Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Digital do Instituto Evandro Chagas (Patuá) |
Texto Completo: | https://patua.iec.gov.br/handle/iec/3924 |
Resumo: | A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America. |
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Chavy, AgatheNava, Alessandra Ferreira DalesLuz, Sergio Luiz BessaRamírez, Juan DavidHerrera, GiovannySantos, Thiago Vasconcelos dosGinouves, MarineDemar, MagaliePrévot, GhislaineGuégan, Jean-FrançoisThoisy, Benoît de2019-09-20T14:21:30Z2019-09-20T14:21:30Z2019CHAVY, Agathe et al. Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome. PLoS Neglected Tropical Diseases, v. 13, n. 8, p. 1-21, e0007629, Aug. 2019.1935-2735https://patua.iec.gov.br/handle/iec/392410.1371/journal.pntd.0007629A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America.This study was conducted within the RESERVOIRS program supported by European (ERDF/FEDER) funds and assistance from Collectivite´ Territoriale de la Guyane and Direction Re´gionale pour la Recherche et la Technologie, and the MicroBIOME project granted by Laboratoire d’Excellence CEBA “Investissement d’Avenir” and managed by the Agence Nationale de la Recherche (CEBA, Ref. ANR-10-LABEX-25-01)Institut Pasteur de la Guyane. Laboratoire des Interactions Virus-Hôtes. Cayenne, French Guiana / Universite´ de Guyane. Medicine Department. Laboratoire des Ecosystèmes Amazoniens et Pathologie Tropicale. Cayenne, French Guiana.Fundação Oswaldo Cruz. Instituto Lêonidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil,Fundação Oswaldo Cruz. Instituto Lêonidas e Maria Deane. Laboratório de Ecologia de Doenças Transmissíveis na Amazônia. Manaus, AM, Brazil.Universidad del Rosario. Programa de Biología, Facultad de Ciencias Naturales y Matemáticas. Grupo de Investigaciones Microbiológicas. Bogotá, Colombia.Universidad del Rosario. Programa de Biología, Facultad de Ciencias Naturales y Matemáticas. Grupo de Investigaciones Microbiológicas. Bogotá, Colombia.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Université de Guyane. Medicine Department. Laboratoire des Ecosystèmes Amazoniens et Pathologie Tropicale. Cayenne, French Guiana.Centre Hospitalier Andrée Rosemon. Laboratoire Hospitalo-Universitaire de Parasitologie-Mycologie. Laboratoire Associé du CNR Leishmaniose. Cayenne, French Guiana.Université de Guyane. Medicine Department. Laboratoire des Ecosystèmes Amazoniens et Pathologie Tropicale. Cayenne, French Guiana.Université de Montpellier. Unité´ Mixte de Recherche MIVEGEC. Montpellier, France / Université de Montpellier. Unité Mixte de Recherche ASTRE Cirad-INRA. Montpellier, France.Institut Pasteur de la Guyane. Laboratoire des Interactions Virus-Hôtes. Cayenne, French Guiana.engPublic Library of ScienceEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biomeinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleLeishmaniose CutâneaLeishmaniose Cutânea / transmissãoEcossistemaEcologia / tendênciasinfo:eu-repo/semantics/openAccessreponame:Repositório Digital do Instituto Evandro Chagas (Patuá)instname:Instituto Evandro Chagas (IEC)instacron:IECORIGINALEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.pdfEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.pdfapplication/pdf2597063https://patua.iec.gov.br/bitstreams/69a064d0-9682-4569-b8ad-b7fb241fad37/download1df6423ba794275c5d7b5e283ff814f7MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82182https://patua.iec.gov.br/bitstreams/f8673cd8-b021-404f-af90-06d3cc2bacda/download11832eea31b16df8613079d742d61793MD52TEXTEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.pdf.txtEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.pdf.txtExtracted texttext/plain78441https://patua.iec.gov.br/bitstreams/f1bf9b23-927c-45c6-8d18-5dd3dfd95217/downloadeff4eba65eeed457602472613d7554ffMD55THUMBNAILEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.pdf.jpgEcological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.pdf.jpgGenerated Thumbnailimage/jpeg6035https://patua.iec.gov.br/bitstreams/76a0cdf9-38f4-4ac9-bbbc-11fe160d6dc7/download9067bd553689782cb07fab6585317f59MD56iec/39242022-10-20 22:07:01.023oai:patua.iec.gov.br:iec/3924https://patua.iec.gov.brRepositório InstitucionalPUBhttps://patua.iec.gov.br/oai/requestclariceneta@iec.gov.br || Biblioteca@iec.gov.bropendoar:2022-10-20T22:07:01Repositório Digital do Instituto Evandro Chagas (Patuá) - 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dc.title.pt_BR.fl_str_mv |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
