Social distancing, as a strategy, was largely effective at decreasing the rates of COVID-19 transmission when the virus first appeared in early 2020 — where it was practiced. But social distancing was unevenly adopted across the United States and the world, leading to unexpected complications in the models that epidemiologists used to forecast the course of the virus. How could policymakers have predicted which regions might take up social distancing wholeheartedly, and how could they have adjusted their messaging in areas that were predisposed against it? Along the same lines, how could modelers have predicted how pandemic social “bubbles” would extend the effective size of households, and how the virus transmitted?
These are the kinds of questions Northeastern University professors Babak Heydari, Gabor Lippner, Daniel T. O’Brien and Silvia Prina hope to answer with their newly funded NSF project “No One Lives in a Bubble: Incorporating Group Dynamics into Epidemic Models.” Heydari, principal investigator on the project and an associate professor of mechanical and industrial engineering — with affiliations in the Network Science Institute and the School for Public Policy and Urban Affairs — says that, in the early days of the pandemic, “there were a lot of debates on whether certain policies — like lockdown, or other policies — were effective.”
