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During the outbreak of an epidemic, individuals may modify their behaviors inresponse to external (including local and global) infection-relatedinformation. However, the difference between local and global information ininfluencing the spread of diseases remains inadequately explored. Here we studya simple epidemic model that incorporates the game-based self-quarantinebehavior of individuals, taking into account the influence of local infectionstatus, global disease prevalence and node heterogeneity (i.e., non-uniformnode degrees). Our findings reveal that local information can effectivelycontain an epidemic, even with only a small proportion of individuals optingfor self-quarantine. On the other hand, global information can induceoscillations in infection evolution curves during the declining phase of anepidemic, owing to the synchronous release of nodes with the same degree fromthe quarantined state. In contrast, the releasing pattern under the localinformation appears to be more random. This oscillation phenomenon can beobserved in various types of networks associated with differentcharacteristics. Significantly, our model is essentially different fromconventional epidemic models in that the network heterogeneity plays a negativerole in the spread of epidemics, which is contrary to the previous findings.

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