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

TL;DR

This paper investigates how local versus global infection information influences self-quarantine behaviors and epidemic spread, revealing that local info effectively contains outbreaks and heterogeneity impacts disease dynamics.

Contribution

It introduces a game-based epidemic model incorporating local and global information, highlighting their distinct effects on disease containment and network heterogeneity influence.

Findings

Local information effectively contains epidemics even with few self-quarantining individuals.

Global information causes oscillations in infection curves during epidemic decline.

Heterogeneous networks hinder disease spread more than homogeneous ones under the model.

Abstract

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.