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Run the Risk

New LANL research provides methods for early assessment of COVID-19 variants

In the early days of the pandemic in the spring of 2020, news of a mutation to the SARS-CoV-2 virus that causes COVID-19 made headlines and stirred controversy.

Bette Korber, a theoretical biologist at Los Alamos National Laboratory, had first identified the D614G mutation, named for the amino acid mutation in the virus’s spike protein, and co-authored a pre-print paper alerting the world to a new and possibly more transmissible strain.

Pushback came swiftly, with many scientists questioning both the study and the reporting about its claims—the original article’s metrics indicate more than 7,000 tweets, some quite outraged.

Fellow LANL scientist Ethan Romero-Severson, a computational epidemiologist in the lab’s Theoretical Division, first heard Korber’s observation about the D614G mutation during an internal talk at the lab and it gave him an idea.

“I heard this and I was thinking, ‘I bet we can use this data to actually estimate exactly how much more contagious these things are,” Romero-Severson tells SFR.

His thinking proved apt. Romero-Severson is the senior author of a peer-reviewed paper published last month in Nature Communications that puts forth methods for quantifying the transmissibility of new COVID-19 variants.

Doing so, he says, takes advantage of the global nature of the data in service of providing new modeling tools to rapidly assess a variant’s transmissibility and risk. Those assessments in turn can help guide vaccination and public health policies.

ICYMI, Korber’s observations turned out to be correct: The variant with the D614G mutation became the dominant strain of the virus around the world. But that did not become evident immediately.

“It’s very important to remember that people were very dismissive of [Korber’s] idea initially,” Romero-Severson says. “Bette faced a lot of pushback…people didn’t want to believe the virus was evolving in real time and adapting to humans, but she was absolutely right. We wanted to really put the things Bette was seeing to a really rigorous test and that was our motivation in doing this work.”

Korber had seen the mutation through intensive study of the SARS-CoV-2 genomes being uploaded to the international GISAID database.

“With COVID, we have a massive collection of COVID sequence data,” Romero-Severson notes. “And there’s a huge global effort to share COVID sequence data, and it’s been growing exponentially…our method focuses on: How do we use that type of data?”

Specifically, he and his colleagues developed three methods for analyzing global SARS-CoV-2 sequence data and applied them to four former variants. They found they were able to find evidence of which variants were more contagious and actually posed greater threats than previous strains even when the frequency of the variant was fairly low. “Understanding contagiousness is really important,” he says. “This research funnels into [providing] information to people who are doing modeling on vaccine efficacy and public health interventions.”

While the new paper doesn’t examine either the Delta or Omicron variants, forthcoming research does, using the population-genetic model the scientists devised, which they describe as providing “a reasonable balance between computability and accuracy.”

The computability element is important to Romero-Severson, who has a background working with public health departments.

“We were really, really sensitive to this,” he says. “We wanted something that could be run in a really low computational environment. One of the things that is really easy for a scientist like myself to get wrong is we come up with these super sophisticated things that take a 200-core computer, 100 hours to run and it’s really hard to do.” Avoiding that outcome was “one the reasons in this paper that we have these three methods is we wanted something that a person could run on a reasonable laptop…doesn’t require specialized resources or libraries or anything like that.”

In addition to computational accessibility, the scientists also make their methods available.

“All of our code and our papers is 100% publicly available,” he says. “It’s not hidden anywhere. Anyone can download it and look at it and anyone can download the data and look at that too.”

That sort of collaborative approach, Romero-Severson notes, has emerged as a dominant theme among scientists during the pandemic.

“I have never seen anything like this in my career,” he says. “Scientists are just like people…they tend to want to protect their own research. The amount of open collaboration and cooperation going on in the scientific community has been unprecedented. We are actively coming up with ways to better understand and document how COVID is spreading and how to prevent it.”

The work Romero-Severson and his colleagues have done, he says, is a “little sliver,” in the service of helping quantify variants’ risks. More broadly: “What we want to facilitate here is knowledge and we want that knowledge to be public and disseminated.”

Currently, of course, New Mexico—and everywhere else—is grappling with the ramifications of the highly transmissible Omicron variant and the glimmer of hope it might be more mild than previous variants. The model Romero-Severson’s team has devised looks to distinguish between some of the different factors that play into analyzing a variant’s impact and tease apart its innate contagiousness versus, for example, a mass gathering that might cause it to spread more widely.

“We try to be as conservative as possible,” he says. “We want to avoid falsely claiming something is going to be a concern; that’s a big concern for us.”

At the same time, he cautions about drawing conclusions too quickly that Omicron, for instance, might be less of a threat to public health.

“There’s a lot of confounding factors to think about here,” he says, such as the possibility that Omicron’s capacity to infect already vaccinated people could play a role in its seemingly more mild attributes.

But a few things are certain: “the virus has been adapting consistently over time.” And “the data is really clear that three shots, full vaccination plus a booster, does provide protection against Omicron. That is clear and unambiguous.”

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