Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species

Detalhes bibliográficos
Autor(a) principal: Peterson,A. Townsend
Data de Publicação: 2004
Outros Autores: Pereira,Ricardo Scachetti, Neves,Vera Fonseca de Camargo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista da Sociedade Brasileira de Medicina Tropical
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822004000100003
Resumo: An important aspect of tropical medicine is analysis of geographic aspects of risk of disease transmission, which for lack of detailed public health data must often be reduced to an understanding of the distributions of critical species such as vectors and reservoirs. We examine the applicability of a new technique, ecological niche modeling, to the challenge of understanding distributions of such species based on municipalities in the state of São Paulo in which a group of 5 Lutzomyia sandfly species have been recorded. The technique, when tested based on independent occurrence data, yielded highly significant predictions of species' distributions; minimum sample sizes for effective predictions were around 40 municipalities.
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spelling Using epidemiological survey data to infer geographic distributions of leishmaniasis vector speciesEcological niche modelingGenetic algorithm for rule-set predictionLutzomyiaLeishmaniasisAn important aspect of tropical medicine is analysis of geographic aspects of risk of disease transmission, which for lack of detailed public health data must often be reduced to an understanding of the distributions of critical species such as vectors and reservoirs. We examine the applicability of a new technique, ecological niche modeling, to the challenge of understanding distributions of such species based on municipalities in the state of São Paulo in which a group of 5 Lutzomyia sandfly species have been recorded. The technique, when tested based on independent occurrence data, yielded highly significant predictions of species' distributions; minimum sample sizes for effective predictions were around 40 municipalities.Sociedade Brasileira de Medicina Tropical - SBMT2004-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822004000100003Revista da Sociedade Brasileira de Medicina Tropical v.37 n.1 2004reponame:Revista da Sociedade Brasileira de Medicina Tropicalinstname:Sociedade Brasileira de Medicina Tropical (SBMT)instacron:SBMT10.1590/S0037-86822004000100003info:eu-repo/semantics/openAccessPeterson,A. TownsendPereira,Ricardo ScachettiNeves,Vera Fonseca de Camargoeng2004-03-19T00:00:00Zoai:scielo:S0037-86822004000100003Revistahttps://www.sbmt.org.br/portal/revista/ONGhttps://old.scielo.br/oai/scielo-oai.php||dalmo@rsbmt.uftm.edu.br|| rsbmt@rsbmt.uftm.edu.br1678-98490037-8682opendoar:2004-03-19T00:00Revista da Sociedade Brasileira de Medicina Tropical - Sociedade Brasileira de Medicina Tropical (SBMT)false
dc.title.none.fl_str_mv Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
title Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
spellingShingle Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
Peterson,A. Townsend
Ecological niche modeling
Genetic algorithm for rule-set prediction
Lutzomyia
Leishmaniasis
title_short Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
title_full Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
title_fullStr Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
title_full_unstemmed Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
title_sort Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
author Peterson,A. Townsend
author_facet Peterson,A. Townsend
Pereira,Ricardo Scachetti
Neves,Vera Fonseca de Camargo
author_role author
author2 Pereira,Ricardo Scachetti
Neves,Vera Fonseca de Camargo
author2_role author
author
dc.contributor.author.fl_str_mv Peterson,A. Townsend
Pereira,Ricardo Scachetti
Neves,Vera Fonseca de Camargo
dc.subject.por.fl_str_mv Ecological niche modeling
Genetic algorithm for rule-set prediction
Lutzomyia
Leishmaniasis
topic Ecological niche modeling
Genetic algorithm for rule-set prediction
Lutzomyia
Leishmaniasis
description An important aspect of tropical medicine is analysis of geographic aspects of risk of disease transmission, which for lack of detailed public health data must often be reduced to an understanding of the distributions of critical species such as vectors and reservoirs. We examine the applicability of a new technique, ecological niche modeling, to the challenge of understanding distributions of such species based on municipalities in the state of São Paulo in which a group of 5 Lutzomyia sandfly species have been recorded. The technique, when tested based on independent occurrence data, yielded highly significant predictions of species' distributions; minimum sample sizes for effective predictions were around 40 municipalities.
publishDate 2004
dc.date.none.fl_str_mv 2004-02-01
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dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822004000100003
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0037-86822004000100003
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dc.publisher.none.fl_str_mv Sociedade Brasileira de Medicina Tropical - SBMT
publisher.none.fl_str_mv Sociedade Brasileira de Medicina Tropical - SBMT
dc.source.none.fl_str_mv Revista da Sociedade Brasileira de Medicina Tropical v.37 n.1 2004
reponame:Revista da Sociedade Brasileira de Medicina Tropical
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repository.name.fl_str_mv Revista da Sociedade Brasileira de Medicina Tropical - Sociedade Brasileira de Medicina Tropical (SBMT)
repository.mail.fl_str_mv ||dalmo@rsbmt.uftm.edu.br|| rsbmt@rsbmt.uftm.edu.br
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