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A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic

BACKGROUND: Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this s...

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Autores principales: Cui, Yapeng, Ni, Shunjiang, Shen, Shifei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803001/
https://www.ncbi.nlm.nih.gov/pubmed/33435892
http://dx.doi.org/10.1186/s12879-020-05750-9
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author Cui, Yapeng
Ni, Shunjiang
Shen, Shifei
author_facet Cui, Yapeng
Ni, Shunjiang
Shen, Shifei
author_sort Cui, Yapeng
collection PubMed
description BACKGROUND: Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases. METHODS: We simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases. RESULTS: The numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals’ infection probability by 10%. The proposed model was validated using COVID-19 testing data. CONCLUSIONS: Although testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics.
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spelling pubmed-78030012021-01-13 A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic Cui, Yapeng Ni, Shunjiang Shen, Shifei BMC Infect Dis Research Article BACKGROUND: Testing is one of the most effective means to manage the COVID-19 pandemic. However, there is an upper bound on daily testing volume because of limited healthcare staff and working hours, as well as different testing methods, such as random testing and contact-tracking testing. In this study, a network-based epidemic transmission model combined with a testing mechanism was proposed to study the role of testing in epidemic control. The aim of this study was to determine how testing affects the spread of epidemics and the daily testing volume needed to control infectious diseases. METHODS: We simulated the epidemic spread process on complex networks and introduced testing preferences to describe different testing strategies. Different networks were generated to represent social contact between individuals. An extended susceptible-exposed-infected-recovered (SEIR) epidemic model was adopted to simulate the spread of epidemics in these networks. The model establishes a testing preference of between 0 and 1; the larger the testing preference, the higher the testing priority for people in close contact with confirmed cases. RESULTS: The numerical simulations revealed that the higher the priority for testing individuals in close contact with confirmed cases, the smaller the infection scale. In addition, the infection peak decreased with an increase in daily testing volume and increased as the testing start time was delayed. We also discovered that when testing and other measures were adopted, the daily testing volume required to keep the infection scale below 5% was reduced by more than 40% even if other measures only reduced individuals’ infection probability by 10%. The proposed model was validated using COVID-19 testing data. CONCLUSIONS: Although testing could effectively inhibit the spread of infectious diseases and epidemics, our results indicated that it requires a huge daily testing volume. Thus, it is highly recommended that testing be adopted in combination with measures such as wearing masks and social distancing to better manage infectious diseases. Our research contributes to understanding the role of testing in epidemic control and provides useful suggestions for the government and individuals in responding to epidemics. BioMed Central 2021-01-12 /pmc/articles/PMC7803001/ /pubmed/33435892 http://dx.doi.org/10.1186/s12879-020-05750-9 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Cui, Yapeng
Ni, Shunjiang
Shen, Shifei
A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic
title A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic
title_full A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic
title_fullStr A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic
title_full_unstemmed A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic
title_short A network-based model to explore the role of testing in the epidemiological control of the COVID-19 pandemic
title_sort network-based model to explore the role of testing in the epidemiological control of the covid-19 pandemic
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803001/
https://www.ncbi.nlm.nih.gov/pubmed/33435892
http://dx.doi.org/10.1186/s12879-020-05750-9
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