Control of Epidemic Process via Link Removal: Flattening the Infection Curve by Social Distancing

Abstract Spreading processes on complex networks have gained significant attention as a means to model the spread of infectious diseases in real social networks. This paper focuses on controlling epidemic processes and reducing the peak of contagion by removing highly central links. In practice, this translates to flattening the infection curve through the implementation of social distancing measures for high-risk social contacts. The identification of important links relies on centrality measures, e.g., node-degrees and edge-betweenness. Edge-betweenness evaluates link importance based on the fraction of shortest paths that contain the link, while node-degree-based centrality defines link centrality as the average degrees of the connected nodes. Our findings demonstrate that degree-based link removal is more effective in flattening the infection curve as compared to many other centralities including edge-betweenness. We analyze small-world and scale-free networks, examining the infection curves across various network clustering coefficients and small-worldness.