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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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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 |
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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 |
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