He explains that this is in part due to people being conformist and therefore slow to adopt new behaviors such as mask wearing, until the disease reaches levels so high that the risk perception overrides conformity, when the population tips over. "Our model found that small changes in certain factors like the effectiveness of NPIs, transmission rate, and costs of interventions can lead to large changes in rate of disease spread, or attack rate," Akçay says.
The model describes threshold dynamics in the number of individuals needed to support a behavioral change, which creates "tipping points" in the adoption of NPI behaviors where a small change in the disease prevalence can cause a significant shift in population behavior. To achieve this, they developed a model that considers the risk of infection, the cost of NPIs, and the social cost of deviating from NPI-usage norms. The researchers aimed to better understand of how the prioritization of risk and social norms affects the adoption of NPIs during a pandemic. "But it turns out populations, and by extension disease transmission rates, are equally, if not more, affected by social norms." "Generally, when there's an infectious disease going around, rational actors are uncomfortable taking risks and will try to avoid getting sick, so naturally you'd think that they'd change their behaviors based on these concerns," says Erol Akçay, associate professor of biology at Penn. The research, published in the Proceedings of the National Academy of Sciences, shows that social conformity creates a type of "stickiness" wherein individuals are reluctant to change their NPI usage if it differs from what others are doing. Now, researchers from the School of Arts & Science at the University of Pennsylvania and Queen's University in Canada have produced a theoretical model for disease transmission that factors in the effects of social dynamics, specifically, how non-pharmaceutical interventions (NPI) like masking and social distancing are affected by social norms.
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