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A dynamic pandemic model evaluating reopening strategies amid COVID-19
Among over 200 COVID-19 affected countries, some are fighting to “flatten the curve”, while some others are considering reopening after lockdown. It remains unclear how different reopening strategies obstruct the local virus containment and impact the economy. We develop a model with travelers acros...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996987/ https://www.ncbi.nlm.nih.gov/pubmed/33770097 http://dx.doi.org/10.1371/journal.pone.0248302 |
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author | Zhong, Ling |
author_facet | Zhong, Ling |
author_sort | Zhong, Ling |
collection | PubMed |
description | Among over 200 COVID-19 affected countries, some are fighting to “flatten the curve”, while some others are considering reopening after lockdown. It remains unclear how different reopening strategies obstruct the local virus containment and impact the economy. We develop a model with travelers across heterogeneous epicenters. A low-risk area attempts to safely reopen utilizing internal policies, such as social distancing and contact tracing, and external policies, including capacity quota, quarantine, and tests. Simulations based on the COVID-19 scenario show that external policies differ in efficacy. They can substitute each other and complement internal policies. Simultaneous relaxation of both channels may lead to a new wave of COVID-19 and large economic costs. This work highlights the importance of quantitative assessment prior to implementing reopening strategies. |
format | Online Article Text |
id | pubmed-7996987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-79969872021-04-05 A dynamic pandemic model evaluating reopening strategies amid COVID-19 Zhong, Ling PLoS One Research Article Among over 200 COVID-19 affected countries, some are fighting to “flatten the curve”, while some others are considering reopening after lockdown. It remains unclear how different reopening strategies obstruct the local virus containment and impact the economy. We develop a model with travelers across heterogeneous epicenters. A low-risk area attempts to safely reopen utilizing internal policies, such as social distancing and contact tracing, and external policies, including capacity quota, quarantine, and tests. Simulations based on the COVID-19 scenario show that external policies differ in efficacy. They can substitute each other and complement internal policies. Simultaneous relaxation of both channels may lead to a new wave of COVID-19 and large economic costs. This work highlights the importance of quantitative assessment prior to implementing reopening strategies. Public Library of Science 2021-03-26 /pmc/articles/PMC7996987/ /pubmed/33770097 http://dx.doi.org/10.1371/journal.pone.0248302 Text en © 2021 Ling Zhong http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhong, Ling A dynamic pandemic model evaluating reopening strategies amid COVID-19 |
title | A dynamic pandemic model evaluating reopening strategies amid COVID-19 |
title_full | A dynamic pandemic model evaluating reopening strategies amid COVID-19 |
title_fullStr | A dynamic pandemic model evaluating reopening strategies amid COVID-19 |
title_full_unstemmed | A dynamic pandemic model evaluating reopening strategies amid COVID-19 |
title_short | A dynamic pandemic model evaluating reopening strategies amid COVID-19 |
title_sort | dynamic pandemic model evaluating reopening strategies amid covid-19 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7996987/ https://www.ncbi.nlm.nih.gov/pubmed/33770097 http://dx.doi.org/10.1371/journal.pone.0248302 |
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