Traffic-driven SI epidemic spreading on scale-free networks

Yongqiang Zhang, Shuang Li, Junfeng Zhang, Jinlong Ma and Haigang An
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Yongqiang Zhang: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Shuang Li: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Junfeng Zhang: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Jinlong Ma: School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, P. R. China
Haigang An: School of Management, Hebei GEO University, Shijiazhuang 050031, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 08, 1-12

Abstract: As COVID-19 spread globally in 2020, the interaction between the traffic dynamics and the spread of the epidemic has attracted much attention. However, controlling the spread of the epidemic remains a challenging issue. In this paper, we have investigated the relationships between link-closure strategies and the traffic-driven epidemic spreading. It is found that the epidemic spreading can be suppressed by the targeted closing of links between small-degree nodes. In contrast, closing links between large-degree nodes can accelerate the outbreak of the epidemic. These findings have significance for controlling the spread of the epidemic.

Keywords: Complex networks; epidemic spreading; traffic flow (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S012918312250111X

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