Epidemic spreading under game-based self-quarantine behaviors guided by local and global infection information

During the outbreak of an epidemic, individuals may modify their behaviors in response to external (including local and global) infection-related information. However, the difference between local and global information in influencing the spread of diseases remains inadequately explored. Here we study a simple epidemic model that incorporates the game-based self-quarantine behavior of individuals, taking into account the influence of local infection status, global disease prevalence and node heterogeneity (i.e., non-uniform node degrees). Our findings reveal that local information can effectively contain an epidemic, even with only a small proportion of individuals opting for self-quarantine. On the other hand, global information can induce oscillations in infection evolution curves during the declining phase of an epidemic, owing to the synchronous release of nodes with the same degree from the quarantined state. In contrast, the releasing pattern under the local information appears to be more random. This oscillation phenomenon can be observed in various types of networks associated with different characteristics. Significantly, our model is essentially different from conventional epidemic models in that the network heterogeneity plays a negative role in the spread of epidemics, which is contrary to the previous findings.