A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious.
The model, developed by lead researcher Professor Guowei Wei, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples.
“Knowledge about the infectivity of SARS-CoV-2 is a vital factor for preventive measurements against COVID-19 and reopening the global economy,” Wei said.
“A crucial question is what are the ramifications of these mutations to COVID-19 transmission, diagnostics, prevention, and treatment.
“Wei’s machine learning model, an advanced neural network, analyzed more than 8,000 protein interaction records to determine the impact of the current known mutations on the binding affinity of the SARS-CoV-2 spike protein.
The result, which suggested increased binding affinity in five of the six known subtypes, indicated that infectivity may have increased as a result of the mutations.
“It’s extremely important to know whether future SARS-CoV-2 subtypes would pose an imminent danger to public health,” Wei said
“He cautions that although AI-based predictions are consistent with available experimental findings, further studies are needed to fully understand mutation impacts on COVID-19 infectivity, which is vital to the public health response to COVID-19.