A Data-driven Horizon Scan of Bacterial Pathogens at the Wildlife-livestock Interface.

Abstract

Many livestock diseases rely on wildlife for the transmission or maintenance of the pathogen, and the wildlife-livestock interface represents a potential site of disease emergence for novel pathogens in livestock. Predicting which pathogen species are most likely to emerge in the future is an important challenge for infectious disease surveillance and intelligence. We used a machine learning approach to conduct a data-driven horizon scan of bacterial associations at the wildlife-livestock interface for cows, sheep, and pigs. Our model identified and ranked from 76 to 189 potential novel bacterial species that might associate with each livestock species. Wildlife reservoirs of known and novel bacteria were shared among all three species, suggesting that targeting surveillance and/or control efforts towards these reservoirs could contribute disproportionately to reducing spillover risk to livestock. By predicting pathogen-host associations at the wildlife-livestock interface, we demonstrate one way to plan for and prevent disease emergence in livestock.