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January 28, 2021

UConn researcher maps COVID-19 spread by town using travel data

By Liese Klein

A UConn researcher and his collaborators have figured out how to predict how many COVID-19 cases are likely in specific Connecticut towns with a new model powered by travel data.

Geography Professor Peter Chen created a predictive model for COVID-19 in Connecticut based on travel distances and broken down by towns rather than states or counties.  

Using state Department of Public Health data, Chen and his colleagues modified a traditional model for epidemic simulations and added in social distancing metrics and residents’ travel activities taken from survey data.

Due to travel bans and pandemic restrictions, most COVID-19 infections in Connecticut are being transmitted from town to town, according to the state data. The new model can help determine which towns are at higher risk and deploy resources to help.
 
“I think this will be very important not only to people at the Connecticut Department of Health managing resources and leveraging them to serve high-risk towns, but also on the local government level,” Chen said. “If they realize they have a high risk, they can be more cautious.”

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