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AI tool tracks detailed evolution of viral pandemics–could have predicted COVID-19 before WHO

Jul. 24, 2023.
1 min. read Interactions

Scripps Research also developing treatments and vaccines using AI

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Amara Angelica

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Amara Angelica is Senior Editor, Mindplex

Pathology alert levels could have been raised months before WHO assignation of COVID-19 (credit: Scripps Research)

In a paper in Patterns on July 21, 2023, Scripps Research scientists demonstrated an AI system for tracking future viral pandemics by using data on recorded SARS-CoV-2 (the virus that causes COVID-19) variants and COVID-19 mortality rates.

The scientists showed that the system could have predicted the emergence of new SARS-CoV-2 “variants of concern” (VOCs) months ahead of the official designations by the World Health Organization (WHO).

COVID early warning “anomaly detector” of “variant dark matter”

Co-first author of the study Salvatore Loguercio, PhD, a staff scientist in the Scripps Research Balch lab at the time of the study, and his team showed that they could use this SARS-CoV-2 tracking system as an early warning “anomaly detector” for gene variants associated with significant changes in viral spread and mortality rates.*

“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,’” says study senior author William Balch, PhD, professor in the Department of Molecular Medicine at Scripps Research..

Developing treatments and vaccines using AI

The researchers also visualize the use of their approach to better understand virus biology and thereby enhance the development of treatments and vaccines. Currently, they are using their AI system to uncover key details of how different SARS-CoV-2 proteins worked together in the evolution of the pandemic.

Citation: Ben C. Calverley et al. Understanding the host-pathogen evolutionary balance through Gaussian process modeling of SARS-CoV-2. Patterns. July 21, 2023. DOI: (open-access)

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