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Simulated dynamics of virus spreading on social networks with various topologies

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Listed:
  • Li, Kun
  • Chen, Zhiyu
  • Cong, Rui
  • Zhang, Jianlei
  • Wei, Zhenlin

Abstract

How to effectively control virus spreading remains an open challenging problem since the environments for virus propagation are complex and heterogeneous, and more importantly, the dynamics of virus spreading usually co-evolves with that of human beings' travelling behavior. Motivated by this, we combine evolutionary game theory and complex network theory to investigate the influence of the competition between different travelling strategies on virus propagation. Simulation results show that the strategy of self-isolation can substantially inhibit the spread of infectious diseases on complex social networks, and introducing rewarding mechanism would further enhance this effect. Moreover, counterintuitively, larger network degree is conducive to the prevalence of self-isolation, thereby hindering virus spreading. We hope our work can provide more insight into the effective control of virus propagation in the real world.

Suggested Citation

  • Li, Kun & Chen, Zhiyu & Cong, Rui & Zhang, Jianlei & Wei, Zhenlin, 2024. "Simulated dynamics of virus spreading on social networks with various topologies," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    • Handle: RePEc:eee:apmaco:v:470:y:2024:i:c:s0096300324000523
    DOI: 10.1016/j.amc.2024.128580
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    References listed on IDEAS

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    1. Wang, Chengjie & Deng, Juan & Zhao, Hui & Li, Li, 2024. "Effect of Q-learning on the evolution of cooperation behavior in collective motion: An improved Vicsek model," Applied Mathematics and Computation, Elsevier, vol. 482(C).

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