  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 (non-identical degree
distribution). 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 cause infection
evolution curves shaking 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 shaking phenomenon can be observed in various types of
networks associated with different characteristics. Moreover, it is found that
under the proposed game-epidemic framework, a disease is more difficult to
spread in heterogeneous networks than in homogeneous networks, which differs
from conventional epidemic models.
