Machine learning can predict which animal viruses such as Covid-19 risk infecting humans – iNews

A machine learning method that can accurately predict which animal viruses such as SARS or Covid-19 could go on to infect humans in the future has been developed by scientists.
It interprets information encoded in the viral genome – often the only thing scientifically known about new animal viruses.
The new method can also rank the virus as low, medium, high, or very high risk.
Without any prior knowledge of the previous SARS outbreak in humans, this model was able to accurately predict that SARS-CoV-2, the virus that caused the Covid-19 pandemic, and its closest viral relatives found in animals, had a high risk of being able to infect humans. This finding, together with more formal testing on hundreds of viruses with known zoonotic status demonstrated that the model could make accurate predictions on viruses that are entirely new to science. 
Most emerging infectious diseases of humans are caused by zoonotic viruses that originate from other animal species. However, of the many millions of viruses that circulate in animals, only a few are likely able to infect humans.
Scientists currently have very limited ability to rapidly assess zoonotic risk at the time that viruses are discovered, making it difficult to know which newly discovered viruses should be prioritised for early investigation.
The University of Glasgow researchers behind the study is an important step towards future human outbreak preparedness and planning.
Senior author Daniel Streicker said: “Identifying high risk viruses amid the vast diversity of animal-infecting viruses that are unlikely to infect humans has been a needle in a haystack challenge.
“Our new genome-based zoonotic risk assessment represents a step towards solving that challenge and, along with our earlier efforts showing that the reservoir hosts and arthropod vectors of viruses can be predicted from viral genomes, shows that a surprising amount of ecological insight is possible from genome sequences alone, hinting at the existence of poorly understood ways that viruses adapt to their hosts.
“More immediately, since these models use nothing more than genetic sequences, they can be applied at the time that viruses are discovered, creating a rapid, low-cost triage system to decide which viruses merit extra attention.”
The study is published in PLOS Biology.
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