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

This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. You must log in to post a comment. There are no comments or no comments have been made public for this article. This is a Preprint and has not been peer reviewed. This is version 2 of this Preprint. Add a Comment You must log in to post a comment. Comments There are no comments or no comments have been made public for this article. Forecasting zoonotic mosquito-borne viruses remains a critical challenge because transmission depends on dynamic, multitrophic interactions among vectors, hosts, pathogens, and the environment. Here, we integrate long-term sentinel chicken surveillance across much of 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. Our models captured both environmental drivers and latent spatiotemporal structure, achieving strong predictive accuracy. Models revealed strong nonlinear effects of moderate precipitation a year prior to sampling and higher minimum temperature 1 month prior to sampling, as well as moderate and high percentages of forest and wetland cover on increased EEEV seroconversion. Retrospective predictions showed shifting virus activity across regions, consistent with Culiseta melanura mosquito vector ecology. We also calculated associations between EEEV and abundance estimates for key bird species that are suspected virus hosts using eBird data. Seasonal shifts among migratory and resident birds with predicted virus activity for key species suspected of being important EEEV hosts suggests spring migrants play a role in amplification, residents in summer persistence, and overwintering groups as potential reservoirs. These results demonstrate ecological forecasting of arboviruses is feasible at management-relevant scales, with broad potential to extend to other arbovirus systems. By integrating traditional surveillance with community science, our framework advances both predictive capacity and ecological understanding of zoonotic arboviruses. https://doi.org/10.32942/X2VM0K Life Sciences ecological forecasting, zoonotic arboviruses, landscape ecology, sdmTMB Published: 2025-10-08 14:53 Last Updated: 2025-10-08 14:53 CC BY Attribution 4.0 International Conflict of interest statement: None Data and Code Availability Statement: All code necessary to conduct these analyses are stored in the following Github repository: https://github.com/Campbell-Lab-FMEL/EEEV_forecasting/tree/main. Georeferenced sentinel chicken seroconversion data is available upon request through the Florida Department of Health Arbovirus Surveillance program upon agreement from participating Florida mosquito control programs through a memorandum of understanding. The authors did not receive special privileges in accessing the data that other researchers would not have. Contact information for data requests are available through the FDOH website: https://www.floridahealth.gov/diseases-and-conditions/mosquito-borne-diseases/surveillance.html. Language: English