With flu season upon us and coronavirus a persistent threat, Dartmouth researchers propose a new way of thinking about masking and social distancing rules that is more responsive to what people feel is necessary at a given time-;and may help increase cooperation.

Based on game theory, the study breaks from the existing science by considering mask wearing and social distancing as two distinct and competing actions. This dynamic gives public health officials more flexibility to adapt to epidemics and encourage cooperation, the researchers report in the Proceedings of the National Academy of Sciences. The authors used the standard computer model epidemiologists employ to simulate how people behave during outbreaks and develop public health guidelines.

The error officials often make is considering masking and social distancing as two sides of the same coin, says Feng Fu, the study’s corresponding author and an associate professor of mathematics at Dartmouth. Both measures have typically been thought of as a single action known as nonpharmaceutical interventions, or NPIs, that are intended to control disease without using medication.

Instead, Fu and first author Alina Glaubitz, who was a doctoral student under Fu and received her PhD from Dartmouth this year, found that people respond to the two actions very differently. In their model, people switched between masking and social distancing-;or rejected both-;depending on how serious and widespread they perceived a disease as being. The model did not factor in a public health mandate to mask or social distance, but rather considered them as actions people take up voluntarily.

Fu, who specializes in game theory, says that masking, distancing, and doing nothing are in competition with each other when people choose an NPI independently. Infection levels and cost effectiveness determine which actions win out.

For example, Fu’s research group analyzed public sentiment during the COVID-19 pandemic. They found that the public initially resisted social distancing-;defined as low-density gatherings at which people can maintain at least six feet of separation from each other or avoid physical contact altogether-;because of the economic costs and mental health effects, but transitioned toward it as infections spread.

Fu and Glaubitz report that over the long-term, however, people trend toward masking or taking no protective action. And once people prefer a less invasive public health measure such as masking, it’s difficult for them to re-adopt a more stringent one like social distancing, which is the most costly and disruptive of the NPIs studied.

Understanding these shifts in how people are perceiving the benefit versus the cost of an intervention is key to timing them effectively and enhancing cooperation.”

Feng Fu, associate professor of mathematics at Dartmouth

“Mathematical models have become an increasingly important part of understanding and fighting against infectious diseases,” says Glaubitz, whose PhD is in evolutionary game theory and infectious disease dynamics. “Our work provides a foundation for understanding the conditions under which the public may favor certain protective behaviors.”

Policymakers could gauge which measures people are likely to adopt based on official surveys, public sentiment such as on social media, and what the local economy can absorb, Fu says.

“We show that choices matter-;both the measures policymakers implement and the timing of them,” he says. “Recommendations need to align with the public’s natural preferences to minimize resistance.”

But the coronavirus has borne out repeatedly that public sentiment does not consistently align with the measures public health officials think are necessary to contain an outbreak.

Fu and Glaubitz’s model suggests that health officials adopt a dual behavioral response that bundles multiple lighter interventions such as masking and moderate reductions in social contact. They refer to this approach as a “Swiss cheese” strategy that provides mostly solid disease mitigation, but with more “holes” for infection to spread than stringent measures would permit.

“While imperfect, layering multiple lighter measures can achieve effective mitigation while aligning with the natural progression of public preferences,” Fu says.

“Communication is essential-;clearly explaining why a particular measure is necessary and presenting less burdensome options can help foster compliance and trust,” he says. “Individuals’ decisions, while not always perfectly rational, can still lead to disease mitigation in most cases.”