Michelle Evans, University of Florida

Abstract

Mapping emerging infectious diseases presents a challenge because these diseases tend to be understudied, with little known about the ecology of the disease or its vector. Data-driven predictive mapping allows us to draw inferences about diseases based on the statistical relationship between disease cases and environmental or socio-economic covariates, without explicitly modeling the underlying biology. In this short talk, I will present a case study of predictive mapping of yellow fever spillover risk in Brazil, and what it can tell us about the drivers of spillover. I will end by introducing recently georeferenced, crowd-sourced data for Zika in the Americas, and propose some opportunities to use this data to further our understanding of emerging vector-borne diseases.