The model, which uses machine learning to track the fitness of different viral strains, accurately predicted the rise of Omicron’s BA.2 subvariant and the Alpha variant
Despite having only been around for fewer than three years, the COVID-causing virus SARS-CoV-2 is perhaps the most studied and genetically sequenced pathogen in history. Disease surveillance teams around the world have uploaded millions of viral sequences to public databases that allow researchers to track how the virus spreads.
Most COVID vaccines target the virus’s spike protein, which it uses to enter cells. Mutations in this protein appear to allow certain variants to escape the body’s immune response to the virus from vaccination or prior infection. The PyR0 model found that simply having numerous spike protein mutations didn’t necessarily make a strain more evolutionarily fit. But a few specific spike mutations in late 2021 helped the Omicron subvariants BA.1 and BA.2 evade the immune system.
We can’t necessarily say what’s going to happen next in terms of mutations. We can say what’s going to happen next in terms of which lineages are most likely to increase in frequency. But the things that allow the cars to go fast—the properties that confer this fitness advantage—seem to have changed over time. Omicron in particular seems to be very immune-evasive, particularly by escaping the human antibody response. That property has been increasingly important for the virus, and that makes sense because so many people have either had COVID or been vaccinated, or both.
It’s a real computational challenge to actually implement that model and fit it to the data. Lead study author Fritz Obermeyer had come to the Broad Institute from Uber AI, where researchers had developed a programming language and a software framework that uses machine learning to model probabilities and apply them to large datasets. It was really amazing to be able to apply these methods to the scale of data we’ve never had before.
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