Suppressing traffic-driven epidemic spreading by edge-removal strategies

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.

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Cited by in corpus (6)

• Immunization of traffic-driven epidemic spreading
• Epidemic spreading driven by biased random walks
• Traffic-driven SIR epidemic model on networks
• Traffic-driven epidemic spreading in correlated networks
• Traffic-driven epidemic spreading on scale-free networks with tunable degree distribution
• Where to cut to delay a pandemic with minimum disruption? Mathematical analysis based on the SIS model