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AI System Detects COVID-19 Variants Of Concern Before WHO Designation

Scientists develop machine-learning tool to track virus evolution and predict potential pandemics
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A pandemic “early warning system” has been developed by scientists using artificial intelligence.

Using it to study COVID-19 data, researchers found that they would have been aware of ‘variants of concern’ before their designation by the World Health Organisation.

The team from Scripps Research Institute in California believes their system can pick out potential viral pandemics at an early stage before they become global.

Their machine-learning system, a type of artificial intelligence (AI) application, can track the detailed evolution of epidemic viruses and predict the emergence of variants that could endanger humans.

Study senior author Dr. William Balch, professor in the Department of Molecular Medicine at Scripps Research said: “There are rules of pandemic virus evolution that we have not understood but can be discovered, and used in an actionable sense by private and public health organizations, through this unprecedented machine-learning approach.”

The system can use data from publicly available repositories as well as genetic data that is discovered.

The team from Scripps Research Institute in California believes their system can pick out potential viral pandemics at an early stage before they become global. PHOTO BY ANNA SHVETS/PEXELS

The study, published in the journal Cell Patterns, says the software enabled the researchers to track sets of genetic changes appearing in SARS-CoV-2 variants around the world.

They developed machine-learning software, using a strategy called Gaussian process-based spatial covariance, to relate three data sets spanning the course of the pandemic: the genetic sequences of SARS-CoV-2 variants found in infected people worldwide, the frequencies of those variants, and the global mortality rate for COVID-19.

They detected changes typically trending towards increased spread rates and decreased mortality rates.

This signified the virus’ adaptations to lockdowns, mask-wearing, vaccines, increasing natural immunity in the global population and the relentless competition among SARS-CoV-2 variants themselves.

Balch said: “We could see key gene variants appearing and becoming more prevalent, as the mortality rate also changed, and all this was happening weeks before the VOCs containing these variants were officially designated by the WHO.

“One of the big lessons of this work is that it is important to take into account not just a few prominent variants, but also the tens of thousands of other undesignated variants, which we call the ‘variant dark matter.”

They say a similar system could be used to track the detailed evolution of future viral pandemics in real-time.

This would enable scientists to predict changes in a pandemic’s trajectory such as big increases in infection rates in time to adopt countermeasures.

The team also hopes the use of their approach to better understand virus biology and thereby enhance the development of treatments and vaccines.

Co-author Dr. Ben Calverley added: “This system and its underlying technical methods have many possible future applications.”

Produced in association with SWNS Talker

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