Which animals are likely to catch and spread COVID-19?

Embargoed until: Publicly released: 2021-11-17 11:01

International

International scientists have tried to predict which species of animals are most likely to be able to carry and transmit COVID-19 by expanding the search to include over 5,000 mammals species —an order of magnitude more than previously possible. The 500 species that rank in the top 10 per cent in terms of the likelihood of transmitting the virus includes several farmed and captive species such as water buffalo, American mink and sika deer. The list also includes other species humans are likely to come into contact with, including live traded and hunted wildlife species, bats and rodents.

Media release

From: The Royal Society

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2

Proceedings of the Royal Society B: Biological Sciences

This study combines structure-based 3D modeling of SARS-CoV-2 binding with machine learning of organismal traits to predict species’ zoonotic capacity – the ability to become infected and also transmit SARS-CoV-2 – a prerequisite for becoming a zoonotic reservoir for SARS-CoV-2. Models predict zoonotic capacity with high accuracy, are strongly corroborated by in vivo empirical studies, and expand predictive capacity to 5000+ mammal species, identifying species found in COVID-19 hotspots and in regular contact with people. This approach identifies urgent surveillance priorities, and develops a new method that extends our predictive capacity for other zoonotic pathogens with similarly undetermined host ranges.

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Journal/
conference: Proceedings of the Royal Society B

Research:Paper

Organisation/s: Cary Institute of Ecosystem Studies, USA

Funder: This work was supported by the NSF EEID program (grant no. DEB 1717282), DARPA PREEMPT program (grant no. D18AC00031), CREATE-NEO, a member of the NIH NIAID CREID program (grant no. 1U01 AI151807-01) and the NVIDIA Corporation GPU grant program (B.A.H.); by the NSF Polar program (grant no. OPP 1935870 and 1947040) (A.V.); and by NIH NIGMS (grant no. R35GM122543) (J.P.G.L.M.R.).

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