Title
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 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.
Year
DOI
Venue
2021
10.1371/journal.pcbi.1009351
PLoS Comput. Biol.
DocType
Volume
Issue
Journal
17
8
ISSN
Citations 
PageRank 
1553-7358
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Shenghao Yang13313.84
Senapati Priyabrata200.34
Di Wang387.07
Bauch Chris T.400.34
Fountoulakis Kimon500.34