Using drivers and transmission pathways to identify SARS-like coronavirus spillover risk hotspots

Abstract

The emergence of SARS-like coronaviruses is a multi-stage process from wildlife reservoirs to people. Here we characterize multivariate indicators associated with the risk of zoonotic spillover of SARS-like coronaviruses in different areas to help inform surveillance and mitigation activities. We consider direct and indirect transmission pathways by modeling four scenarios with livestock and mammalian wildlife as potential and known reservoirs, before examining how access to healthcare varies within areas. We found 19 multivariate clusters that had differing risk factor contributions. High-risk areas were mostly close (11-20%) rather than far (<1%) from healthcare. With the presented framework, areas with the highest estimated risk can be priority intervention targets in which risk management strategies can be implemented, such as land use planning and preventive measures to reduce contact between people and potential hosts.