Information-epidemic co-evolution propagation under policy intervention in multiplex networks

Area of Science:

• Epidemiology
• Network Science
• Public Health

Background:

• Epidemics like COVID-19 pose significant societal threats.
• External factors and policy interventions critically influence epidemic transmission dynamics.

Purpose of the Study:

• To develop a novel model analyzing the co-evolution of epidemic-related information and infectious disease spread.
• To investigate the impact of policy interventions on epidemic propagation and social distancing.

Main Methods:

• Established a model with two dynamic processes: information diffusion and epidemic transmission.
• Introduced a weighted network to represent policy impacts on social distancing.
• Utilized the micro-Markov chain (MMC) method to derive dynamic equations and epidemic thresholds.

Main Results:

• Analytical expressions confirmed that network topology, information diffusion, and policy interventions directly affect the epidemic threshold.
• Numerical simulations validated the model's dynamic equations and threshold predictions.
• Demonstrated that enhanced information diffusion and policy interventions significantly inhibit disease outbreaks.

Conclusions:

• Co-evolutionary dynamics of information and disease are significantly influenced by policy interventions.
• Strengthening information dissemination and implementing robust policies are crucial for controlling infectious diseases.
• Findings offer valuable insights for public health strategies in epidemic prevention and control.