Kemeny-based testing for COVID-19

Yilmaz, S., Dudkina, E., Bin, M., Crisostomi, E., Ferraro, P., Murray-Smith, R. , Parisini, T., Stone, L. and Shorten, R. (2020) Kemeny-based testing for COVID-19. PLoS ONE, 15(11), e0242401. (doi: 10.1371/journal.pone.0242401) (PMID:33211725) (PMCID:PMC7676669)

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Testing, tracking and tracing abilities have been identified as pivotal in helping countries to safely reopen activities after the first wave of the COVID-19 virus. Contact tracing apps give the unprecedented possibility to reconstruct graphs of daily contacts, so the question is: who should be tested? As human contact networks are known to exhibit community structure, in this paper we show that the Kemeny constant of a graph can be used to identify and analyze bridges between communities in a graph. Our ‘Kemeny indicator’ is the value of the Kemeny constant in the new graph that is obtained when a node is removed from the original graph. We show that testing individuals who are associated with large values of the Kemeny indicator can help in efficiently intercepting new virus outbreaks, when they are still in their early stage. Extensive simulations provide promising results in early identification and in blocking the possible ‘super-spreaders’ links that transmit disease between different communities.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick
Creator Roles:
Murray-Smith, R.Investigation, Methodology
Authors: Yilmaz, S., Dudkina, E., Bin, M., Crisostomi, E., Ferraro, P., Murray-Smith, R., Parisini, T., Stone, L., and Shorten, R.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN (Online):1932-6203
Copyright Holders:Copyright © 2020 Yilmaz et al.
First Published:First published in PLoS ONE 15(11):e0242401
Publisher Policy:Reproduced under a Creative Commons license

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300982Exploiting Closed-Loop Aspects in Computationally and Data Intensive AnalyticsRoderick Murray-SmithEngineering and Physical Sciences Research Council (EPSRC)EP/R018634/1Computing Science