Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
09 2023
Historique:
received: 19 03 2023
accepted: 09 08 2023
revised: 14 09 2023
medline: 18 9 2023
pubmed: 1 9 2023
entrez: 1 9 2023
Statut: epublish

Résumé

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

Identifiants

pubmed: 37656743
doi: 10.1371/journal.pcbi.1011423
pii: PCOMPBIOL-D-23-00428
pmc: PMC10501547
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1011423

Informations de copyright

Copyright: © 2023 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

M.A. has received consultancy fees from Seqirus unrelated to this work. All other authors report no competing interests.

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Auteurs

Kun Zhang (K)

College of Computer Science, Sichuan University, Chengdu, China.

Zhichu Xia (Z)

Glasgow College, University of Electronic Science and Technology of China, Chengdu, China.

Shudong Huang (S)

College of Computer Science, Sichuan University, Chengdu, China.

Gui-Quan Sun (GQ)

Department of Mathematics, North University of China, Taiyuan, China.
Complex Systems Research Center, Shanxi University, Taiyuan, China.

Jiancheng Lv (J)

College of Computer Science, Sichuan University, Chengdu, China.

Marco Ajelli (M)

Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America.

Keisuke Ejima (K)

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

Quan-Hui Liu (QH)

College of Computer Science, Sichuan University, Chengdu, China.

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Classifications MeSH