A weather-driven model of malaria transmission

BACKGROUND: Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. METHODS: This paper describes a mathematical-biological model of the parasite dynamics, comprising both the...

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Detalles Bibliográficos
Autores principales: Hoshen, Moshe B, Morse, Andrew P
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC520827/
https://www.ncbi.nlm.nih.gov/pubmed/15350206
http://dx.doi.org/10.1186/1475-2875-3-32
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author Hoshen, Moshe B
Morse, Andrew P
author_facet Hoshen, Moshe B
Morse, Andrew P
author_sort Hoshen, Moshe B
collection PubMed
description BACKGROUND: Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. METHODS: This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. RESULTS: Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. CONCLUSION: A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts.
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spelling pubmed-5208272004-10-01 A weather-driven model of malaria transmission Hoshen, Moshe B Morse, Andrew P Malar J Research BACKGROUND: Climate is a major driving force behind malaria transmission and climate data are often used to account for the spatial, seasonal and interannual variation in malaria transmission. METHODS: This paper describes a mathematical-biological model of the parasite dynamics, comprising both the weather-dependent within-vector stages and the weather-independent within-host stages. RESULTS: Numerical evaluations of the model in both time and space show that it qualitatively reconstructs the prevalence of infection. CONCLUSION: A process-based modelling structure has been developed that may be suitable for the simulation of malaria forecasts based on seasonal weather forecasts. BioMed Central 2004-09-06 /pmc/articles/PMC520827/ /pubmed/15350206 http://dx.doi.org/10.1186/1475-2875-3-32 Text en Copyright © 2004 Hoshen and Morse; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open-access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Hoshen, Moshe B
Morse, Andrew P
A weather-driven model of malaria transmission
title A weather-driven model of malaria transmission
title_full A weather-driven model of malaria transmission
title_fullStr A weather-driven model of malaria transmission
title_full_unstemmed A weather-driven model of malaria transmission
title_short A weather-driven model of malaria transmission
title_sort weather-driven model of malaria transmission
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC520827/
https://www.ncbi.nlm.nih.gov/pubmed/15350206
http://dx.doi.org/10.1186/1475-2875-3-32
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