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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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Autores principales: Diouf, Ibrahima, Rodriguez-Fonseca, Belen, Deme, Abdoulaye, Caminade, Cyril, Morse, Andrew P., Cisse, Moustapha, Sy, Ibrahima, Dia, Ibrahima, Ermert, Volker, Ndione, Jacques-André, Gaye, Amadou Thierno
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664620/
https://www.ncbi.nlm.nih.gov/pubmed/28946705
http://dx.doi.org/10.3390/ijerph14101119
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author Diouf, Ibrahima
Rodriguez-Fonseca, Belen
Deme, Abdoulaye
Caminade, Cyril
Morse, Andrew P.
Cisse, Moustapha
Sy, Ibrahima
Dia, Ibrahima
Ermert, Volker
Ndione, Jacques-André
Gaye, Amadou Thierno
author_facet Diouf, Ibrahima
Rodriguez-Fonseca, Belen
Deme, Abdoulaye
Caminade, Cyril
Morse, Andrew P.
Cisse, Moustapha
Sy, Ibrahima
Dia, Ibrahima
Ermert, Volker
Ndione, Jacques-André
Gaye, Amadou Thierno
author_sort Diouf, Ibrahima
collection PubMed
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.
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spelling pubmed-56646202017-11-06 Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal Diouf, Ibrahima Rodriguez-Fonseca, Belen Deme, Abdoulaye Caminade, Cyril Morse, Andrew P. Cisse, Moustapha Sy, Ibrahima Dia, Ibrahima Ermert, Volker Ndione, Jacques-André Gaye, Amadou Thierno Int J Environ Res Public Health Article 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. MDPI 2017-09-25 2017-10 /pmc/articles/PMC5664620/ /pubmed/28946705 http://dx.doi.org/10.3390/ijerph14101119 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Diouf, Ibrahima
Rodriguez-Fonseca, Belen
Deme, Abdoulaye
Caminade, Cyril
Morse, Andrew P.
Cisse, Moustapha
Sy, Ibrahima
Dia, Ibrahima
Ermert, Volker
Ndione, Jacques-André
Gaye, Amadou Thierno
Comparison of Malaria Simulations Driven by Meteorological Observations and Reanalysis Products in Senegal
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5664620/
https://www.ncbi.nlm.nih.gov/pubmed/28946705
http://dx.doi.org/10.3390/ijerph14101119
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