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A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission

BACKGROUND: Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and para...

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Autores principales: Depinay, Jean-Marc O, Mbogo, Charles M, Killeen, Gerry, Knols, Bart, Beier, John, Carlson, John, Dushoff, Jonathan, Billingsley, Peter, Mwambi, Henry, Githure, John, Toure, Abdoulaye M, Ellis McKenzie, F
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2004
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC514565/
https://www.ncbi.nlm.nih.gov/pubmed/15285781
http://dx.doi.org/10.1186/1475-2875-3-29
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author Depinay, Jean-Marc O
Mbogo, Charles M
Killeen, Gerry
Knols, Bart
Beier, John
Carlson, John
Dushoff, Jonathan
Billingsley, Peter
Mwambi, Henry
Githure, John
Toure, Abdoulaye M
Ellis McKenzie, F
author_facet Depinay, Jean-Marc O
Mbogo, Charles M
Killeen, Gerry
Knols, Bart
Beier, John
Carlson, John
Dushoff, Jonathan
Billingsley, Peter
Mwambi, Henry
Githure, John
Toure, Abdoulaye M
Ellis McKenzie, F
author_sort Depinay, Jean-Marc O
collection PubMed
description BACKGROUND: Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and parasite resistance to insecticides and drugs. METHODS: This study presents a simulation model of African malaria vectors. This individual-based model incorporates current knowledge of the mechanisms underlying Anopheles population dynamics and their relations to the environment. One of its main strengths is that it is based on both biological and environmental variables. RESULTS: The model made it possible to structure existing knowledge, assembled in a comprehensive review of the literature, and also pointed out important aspects of basic Anopheles biology about which knowledge is lacking. One simulation showed several patterns similar to those seen in the field, and made it possible to examine different analyses and hypotheses for these patterns; sensitivity analyses on temperature, moisture, predation and preliminary investigations of nutrient competition were also conducted. CONCLUSIONS: Although based on some mathematical formulae and parameters, this new tool has been developed in order to be as explicit as possible, transparent in use, close to reality and amenable to direct use by field workers. It allows a better understanding of the mechanisms underlying Anopheles population dynamics in general and also a better understanding of the dynamics in specific local geographic environments. It points out many important areas for new investigations that will be critical to effective, efficient, sustainable interventions.
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spelling pubmed-5145652004-08-27 A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission Depinay, Jean-Marc O Mbogo, Charles M Killeen, Gerry Knols, Bart Beier, John Carlson, John Dushoff, Jonathan Billingsley, Peter Mwambi, Henry Githure, John Toure, Abdoulaye M Ellis McKenzie, F Malar J Research BACKGROUND: Malaria is one of the oldest and deadliest infectious diseases in humans. Many mathematical models of malaria have been developed during the past century, and applied to potential interventions. However, malaria remains uncontrolled and is increasing in many areas, as are vector and parasite resistance to insecticides and drugs. METHODS: This study presents a simulation model of African malaria vectors. This individual-based model incorporates current knowledge of the mechanisms underlying Anopheles population dynamics and their relations to the environment. One of its main strengths is that it is based on both biological and environmental variables. RESULTS: The model made it possible to structure existing knowledge, assembled in a comprehensive review of the literature, and also pointed out important aspects of basic Anopheles biology about which knowledge is lacking. One simulation showed several patterns similar to those seen in the field, and made it possible to examine different analyses and hypotheses for these patterns; sensitivity analyses on temperature, moisture, predation and preliminary investigations of nutrient competition were also conducted. CONCLUSIONS: Although based on some mathematical formulae and parameters, this new tool has been developed in order to be as explicit as possible, transparent in use, close to reality and amenable to direct use by field workers. It allows a better understanding of the mechanisms underlying Anopheles population dynamics in general and also a better understanding of the dynamics in specific local geographic environments. It points out many important areas for new investigations that will be critical to effective, efficient, sustainable interventions. BioMed Central 2004-07-30 /pmc/articles/PMC514565/ /pubmed/15285781 http://dx.doi.org/10.1186/1475-2875-3-29 Text en Copyright © 2004 Depinay et al; licensee BioMed Central Ltd.
spellingShingle Research
Depinay, Jean-Marc O
Mbogo, Charles M
Killeen, Gerry
Knols, Bart
Beier, John
Carlson, John
Dushoff, Jonathan
Billingsley, Peter
Mwambi, Henry
Githure, John
Toure, Abdoulaye M
Ellis McKenzie, F
A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
title A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
title_full A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
title_fullStr A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
title_full_unstemmed A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
title_short A simulation model of African Anopheles ecology and population dynamics for the analysis of malaria transmission
title_sort simulation model of african anopheles ecology and population dynamics for the analysis of malaria transmission
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC514565/
https://www.ncbi.nlm.nih.gov/pubmed/15285781
http://dx.doi.org/10.1186/1475-2875-3-29
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