Coupled spreading between information and epidemics on multiplex networks with simplicial complexes.

Area of Science:

• Complex systems
• Epidemiology
• Network science

Background:

• Information diffusion is complex, influenced by multiple channels and social thresholds.
• Higher-order network structures like simplicial complexes offer more realistic models of social phenomena.
• Existing epidemic models often simplify social interactions and information spread.

Purpose of the Study:

• To propose a novel epidemic model integrating physical contact networks and online social networks.
• To investigate the impact of simplicial complexes and herd-like behavior on epidemic dynamics.
• To analyze epidemic thresholds and steady-state sizes using theoretical and simulation methods.

Main Methods:

• Developed a multiplex network model coupling disease (physical layer) and information (social layer).
• Utilized random simplicial complexes for the social layer with a herd-like threshold model for information diffusion.
• Employed the microscopic Markov chain approach for theoretical analysis.
• Validated findings through extensive Monte Carlo simulations.

Main Results:

• The proposed model demonstrates that simplicial complexes can induce abrupt transitions in epidemic thresholds.
• Herd-like behavior within simplicial complexes significantly impacts information diffusion dynamics.
• Simplicial complex structures were found to substantially influence the epidemic size at a steady state.

Conclusions:

• The integration of simplicial complexes into epidemic models provides a more nuanced understanding of disease and information spread.
• Abrupt epidemic threshold shifts are possible due to higher-order network structures and herd behavior.
• Network topology, particularly simplicial complexes, plays a critical role in determining epidemic outcomes.