Linking hosts, landscapes, and climate to advance zoonotic arbovirus forecasting.

Forecasting zoonotic mosquito-borne viruses remains a critical challenge because transmission depends on dynamic, multitrophic interactions among vectors, hosts, pathogens, and the environment. We integrate long-term sentinel chicken surveillance in Florida with environmental data to build a predictive framework for eastern equine encephalitis virus (EEEV), a zoonotic mosquito borne disease of concern to human and equine health. Models captured environmental drivers and latent spatiotemporal structure, achieving strong predictive accuracy. Models also revealed nonlinear effects of moderate precipitation a year prior to sampling, higher minimum temperature a month prior to sampling, and moderate and high forest and wetland cover on increased EEEV seroconversion. Retrospective predictions showed distributions of virus activity across regions, consistent with Culiseta melanura vector ecology. We also calculated associations between predicted EEEV seroconversion and abundance estimates for suspected avian hosts using eBird data. Seasonal shifts among migratory and resident birds with predicted virus activity for key species suggests spring migrants play a role in amplification, residents in summer persistence, and overwintering groups as potential reservoirs. These results demonstrate ecological forecasting is feasible at management-relevant scales, with broad potential to extend to other arbovirus systems. By integrating traditional surveillance with community science eBird data, our framework advances predictive capacity and ecological understanding of zoonotic arboviruses.