A high-resolution, US-scale digital similar of interacting livestock, wild birds, and human ecosystems for multihost epidemic spread

One Health issues, such as the spread of highly pathogenic avian influenza, present unique challenges at the human–animal–environmental interface. Ongoing H5N1 outbreaks underscore the urgent need for comprehensive modeling efforts that capture the complex interactions between various entities in these interconnected ecosystems. To support such efforts, we develop a methodology to construct a realistic spatiotemporal gridded digital similar of livestock production and processing, human population, and wild birds for the contiguous United States. It involves multiscale and multisource data fusion and synthesis using statistical and optimization techniques, followed by verification and validation. This framework, called FIELD, consists of multiple layers and sublayers. It includes farm-level representations of four major livestock types—cattle, poultry, swine, and sheep—with further categorization into commodities such as dairy cows, beef cows, chickens, and turkeys. Abundance data for relevant wild bird species are included. Gridded distributions of the human population, with demographic and occupational features, capture the agricultural workers and the general population. We apply FIELD to evaluate the evolving incidence likelihood risk to dairy and poultry operations and validate these results using historical incidences and phylogenetic analysis. The resulting commodity-specific spatiotemporal risk maps identify high-risk hotspots, enabling prioritization of surveillance efforts. While regional infection presence significantly enhances outbreak risk, the models reveal substantial differences in spread dynamics across poultry and dairy cattle. Furthermore, the colocation of these high-risk agricultural areas with dense human populations suggests heightened potential for zoonotic spillover and underscores the need for targeted surveillance in these coupled socioecological systems.