Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data

Autores
Rotela, Camilo Hugo; Spinsanti, Lorena Ivana; Lamfri, Mario; Contigiani de Minio, Marta Silvia; Almiron, Walter Ricardo; Scavuzzo, Carlos Marcelo
Año de publicación
2011
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In response to the first human outbreak (January - May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.
Fil: Rotela, Camilo Hugo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Spinsanti, Lorena Ivana. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología "Dr. J. M. Vanella"; Argentina
Fil: Lamfri, Mario. Comision Nacional de Actividades Espaciales; Argentina
Fil: Contigiani de Minio, Marta Silvia. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología "Dr. J. M. Vanella"; Argentina
Fil: Almiron, Walter Ricardo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Centro de Investigaciones Entomológicas de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina
Fil: Scavuzzo, Carlos Marcelo. Comision Nacional de Actividades Espaciales; Argentina
Materia
LANDSCAPE EPIDEMIOLOGY
REMOTE SENSING
RISK MAP
SAINT LOUIS ENCEPHALITIS
ARGENTINA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/42990
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed dataRotela, Camilo HugoSpinsanti, Lorena IvanaLamfri, MarioContigiani de Minio, Marta SilviaAlmiron, Walter RicardoScavuzzo, Carlos MarceloLANDSCAPE EPIDEMIOLOGYREMOTE SENSINGRISK MAPSAINT LOUIS ENCEPHALITISARGENTINAhttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1In response to the first human outbreak (January - May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.Fil: Rotela, Camilo Hugo. Comision Nacional de Actividades Espaciales; ArgentinaFil: Spinsanti, Lorena Ivana. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología "Dr. J. M. Vanella"; ArgentinaFil: Lamfri, Mario. Comision Nacional de Actividades Espaciales; ArgentinaFil: Contigiani de Minio, Marta Silvia. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología "Dr. J. M. Vanella"; ArgentinaFil: Almiron, Walter Ricardo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Centro de Investigaciones Entomológicas de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; ArgentinaFil: Scavuzzo, Carlos Marcelo. Comision Nacional de Actividades Espaciales; ArgentinaUniv Naples Federico Ii2011-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/42990Rotela, Camilo Hugo; Spinsanti, Lorena Ivana; Lamfri, Mario; Contigiani de Minio, Marta Silvia; Almiron, Walter Ricardo; et al.; Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data; Univ Naples Federico Ii; Geospatial Health; 6; 1; 11-2011; 85-941827-19871970-7096CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://geospatialhealth.net/index.php/gh/article/view/160info:eu-repo/semantics/altIdentifier/doi/10.4081/gh.2011.160info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:55:47Zoai:ri.conicet.gov.ar:11336/42990instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:55:47.346CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
title Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
spellingShingle Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
Rotela, Camilo Hugo
LANDSCAPE EPIDEMIOLOGY
REMOTE SENSING
RISK MAP
SAINT LOUIS ENCEPHALITIS
ARGENTINA
title_short Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
title_full Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
title_fullStr Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
title_full_unstemmed Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
title_sort Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data
dc.creator.none.fl_str_mv Rotela, Camilo Hugo
Spinsanti, Lorena Ivana
Lamfri, Mario
Contigiani de Minio, Marta Silvia
Almiron, Walter Ricardo
Scavuzzo, Carlos Marcelo
author Rotela, Camilo Hugo
author_facet Rotela, Camilo Hugo
Spinsanti, Lorena Ivana
Lamfri, Mario
Contigiani de Minio, Marta Silvia
Almiron, Walter Ricardo
Scavuzzo, Carlos Marcelo
author_role author
author2 Spinsanti, Lorena Ivana
Lamfri, Mario
Contigiani de Minio, Marta Silvia
Almiron, Walter Ricardo
Scavuzzo, Carlos Marcelo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv LANDSCAPE EPIDEMIOLOGY
REMOTE SENSING
RISK MAP
SAINT LOUIS ENCEPHALITIS
ARGENTINA
topic LANDSCAPE EPIDEMIOLOGY
REMOTE SENSING
RISK MAP
SAINT LOUIS ENCEPHALITIS
ARGENTINA
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In response to the first human outbreak (January - May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.
Fil: Rotela, Camilo Hugo. Comision Nacional de Actividades Espaciales; Argentina
Fil: Spinsanti, Lorena Ivana. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología "Dr. J. M. Vanella"; Argentina
Fil: Lamfri, Mario. Comision Nacional de Actividades Espaciales; Argentina
Fil: Contigiani de Minio, Marta Silvia. Universidad Nacional de Córdoba. Facultad de Medicina. Instituto de Virología "Dr. J. M. Vanella"; Argentina
Fil: Almiron, Walter Ricardo. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Centro de Investigaciones Entomológicas de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Biológicas y Tecnológicas. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales. Instituto de Investigaciones Biológicas y Tecnológicas; Argentina
Fil: Scavuzzo, Carlos Marcelo. Comision Nacional de Actividades Espaciales; Argentina
description In response to the first human outbreak (January - May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.
publishDate 2011
dc.date.none.fl_str_mv 2011-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/42990
Rotela, Camilo Hugo; Spinsanti, Lorena Ivana; Lamfri, Mario; Contigiani de Minio, Marta Silvia; Almiron, Walter Ricardo; et al.; Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data; Univ Naples Federico Ii; Geospatial Health; 6; 1; 11-2011; 85-94
1827-1987
1970-7096
CONICET Digital
CONICET
url http://hdl.handle.net/11336/42990
identifier_str_mv Rotela, Camilo Hugo; Spinsanti, Lorena Ivana; Lamfri, Mario; Contigiani de Minio, Marta Silvia; Almiron, Walter Ricardo; et al.; Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotelysensed data; Univ Naples Federico Ii; Geospatial Health; 6; 1; 11-2011; 85-94
1827-1987
1970-7096
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://geospatialhealth.net/index.php/gh/article/view/160
info:eu-repo/semantics/altIdentifier/doi/10.4081/gh.2011.160
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Univ Naples Federico Ii
publisher.none.fl_str_mv Univ Naples Federico Ii
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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