A COVID-19 transmission model inspired by gas-phase chemistry is helping the Centers for Disease Control and Prevention (CDC) forecast COVID-19 deaths across the country.

Developed by Yunfeng Shi, associate professor of materials science and engineering at Rensselaer, and Jeff Ban, professor of civil engineering at the University of Washington, the model uses fatality data collected by Johns Hopkins University and mobility data collected by Google to predict disease spread based on how much a population is moving within its community.

The researchers tested their model against data from 20 of the hardest hit counties in the United States and found it to be valid. The team has also been able to show how the forecasts change as schools open, communities lock down, and masks are mandated.

“There’s no mystery as to why there’s an outbreak,” Shi says. “There’s no mystery to how we control it. The science is absolutely there. We want to use the model to give the local government some concrete predictive insight to implement certain policies.”

Shi is a computational materials scientist who was curious about how simple chemical reaction analogs could be applied to forecasting COVID-19 transmission. Combined with Ban’s expertise in transportation and mobility, the two have developed a straightforward model that has been accurately predicting disease transmission. They are now sharing their unique approach to forecasting COVID-19 spread with the CDC on a weekly basis, along with a collective of other research teams made up of infectious disease specialists, machine learning experts, and modelers from across the nation. Combined, the models form an ensemble forecast from a multitude of perspectives.

Shi discusses his approach to the model in “What Molecules Can Teach Us During A Pandemic,” a post on the Rensselaer blog, Every Day Matters.