Spatial epidemiology of yellow fever: Identification of determinants of the 2016-2018 epidemics and at-risk areas in Brazil

PLoS Negl Trop Dis. 2020 Oct 1;14(10):e0008691. doi: 10.1371/journal.pntd.0008691. eCollection 2020 Oct.

Abstract

Optimise control strategies of infectious diseases, identify factors that favour the circulation of pathogens, and propose risk maps are crucial challenges for global health. Ecological niche modelling, once relying on an adequate framework and environmental descriptors can be a helpful tool for such purposes. Despite the existence of a vaccine, yellow fever (YF) is still a public health issue. Brazil faced massive sylvatic YF outbreaks from the end of 2016 up to mid-2018, but cases in human and non-human primates have been recorded until the beginning of 2020. Here we used both human and monkey confirmed YF cases from two epidemic periods (2016/2017 and 2017/2018) to describe the spatial distribution of the cases and explore how biotic and abiotic factors drive their occurrence. The distribution of YF cases largely overlaps for humans and monkeys, and a contraction of the spatial extent associated with a southward displacement is observed during the second period of the epidemics. More contributive variables to the spatiotemporal heterogeneity of cases were related to biotic factors (mammal richness), abiotic factors (temperature and precipitation), and some human-related variables (population density, human footprint, and human vaccination coverage). Both projections of the most favourable conditions showed similar trends with a contraction of the more at-risk areas. Once extrapolated at a large scale, the Amazon basin remains at lower risk, although surrounding forest regions and notably the North-West region, would face a higher risk. Spatial projections of infectious diseases often relied on climatic variables only; here for both models, we instead highlighted the importance of considering local biotic conditions, hosts vulnerability, social and epidemiological factors to run the spatial risk analysis correctly: all YF cases occurring later on, in 2019 and 2020, were observed in the predicted at-risk areas.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brazil / epidemiology
  • Demography
  • Ecosystem
  • Epidemics*
  • Humans
  • Models, Biological
  • Risk Factors
  • Yellow Fever / epidemiology*

Grants and funding

BdT was supported by the RESERVOIRS program funded by European (ERDF/FEDER) and assistance from Collectivité Territoriale de la Guyane and Direction Régionale 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). BPD was supported by the Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) (grant number APQ-01574-17). GST was supported by Decit/SCTIE/MoH. BPD and GST were supported by the Secretaria de Estado de Saúde de Minas Gerais (SES-MG)/Secretaria de Estado de Planejamento de Minas Gerais (SEPLAG-MG)/ Fundação Oswaldo Cruz (FIOCRUZ) (grant Yellow fever). BPD and GST were supported by the National Council for Scientific and Technological Development (CNPq) (grant: Research fellowship). LS was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)/BRAZIL (grant 0001). NIOS: National Council for Scientific and Technological Development (CNPq) - (grant Graduate Scholarship). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.