Predicting mammalian hosts in which novel coronaviruses can be generated

Maya Wardeh (), Matthew Baylis and Marcus S. C. Blagrove ()
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Maya Wardeh: University of Liverpool
Matthew Baylis: University of Liverpool
Marcus S. C. Blagrove: University of Liverpool

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Novel pathogenic coronaviruses – such as SARS-CoV and probably SARS-CoV-2 – arise by homologous recombination between co-infecting viruses in a single cell. Identifying possible sources of novel coronaviruses therefore requires identifying hosts of multiple coronaviruses; however, most coronavirus-host interactions remain unknown. Here, by deploying a meta-ensemble of similarity learners from three complementary perspectives (viral, mammalian and network), we predict which mammals are hosts of multiple coronaviruses. We predict that there are 11.5-fold more coronavirus-host associations, over 30-fold more potential SARS-CoV-2 recombination hosts, and over 40-fold more host species with four or more different subgenera of coronaviruses than have been observed to date at >0.5 mean probability cut-off (2.4-, 4.25- and 9-fold, respectively, at >0.9821). Our results demonstrate the large underappreciation of the potential scale of novel coronavirus generation in wild and domesticated animals. We identify high-risk species for coronavirus surveillance.

Date: 2021
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DOI: 10.1038/s41467-021-21034-5

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