$1M NSF Award for Modeling Group Behavior To Fight Epidemics
MIE Associate Professor Babak Heydari, in collaboration with CSSH Professor Dan O’Brien (Public Policy), COS Associate Professor Gabor Lippner (Math), and CSSH Professor Silvia Prina (Economics), was awarded a $1 million NSF grant for “No One Lives in a Bubble: Incorporating Group Dynamics Into Epidemic Models,” to build computational models for group behaviors, such as the formation of social bubbles and changes in risk-mitigating norms and conventions.
This article originally appeared on Northeastern Global News. It was published by Noah Lloyd.
To understand the next pandemic, we must understand our own collective behavior—these researchers want to be ready
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.”
But there was a crucial insight not taken into account during some of these debates: “It’s not just the virus that’s moving, it’s also people adjusting their behavior according to the virus,” he says.
“For example,” Heydari wrote in a follow-up email, the social “pods” that many eventually formed with friends and family “aimed to balance the risk of infection with the benefits of social interaction.”
“Risk-mitigation norms,” he continued, varied across regions, “even when mandates and policies were similar, reflected in their differing attitudes toward mask-wearing and social distancing in public spaces.”
“We need to not just understand the behavior of the virus,” Heydari said, “but also understand the behavior of people, and how they react to the virus, and how they react to the policies.”
“Probably the single most controversial policy question was how much social distancing” would be the most effective, Heydari says. “If you want to provide a more informed answer to those questions, we need to anticipate how people will react, both to the virus and to our policies.”
The uneven response to policies at the group level presents a major problem to epidemic modelers trying to incorporate the effects of policies into their predictions.
Integrating human behavior is an important step, but not just at the individual level, Heydari says. As society moved past the initial shocks of an emerging pandemic, “the importance of group-level—or collective—behavior becomes, if not more important, as important as individual responses and individual behavior.
“But that’s often the missing part of a lot of the current research.”
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Abstract: NSF