ArticleProceedings. Biological sciences2021
Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.
Article in Proceedings. Biological sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
57 citing papers in PubMed.
- Article
- Article
- Serological Survey of SARS-CoV-2 in Wild Canids in Serbia: First Report in Red Foxes and Golden Jackals.Veterinary sciences · 2026Article
- Systematic multi-reference vertebrate ACE2 sequence similarity analysis predicts species susceptibility to SARS-related sarbecoviruses.Scientific reports · 2026Article
- Selecting Sites for Strategic Surveillance of Zoonotic Pathogens: A Case Study in Panamá.EcoHealth · 2025Article
- Detection of avian, murine, bovine, shrew, and bat coronaviruses in wild mammals from Mexico.Virology journal · 2025Article
- Viral genomic features predict Orthopoxvirus reservoir hosts.Communications biology · 2025Article
- Pathogens and planetary change.Nature reviews. Biodiversity · 2025Article
- Rapid Risk Assessment Framework to Estimate Potential for Spillback at Human-Wildlife Interfaces.Transboundary and emerging diseases · 2025Article
- Viral genomic features predictbioRxiv : the preprint server for biology · 2024Article
- Review
- Widespread exposure to SARS-CoV-2 in wildlife communities.Nature communications · 2024Article
- Significance of Artificial Intelligence in the Study of Virus-Host Cell Interactions.Biomolecules · 2024Review
- The reverse zoonotic potential of SARS-CoV-2.Heliyon · 2024Review
- Perspectives on SARS-CoV-2 Cases in Zoological Institutions.Veterinary sciences · 2024Review
- Experimental SARS-CoV-2 Infection of Elk and Mule Deer.Emerging infectious diseases · 2024Article
- Evidence of SARS-CoV-2 infection in companion animals from owners who tested positive for COVID-19 in the Valley of Mexico.Molecular biology reports · 2024Article
- In Silico Analysis of Cross-Species Sequence Variability in Host Interferon Antiviral Pathway Proteins and SARS-CoV-2 Susceptibility.Zoonoses (Shannon, Ireland) · 2024Article
- Survey of white-footed mice (Peromyscus leucopus) in Connecticut, USA reveals low SARS-CoV-2 seroprevalence and infection with divergent betacoronaviruses.Npj viruses · 2023Article
- Engineering minimal tissue inhibitors of metalloproteinase targeting MMPs via gene shuffling and yeast surface display.Protein science : a publication of the Protein Society · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.