Epidemic dynamics in physical-information-social multilayer networks

TL;DR

This paper models epidemic spread in multilayer networks combining physical contact, information, and social interactions, revealing how information exchange and government actions influence disease transmission.

Contribution

It introduces a tripartite epidemic model with kinetic equations and derives explicit thresholds, highlighting the impact of information and government influence on epidemic dynamics.

Findings

Active information exchange reduces disease spread.

Early and strong government responses lower epidemic size.

Stronger government influence on media and hospitals further decreases transmission.

Abstract

During epidemic outbreaks, information dissemination enhances individual protection, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in different scales by using the Microscopic Markov Chain Approach and mean-field approximations. The basic reproduction number and epidemic thresholds are explicitly derived by the next generation matrix method. Our results reveal that (1) active information exchange curbs disease transmission, (2) earlier and stronger government responses reduce the epidemic size, and (3) stronger governmental influence on media and hospitals further decreases disease transmission, particularly in hospital nodes. In fixed community structures, groups with frequent physical contact but weak information access (e.g., students) exhibit higher infection rates. For diverse communities, weaker physical layer heterogeneity but stronger information layer heterogeneity (e.g., high internet penetration in rural areas) inhibits epidemic outbreaks. These findings offer valuable insights for epidemic prevention and control strategies.