Epidemic spreading under game-based self-quarantine behaviors: The different effects of local and global information.

The article focuses on a game-based extension of the classical susceptible-infected-removed (SIR) epidemic model, termed the SIR-Q model, which incorporates individual self-quarantine behavior influenced by local and global infection information and node heterogeneity in networks. The study finds that local infection information can effectively suppress epidemics with only a small fraction of individuals self-isolating, whereas global information leads to synchronous quarantine releases among nodes with the same degree, causing oscillations ("shaking") in infection curves during the epidemic’s decline. Additionally, contrary to traditional models, the heterogeneous degree distribution of networks in this framework impedes rather than facilitates disease spread, as low-degree nodes tend to self-isolate more readily. These findings are supported by simulations on synthetic (Erdős–Rényi and Barabási–Albert) and real-world networks, highlighting the distinct roles of information sources and network structure in epidemic dynamics.