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Suppressing disease spreading by using information diffusion on multiplex networks.

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Information and disease spread interact dynamically. An optimal information transmission rate can suppress disease spreading, aligning with real-world processes.

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Area of Science:

  • Complex Systems Science
  • Epidemiology
  • Network Science
  • Information Diffusion

Background:

  • Information diffusion and disease spreading are distinct yet interconnected phenomena.
  • Empirical research on the coevolutionary mechanisms linking these dynamics is limited.
  • Understanding these links is crucial for public health and communication strategies.

Purpose of the Study:

  • To investigate the coevolutionary mechanisms and dynamics between information and disease spreading.
  • To analyze the interplay using real-world data and a novel multiplex network model.
  • To identify conditions that influence or mitigate disease spread through information dynamics.

Main Methods:

  • Utilized real-world data to analyze spreading dynamics.
  • Developed and applied a proposed spreading model on a multiplex network.
  • Conducted extensive stochastic numerical simulations and theoretical analysis.

Main Results:

  • Empirical analysis revealed asymmetrical interactions between information and disease spreading.
  • Information outbreaks can be initiated by internal dynamics or disease outbreaks.
  • Disease spreading thresholds are unaffected by information dynamics.
  • An optimal information transmission rate was identified that significantly suppresses disease spread.

Conclusions:

  • The study demonstrates a significant interplay between information and disease spreading on multiplex networks.
  • Optimal information transmission rates offer a potential strategy for mitigating disease spread.
  • The proposed model's dynamics align with real-world spreading processes under optimal conditions.