The actions and behavior of hypersocial species like humans are greatly influenced by the actions and actions of the people around them. This has been proven throughout the COVID-19 pandemic. Because protective measures such as masks and social distancing were influenced by where people were and who was around them, their behavior varied widely, which in turn affected the spread of the disease and infection rates.
Now, researchers from the University of Pennsylvania’s College of Arts and Sciences and Queen’s University in Canada are investigating how social dynamics, specifically non-pharmaceutical interventions (NPIs) such as masking and masking, affect disease transmission. A theoretical model was created. Social distancing is influenced by social norms.
This study Proceedings of the National Academy of Sciencesthat social conformity creates a kind of “stickiness” in which individuals are reluctant to change their NPI usage if it differs from what others are doing. is shown.
“In general, when an infectious disease is prevalent, rational actors are reluctant to take risks and seek to avoid getting sick, so it is natural to think that they will change their behavior based on these concerns. ” said associate professor Erol Akchai. He majored in biology at the University of Pennsylvania. “However, we found that population, and therefore disease transmission rates, are equally, if not more, influenced by social norms.”
The researchers aimed to better understand how prioritization of risk and social norms influence the adoption of NPIs during a pandemic.
To achieve this, they developed a model that takes into account the risk of infection, the cost of NPIs, and the societal costs of deviations from NPI usage standards. This model describes the dynamics of a threshold number of individuals required to support behavioral change, creating a “tipping point” in the adoption of NPI behaviors, where small changes in disease prevalence It can cause significant changes in behavior.
“Our model shows that small changes in certain factors, such as NPI effectiveness, infection rates, and intervention costs, can lead to large changes in disease prevalence and attack rates.” Akchai says.
This is partly because people are adaptable and therefore slow to adopt new behaviors such as mask-wearing, and as infections reach very high levels and the population tilts, risk perceptions become less adaptive. Until you surpass it, he explains. Conformism also works in reverse. New behaviors persist with people longer than if they cared only about their own risks and costs. This creates a distinct wave in infection and her NPI behavior.
Changes in variables such as the cost and effectiveness of NPI actions can lead to more or less waves of change, leading to higher or lower numbers of infections at the end of an epidemic. The researchers found that attack rates did not increase as smoothly as expected. Rather, it looks like a “saw” when graphed. These results highlight the complex relationship between social norms and the spread of disease.
“We saw a cycle of increases and decreases, and we noticed this trend with other parameters as well, even transmission speed,” says lead author Bryce Morsky. He began working on this project as a postdoctoral fellow in Akchai’s lab and is currently an assistant. He is a professor at Florida State University.
The team, which also includes Felicia Magpantai and Troy Day from Queen’s University in Canada, ran epidemiological simulations without NPIs early in the outbreak and found that attack rates were as high as expected and that the final explains that individuals have begun using NPI. Due to concerns about the risk of infection.
However, the use of NPI begins much later, when the cost parameter of deviating from social norms is set high. “Because if no one is wearing a mask, you don’t want to be the first one,” Akchai said.
“Increasing this parameter therefore causes a delay in masking, making the first wave of the epidemic much higher than it would have been if individuals had reacted according to their own risk levels. We ran a simulation in which when the number of cases started to decline, there was a reluctance to stop masking because no one wanted to stop masking in the first place, which we called stickiness. .”
Morsky explains that the model was initially motivated by the results of previous research examining the social norms and effects associated with reciprocal behavior, suggesting that same-sex behavior undergoes boom-bust cycles in supportive communities. may cause. Here, even in the absence of external factors such as seasonal variations in infection rates, conformist behavior makes epidemic waves inherently more distinct than they would otherwise be.
Akchai said information about these trends and social dynamics could be useful to policymakers as they consider decisions about responding to human behavior. The researchers then hope to investigate how the interactions of different populations and socio-economic backgrounds influence the social behavior of disease interventions.
Erol Akchai is an associate professor in the Department of Biology in the College of Arts and Sciences at the University of Pennsylvania.
Bryce Morsky is a postdoctoral fellow at the University of Pennsylvania and currently an assistant professor at Florida State University.
This research was supported by the One Society Network, which received funding from the Natural Sciences and Engineering Research Council of Canada and the U.S.-Israel Binational Science Foundation.