Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks

Understand

Decision-making about pandemic mitigation often relies upon simulation modelling.

• Models of disease transmission through networks of contacts--between individuals or between population centres--are increasingly used for these purposes.
• Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
• In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks.