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A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures
The World Health Organization (WHO) classified the spread of COVID-19 (Coronavirus Disease 2019) as a global pandemic in March. Scholars predict that the pandemic will continue into the coming winter and will become a seasonal epidemic in the following year. Therefore, the identification of effectiv...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870204/ https://www.ncbi.nlm.nih.gov/pubmed/33592845 http://dx.doi.org/10.1097/MD.0000000000023925 |
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author | Yang, Shuhang Liu, Yu Chen, Ke Li, Tong Huang, Yi Chen, Xiaolei Qi, Pengfang Xu, Yazhi Yu, Feifei Yang, Yuling Chen, Youhua |
author_facet | Yang, Shuhang Liu, Yu Chen, Ke Li, Tong Huang, Yi Chen, Xiaolei Qi, Pengfang Xu, Yazhi Yu, Feifei Yang, Yuling Chen, Youhua |
author_sort | Yang, Shuhang |
collection | PubMed |
description | The World Health Organization (WHO) classified the spread of COVID-19 (Coronavirus Disease 2019) as a global pandemic in March. Scholars predict that the pandemic will continue into the coming winter and will become a seasonal epidemic in the following year. Therefore, the identification of effective control measures becomes extremely important. Although many reports have been published since the COVID-19 outbreak, no studies have identified the relative effectiveness of a combination of control measures implemented in Wuhan and other areas in China. To this end, a retrospective analysis by the collection and modeling of an unprecedented number of epidemiology records in China of the early stage of the outbreaks can be valuable. In this study, we developed a new dynamic model to describe the spread of COVID-19 and to quantify the effectiveness of control measures. The transmission rate, daily close contacts, and the average time from onset to isolation were identified as crucial factors in viral spreading. Moreover, the capacity of a local health-care system is identified as a threshold to control an outbreak in its early stage. We took these factors as controlling parameters in our model. The parameters are estimated based on epidemiological reports from national and local Center for Disease Control (CDCs). A retrospective simulation showed the effectiveness of combinations of 4 major control measures implemented in Wuhan: hospital isolation, social distancing, self-protection by wearing masks, and extensive medical testing. Further analysis indicated critical intervention conditions and times required to control an outbreak in the early stage. Our simulations showed that South Korea has kept the spread of COVID-19 at a low level through extensive medical testing. Furthermore, a predictive simulation for Italy indicated that Italy would contain the outbreak in late May under strict social distancing. In our general analysis, no single measure could contain a COVID-19 outbreak once a health-care system is overloaded. Extensive medical testing could keep viral spreading at a low level. Wearing masks functions as favorably as social distancing but with much lower socioeconomic costs. |
format | Online Article Text |
id | pubmed-7870204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-78702042021-02-10 A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures Yang, Shuhang Liu, Yu Chen, Ke Li, Tong Huang, Yi Chen, Xiaolei Qi, Pengfang Xu, Yazhi Yu, Feifei Yang, Yuling Chen, Youhua Medicine (Baltimore) 4400 The World Health Organization (WHO) classified the spread of COVID-19 (Coronavirus Disease 2019) as a global pandemic in March. Scholars predict that the pandemic will continue into the coming winter and will become a seasonal epidemic in the following year. Therefore, the identification of effective control measures becomes extremely important. Although many reports have been published since the COVID-19 outbreak, no studies have identified the relative effectiveness of a combination of control measures implemented in Wuhan and other areas in China. To this end, a retrospective analysis by the collection and modeling of an unprecedented number of epidemiology records in China of the early stage of the outbreaks can be valuable. In this study, we developed a new dynamic model to describe the spread of COVID-19 and to quantify the effectiveness of control measures. The transmission rate, daily close contacts, and the average time from onset to isolation were identified as crucial factors in viral spreading. Moreover, the capacity of a local health-care system is identified as a threshold to control an outbreak in its early stage. We took these factors as controlling parameters in our model. The parameters are estimated based on epidemiological reports from national and local Center for Disease Control (CDCs). A retrospective simulation showed the effectiveness of combinations of 4 major control measures implemented in Wuhan: hospital isolation, social distancing, self-protection by wearing masks, and extensive medical testing. Further analysis indicated critical intervention conditions and times required to control an outbreak in the early stage. Our simulations showed that South Korea has kept the spread of COVID-19 at a low level through extensive medical testing. Furthermore, a predictive simulation for Italy indicated that Italy would contain the outbreak in late May under strict social distancing. In our general analysis, no single measure could contain a COVID-19 outbreak once a health-care system is overloaded. Extensive medical testing could keep viral spreading at a low level. Wearing masks functions as favorably as social distancing but with much lower socioeconomic costs. Lippincott Williams & Wilkins 2021-02-05 /pmc/articles/PMC7870204/ /pubmed/33592845 http://dx.doi.org/10.1097/MD.0000000000023925 Text en Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 |
spellingShingle | 4400 Yang, Shuhang Liu, Yu Chen, Ke Li, Tong Huang, Yi Chen, Xiaolei Qi, Pengfang Xu, Yazhi Yu, Feifei Yang, Yuling Chen, Youhua A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures |
title | A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures |
title_full | A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures |
title_fullStr | A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures |
title_full_unstemmed | A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures |
title_short | A dynamic model of the Coronavirus Disease 2019 outbreak to analyze the effectiveness of control measures |
title_sort | dynamic model of the coronavirus disease 2019 outbreak to analyze the effectiveness of control measures |
topic | 4400 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870204/ https://www.ncbi.nlm.nih.gov/pubmed/33592845 http://dx.doi.org/10.1097/MD.0000000000023925 |
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