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dc.contributor.authorAlmeida, Marco Antônio Barreto de-
dc.contributor.authordos Santos, Edmilson-
dc.contributor.authorCardoso, Jáder da Cruz-
dc.contributor.authorSilva, L. G. da-
dc.contributor.authorRabelo, Rafael M.-
dc.contributor.authorBicca-Marques, Júlio César-
dc.date.accessioned2020-06-15T21:35:55Z-
dc.date.available2020-06-15T21:35:55Z-
dc.date.issued2019-
dc.identifier.urihttps://repositorio.inpa.gov.br/handle/1/16716-
dc.description.abstractMapping 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.en
dc.language.isoenpt_BR
dc.relation.ispartofVolume 16, Número 1, Pags. 95-108pt_BR
dc.rightsRestrito*
dc.subjectAnimalsen
dc.subjectBrasilen
dc.subjectEcosystemen
dc.subjectEpidemiologyen
dc.subjectHaplorhinien
dc.subjectIsolation And Purificationen
dc.subjectMosquito Vectoren
dc.subjectRisk Factoren
dc.subjectSeasonen
dc.subjectVeterinary Medicineen
dc.subjectVirologyen
dc.subjectYellow Feveren
dc.subjectYellow Fever Virusen
dc.subjectAnimalen
dc.subjectBrasilen
dc.subjectEcosystemen
dc.subjectHaplorhinien
dc.subjectMosquito Vectorsen
dc.subjectRisk Factorsen
dc.subjectSeasonsen
dc.subjectYellow Feveren
dc.subjectYellow Fever Virusen
dc.titlePredicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeysen
dc.typeArtigopt_BR
dc.identifier.doi10.1007/s10393-018-1388-4-
dc.publisher.journalEcoHealthpt_BR
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