Connect with us

News

Researchers use AI to guide search for next SARS-like virus

Published

on

Researchers say artificial intelligence (AI) can predict which viruses could infect humans, like SARS-CoV-2, the virus that led to the COVID-19 pandemic, which animals host them and where they could emerge.

In the first quarter of 2020, a team led by scientists at Georgetown University trained eight statistical models that could predict which kinds of animals could host beta coronaviruses, the group that includes SARS-like viruses.

Over more than a year, the team then tracked the discovery of 40 new bat hosts of beta coronaviruses to validate initial predictions and dynamically update their models.

The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts.

In contrast, cutting-edge models from network science that used high-level mathematics, but less biological data, performed roughly as well or worse than expected at random.

The study’s senior author, Colin Carlson, PhD, an assistant research professor in the department of microbiology and immunology at Georgetown University, said although the origin of SARS-CoV-2 remains uncertain, the spillover of other viruses from bats is a growing problem due to factors like agricultural expansion and climate change.

“If you want to find these viruses, you have to start by profiling their hosts, their ecology, their evolution, even the shape of their wings.

“Artificial intelligence lets us take data on bats and turn it into concrete predictions: where should we be looking for the next SARS?

“If we spend less money, resources, and time looking for these viruses, we can put all of those resources into the things that actually save lives down the road.

“We can invest in building universal vaccines to target those viruses, or monitoring for spillover in people that live near bats.

“It’s a win-win for science and public health.”

Despite global investments in disease surveillance, it remains difficult to identify and monitor wildlife reservoirs of viruses that could someday infect humans.

Statistical models are increasingly being used to prioritise which wildlife species to sample in the field, but the predictions being generated from any one model can be highly uncertain.

Scientists also rarely track the success or failure of their predictions after they make them, making it hard to learn and make better models in the future.

Together, these limitations mean that there is high uncertainty in which models may be best suited to the task. The researchers found that models harnessing data on bat ecology and evolution performed extremely well at predicting new hosts.

Daniel Becker, PhD, assistant professor of biology at the University of Oklahoma, said the team is now working with other scientists around the world to test bat samples for coronaviruses based on their predictions.

“One of the most important things our study gives us is a data-driven shortlist of which bat species should be studied further.

“After identifying these likely hosts, the next step is then to invest in monitoring to understand where and when beta coronaviruses are likely to spill over.”

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Trending stories

Copyright © 2022 Aspect Publishing Ltd.

[vgoAlias]
[vgoAlias]
[id^="_form"]
[id^="_form"]
[id$="_submit"]
[id$="_submit"]
[^;]
[^;]
[(d+)]
[(d+)]
[elem.name]
[elem.name]
[+_a-z0-9-'&=]
[+_a-z0-9-'&=]
[+_a-z0-9-']
[+_a-z0-9-']
[a-z0-9-]
[a-z0-9-]
[a-z]
[a-z]
[el.name]
[el.name]