A trip to the grocery store or a friend’s house may seem harmless enough, but the spread of COVID-19 is closely tied to people’s mobility. To help determine the level of travel that is safe for communities, as well as when an outbreak is likely to occur, researchers have developed a new type of mobility informed infectious disease model. It is already being used by the Centers for Disease Control and Prevention (CDC) to forecast COVID-19 deaths across the country.

A new COVID-19 transmission model is based on the theory that the number of infections increases with the average mobility.

“Overall mobility is measured by the number of places an average person visits in a day,” says professor Jeff Ban. “If a critical value is exceeded, our model will indicate an outbreak of more COVID-19 cases. Of course, public health measures such as social distancing and masks will change the critical value and other parameters of the model.”

Ban’s model — which he developed in collaboration with Yunfeng Shi, a materials science and engineering researcher at Rensselaer Polytechnic Institute — was inspired by the idea that simple chemical reactions, such as how molecules collide to form reactions, could be applied to forecasting COVID-19 transmission. Similar to how the likelihood of molecules colliding increases according to distance traveled, the researchers speculated that the risk of encountering someone infected with COVID-19 also increases the more a person travels.

Therefore, to control COVID-19 outbreaks, the average mobility over a period of time must be lower than a critical value, which varies by city and considers public health measures such as social distancing and city-wide mask usage. For example, the critical value was 30% of the pre-COVID average mobility for New York City and 60% of the pre-COVID average mobility for all other counties in New York in mid-March.

Although this is not the first COVID-19 transmission model that incorporates mobility, the researchers say it is unique in its simplicity and accuracy. The model incorporates two data sources: COVID-19 fatality data from Johns Hopkins University and mobility data from Google.