Use of a mixture statistical model in studying malaria vectors density.

Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion...

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Autores principales:	Olayidé Boussari, Nicolas Moiroux, Jean Iwaz, Armel Djènontin, Sahabi Bio-Bangana, Vincent Corbel, Noël Fonton, René Ecochard
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6898e37c34a2446bbcb55ae55275ea3c
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spelling	oai:doaj.org-article:6898e37c34a2446bbcb55ae55275ea3c2021-11-18T08:07:42ZUse of a mixture statistical model in studying malaria vectors density.1932-620310.1371/journal.pone.0050452https://doaj.org/article/6898e37c34a2446bbcb55ae55275ea3c2012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23185626/?tool=EBIhttps://doaj.org/toc/1932-6203Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with "village" as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.Olayidé BoussariNicolas MoirouxJean IwazArmel DjènontinSahabi Bio-BanganaVincent CorbelNoël FontonRené EcochardPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 11, p e50452 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Olayidé Boussari Nicolas Moiroux Jean Iwaz Armel Djènontin Sahabi Bio-Bangana Vincent Corbel Noël Fonton René Ecochard Use of a mixture statistical model in studying malaria vectors density.
description	Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with "village" as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.
format	article
author	Olayidé Boussari Nicolas Moiroux Jean Iwaz Armel Djènontin Sahabi Bio-Bangana Vincent Corbel Noël Fonton René Ecochard
author_facet	Olayidé Boussari Nicolas Moiroux Jean Iwaz Armel Djènontin Sahabi Bio-Bangana Vincent Corbel Noël Fonton René Ecochard
author_sort	Olayidé Boussari
title	Use of a mixture statistical model in studying malaria vectors density.
title_short	Use of a mixture statistical model in studying malaria vectors density.
title_full	Use of a mixture statistical model in studying malaria vectors density.
title_fullStr	Use of a mixture statistical model in studying malaria vectors density.
title_full_unstemmed	Use of a mixture statistical model in studying malaria vectors density.
title_sort	use of a mixture statistical model in studying malaria vectors density.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/6898e37c34a2446bbcb55ae55275ea3c
work_keys_str_mv	AT olayideboussari useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT nicolasmoiroux useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT jeaniwaz useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT armeldjenontin useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT sahabibiobangana useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT vincentcorbel useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT noelfonton useofamixturestatisticalmodelinstudyingmalariavectorsdensity AT reneecochard useofamixturestatisticalmodelinstudyingmalariavectorsdensity
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Use of a mixture statistical model in studying malaria vectors density.

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