndione, Amadou Thierno Gaye
Format: Article
Language:English
Published: MDPI AG 2017-09-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/14/10/1119
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author Ibrahima Diouf
Belen Rodriguez-Fonseca
Abdoulaye Deme
Cyril Caminade
Andrew P. Morse
Moustapha Cisse
Ibrahima Sy
Ibrahima Dia
Volker Ermert
Jacques-André Ndione
Amadou Thierno Gaye
spellingShingle Ibrahima Diouf
Belen Rodriguez-Fonseca
Abdoulaye Deme
Cyril Caminade
Andrew P. Morse
Moustapha Cisse
Ibrahima Sy
Ibrahima Dia
Volker Ermert
Jacques-André Ndione
Amadou Thierno Gaye
Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
International Journal of Environmental Research and Public Health
climate
malaria
observations
simulations
stations
Senegal
model
author_facet Ibrahima Diouf
Belen Rodriguez-Fonseca
Abdoulaye Deme
Cyril Caminade
Andrew P. Morse
Moustapha Cisse
Ibrahima Sy
Ibrahima Dia
Volker Ermert
Jacques-André Ndione
Amadou Thierno Gaye
author_sort Ibrahima Diouf
title Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
title_short Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
title_full Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
title_fullStr Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
title_full_unstemmed Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
title_sort comparison of malaria simulations driven by meteorological observations and reanalysis products in senegal
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1660-4601
publishDate 2017-09-01
description 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.
topic climate
malaria
observations
simulations
stations
Senegal
model
url https://www.mdpi.com/1660-4601/14/10/1119
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spelling doaj-10105bb638644c2199c4b36ebfd679222020-11-25T00:53:00ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012017-09-011410111910.3390/ijerph14101119ijerph14101119Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in SenegalIbrahima Diouf0Belen Rodriguez-Fonseca1Abdoulaye Deme2Cyril Caminade3Andrew P. Morse4Moustapha Cisse5Ibrahima Sy6Ibrahima Dia7Volker Ermert8Jacques-André Ndione9Amadou Thierno Gaye10Laboratoire 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, SenegalDepartment of Geophysics and Meteorology, Universidad Complutense de, Plaza de las Ciencias s/n, Madrid 28040, SpainUnité de Formation et de Recherche de Sciences Appliquées et de Technologie, Université Gaston Berger de Saint-Louis, BP 234, Saint-Louis 32000, SenegalDepartment of Epidemiology and Population Health, Institute of Infection and Global Health, University of Liverpool, Water House Building, Liverpool L693GL, UKNational Institute for Health Research [M1] (NIHR), Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool L69 3GL, UKProgramme National de Lutte contre le Paludisme (PNLP), BP 25 270 Dakar-Fann, Dakar 10700, SenegalCentre de Suivi Ecologique, BP 15532, Fann Résidense, Dakar 10700, SenegalInstitut Pasteur de Dakar (IPD), Unité d’Entomologie Médicale, 36 Av. Pasteur, BP 220 Dakar, Dakar 12900, SenegalInstitute of Geophysics and Meteorology, University of Cologne, Kerpenerstr. 13, D-50923 Cologne, GermanyCentre de Suivi Ecologique, BP 15532, Fann Résidense, Dakar 10700, SenegalLaboratoire 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, SenegalThe 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.https://www.mdpi.com/1660-4601/14/10/1119climatemalariaobservationssimulationsstationsSenegalmodel