Coupled Information-Epidemic Spreading Dynamics with Selective Mass Media.

Area of Science:

• Epidemiology
• Network Science
• Information Dynamics

Background:

• The COVID-19 pandemic highlighted the critical role of mass media in disseminating public health information.
• Existing models often assume uniform information spread, neglecting resource constraints and network structures.
• Understanding the interplay between information diffusion and disease propagation is crucial for effective pandemic response.

Purpose of the Study:

• To develop and analyze a novel coupled information-epidemic spreading model incorporating targeted mass media dissemination.
• To investigate the impact of selective information promotion to high-degree nodes on epidemic dynamics.
• To evaluate the influence of network topology, specifically interlayer degree correlation in multiplex networks, on the effectiveness of media interventions.

Main Methods:

• Development of a coupled information-epidemic spreading model.
• Utilization of microscopic Markov chain methodology for model analysis.
• Simulation and examination of various model parameters, including broadcast proportion and network characteristics.

Main Results:

• Targeted mass media dissemination to high-degree nodes significantly reduces epidemic infection density and elevates the epidemic threshold.
• Increasing the proportion of targeted broadcasts amplifies the disease suppression effect.
• The inhibitory effect of mass media is more pronounced in multiplex networks with negative interlayer degree correlation.

Conclusions:

• Selective mass media promotion targeting influential nodes is an effective strategy for mitigating epidemics.
• Network structure, particularly in multiplex systems, plays a significant role in the efficacy of public health communication during outbreaks.
• The findings offer valuable insights for optimizing public health communication strategies in complex social networks.