Mapping malaria risk using environmental and anthropic variables

Detalhes bibliográficos
Autor(a) principal: Rincón-Romero,Mauricio Edilberto
Data de Publicação: 2009
Outros Autores: Londoño,Julián Esteban
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista brasileira de epidemiologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2009000300005
Resumo: Despite much research in the identification of areas with malaria, it is urgent to further investigate mapping techniques to achieve better approaches in strategies to prevent, mitigate, and eradicate the mosquito and the illness eventually. By using spatial distributed modeling techniques with Geographical Information Systems (GIS), the study proposes methodology to map malaria risk zoning for the municipality of Buenaventura in Colombia. The model proposed by Craig et al.¹ using climatic information was adapted to the conditions of the study area regarding scale and spatial resolution. Geomorphologic and anthropic variables were added to improve spatial allocation of areas with higher risk of contracting the illness, refining zoning. Then, they were contrasted with the locations reported by health entities², taking into account spatial distribution. The comparison of results shows a decrease in the area obtained initially using the Craig et al. model¹ (1999), from 5,422.4 km² (89.1% of the municipality's territory) to 624.3km² (approximately 10% of the municipality's area), yielding a total reduction of 78.8% when environmental and anthropic variables were included in the model. Data show that of the 9,863 cases reported during 2001 to 2005 for 20 selected towns as basis for the amount of surveyed malaria cases², 1,132 were located in the very high-risk areas, 7,662 were in the areas of moderate risk, and 1,066 cases in low-risk areas, showing that 89% of the cases reported fell into the areas with higher risk for malaria.
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spelling Mapping malaria risk using environmental and anthropic variablesMalariaGISSpatial modelingEnvironmental modelingMalaria risk zoningDespite much research in the identification of areas with malaria, it is urgent to further investigate mapping techniques to achieve better approaches in strategies to prevent, mitigate, and eradicate the mosquito and the illness eventually. By using spatial distributed modeling techniques with Geographical Information Systems (GIS), the study proposes methodology to map malaria risk zoning for the municipality of Buenaventura in Colombia. The model proposed by Craig et al.¹ using climatic information was adapted to the conditions of the study area regarding scale and spatial resolution. Geomorphologic and anthropic variables were added to improve spatial allocation of areas with higher risk of contracting the illness, refining zoning. Then, they were contrasted with the locations reported by health entities², taking into account spatial distribution. The comparison of results shows a decrease in the area obtained initially using the Craig et al. model¹ (1999), from 5,422.4 km² (89.1% of the municipality's territory) to 624.3km² (approximately 10% of the municipality's area), yielding a total reduction of 78.8% when environmental and anthropic variables were included in the model. Data show that of the 9,863 cases reported during 2001 to 2005 for 20 selected towns as basis for the amount of surveyed malaria cases², 1,132 were located in the very high-risk areas, 7,662 were in the areas of moderate risk, and 1,066 cases in low-risk areas, showing that 89% of the cases reported fell into the areas with higher risk for malaria.Associação Brasileira de Saúde Coletiva2009-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2009000300005Revista Brasileira de Epidemiologia v.12 n.3 2009reponame:Revista brasileira de epidemiologia (Online)instname:Associação Brasileira de Saúde Coletiva (ABRASCO)instacron:ABRASCO10.1590/S1415-790X2009000300005info:eu-repo/semantics/openAccessRincón-Romero,Mauricio EdilbertoLondoño,Julián Estebaneng2009-09-02T00:00:00Zoai:scielo:S1415-790X2009000300005Revistahttp://www.scielo.br/rbepidhttps://old.scielo.br/oai/scielo-oai.php||revbrepi@usp.br1980-54971415-790Xopendoar:2009-09-02T00:00Revista brasileira de epidemiologia (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO)false
dc.title.none.fl_str_mv Mapping malaria risk using environmental and anthropic variables
title Mapping malaria risk using environmental and anthropic variables
spellingShingle Mapping malaria risk using environmental and anthropic variables
Rincón-Romero,Mauricio Edilberto
Malaria
GIS
Spatial modeling
Environmental modeling
Malaria risk zoning
title_short Mapping malaria risk using environmental and anthropic variables
title_full Mapping malaria risk using environmental and anthropic variables
title_fullStr Mapping malaria risk using environmental and anthropic variables
title_full_unstemmed Mapping malaria risk using environmental and anthropic variables
title_sort Mapping malaria risk using environmental and anthropic variables
author Rincón-Romero,Mauricio Edilberto
author_facet Rincón-Romero,Mauricio Edilberto
Londoño,Julián Esteban
author_role author
author2 Londoño,Julián Esteban
author2_role author
dc.contributor.author.fl_str_mv Rincón-Romero,Mauricio Edilberto
Londoño,Julián Esteban
dc.subject.por.fl_str_mv Malaria
GIS
Spatial modeling
Environmental modeling
Malaria risk zoning
topic Malaria
GIS
Spatial modeling
Environmental modeling
Malaria risk zoning
description Despite much research in the identification of areas with malaria, it is urgent to further investigate mapping techniques to achieve better approaches in strategies to prevent, mitigate, and eradicate the mosquito and the illness eventually. By using spatial distributed modeling techniques with Geographical Information Systems (GIS), the study proposes methodology to map malaria risk zoning for the municipality of Buenaventura in Colombia. The model proposed by Craig et al.¹ using climatic information was adapted to the conditions of the study area regarding scale and spatial resolution. Geomorphologic and anthropic variables were added to improve spatial allocation of areas with higher risk of contracting the illness, refining zoning. Then, they were contrasted with the locations reported by health entities², taking into account spatial distribution. The comparison of results shows a decrease in the area obtained initially using the Craig et al. model¹ (1999), from 5,422.4 km² (89.1% of the municipality's territory) to 624.3km² (approximately 10% of the municipality's area), yielding a total reduction of 78.8% when environmental and anthropic variables were included in the model. Data show that of the 9,863 cases reported during 2001 to 2005 for 20 selected towns as basis for the amount of surveyed malaria cases², 1,132 were located in the very high-risk areas, 7,662 were in the areas of moderate risk, and 1,066 cases in low-risk areas, showing that 89% of the cases reported fell into the areas with higher risk for malaria.
publishDate 2009
dc.date.none.fl_str_mv 2009-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2009000300005
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2009000300005
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1415-790X2009000300005
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Saúde Coletiva
publisher.none.fl_str_mv Associação Brasileira de Saúde Coletiva
dc.source.none.fl_str_mv Revista Brasileira de Epidemiologia v.12 n.3 2009
reponame:Revista brasileira de epidemiologia (Online)
instname:Associação Brasileira de Saúde Coletiva (ABRASCO)
instacron:ABRASCO
instname_str Associação Brasileira de Saúde Coletiva (ABRASCO)
instacron_str ABRASCO
institution ABRASCO
reponame_str Revista brasileira de epidemiologia (Online)
collection Revista brasileira de epidemiologia (Online)
repository.name.fl_str_mv Revista brasileira de epidemiologia (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO)
repository.mail.fl_str_mv ||revbrepi@usp.br
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