Scientists from MIT, Harvard, and elsewhere have developed a computer model that can analyze millions of SARS-CoV-2 genomes and predict which viral variants will likely dominate and cause surges in COVID-19 cases.
The research is published in Science and was conducted by Fritz Obermeyer et al.
In the study, the team trained the machine-learning model using 6 million SARS-CoV-2 genomes that were in the GISAID database in January 2022.
They showed how their tool can also estimate the effect of genetic mutations on the virus’s fitness—its ability to multiply and spread through a population.
When the team tested their model on viral genomic data from January 2022, it predicted the rise of the BA.2 variant, which became dominant in many countries in March 2022.
PyR0 would have also identified the alpha variant (B.1.1.7) by late November 2020, a month before the World Health Organization listed it as a variant of concern.
Researchers around the world have been working to predict the fitness of different SARS-CoV-2 viral variants since early in the pandemic.
But previous models could not compare all variants simultaneously or took days to process only a few thousand genomes.
By contrast, PyR0 can analyze millions of genomes—all of the publicly available SARS-CoV-2 data—in about an hour.
It does this by grouping similar sequences together and then defining “clusters” of genomes by the constellation of mutations they share.
By focusing on mutations, which can appear in multiple variants, PyR0 has more statistical power than models that focus on viral variants.
Next, the model determines which mutations are becoming more common and estimates how quickly each mutation can cause the virus to spread.
It also estimates how rapidly the number of cases of different variants will increase based on their genetic makeup.
By identifying which mutations are important for the fitness of particular variants, the model also offers biological insight into how COVID-19 spreads and develops.
The researchers say their study suggests that current increases in viral fitness stem from the virus’s ability to escape immune responses.
New versions of this or similar models could further improve predictions by taking into account interactions between mutations.
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