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Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall

BACKGROUND: To support malaria control strategies, prior knowledge of disease risk is necessary. Developing a model to explain the transmission of malaria, in endemic and epidemic regions, is of high priority in developing health system interventions. We develop, fit and validate a non-spatial dynam...

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Autores principales: Yé, Yazoumé, Hoshen, Moshe, Kyobutungi, Catherine, Louis, Valérie R., Sauerborn, Rainer
Formato: Texto
Lenguaje:English
Publicado: CoAction Publishing 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2799324/
https://www.ncbi.nlm.nih.gov/pubmed/20052379
http://dx.doi.org/10.3402/gha.v2i0.1923
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author Yé, Yazoumé
Hoshen, Moshe
Kyobutungi, Catherine
Louis, Valérie R.
Sauerborn, Rainer
author_facet Yé, Yazoumé
Hoshen, Moshe
Kyobutungi, Catherine
Louis, Valérie R.
Sauerborn, Rainer
author_sort Yé, Yazoumé
collection PubMed
description BACKGROUND: To support malaria control strategies, prior knowledge of disease risk is necessary. Developing a model to explain the transmission of malaria, in endemic and epidemic regions, is of high priority in developing health system interventions. We develop, fit and validate a non-spatial dynamic model driven by meteorological conditions that can capture seasonal malaria transmission dynamics at the village level in a malaria holoendemic area of north-western Burkina Faso. METHODS: A total of 676 children aged 6–59 months took part in this study. Trained interviewers visited children at home weekly from December 2003 to November 2004 for Plasmodium falciparum malaria infection detection. Anopheles daily biting rate, mortality rate and growth rate were evaluated. Digital meteorological stations measured ambient temperature, humidity and rainfall in each site. RESULTS: The overall P. falciparum malaria infection incidence was 1.1 episodes per person year. There was strong seasonal variation in P. falciparum malaria infection incidence with a peak observed in August and September, corresponding to the rainy season and a high number of mosquitoes. The model estimates of monthly mosquito abundance and the incidence of malaria infection correlated well with observed values. The fit was sensitive to daily mosquito survival and daily human parasite clearance. CONCLUSION: The model has demonstrated potential for local scale seasonal prediction of P. falciparum malaria infection. It could therefore be used to understand malaria transmission dynamics using meteorological parameters as the driving force and to help district health managers in identifying high-risk periods for more focused interventions.
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spelling pubmed-27993242010-01-05 Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall Yé, Yazoumé Hoshen, Moshe Kyobutungi, Catherine Louis, Valérie R. Sauerborn, Rainer Glob Health Action Climate change and infectious diseases BACKGROUND: To support malaria control strategies, prior knowledge of disease risk is necessary. Developing a model to explain the transmission of malaria, in endemic and epidemic regions, is of high priority in developing health system interventions. We develop, fit and validate a non-spatial dynamic model driven by meteorological conditions that can capture seasonal malaria transmission dynamics at the village level in a malaria holoendemic area of north-western Burkina Faso. METHODS: A total of 676 children aged 6–59 months took part in this study. Trained interviewers visited children at home weekly from December 2003 to November 2004 for Plasmodium falciparum malaria infection detection. Anopheles daily biting rate, mortality rate and growth rate were evaluated. Digital meteorological stations measured ambient temperature, humidity and rainfall in each site. RESULTS: The overall P. falciparum malaria infection incidence was 1.1 episodes per person year. There was strong seasonal variation in P. falciparum malaria infection incidence with a peak observed in August and September, corresponding to the rainy season and a high number of mosquitoes. The model estimates of monthly mosquito abundance and the incidence of malaria infection correlated well with observed values. The fit was sensitive to daily mosquito survival and daily human parasite clearance. CONCLUSION: The model has demonstrated potential for local scale seasonal prediction of P. falciparum malaria infection. It could therefore be used to understand malaria transmission dynamics using meteorological parameters as the driving force and to help district health managers in identifying high-risk periods for more focused interventions. CoAction Publishing 2009-11-11 /pmc/articles/PMC2799324/ /pubmed/20052379 http://dx.doi.org/10.3402/gha.v2i0.1923 Text en © 2009 Yazoumé Yé et al. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License, permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Climate change and infectious diseases
Yé, Yazoumé
Hoshen, Moshe
Kyobutungi, Catherine
Louis, Valérie R.
Sauerborn, Rainer
Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
title Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
title_full Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
title_fullStr Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
title_full_unstemmed Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
title_short Local scale prediction of Plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
title_sort local scale prediction of plasmodium falciparum malaria transmission in an endemic region using temperature and rainfall
topic Climate change and infectious diseases
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2799324/
https://www.ncbi.nlm.nih.gov/pubmed/20052379
http://dx.doi.org/10.3402/gha.v2i0.1923
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