Traffic-driven epidemic spreading dynamics with heterogeneous infection rates

Jie Chen, Mao-Bin Hu and Ming Li

Chaos, Solitons & Fractals, 2020, vol. 132, issue C

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:132:y:2020:i:c:s096007791930534x

DOI: 10.1016/j.chaos.2019.109577

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