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Epidemic dynamics on information-driven adaptive networks

Author

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  • Zhan, Xiu-Xiu
  • Liu, Chuang
  • Sun, Gui-Quan
  • Zhang, Zi-Ke

Abstract

Research on the interplay between the dynamics on the network and the dynamics of the network has attracted much attention in recent years. In this work, we propose an information-driven adaptive model, where disease and disease information can evolve simultaneously. For the information-driven adaptive process, susceptible (infected) individuals who have abilities to recognize the disease would break the links of their infected (susceptible) neighbors to prevent the epidemic from further spreading. Simulation results and numerical analyses based on the pairwise approach indicate that the information-driven adaptive process can not only slow down the speed of epidemic spreading, but can also diminish the epidemic prevalence at the final state significantly. In addition, the disease spreading and information diffusion pattern on the lattice as well as on a real-world network give visual representations about how the disease is trapped into an isolated field with the information-driven adaptive process. Furthermore, we perform the local bifurcation analysis on four types of dynamical regions, including healthy, a continuous dynamic behavior, bistable and endemic, to understand the evolution of the observed dynamical behaviors. This work may shed some lights on understanding how information affects human activities on responding to epidemic spreading.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:chsofr:v:108:y:2018:i:c:p:196-204
    DOI: 10.1016/j.chaos.2018.02.010
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    References listed on IDEAS

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    1. Zhou, Yinzuo & Xia, Yingjie, 2014. "Epidemic spreading on weighted adaptive networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 16-23.
    2. Ying-Yeh Chen & Feng Chen & David Gunnell & Paul S F Yip, 2013. "The Impact of Media Reporting on the Emergence of Charcoal Burning Suicide in Taiwan," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-6, January.
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    Cited by:

    1. Fang, Fanshu & Ma, Jing & Li, Yanli, 2023. "The coevolution of the spread of a disease and competing opinions in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    2. Li, Wenyao & Cai, Meng & Zhong, Xiaoni & Liu, Yanbing & Lin, Tao & Wang, Wei, 2023. "Coevolution of epidemic and infodemic on higher-order networks," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Liu, Chuang & Zhou, Nan & Zhan, Xiu-Xiu & Sun, Gui-Quan & Zhang, Zi-Ke, 2020. "Markov-based solution for information diffusion on adaptive social networks," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    4. Wang, Jun & Cai, Shimin & Wang, Wei & Zhou, Tao, 2023. "Link cooperation effect of cooperative epidemics on complex networks," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    5. Dun, Han & Shuting, Yan & She, Han & Lingfei, Qian & Chris, Ampimah Benjamin, 2019. "Research on how the difference of personal propagation ability influences the epidemic spreading in activity-driven network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 311-318.
    6. Zhang, Yaming & Su, Yanyuan & Weigang, Li & Liu, Haiou, 2019. "Interacting model of rumor propagation and behavior spreading in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 168-177.
    7. 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.
    8. Kumar, Viney & Bhattacharyya, Samit, 2023. "Nonlinear effect of sentiments and opinion sharing on vaccination decision in face of an outbreak: A multiplex network approach," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).

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