Impact of heterogeneous infection rates on traffic-driven epidemic spreading dynamics in complex networks

Xing-Li Jing, Jie Chen, Xiang Ling, Mao-Bin Hu and Yan-Qiang Li
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Xing-Li Jing: School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, P. R. China2Jiyuan Vocational and Technical College, Jiyuan 459000, P. R. China
Jie Chen: School of Computer and Information, Anhui Normal University, Wuhu 241003, P. R. China
Xiang Ling: School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, P. R. China
Mao-Bin Hu: School of Engineering Science, University of Science and Technology of China, Hefei 230026, P. R. China
Yan-Qiang Li: Department of Engineering Mechanics, Shijiazhuang Tiedao University, Shijiazhuang 050043, P. R. China

International Journal of Modern Physics C (IJMPC), 2025, vol. 36, issue 09, 1-14

Abstract: Although much attention has been focused on traffic-driven epidemic spreading on complex networks, the vast majority of theoretical approaches assumes an identical infection rate for all nodes. This paper investigates the effect of heterogenous infection rates on epidemic spreading, in which the infection rate obeys Binomial distribution. Mean-field approximation and continuous-time Markov chain are adopted to establish the control equation for the model. Results show that heterogeneous infection rates can suppress or promote the epidemic spreading. By adjusting the parameters of infection heterogeneity or the proportion of different nodes, the scope and the threshold of epidemic spreading can be controlled. Simulations on Erdös–Rényi random networks and Barabási–Albert scale-free networks prove the accuracy of theoretical predictions.

Keywords: Complex network; traffic-flow; epidemic spreading (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0129183125500032

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