title |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
spellingShingle |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome Chavy, Agathe Leishmaniose Cutânea Leishmaniose Cutânea / transmissão Ecossistema Ecologia / tendências |
title_short |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
title_full |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
title_fullStr |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
title_full_unstemmed |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
title_sort |
Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome |
author |
Chavy, Agathe |
author_facet |
Chavy, Agathe Nava, Alessandra Ferreira Dales Luz, Sergio Luiz Bessa Ramírez, Juan David Herrera, Giovanny Santos, Thiago Vasconcelos dos Ginouves, Marine Demar, Magalie Prévot, Ghislaine Guégan, Jean-François Thoisy, Benoît de |
author_role |
author |
author2 |
Nava, Alessandra Ferreira Dales Luz, Sergio Luiz Bessa Ramírez, Juan David Herrera, Giovanny Santos, Thiago Vasconcelos dos Ginouves, Marine Demar, Magalie Prévot, Ghislaine Guégan, Jean-François Thoisy, Benoît de |
author2_role |
author author author author author author author author author author |
dc.contributor.author.fl_str_mv |
Chavy, Agathe Nava, Alessandra Ferreira Dales Luz, Sergio Luiz Bessa Ramírez, Juan David Herrera, Giovanny Santos, Thiago Vasconcelos dos Ginouves, Marine Demar, Magalie Prévot, Ghislaine Guégan, Jean-François Thoisy, Benoît de |
dc.subject.decsPrimary.pt_BR.fl_str_mv |
Leishmaniose Cutânea Leishmaniose Cutânea / transmissão Ecossistema Ecologia / tendências |
topic |
Leishmaniose Cutânea Leishmaniose Cutânea / transmissão Ecossistema Ecologia / tendências |
description |
A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America. |
publishDate |
2019 |
dc.date.accessioned.fl_str_mv |
2019-09-20T14:21:30Z |
dc.date.available.fl_str_mv |
2019-09-20T14:21:30Z |
dc.date.issued.fl_str_mv |
2019 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
CHAVY, Agathe et al. Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome. PLoS Neglected Tropical Diseases, v. 13, n. 8, p. 1-21, e0007629, Aug. 2019. |
dc.identifier.uri.fl_str_mv |
https://patua.iec.gov.br/handle/iec/3924 |
dc.identifier.issn.-.fl_str_mv |
1935-2735 |
dc.identifier.doi.-.fl_str_mv |
10.1371/journal.pntd.0007629 |
identifier_str_mv |
CHAVY, Agathe et al. Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome. PLoS Neglected Tropical Diseases, v. 13, n. 8, p. 1-21, e0007629, Aug. 2019. 1935-2735 10.1371/journal.pntd.0007629 |
url |
https://patua.iec.gov.br/handle/iec/3924 |
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eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
dc.publisher.none.fl_str_mv |
Public Library of Science |
publisher.none.fl_str_mv |
Public Library of Science |
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reponame:Repositório Digital do Instituto Evandro Chagas (Patuá) instname:Instituto Evandro Chagas (IEC) instacron:IEC |
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Instituto Evandro Chagas (IEC) |
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IEC |
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IEC |
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Repositório Digital do Instituto Evandro Chagas (Patuá) |
collection |
Repositório Digital do Instituto Evandro Chagas (Patuá) |
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