  It is critical to understand and model the behavior of individuals in a
pandemic, as well as identify effective ways to guide people's behavior in
order to better control the epidemic spread. However, current research fails to
account for the impact of users' irrationality in decision-making, which is a
prevalent factor in real-life scenarios. Additionally, existing disease control
methods rely on measures such as mandatory isolation and assume that
individuals will fully comply with these policies, which may not be true in
reality. Thus, it is critical to find effective ways to guide people's behavior
during an epidemic. To address these gaps, we propose a Prospect Theory-based
theoretical framework to model individuals' decision-making process in an
epidemic and analyze the impact of irrationality on the co-evolution of user
behavior and the epidemic. Our analysis shows that irrationality can lead
individuals to be more conservative when the risk of being infected is small,
while irrationality tends to make users be more risk-seeking when the risk of
being infected is high. We then propose a behavior inducement algorithm to
guide user behavior and control the spread of disease. Simulations and real
user tests validate our proposed model and analysis, and simulation results
show that our proposed behavior inducement algorithm can effectively guide
users' behavior.
