  Understanding the social determinants that influence the adoption of
preventive measures during a disease outbreak is crucial for building effective
epidemic models and hence for policy making. While traditional behavioral
epidemic models focused on rational decision-making, psychological biases and
the incorporation of simple imitation-based mechanisms to account for social
influence, recent experimental studies highlight the necessity of considering
the role of social norm dynamics. Here, we develop a behavioral epidemic model
on a multilayer network, by integrating an Experience Weighted Attractor (EWA)
learning mechanism with the dynamics of social norms. The resulting decision
making process, can potentially lead to a boost in vaccination coverage and
hence to a significantly reduced final infected fraction, offering an
alternative social mechanism to altruism that can achieve this. Furthermore, we
examine the importance of the dynamics of each one of the social norms' types,
injunctive or descriptive, in reducing the infected fraction, finding that the
former have a more significant effect. We also explore the effect that external
interventions on the dynamics of social norms can have on the expansion of an
epidemic, aiming in identifying improved public communication protocols.
Enhanced models of social norm dynamics, if validated and tested, can better
capture the complexities of human social behavior and mitigate various societal
challenges beyond pandemics.
