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Predicting Yellow Fever Through Species Distribution Modeling of Virus, Vector, and Monkeys

  • Marco A. B. de AlmeidaEmail author
  • Edmilson dos Santos
  • Jáder da C. Cardoso
  • Lucas G. da Silva
  • Rafael M. Rabelo
  • Júlio César Bicca-Marques
Original Contribution

Abstract

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.

Keywords

Alouatta Disease modeling Epizootic Maxent Haemagogus leucocelaenus Nonhuman primates 

Notes

Acknowledgements

We acknowledge the invaluable contributions of the Municipal Health Secretariats and Regional Health Coordinators of the Rio Grande do Sul State for their assistance in the field. We are grateful for the support of colleagues from the State Health Surveillance Center and the Yellow Fever team from the Brazilian Ministry of Health. Marco Antonio Barreto de Almeida acknowledges the suggestions, patience, and support of Vivyanne Santiago Magalhães. Júlio César Bicca-Marques acknowledges the financial support of the Brazilian National Council for Scientific and Technological Development/CNPq (PQ # 303306/2013-0). Lucas Gonçalves da Silva acknowledges the financial support of CNPq and FACEPE. Rafael Magalhães Rabelo received a scholarship from CNPq (#142352/2017-9). We thank two anonymous reviewers for their suggestions and Andrew Townsend Peterson and an anonymous reviewer for critical comments on an earlier version of this manuscript.

Supplementary material

10393_2018_1388_MOESM1_ESM.docx (9.2 mb)
Supplementary material 1 (DOCX 9396 kb)

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Copyright information

© EcoHealth Alliance 2018

Authors and Affiliations

  • Marco A. B. de Almeida
    • 1
    • 4
    Email author
  • Edmilson dos Santos
    • 1
  • Jáder da C. Cardoso
    • 1
  • Lucas G. da Silva
    • 2
  • Rafael M. Rabelo
    • 3
  • Júlio César Bicca-Marques
    • 4
  1. 1.Divisão de Vigilância Ambiental em Saúde, Centro Estadual de Vigilância em SaúdeSecretaria da Saúde do Estado do Rio Grande do SulPorto AlegreBrazil
  2. 2.Universidade Federal Rural de PernambucoRecifeBrazil
  3. 3.Instituto Nacional de Pesquisas da AmazôniaManausBrazil
  4. 4.Escola de CiênciasPontifícia Universidade Católica do Rio Grande do SulPorto AlegreBrazil

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