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However, our understanding of the global distribution and drivers of animal infection risk remains limited. To bridge this knowledge gap, we conducted extensive data mining from various sources and developed machine learning (ML) models to estimate the global probability of SARS\u2010CoV\u20102 infections in animals. We trained and evaluated three ML models, mapping the distribution of infection risk in well\u2010documented regions and projecting risk in areas with sparse infection records. Our models pinpointed high\u2010risk areas in Europe and the United States, where infection records are scattered, as well as in the southern regions of Brazil and Asia, which have sparse infection records. Notably, our projections indicated overlaps between predicted high\u2010risk areas and the known distribution of white\u2010tailed deer, American minks, and Asian small\u2010clawed otters. Anthropogenic factors were found to be more predictive of animal infection than biophysical factors, highlighting the importance of accessibility, population density, and COVID\u201019 mortality rates. These findings suggest the potential for synergies between public and animal health policies.<\/jats:p>","DOI":"10.1155\/tbed\/3959370","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T16:05:45Z","timestamp":1759248345000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictive Modeling of Global SARS\u2010CoV\u20102 Infection Risk in Animals: Unveiling Potential Reservoirs and Informing Health Policy 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