Decision-making about pandemic mitigation often relies upon mathematical modelling. Models of contact networks are increasingly used for these purposes , and are often appropriate for infections that spread from person to person . Real-world human 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 communities in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using mathematical models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10 %DIFDELCMD < \\%%% %DIF < more than state-of-the-art edge-betweenness methods. Furthermore, we demonstrate empirically that the proposed method is orders of magnitude faster than existing methods.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 demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10 %DIFDELCMD < \\%%% %DIF < more than state-of-the-art edge-betweenness methods. Furthermore, we demonstrate empirically that the proposed method is orders of magnitude faster than existing methods.abstract \% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.
R
mathematical
simulation
R
contact networks are
disease transmission through networks of contacts--between individuals or between population centres--are
D
, and are often appropriate for infections that spread from person to person
D
human
R
communities
nodes
R
mathematical
simulation
A
\% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.