To understand the next pandemic, we must understand our own collective behavior

Northeastern University researchers are developing epidemic models that incorporate collective behavioral patterns, which will help policymakers make better decisions in both future pandemics and other public crises.

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 new 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.”

Even “deliberately coordinated behaviors,” Heydari wrote, like pandemic pods or masking, “cannot be adequately modeled as the sum of individual behaviors. They require new theoretical and empirical frameworks.”

Using computational models, the researchers will incorporate collective behavior into existing epidemic modeling techniques, increasing their accuracy and providing a template for better policymaking decisions in the future.

Of the team, Heydari says that Lippner—an associate professor of mathematics—is “a graph theorist and also an affiliated member of network science.

“His expertise is on creating mathematical dynamic models on graphs, which is something that we very much need for this project.”

Prina, a professor of economics, will help extract “causality out of the data,” Heydari says, “because all we care about is causality.

“We need to use these models for policy implications, not just for policy design,” he continues.

“We want to measure how much risk and risk perception affect the formation and evolution of group-level behavior” like social bubbles, Prina wrote in an email, “and how changes in people’s behavioral responses to health risks can affect group-level behavior.”

One part of Prina’s role in the project is “causal identification,” which uses “data and exogenous variation stemming from various natural experiments,” she wrote, “to estimate the input parameters used in the pandemic models.”

O’Brien, a professor of public policy and urban affairs and criminology and criminal justice, wrote by email how every individual has “a variety of contacts with whom we share different levels of contact.”

These connections “form bridges between subgroups. This structure creates the opportunity for infection, norms, beliefs or anything else to be incubated in a localized group and then transmitted outward into broader society.”

Heydari and the rest of the team see a potential for this new modeling strategy, which incorporates these social networks, to assist with not only future epidemics, but also other public crises.

“Post natural disaster,” Heydari says, if policymakers want to produce a “more efficient management of the crisis, some of the insights from these models can be useful.”

Any individual model will still have its limits. “For every separate case, we need to redesign a model, but the insights and the framework can be used,” Heydari notes.

But he also foresees quickly developed, adaptive and responsive models that can be designed alongside recent advances in artificial intelligence.

“Thanks to our combination of theoretical modeling and real-world data and applications, our hope is that we can translate the deep insights into tangible value and impact,” O’Brien wrote.

“If you think about the opioid epidemic, there is a strong notion of social norms emerging and evolving,” Heydari says, considering another current crisis.

But these norms are also different in different areas, he says. “If you want to have the right intervention, sometimes the intervention can be in the form of trying to steer that collective behavior, rather than having mandates at the individual level.”

“The big question is, how can we steer collective behavior to something for the social good?”

This story is republished courtesy of Northeastern Global News news.northeastern.edu.