COVID-19 News

Argonne taps HPC consortium to model impacts of COVID-19 interventions

With the help of the Department of Energy (DOE) COVID-19 High Performance Computing Consortium, researchers at Argonne National Laboratory are constructing and assessing epidemiological models to simulate the spread of COVID-19 through the population and more accurately predict the impacts of various interventions.

These agent-based computational models, which take into account on-the-fly reports of the properties of the virus’s virulence that are being published every day in the scientific literature, can help represent what impacts the actions and interactions of agents (individuals or groups) may have on a system as a whole.

“This is modeling people going about their normal business,” Rick Stevens, Associate Laboratory Director for Computing, Environment and Life Sciences at Argonne, told Nextgov. “Think of it as, you know, like SimCity, right?”

The agent-based model that Argonne researchers have developed includes almost 3 million separate agents, each of whom can travel to any of 1.2 million different locations. The actions of each agent are determined by hourly schedules, which might include a trip to the gym or going to school.

Currently, the Argonne team is developing a baseline simulation — in essence, to see what would happen to our communities if people carried on with business as usual. But the true goal is to be able to extensively model the various interventions — such as closing schools and restaurants or restricting flights — that decision makers can implement in order to slow the virus’s spread. Stevens said the epidemiological efforts are aimed at addressing questions around the virus’ potential impact on hospitals and critical services, as well as elements of the infection curve and peak.

“Our models simulate individuals in a city interacting with each other,” said Argonne computational scientist Jonathan Ozik, who helps to lead Argonne’s epidemiological modeling research. ​“If there’s a school closure, we see people who are supposed to go to school not go to school, and we can look at population level outcomes, such as how does the school closure affect how many people get exposed to the virus.”

The advantage of having a computer model of an entire city is that it represents an in silico laboratory for decision-makers to see how different decisions might affect a population without actually having to implement them. ​“Knowing what decisions to make on a regional or national scale and when are crucial in this worldwide fight,” said Argonne distinguished fellow Charles (Chick) Macal, who also leads the research. ​“We’re developing a model that will help give information about what decisions will be most effective.”

The team includes Argonne software engineer Nick Collier and computer scientist Justin Wozniak, providing critical expertise in deploying the large-scale computational experiments needed for the effort on DOE leadership computing resources.

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