Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species
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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Sociedade Brasileira de Medicina Tropical - SBMT
2004
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oai:scielo:S0037-868220040001000032004-03-19Using epidemiological survey data to infer geographic distributions of leishmaniasis vector speciesPeterson,A. TownsendPereira,Ricardo ScachettiNeves,Vera Fonseca de Camargo Ecological niche modeling Genetic algorithm for rule-set prediction Lutzomyia Leishmaniasis 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.info:eu-repo/semantics/openAccessSociedade Brasileira de Medicina Tropical - SBMTRevista da Sociedade Brasileira de Medicina Tropical v.37 n.1 20042004-02-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822004000100003en10.1590/S0037-86822004000100003 |
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Digital |
| author |
Peterson,A. Townsend Pereira,Ricardo Scachetti Neves,Vera Fonseca de Camargo |
| spellingShingle |
Peterson,A. Townsend Pereira,Ricardo Scachetti Neves,Vera Fonseca de Camargo Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species |
| author_facet |
Peterson,A. Townsend Pereira,Ricardo Scachetti Neves,Vera Fonseca de Camargo |
| author_sort |
Peterson,A. Townsend |
| title |
Using epidemiological survey data to infer geographic distributions of leishmaniasis vector species |
| 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 |
| 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. |
| publisher |
Sociedade Brasileira de Medicina Tropical - SBMT |
| publishDate |
2004 |
| url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0037-86822004000100003 |
| work_keys_str_mv |
AT petersonatownsend usingepidemiologicalsurveydatatoinfergeographicdistributionsofleishmaniasisvectorspecies AT pereiraricardoscachetti usingepidemiologicalsurveydatatoinfergeographicdistributionsofleishmaniasisvectorspecies AT nevesverafonsecadecamargo usingepidemiologicalsurveydatatoinfergeographicdistributionsofleishmaniasisvectorspecies |
| _version_ |
1756380285731078144 |