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Traffic-driven epidemic spreading dynamics with heterogeneous infection rates

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  • Chen, Jie
  • Hu, Mao-Bin
  • Li, Ming

Abstract

Despite extensive work on traffic dynamics and epidemic spreading on complex networks, the vast majority of theoretical approaches assumes an identical infection rate for all nodes. Here we study the influence of heterogeneous infection rates, and show that the threshold of epidemic can be adjusted by heterogeneous susceptibility, network structure and routing strategy. When the traffic is in free flow state, an appropriate coupling between routing protocol and infection rates can significantly increase the epidemic threshold. The epidemic spreading can be effectively controlled by a negative correlation between infection rate and node degree. When the traffic is congested, we find that the epidemic threshold decreases significantly under the condition of strong heterogeneous infection rate. This indicates that even in congested conditions, excessive traffic load will promote the spread of epidemic.

Suggested Citation

  • Chen, Jie & Hu, Mao-Bin & Li, Ming, 2020. "Traffic-driven epidemic spreading dynamics with heterogeneous infection rates," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    • Handle: RePEc:eee:chsofr:v:132:y:2020:i:c:s096007791930534x
    DOI: 10.1016/j.chaos.2019.109577
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    References listed on IDEAS

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    1. Bamaarouf, O. & Alweimine, A. Ould Baba & Rachadi, A. & EZ-Zahraouy, H., 2018. "Selective epidemic vaccination under the performant routing algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 209-219.
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    4. 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.
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    Cited by:

    1. 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).
    2. 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).

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