The severity of an epidemic has a significant impact on individual vaccinating decisions under voluntary vaccination. During the epidemic of a vaccine-preventable disease, individuals in a social network can perceive the infection risks based on global information announced by public health authorities, or local information obtained from their social neighbors. After that, they can rationally decide whether or not to take the vaccine through weighing the relative cost of vaccination and infection (i.e., relative vaccine cost). In this case, both social network structure and individuals' risk perception strategies will affect the final vaccine coverage. In this paper, we focus on the problem of how individuals' perceptions on epidemic severity affect their vaccinating behaviors in the face of flu-like seasonal diseases in social networks, and vice versa. Specifically, we first present three types of static decision-making mechanisms, each of which simulates human vaccinating behaviors based on different local/global information. On this basis, we further present a reinforcement-learning-based mechanism, where individuals can use their historical vaccination experiences to determine what information is more suitable to estimate the severity of the epidemic. Finally, we carry out simulations on three types of social networks to investigate the effects of network structure, source of information, relative vaccine cost, and individual social connections on the final vaccine coverage and epidemic size. The results and findings can provide a new insight for designing incentive-based vaccination policies and intervention strategies for flu-like seasonal diseases.
Scopus Subject Areas
- Computer Science(all)