Suppressing traffic-driven epidemic spreading by edge-removal strategies

TL;DR

This paper proposes edge-removal strategies to control traffic-driven epidemic spreading on networks, showing targeted removal increases epidemic thresholds while random removal decreases them, effectively reducing infection risk.

Contribution

It introduces a targeted edge-removal method to suppress epidemic outbreaks driven by traffic flow, improving epidemic control on complex networks.

Findings

Targeted removal of links among high-degree nodes raises epidemic threshold.

Random edge removal lowers epidemic threshold.

Targeted edge shutdown reduces traffic load and infection probability at key nodes.

Abstract

The interplay between traffic dynamics and epidemic spreading on complex networks has received increasing attention in recent years. However, the control of traffic-driven epidemic spreading remains to be a challenging problem. In this Brief Report, we propose a method to suppress traffic-driven epidemic outbreak by properly removing some edges in a network. We find that the epidemic threshold can be enhanced by the targeted cutting of links among large-degree nodes or edges with the largest algorithmic betweeness. In contrast, the epidemic threshold will be reduced by the random edge removal. These findings are robust with respect to traffic-flow conditions, network structures and routing strategies. Moreover, we find that the shutdown of targeted edges can effectively release traffic load passing through large-degree nodes, rendering a relatively low probability of infection to these nodes.