ArticleNature communications2021
Predicting mammalian hosts in which novel coronaviruses can be generated.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed.
- Narrowed host ranges do not constrain future host range expansion in RNA phage Φ6.Journal of virology · 2026Article
- Identifying host-specific patterns in viral protein sequences to predict host spillover risk in animal and plant kingdoms.Scientific reports · 2026Article
- Article
- Viral genomic features predict Orthopoxvirus reservoir hosts.Communications biology · 2025Article
- Viral genomic features predictbioRxiv : the preprint server for biology · 2024Article
- Features that matter: Evolutionary signatures can predict viral transmission routes.PLoS pathogens · 2024Article
- Article
- Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.Frontiers in veterinary science · 2024Article
- The ecology of viruses in urban rodents with a focus on SARS-CoV-2.Emerging microbes & infections · 2023Article
- Praemonitus praemunitus: can we forecast and prepare for future viral disease outbreaks?FEMS microbiology reviews · 2023Review
- Concurrent loss of ciliary genesRoyal Society open science · 2023Article
- Review
- Macro-level drivers of SARS-CoV-2 transmission: A data-driven analysis of factors contributing to epidemic growth during the first wave of outbreaks in the United States.Spatial and spatio-temporal epidemiology · 2022Article
- The Lancet Commission on lessons for the future from the COVID-19 pandemic.Lancet (London, England) · 2022Review
- Variations in Cell Surface ACE2 Levels Alter Direct Binding of SARS-CoV-2 Spike Protein and Viral Infectivity: Implications for Measuring Spike Protein Interactions with Animal ACE2 Orthologs.Journal of virology · 2022Article
- Reply to: Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.Nature communications · 2022Article
- Machine-learning prediction of hosts of novel coronaviruses requires caution as it may affect wildlife conservation.Nature communications · 2022Article
- Understanding the Role of Environmental Transmission on COVID-19 Herd Immunity and Invasion Potential.Bulletin of mathematical biology · 2022Article
- Optimising predictive models to prioritise viral discovery in zoonotic reservoirs.The Lancet. Microbe · 2022Review
- Complete mitogenome of the endangered and endemic Nicobar treeshrew (Tupaia nicobarica) and comparison with other Scandentians.Scientific reports · 2022Article
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3 authors.
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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.
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