globalchange  > 气候变化事实与影响
DOI: 10.1007/s10393-018-1388-4
WOS记录号: WOS:000462144000007
论文题名:
Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys
作者: de Almeida, Marco A. B.1,4; dos Santos, Edmilson1; Cardoso, Jader da C.1; da Silva, Lucas G.2; Rabelo, Rafael M.3; Bicca-Marques, Julio Cesar4
通讯作者: de Almeida, Marco A. B.
刊名: ECOHEALTH
ISSN: 1612-9202
EISSN: 1612-9210
出版年: 2019
卷: 16, 期:1, 页码:95-108
语种: 英语
英文关键词: Alouatta ; Disease modeling ; Epizootic ; Maxent ; Haemagogus leucocelaenus ; Nonhuman primates
WOS关键词: DO-SUL STATE ; HAEMAGOGUS-JANTHINOMYS DIPTERA ; ALOUATTA-GUARIBA-CLAMITANS ; NONHUMAN-PRIMATES ; ESPIRITO-SANTO ; CLIMATE-CHANGE ; AEDES-AEGYPTI ; SAO-PAULO ; BRAZIL ; CULICIDAE
WOS学科分类: Biodiversity Conservation ; Ecology ; Environmental Sciences
WOS研究方向: Biodiversity & Conservation ; Environmental Sciences & Ecology
英文摘要:

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.


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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/130626
Appears in Collections:气候变化事实与影响

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作者单位: 1.Secretaria Saude Estado Rio Grande do Sul, Ctr Estadual Vigilancia Saude, Div Vigilancia Ambiental Saude, Ave Ipiranga 5400 Sala 95, BR-90610030 Porto Alegre, RS, Brazil
2.Univ Fed Rural Pernambuco, Recife, PE, Brazil
3.Inst Nacl de Pesquisas da Amazonia, Manaus, Amazonas, Brazil
4.Pontificia Univ Catolica Rio Grande do Sul, Escola Ciencias, Porto Alegre, RS, Brazil

Recommended Citation:
de Almeida, Marco A. B.,dos Santos, Edmilson,Cardoso, Jader da C.,et al. Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys[J]. ECOHEALTH,2019-01-01,16(1):95-108
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