Forecasting zoonotic vector-borne disease systems remains a persistent challenge for public and veterinary health. These systems are difficult to predict because they often involve multiple vectors and multiple hosts, and a pathogen, each with intrinsic population dynamics influenced by external environmental conditions. Despite substantial investment, surveillance programs often detect hazards after cases in humans or other animals, leading to reactive rather than proactive control. We developed hierarchical spatiotemporal models for two mosquito vector–avian host arbovirus systems in Florida, USA. West Nile virus (WNV) is a generalist involving multiple primary mosquito vectors, and eastern equine encephalitis virus (EEEV) is a specialist with a single primary vector. Models were fitted to 20 years (WNV) and 15 years (EEEV) of Florida Department of Health sentinel chicken seroconversion data. We incorporated nonlinear landscape and lagged climate covariates with spatiotemporal random fields (GMRF-SPDE, AR1) in the 'sdmTMB' R package and evaluated model performance using out-of-sample predictions at monthly and seasonal horizons. Both systems demonstrated potential for operational ecological forecasting. Monthly WNV models outperformed seasonal models. Seroconversion increased with higher precipitation and minimum temperatures at a two-month lag and declined under higher maximum temperatures during the sampling month. EEEV seroconversion increased with intermediate precipitation at a 12-month lag, elevated 1-month lagged minimum temperature, and intermediate forest and wetland cover. Correlations between eBird-derived avian host dynamics and predicted EEEV activity suggest this data stream may improve realized predictability. Next steps include comparative analyses of intrinsic predictability and identifying where strategic data integration can advance operational forecasting.

Keywords: zoonotic arboviruses, predictability, multisource data streams