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Coupling dynamics of epidemic spreading and information diffusion on complex networks

Author

Listed:
  • Zhan, Xiu-Xiu
  • Liu, Chuang
  • Zhou, Ge
  • Zhang, Zi-Ke
  • Sun, Gui-Quan
  • Zhu, Jonathan J.H.
  • Jin, Zhen

Abstract

The interaction between disease and disease information on complex networks has facilitated an interdisciplinary research area. When a disease begins to spread in the population, the corresponding information would also be transmitted among individuals, which in turn influence the spreading pattern of the disease. In this paper, firstly, we analyze the propagation of two representative diseases (H7N9 and Dengue fever) in the real-world population and their corresponding information on Internet, suggesting the high correlation of the two-type dynamical processes. Secondly, inspired by empirical analyses, we propose a nonlinear model to further interpret the coupling effect based on the SIS (Susceptible-Infected-Susceptible) model. Both simulation results and theoretical analysis show that a high prevalence of epidemic will lead to a slow information decay, consequently resulting in a high infected level, which shall in turn prevent the epidemic spreading. Finally, further theoretical analysis demonstrates that a multi-outbreak phenomenon emerges via the effect of coupling dynamics, which finds good agreement with empirical results. This work may shed light on the in-depth understanding of the interplay between the dynamics of epidemic spreading and information diffusion.

Suggested Citation

  • Zhan, Xiu-Xiu & Liu, Chuang & Zhou, Ge & Zhang, Zi-Ke & Sun, Gui-Quan & Zhu, Jonathan J.H. & Jin, Zhen, 2018. "Coupling dynamics of epidemic spreading and information diffusion on complex networks," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 437-448.
    • Handle: RePEc:eee:apmaco:v:332:y:2018:i:c:p:437-448
    DOI: 10.1016/j.amc.2018.03.050
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    References listed on IDEAS

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    1. Zhan, Xiu-Xiu & Liu, Chuang & Zhang, Zi-Ke & Sun, Gui-Quan, 2016. "Roles of edge weights on epidemic spreading dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 228-234.
    2. Zhan, Xiu-Xiu & Liu, Chuang & Sun, Gui-Quan & Zhang, Zi-Ke, 2018. "Epidemic dynamics on information-driven adaptive networks," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 196-204.
    3. Peter Horby, 2013. "H7N9 is a virus worth worrying about," Nature, Nature, vol. 496(7446), pages 399-399, April.
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