Targeted pandemic containment through identifying local contact network bottlenecks

3Citations
Citations of this article
23Readers
Mendeley users who have this article in their library.
Get full text

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 flowbased 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.

Cite

CITATION STYLE

APA

Yang, S., Senapati, P., Wang, D., Bauch, C. T., & Fountoulakis, K. (2021). Targeted pandemic containment through identifying local contact network bottlenecks. PLoS Computational Biology, 17(8). https://doi.org/10.1371/journal.pcbi.1009351

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free