A High-Resolution, US-scale Digital Similar of Interacting Livestock, Wild Birds, and Human Ecosystems with Applications to Multi-host Epidemic Spread

One Health issues, such as the spread of highly pathogenic avian influenza (HPAI), present significant challenges at the intersection of human, animal, and environmental health. Recent H5N1 outbreaks underscore the need for comprehensive modeling that capture the complex interactions between various entities in these interconnected ecosystems, encompassing livestock, wild birds, and human populations. To support such efforts, we present a synthetic spatiotemporal gridded dataset for the contiguous United States, referred to as a digital similar. The methodology for constructing this digital similar involves fusing diverse datasets using statistical and optimization techniques. The livestock component includes farm-level representations of multiple livestock types -- cattle, poultry, hogs, and sheep -- including further categorization into subtypes, such as milk and beef cows, chicken, turkeys, ducks, etc. It also includes location-level data for livestock-product processing centers. Weekly abundance data for key wild bird species involved in avian flu transmission are included along with temporal networks of movements. Gridded distributions of the human population, along with demographic and occupational features, capture the placement of agricultural workers and the general population. The digital similar is verified and validated in multiple ways.This dataset aims to provide a comprehensive basis for modeling complex phenomena at the wild-domestic-human interfaces.