Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

Summary Back and forth transmission of SARS-CoV-2 between humans and animals has the potential to create wild reservoirs of virus that can endanger both long-term control of COVID-19 in people, and vulnerable animal populations that are particularly susceptible to lethal disease. In the near term, SARS-CoV-2 virus variants arising in newly established animal hosts could escape immunity conferred by current human vaccines. In the long-term, animal reservoirs of SARS-CoV-2 increase the overall risk of disease resurgence, making global disease control unlikely. Predicting potential animal host species is key to targeting critical surveillance as well as lab experiments testing susceptibility of potential hosts. A major bottleneck to predicting animal hosts is a paucity of molecular information about the structure of ACE2 across species, a key cellular receptor required for viral cell entry that is highly conserved across thousands of animal species. We overcome this bottleneck by combining 3D modeling of virus and host cell protein interactions with machine learning analysis of species’ ecological and biological traits, enabling predictions about the zoonotic capacity of SARS-CoV-2 for over 5,000 mammals — an order of magnitude more species than previously possible. High accuracy model predictions are strongly corroborated by available and emerging in vivo empirical studies. We also identify numerous common mammal species whose predicted zoonotic capacity and close proximity to humans may facilitate spillover and spillback transmission of SARS-CoV-2. Our results reveal high priority areas of geographic overlap between global COVID-19 hotspots and potential new mammal hosts of SARS-CoV-2. Predictive modeling integrating data across multiple biological scales offers a conceptual advance that may expand our predictive capacity for zoonotic viruses with similarly unknown and potentially broad host ranges.