Integrating field, molecular and surveillance data to predict and control wildlife zoonoses
Endemic and newly emerging infectious diseases of wildlife represent important threats to biodiversity, human health and socio-economic advancement. Management of wildlife diseases is often limited by high system complexity and sparse data. Incorporating molecular sequence data into mechanistic models of disease transmission is one of the most promising avenues to advance understanding of pathogen persistence and thereby improve prospects for control. This research will use vampire bat transmitted rabies to develop a statistical framework that integrates field data from bat colonies in Peru with time series and molecular sequence data from rabies outbreaks in livestock. Analysis of viral sequence data will identify geographic predictors of viral diffusion across landscapes. Data from field surveys and national surveillance programs will inform spatially-explicit transmission models. Transmission trees generated from these models will be compared to observed viral phylogenies for parameter refinement and model selection. Simulation studies will explore the efficacy of alternative culling or oral vaccination plans.
Maximizing biological inference from diverse datasets is a frontier in disease ecology that will improve management of a variety of zoonotic diseases. This project will provide a framework to integrate three types of data that are increasingly collected in studies that seek mechanistic understanding of pathogen persistence: longitudinal field surveillance, time series and molecular sequence data. The research will generate a sophisticated understanding of rabies epidemiology in vampire bats, a disease responsible for thousands of domestic animal deaths yearly and increasing human rabies mortality. The tools developed will inform control programs throughout Latin America and constitute a first step towards viral elimination, while expanding scientific capacity in a developing country.
