article · Communications Biology
Cross-species virus transmission and spillover into humans are driven by co-evolved ecological and evolutionary selection pressures. Predictive models of virus-host networks can evaluate newly discovered wildlife pathogens to identify potential pathways of human infection and address gaps in host-range knowledge. Applying these predictive tools to 513 novel viruses gathered from large-scale wildlife surveillance across high-risk human-animal interfaces in Africa, Asia, and Latin America revealed notable patterns in cross-species infectivity. In particular, newly discovered coronaviruses demonstrated a likelihood of infecting a wider variety of host species compared to viruses belonging to other viral families. In addition to identifying potential human hosts, these modelling approaches generate prioritisation scores to guide and focus future surveillance targets, helping researchers more effectively determine the host ranges of emerging wildlife viruses.
Thousands of unknown viruses circulate in wildlife, but identifying which ones threaten human health is challenging. By using predictive modelling to assess the likelihood of spillover and prioritise surveillance, public health bodies and wildlife monitoring programmes can allocate screening resources more effectively towards the pathogens and host species most capable of crossing into human populations.
The predictive models offer decision-support tools for public health agencies, epidemiology researchers, and wildlife disease surveillance organisations. The methodology functions as an early-stage prioritisation framework, helping users target laboratory screening and field monitoring towards high-risk pathogens. Real-world application appears to be at an applied analytical stage, relying on computational risk scoring to guide ongoing surveillance rather than providing a stand-alone commercial diagnostic product.
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Host-virus associations have co-evolved under ecological and evolutionary selection pressures that shape cross-species transmission and spillover to humans. Observed virus-host associations provide relevant context for newly discovered wildlife viruses to assess knowledge gaps in host-range and estimate pathways for potential human infection. Using models to predict virus-host networks, we predicted the likelihood of humans as hosts for 513 newly discovered viruses detected by large-scale wildlife surveillance at high-risk animal-human interfaces in Africa, Asia, and Latin America. Predictions indicated that novel coronaviruses are likely to infect a greater number of host species than viruses from other families. Our models further characterize novel viruses through prioritization scores and directly inform surveillance targets to identify host ranges for newly discovered viruses.
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DOI: 10.1038/s42003-022-03797-9
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