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dc.contributor.authorProtopopoff, Natacha*
dc.contributor.authorVan Bortel, Wim*
dc.contributor.authorSpeybroeck, Niko*
dc.contributor.authorVan Geertruyden, Jean-Pierre*
dc.contributor.authorBaza, Dismas*
dc.contributor.authorD'Alessandro, Umberto*
dc.contributor.authorCoosemans, Marc*
dc.date.accessioned2010-10-29T13:48:55Z
dc.date.available2010-10-29T13:48:55Z
dc.date.issued2009-11-25
dc.identifier.citationPLoS ONE 2009;4(11):e8022en
dc.identifier.issn1932-6203
dc.identifier.pmid19946627
dc.identifier.doi10.1371/journal.pone.0008022
dc.identifier.urihttp://hdl.handle.net/10144/114014
dc.description.abstractINTRODUCTION: Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies. METHODS AND FINDINGS: A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through "classification and regression trees", an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density. CONCLUSIONS: In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.
dc.language.isoenen
dc.relation.urlhttp://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0008022en
dc.rightsPublished by Public Library of Science, [url]http://www.plosone.org/[/url] Archived on this site by Open Access permissionen
dc.subject.meshAdolescenten
dc.subject.meshAdulten
dc.subject.meshAnimalsen
dc.subject.meshAnophelesen
dc.subject.meshBedding and Linensen
dc.subject.meshBurundien
dc.subject.meshChilden
dc.subject.meshDecision Support Techniquesen
dc.subject.meshFemaleen
dc.subject.meshHumansen
dc.subject.meshInsect Vectorsen
dc.subject.meshInsecticidesen
dc.subject.meshMalariaen
dc.subject.meshMaleen
dc.subject.meshMosquito Controlen
dc.subject.meshMosquito Netsen
dc.subject.meshMultivariate Analysisen
dc.subject.meshRegression Analysisen
dc.subject.meshRisk Factorsen
dc.subject.meshTemperatureen
dc.titleRanking malaria risk factors to guide malaria control efforts in African highlandsen
dc.typeArticleen
dc.contributor.departmentDepartment of Parasitology, Prince Leopold Institute of Tropical Medicine, Antwerp, Belgium; Medecins Sans Frontieres Brussels, Belgium; Department of Animal Health, Prince Leopold Institute of Tropical Medicine, Antwerp, Belgium; School of Public Health, Universite Catholique de Louvain, Brussels, Belgium; Programme de Lutte contre les Maladies Transmissibles et Carentielles, Ministry of Health, Bujumbura, Burundi; Department of Biomedical Sciences, Faculty of Pharmaceutical, Veterinary and Biomedical Sciences, University of Antwerp, Antwerp, Belgiumen
dc.identifier.journalPloS Oneen
refterms.dateFOA2019-03-04T08:23:09Z
html.description.abstractINTRODUCTION: Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies. METHODS AND FINDINGS: A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through "classification and regression trees", an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density. CONCLUSIONS: In Burundi highlands monitoring Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.


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