Targeted pandemic containment through identifying local contact network bottlenecks.

ABSTRACT: 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. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demon

SUBMITTER: Yang S 

PROVIDER: S-EPMC8432902 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature