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Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model
While the Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten public health and safety, every state has strategically reopened the business in the United States. It is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
KeAi Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901308/ https://www.ncbi.nlm.nih.gov/pubmed/33644499 http://dx.doi.org/10.1016/j.idm.2021.02.001 |
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author | Yu, Duo Zhu, Gen Wang, Xueying Zhang, Chenguang Soltanalizadeh, Babak Wang, Xia Tang, Sanyi Wu, Hulin |
author_facet | Yu, Duo Zhu, Gen Wang, Xueying Zhang, Chenguang Soltanalizadeh, Babak Wang, Xia Tang, Sanyi Wu, Hulin |
author_sort | Yu, Duo |
collection | PubMed |
description | While the Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten public health and safety, every state has strategically reopened the business in the United States. It is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the control measures and medical resource allocations. In this study, a novel SEIR model was developed to evaluate the effect of reopening policies based on the real-world reported COVID-19 data in Texas. The earlier reported data before the reopening were used to develop the SEIR model; data after the reopening were used for evaluation. The simulation results show that if continuing the “stay-at-home order” without reopening the business, the COVID-19 pandemic could end in December 2020 in Texas. On the other hand, the pandemic could be controlled similarly as the case of no-reopening only if the contact rate was low and additional high magnitude of control measures could be implemented. If the control measures are only slightly enhanced after reopening, it could flatten the curve of the COVID-19 epidemic with reduced numbers of infections and deaths, but it might make the epidemic last longer. Based on the reported data up to July 2020 in Texas, the real-world epidemic pattern is between the cases of the low and high magnitude of control measures with a medium risk of contact rate after reopening. In this case, the pandemic might last until summer 2021 to February 2022 with a total of 4–10 million infected cases and 20,080–58,604 deaths. |
format | Online Article Text |
id | pubmed-7901308 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | KeAi Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-79013082021-02-24 Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model Yu, Duo Zhu, Gen Wang, Xueying Zhang, Chenguang Soltanalizadeh, Babak Wang, Xia Tang, Sanyi Wu, Hulin Infect Dis Model Original Research Article While the Coronavirus Disease 2019 (COVID-19) pandemic continues to threaten public health and safety, every state has strategically reopened the business in the United States. It is urgent to evaluate the effect of reopening policies on the COVID-19 pandemic to help with the decision-making on the control measures and medical resource allocations. In this study, a novel SEIR model was developed to evaluate the effect of reopening policies based on the real-world reported COVID-19 data in Texas. The earlier reported data before the reopening were used to develop the SEIR model; data after the reopening were used for evaluation. The simulation results show that if continuing the “stay-at-home order” without reopening the business, the COVID-19 pandemic could end in December 2020 in Texas. On the other hand, the pandemic could be controlled similarly as the case of no-reopening only if the contact rate was low and additional high magnitude of control measures could be implemented. If the control measures are only slightly enhanced after reopening, it could flatten the curve of the COVID-19 epidemic with reduced numbers of infections and deaths, but it might make the epidemic last longer. Based on the reported data up to July 2020 in Texas, the real-world epidemic pattern is between the cases of the low and high magnitude of control measures with a medium risk of contact rate after reopening. In this case, the pandemic might last until summer 2021 to February 2022 with a total of 4–10 million infected cases and 20,080–58,604 deaths. KeAi Publishing 2021-02-23 /pmc/articles/PMC7901308/ /pubmed/33644499 http://dx.doi.org/10.1016/j.idm.2021.02.001 Text en © 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Original Research Article Yu, Duo Zhu, Gen Wang, Xueying Zhang, Chenguang Soltanalizadeh, Babak Wang, Xia Tang, Sanyi Wu, Hulin Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model |
title | Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model |
title_full | Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model |
title_fullStr | Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model |
title_full_unstemmed | Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model |
title_short | Assessing effects of reopening policies on COVID-19 pandemic in Texas with a data-driven transmission model |
title_sort | assessing effects of reopening policies on covid-19 pandemic in texas with a data-driven transmission model |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901308/ https://www.ncbi.nlm.nih.gov/pubmed/33644499 http://dx.doi.org/10.1016/j.idm.2021.02.001 |
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