Targeted Pandemic Containment Through Identifying Local Contact Network Bottlenecks

TL;DR

This paper introduces a new flow-based method to identify critical contact network bottlenecks, enabling more effective pandemic containment strategies with improved efficiency and reduced infection spread.

Contribution

A novel flow-based edge-betweenness centrality approach using convex optimization for detecting network bottlenecks in contact networks.

Findings

Targeting bottleneck edges reduces infections by up to 10% more than existing methods.

The proposed method is significantly faster than current approaches.

Effective in real COVID-19 transmission network data.

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% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.