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Interplay between epidemic and information spreading on multiplex networks

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  • Zhu, Linhe
  • Liu, Wenshan
  • Zhang, Zhengdi

Abstract

Every outbreak of a serious infectious disease has the potential to pose unprecedented challenges to humanity. Understanding how epidemic spreads among populations is a key step in preventing and controlling it to spread. In this paper, for modeling the epidemic spreading and its associated information spreading, we put forward a novel coupled two-layered networking framework. One layer deals with the modeling of an SI1I2R process, where each node in the network may be in four states: susceptible, mildly infected, severely infected and recovery. Whereas, for the other layer, the transmission dynamics can be represented by an unaware–aware–refractory (UAT) model, which is significantly different from the classical unaware–aware–unaware (UAU) process since there exist individuals who are unwilling to share information. Moreover, we set a group of discrete time Microscopic Markov equations and derive the epidemic threshold. Finally, some numerical simulations are carried out to validate the analytical results. This work is of great significance to prevent epidemic, and it can be applicable in guiding the input on disease-related information on complex networks.

Suggested Citation

  • Zhu, Linhe & Liu, Wenshan & Zhang, Zhengdi, 2021. "Interplay between epidemic and information spreading on multiplex networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 188(C), pages 268-279.
    • Handle: RePEc:eee:matcom:v:188:y:2021:i:c:p:268-279
    DOI: 10.1016/j.matcom.2021.04.017
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    References listed on IDEAS

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    1. Wang, Zhishuang & Guo, Quantong & Sun, Shiwen & Xia, Chengyi, 2019. "The impact of awareness diffusion on SIR-like epidemics in multiplex networks," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 134-147.
    2. Huo, Liang’an & Guo, Hongyuan & Cheng, Yingying & Xie, Xiaoxiao, 2020. "A new model for supply chain risk propagation considering herd mentality and risk preference under warning information on multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    3. Kabir, K.M. Ariful & Kuga, Kazuki & Tanimoto, Jun, 2019. "Effect of information spreading to suppress the disease contagion on the epidemic vaccination game," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 180-187.
    4. Miao Peng & Zhengdi Zhang & C. W. Lim & Xuedi Wang, 2018. "Hopf Bifurcation and Hybrid Control of a Delayed Ecoepidemiological Model with Nonlinear Incidence Rate and Holling Type II Functional Response," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-12, June.
    5. Zhu, Linhe & Liu, Wenshan & Zhang, Zhengdi, 2020. "Delay differential equations modeling of rumor propagation in both homogeneous and heterogeneous networks with a forced silence function," Applied Mathematics and Computation, Elsevier, vol. 370(C).
    6. Huang, He & Chen, Yahong & Ma, Yefeng, 2021. "Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading," Applied Mathematics and Computation, Elsevier, vol. 388(C).
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    Cited by:

    1. Wang, Runzhou & Zhang, Xinsheng & Wang, Minghu, 2024. "A two-layer model with partial mapping: Unveiling the interplay between information dissemination and disease diffusion," Applied Mathematics and Computation, Elsevier, vol. 468(C).
    2. Li, Ling & Dong, Gaogao & Zhu, Huaiping & Tian, Lixin, 2024. "Impact of multiple doses of vaccination on epidemiological spread in multiple networks," Applied Mathematics and Computation, Elsevier, vol. 472(C).

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