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AI Used to Predict Unknown Links between Viruses and Mammals

Machine learning used to predict more than 20,000 unknown associations between viruses and mammalian species

by University of Liverpool
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A new University of Liverpool study could help scientists mitigate the future spread of zoonotic and livestock diseases caused by existing viruses.

Researchers have used a form or artificial intelligence (AI) called machine learning to predict more than 20,000 unknown associations between known viruses and susceptible mammalian species. The findings, which are published in Nature Communications, could be used to help target disease surveillance programs.

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Thousands of viruses are known to affect mammals, with recent estimates indicating that less than one percent of mammalian viral diversity has been discovered to date. Some of these viruses such as human and feline immunodeficiency viruses have a very narrow host range, whereas others such as rabies and West Nile viruses have very wide host ranges.

"Host range is an important predictor of whether a virus is zoonotic and therefore poses a risk to humans. Most recently, SARS-CoV-2 has been found to have a relatively broad host range that may have facilitated its spillover to humans. However, our knowledge of the host range of most viruses remains limited," explains lead researcher Dr. Maya Wardeh from the University's Institute of Infection, Veterinary and Ecological Sciences.

To address this knowledge gap, the researchers developed a novel machine learning framework to predict unknown associations between known viruses and susceptible mammalian species by consolidating three distinct perspectives—that of each virus, each mammal, and the network connecting them, respectively.

Their results suggests that there are more than five times as many associations between known zoonotic viruses and wild and semidomesticated mammals than previously thought. In particular, bats and rodents, which have been associated with recent outbreaks of emerging viruses such as coronaviruses and hantaviruses, were linked with increased risk of zoonotic viruses.

The model also predicts a five-fold increase in associations between wild and semi-domesticated mammals and viruses of economically important domestic species such as livestock and pets.

Wardeh said: "As viruses continue to move across the globe, our model provides a powerful way to assess potential hosts they have yet to encounter. Having this foresight could help to identify and mitigate zoonotic and animal-disease risks, such as spillover from animal reservoirs into human populations."

Wardeh is currently expanding the approach to predict the ability of ticks and insects to transmit viruses to birds and mammals, which will enable prioritization of laboratory-based vector competence studies worldwide to help mitigate future outbreaks of vector-borne diseases. 

- This press release was originally published on the University of Liverpool website