ArticlePLoS computational biology2021
Targeted pandemic containment through identifying local contact network bottlenecks.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Complex network analysis of mobility dynamics in Seoul during the COVID-19 pandemic 2020-2022.Scientific reports · 2026Article
- Designing network based intervention strategies for epidemics of infectious diseases from edge based infection probability.Scientific reports · 2026Article
- A spatial vaccination strategy to reduce the risk of vaccine-resistant variants.PLoS computational biology · 2022Article
- Introducing critical common dysregulated proteins in esophageal, gastric, and intestinal cancers.Gastroenterology and hepatology from bed to bench · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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