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Modeling the competitive diffusions of rumor and knowledge and the impacts on epidemic spreading

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  • Huang, He
  • Chen, Yahong
  • Ma, Yefeng

Abstract

The interaction between epidemic spreading and information diffusion is an interdisciplinary research problem. During an epidemic, people tend to take self-protective measures to reduce the infection risk. However, with the diffusion of rumor, people may be difficult to make an appropriate choice. How to reduce the negative impact of rumor and to control epidemic has become a critical issue in the social network. Elaborate mathematical model is instructive to understand such complex dynamics. In this paper, we develop a two-layer network to model the interaction between the spread of epidemic and the competitive diffusions of information. The results show that knowledge diffusion can eradicate both rumor and epidemic, where the penetration intensity of knowledge into rumor plays a vital role. Specifically, the penetration intensity of knowledge significantly increases the thresholds for rumor and epidemic to break out, even when the self-protective measure is not perfectly effective. But eradicating rumor shouldn’t be equated with eradicating epidemic. The epidemic can be eradicated with rumor still diffusing, and the epidemic may keep spreading with rumor being eradicated. Moreover, the communication-layer network structure greatly affects the spread of epidemic in the contact-layer network. When people have more connections in the communication-layer network, the knowledge is more likely to diffuse widely, and the rumor and epidemic can be eradicated more efficiently. When the communication-layer network is sparse, a larger penetration intensity of knowledge into rumor is required to promote the diffusion of knowledge.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:apmaco:v:388:y:2021:i:c:s0096300320304926
    DOI: 10.1016/j.amc.2020.125536
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    Cited by:

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    2. Dong, Yafang & Huo, Liang'an & Zhao, Laijun, 2022. "An improved two-layer model for rumor propagation considering time delay and event-triggered impulsive control strategy," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Liang’an Huo & Yuqing Zhang, 2022. "Effect of Global and Local Refutation Mechanism on Rumor Propagation in Heterogeneous Network," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    4. Tan, Jipeng & Zhang, Man & Liu, Fengming, 2024. "Online-Offline Higher-Order Rumor Propagation Model Based on Quantum Cellular Automata Considering Social Adaptation," Applied Mathematics and Computation, Elsevier, vol. 461(C).
    5. Huang, He & Chen, Yahong & Yan, Zhijun, 2021. "Impacts of social distancing on the spread of infectious diseases with asymptomatic infection: A mathematical model," Applied Mathematics and Computation, Elsevier, vol. 398(C).
    6. Li, Dandan & Qian, Wenqi & Sun, Xiaoxiao & Han, Dun & Sun, Mei, 2023. "Rumor spreading in a dual-relationship network with diverse propagation abilities," Applied Mathematics and Computation, Elsevier, vol. 458(C).
    7. Song, Le & Ma, Yinghong, 2022. "Evaluating tacit knowledge diffusion with algebra matrix algorithm based social networks," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    8. 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.
    9. Cui, Yajuan & Wei, Ruichen & Tian, Yang & Tian, Hui & Zhu, Xuzhen, 2022. "Information propagation influenced by individual fashion-passion trend on multi-layer weighted network," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    10. Zenghui Yue & Haiyun Xu & Guoting Yuan & Yan Qi, 2022. "Modeling knowledge diffusion in the disciplinary citation network based on differential dynamics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7593-7613, December.
    11. Jia Wang & Zhiping Wang & Ping Yu & Peiwen Wang, 2022. "The SEIR Dynamic Evolutionary Model with Markov Chains in Hyper Networks," Sustainability, MDPI, vol. 14(20), pages 1-16, October.

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