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Suppressing traffic-driven epidemic spreading by adaptive routing strategy

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  • Yang, Han-Xin
  • Wang, Zhen

Abstract

The design of routing strategies for traffic-driven epidemic spreading has received increasing attention in recent years. In this paper, we propose an adaptive routing strategy that incorporates topological distance with local epidemic information through a tunable parameter h. In the case where the traffic is free of congestion, there exists an optimal value of routing parameter h, leading to the maximal epidemic threshold. This means that epidemic spreading can be more effectively controlled by adaptive routing, compared to that of the static shortest path routing scheme. Besides, we find that the optimal value of h can greatly relieve the traffic congestion in the case of finite node-delivering capacity. We expect our work to provide new insights into the effects of dynamic routings on traffic-driven epidemic spreading.

Suggested Citation

  • Yang, Han-Xin & Wang, Zhen, 2016. "Suppressing traffic-driven epidemic spreading by adaptive routing strategy," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 147-150.
    • Handle: RePEc:eee:chsofr:v:93:y:2016:i:c:p:147-150
    DOI: 10.1016/j.chaos.2016.10.012
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    Citations

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    Cited by:

    1. Jing, Xing-Li & Hu, Mao-Bin & Chen, Jie, 2022. "Suppressing traffic-driven epidemic spreading in multiplex networks by effective traffic-flow assignment strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    2. Biswas, Soumyajyoti & Mandal, Amit Kr, 2021. "Parallel Minority Game and it’s application in movement optimization during an epidemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    3. Chen, Xiao-Long & Wang, Rui-Jie & Yang, Chun & Cai, Shi-Min, 2019. "Hybrid resource allocation and its impact on the dynamics of disease spreading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 156-165.
    4. Chen, Jie & Hu, Mao-Bin & Li, Ming, 2020. "Traffic-driven epidemic spreading dynamics with heterogeneous infection rates," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    5. Huo, Liang’an & Yu, Yue, 2023. "The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    6. Chen, Jun-Jie & Hu, Mao-Bin & Wu, Yong-Hong, 2022. "Traffic-driven epidemic spreading with non-uniform origin and destination selection," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    7. Xu, Yuan-Hao & Wang, Hao-Jie & Lu, Zhong-Wen & Hu, Mao-Bin, 2023. "Impact of awareness dissemination on epidemic reaction–diffusion in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    8. Chen, Jie & Tan, Xuegang & Cao, Jinde & Li, Ming, 2022. "Effect of coupling structure on traffic-driven epidemic spreading in interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. Deepti Muley & Md. Shahin & Charitha Dias & Muhammad Abdullah, 2020. "Role of Transport during Outbreak of Infectious Diseases: Evidence from the Past," Sustainability, MDPI, vol. 12(18), pages 1-22, September.

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