Cargando…

Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

OBJECTIVES: This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investiga...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Yiying, Guo, Jianying, Li, Guanqiao, Lu, Xi, Li, Xiang, Zhang, Yuan, Cong, Lin, Kang, Yanni, Jia, Xiaoyu, Shi, Xuanling, Xie, Guotong, Zhang, Linqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266432/
https://www.ncbi.nlm.nih.gov/pubmed/34233974
http://dx.doi.org/10.1136/bmjopen-2020-045886
_version_ 1783719944588886016
author Hu, Yiying
Guo, Jianying
Li, Guanqiao
Lu, Xi
Li, Xiang
Zhang, Yuan
Cong, Lin
Kang, Yanni
Jia, Xiaoyu
Shi, Xuanling
Xie, Guotong
Zhang, Linqi
author_facet Hu, Yiying
Guo, Jianying
Li, Guanqiao
Lu, Xi
Li, Xiang
Zhang, Yuan
Cong, Lin
Kang, Yanni
Jia, Xiaoyu
Shi, Xuanling
Xie, Guotong
Zhang, Linqi
author_sort Hu, Yiying
collection PubMed
description OBJECTIVES: This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated. SETTING: We developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a ‘T’ compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed. PRIMARY AND SECONDARY OUTCOME MEASURES: Simulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people. RESULTS: (1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate. CONCLUSIONS: Reducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.
format Online
Article
Text
id pubmed-8266432
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-82664322021-07-09 Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study Hu, Yiying Guo, Jianying Li, Guanqiao Lu, Xi Li, Xiang Zhang, Yuan Cong, Lin Kang, Yanni Jia, Xiaoyu Shi, Xuanling Xie, Guotong Zhang, Linqi BMJ Open Public Health OBJECTIVES: This study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated. SETTING: We developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a ‘T’ compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed. PRIMARY AND SECONDARY OUTCOME MEASURES: Simulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people. RESULTS: (1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate. CONCLUSIONS: Reducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies. BMJ Publishing Group 2021-07-07 /pmc/articles/PMC8266432/ /pubmed/34233974 http://dx.doi.org/10.1136/bmjopen-2020-045886 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Public Health
Hu, Yiying
Guo, Jianying
Li, Guanqiao
Lu, Xi
Li, Xiang
Zhang, Yuan
Cong, Lin
Kang, Yanni
Jia, Xiaoyu
Shi, Xuanling
Xie, Guotong
Zhang, Linqi
Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study
title Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study
title_full Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study
title_fullStr Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study
title_full_unstemmed Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study
title_short Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study
title_sort role of efficient testing and contact tracing in mitigating the covid-19 pandemic: a network modelling study
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8266432/
https://www.ncbi.nlm.nih.gov/pubmed/34233974
http://dx.doi.org/10.1136/bmjopen-2020-045886
work_keys_str_mv AT huyiying roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT guojianying roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT liguanqiao roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT luxi roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT lixiang roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT zhangyuan roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT conglin roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT kangyanni roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT jiaxiaoyu roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT shixuanling roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT xieguotong roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy
AT zhanglinqi roleofefficienttestingandcontacttracinginmitigatingthecovid19pandemicanetworkmodellingstudy