AI tool predicts Covid variations

  • October 24, 2023
  • Steve Rogerson

An artificial intelligence tool developed by researchers at Harvard Medical School and the University of Oxford can help forecast viral outbreaks.

The AI tool called Evescape uses evolutionary and biological information to predict how a virus could change to escape the immune system. The tool successfully predicted the most concerning new variants that occurred during the Covid-19 pandemic.

Researchers say the tool can help inform the development of vaccines and therapies for Sars-CoV-2 and other rapidly mutating viruses.

The tool has two elements: A model of evolutionary sequences that predicts changes that can occur to a virus; and detailed biological and structural information about the virus. Together, they allow Evescape to make predictions about the variants most likely to occur as the virus evolves.

In a study published in Nature (, the researchers show that had it been deployed at the start of the Covid-19 pandemic, Evescape would have predicted the most frequent mutations and identified the most concerning variants for Sars-CoV-2. The tool also made accurate predictions about other viruses, including HIV and influenza.

The researchers are now using Evescape to look ahead at Sars-CoV-2 and predict future variants of concern; every two weeks, they release a ranking of new variants. Eventually, this information could help scientists develop more effective vaccines and therapies. The team is also broadening the work to include more viruses.

“We want to know if we can anticipate the variation in viruses and forecast new variants, because, if we can, that’s going to be extremely important for designing vaccines and therapies,” said senior author Debora Marks, professor of systems biology in the Blavatnik Institute at HMS.

The researchers first developed Eve, short for evolutionary model of variant effect, in a different context: gene mutations that cause human diseases. The core of Eve is a generative model that learns to predict the functionality of proteins based on large-scale evolutionary data across species.

In a previous study, Eve allowed researchers to discern disease-causing from benign mutations in genes linked to various conditions, including cancers and heart rhythm disorders.

“You can use these generative models to learn amazing things from evolutionary information,” Marks said. “The data have hidden secrets that you can reveal.”

As the Covid-19 pandemic hit and progressed, the world was caught off guard by Sars-CoV-2’s impressive ability to evolve. The virus kept morphing, changing its structure in ways subtle and substantial to slip past vaccines and therapies designed to defeat it.

“We underestimate the ability of things to mutate when they’re under pressure and have a large population in which to do so,” Marks said. “Viruses are flexible. It’s almost like they’ve evolved to evolve.”

Watching the pandemic unfold, Marks and her team saw an opportunity to help. They rebuilt Eve into a tool called Evescape for the purpose of predicting viral variants.

They took the generative model from Eve – which can predict mutations in viral proteins that won’t interfere with the virus’s function – and added biological and structural details about the virus, including information about regions most easily targeted by the immune system.

“We’re taking biological information about how the immune system works and layering it on our learnings from the broader evolutionary history of the virus,” said co-lead author Nicole Thadani, a former research fellow in the Marks lab.

Such an approach, Marks said, meant Evescape had a flexible framework that could be easily adapted to any virus.

In the new study, the team turned the clock back to January 2020, just before the Covid-19 pandemic started. Then they asked Evescape to predict what would happen with Sars-CoV-2.

“It’s as if you have a time machine,” Marks said. “You go back to day one, and you say, I only have that data, what am I going to say is happening?”

Evescape predicted which Sars-CoV-2 mutations would occur during the pandemic with accuracy similar to experimental approaches that test the virus’ ability to bind to antibodies made by the immune system. Evescape outperformed experimental approaches in predicting which of those mutations would be most prevalent. More importantly, it could make its predictions more quickly and efficiently than lab-based testing since it didn’t need to wait for relevant antibodies to arise in the population and become available for testing.

Additionally, Evescape predicted which antibody-based therapies would lose their efficacy as the pandemic progressed and the virus developed mutations to escape these treatments.

The tool was also able to sift through the tens of thousands of new Sars-CoV-2 variants produced each week and identify the ones most likely to become problematic.

“By rapidly determining the threat level of new variants, we can help inform earlier public health decisions,” said co-lead author Sarah Gurev, a graduate student in the Marks lab from the electrical engineering and computer science programme at MIT.

In a final step, the team demonstrated that Evescape could be generalised to other common viruses, including HIV and influenza.

The team is now applying Evescape to Sars-CoV-2 in real time, using all of the information available to make predictions about how it might evolve next.

The researchers publish a biweekly ranking of new Sars-CoV-2 variants on their web site ( and share this information with entities such as the World Health Organisation. The complete code for Evescape ( is also freely available online.

They are also testing Evescape on understudied viruses such as Lassa and Nipah, two pathogens of pandemic potential for which relatively little information exists. Such less-studied viruses can have a huge impact on human health across the globe, the researchers said.

Another important application of Evescape would be to evaluate vaccines and therapies against current and future viral variants. The ability to do so can help scientists design treatments that are able to withstand the escape mechanisms a virus acquires.

“Historically, vaccine and therapeutic design has been retrospective, slow and tied to the exact sequences known about a given virus,” Thadani said.

Noor Youssef, a research fellow in the Marks lab, added: “We want to figure out how we can actually design vaccines and therapies that are future-proof.”

Additional authors on the paper included Pascal Notin, Nathan Rollins, Daniel Ritter, Chris Sander and Yarin Gal.

Funding for the research was provided by the National Institutes of Health, the Coalition for Epidemic Preparedness Innovations, Chan Zuckerberg Initiative, GSK, UK Engineering & Physical Sciences Research Council, and Alan Turing Institute.

Marks is an advisor for Dyno Therapeutics, Octant, Jura Bio, Tectonic Therapeutic and Genentech, and is a co-founder of Seismic Therapeutic. Sander is an advisor for CytoReason.