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Decision-making about pandemic mitigation often relies upon simulationmodelling. Models of disease transmission through networks of contacts--betweenindividuals or between population centres--are increasingly used for thesepurposes. Real-world contact networks are rich in structural features thatinfluence infection transmission, such as tightly-knit local communities thatare weakly connected to one another. In this paper, we propose a new flow-basededge-betweenness centrality method for detecting bottleneck edges that connectnodes in contact networks. In particular, we utilize convex optimizationformulations based on the idea of diffusion with p-norm network flow. Usingsimulation models of COVID-19 transmission through real network data at bothindividual and county levels, we demonstrate that targeting bottleneck edgesidentified by the proposed method reduces the number of infected cases by up to10% more than state-of-the-art edge-betweenness methods. Furthermore, theproposed method is orders of magnitude faster than existing methods.

Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks