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Artigo
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys
Mapping yellow fever (YF) risk is often based on place of infection of human cases, whereas the circulation between nonhuman primates (NHP) and vectors is neglected. In 2008/2009, YF devastated NHP at the southern limit of the disease in the Americas. In view of the recent expansion of YF in Brazil,...
Autor principal: | Almeida, Marco Antônio Barreto de |
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Outros Autores: | dos Santos, Edmilson, Cardoso, Jáder da Cruz, Silva, L. G. da, Rabelo, Rafael M., Bicca-Marques, Júlio César |
Grau: | Artigo |
Idioma: | English |
Publicado em: |
EcoHealth
2020
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https://repositorio.inpa.gov.br/handle/1/16716 |
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oai:repositorio:1-16716 Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys Almeida, Marco Antônio Barreto de dos Santos, Edmilson Cardoso, Jáder da Cruz Silva, L. G. da Rabelo, Rafael M. Bicca-Marques, Júlio César Animals Brasil Ecosystem Epidemiology Haplorhini Isolation And Purification Mosquito Vector Risk Factor Season Veterinary Medicine Virology Yellow Fever Yellow Fever Virus Animal Brasil Ecosystem Haplorhini Mosquito Vectors Risk Factors Seasons Yellow Fever Yellow Fever Virus Mapping yellow fever (YF) risk is often based on place of infection of human cases, whereas the circulation between nonhuman primates (NHP) and vectors is neglected. In 2008/2009, YF devastated NHP at the southern limit of the disease in the Americas. In view of the recent expansion of YF in Brazil, we modeled the environmental suitability for YF with data from 2008/2009 epizootic, the distribution of NHP (Alouatta spp.), and the mosquito (Haemagogus leucocelaenus) using the maximum entropy algorithm (Maxent) to define risk areas for YF and their main environmental predictors. We evaluated points of occurrence of YF based on dates of confirmed deaths of NHP in three periods, from October 2008 to: December 2008, March 2009, and June 2009. Variables with greatest influence on suitability for YF were seasonality in water vapor pressure (36%), distribution of NHP (32%), maximum wind speed (11%), annual mean rainfall (7%), and maximum temperature in the warmest month (5%). Models of early periods of the epizootic identified suitability for YF in localities that recorded NHP deaths only months later, demonstrating usefulness of the approach for predicting the disease spread. Our data supported influence of rainfall, air humidity, and ambient temperature on the distribution of epizootics. Wind was highlighted as a predicting variable, probably due to its influence on the dispersal of vectors infected with YF in fragmented landscapes. Further studies on the role of wind are necessary to improve our understanding of the occurrence of YF and other arboviruses and their dispersal in the landscape. © 2018, EcoHealth Alliance. 2020-06-15T21:35:55Z 2020-06-15T21:35:55Z 2019 Artigo https://repositorio.inpa.gov.br/handle/1/16716 10.1007/s10393-018-1388-4 en Volume 16, Número 1, Pags. 95-108 Restrito EcoHealth |
institution |
Instituto Nacional de Pesquisas da Amazônia - Repositório Institucional |
collection |
INPA-RI |
language |
English |
topic |
Animals Brasil Ecosystem Epidemiology Haplorhini Isolation And Purification Mosquito Vector Risk Factor Season Veterinary Medicine Virology Yellow Fever Yellow Fever Virus Animal Brasil Ecosystem Haplorhini Mosquito Vectors Risk Factors Seasons Yellow Fever Yellow Fever Virus |
spellingShingle |
Animals Brasil Ecosystem Epidemiology Haplorhini Isolation And Purification Mosquito Vector Risk Factor Season Veterinary Medicine Virology Yellow Fever Yellow Fever Virus Animal Brasil Ecosystem Haplorhini Mosquito Vectors Risk Factors Seasons Yellow Fever Yellow Fever Virus Almeida, Marco Antônio Barreto de Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys |
topic_facet |
Animals Brasil Ecosystem Epidemiology Haplorhini Isolation And Purification Mosquito Vector Risk Factor Season Veterinary Medicine Virology Yellow Fever Yellow Fever Virus Animal Brasil Ecosystem Haplorhini Mosquito Vectors Risk Factors Seasons Yellow Fever Yellow Fever Virus |
description |
Mapping yellow fever (YF) risk is often based on place of infection of human cases, whereas the circulation between nonhuman primates (NHP) and vectors is neglected. In 2008/2009, YF devastated NHP at the southern limit of the disease in the Americas. In view of the recent expansion of YF in Brazil, we modeled the environmental suitability for YF with data from 2008/2009 epizootic, the distribution of NHP (Alouatta spp.), and the mosquito (Haemagogus leucocelaenus) using the maximum entropy algorithm (Maxent) to define risk areas for YF and their main environmental predictors. We evaluated points of occurrence of YF based on dates of confirmed deaths of NHP in three periods, from October 2008 to: December 2008, March 2009, and June 2009. Variables with greatest influence on suitability for YF were seasonality in water vapor pressure (36%), distribution of NHP (32%), maximum wind speed (11%), annual mean rainfall (7%), and maximum temperature in the warmest month (5%). Models of early periods of the epizootic identified suitability for YF in localities that recorded NHP deaths only months later, demonstrating usefulness of the approach for predicting the disease spread. Our data supported influence of rainfall, air humidity, and ambient temperature on the distribution of epizootics. Wind was highlighted as a predicting variable, probably due to its influence on the dispersal of vectors infected with YF in fragmented landscapes. Further studies on the role of wind are necessary to improve our understanding of the occurrence of YF and other arboviruses and their dispersal in the landscape. © 2018, EcoHealth Alliance. |
format |
Artigo |
author |
Almeida, Marco Antônio Barreto de |
author2 |
dos Santos, Edmilson Cardoso, Jáder da Cruz Silva, L. G. da Rabelo, Rafael M. Bicca-Marques, Júlio César |
author2Str |
dos Santos, Edmilson Cardoso, Jáder da Cruz Silva, L. G. da Rabelo, Rafael M. Bicca-Marques, Júlio César |
title |
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys |
title_short |
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys |
title_full |
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys |
title_fullStr |
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys |
title_full_unstemmed |
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys |
title_sort |
predicting yellow fever through species distribution modeling of virus, vector, and monkeys |
publisher |
EcoHealth |
publishDate |
2020 |
url |
https://repositorio.inpa.gov.br/handle/1/16716 |
_version_ |
1787141625230655488 |
score |
11.674684 |