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This study introduces a new epidemiological model for evolving multiplex networks, revealing how awareness diffusion can control disease outbreaks. Heterogeneity in network activity impacts disease spread differently across layers.

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Area of Science:

  • Complex Systems
  • Epidemiology
  • Network Science

Background:

  • Traditional epidemiological models often assume static networks, which do not reflect real-world dynamic systems.
  • Understanding disease spread in evolving networks, particularly multiplex networks, is crucial for public health interventions.
  • The interplay between disease dynamics and awareness diffusion in time-varying networks remains underexplored.

Purpose of the Study:

  • To propose and analyze a novel epidemiological model for time-varying multiplex networks.
  • To investigate the impact of individual heterogeneity and network structure on disease and awareness dynamics.
  • To identify conditions under which awareness diffusion can mitigate epidemic outbreaks.

Main Methods:

  • Development of a novel epidemiological model incorporating a time-varying disease layer (activity-driven) and a static awareness layer.
  • Extension of the microscopic Markov chain approach to analytically derive the epidemic threshold.
  • Computational simulations to analyze disease spread patterns under varying heterogeneity and distribution assumptions (Gaussian, power-law).

Main Results:

  • Heterogeneity in the physical layer's activity promotes disease spread, while heterogeneity in the virtual layer hinders it.
  • Individual infection ability heterogeneity significantly impacts epidemic thresholds, especially under power-law distributions.
  • A metacritical point was identified where awareness diffusion effectively controls epidemic onset.

Conclusions:

  • The proposed model offers insights into epidemic dynamics within complex, time-varying multiplex networks.
  • Network heterogeneity and individual characteristics play critical, layer-dependent roles in disease transmission.
  • Awareness diffusion presents a viable strategy for controlling epidemics, particularly at the identified metacritical point.