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A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal

The governors of New Jersey, New York, California, Connecticut, Delaware and Oregon announced early in the week of February 7 that select mask mandates in their states would end in two to six weeks. These states together account for 77.9 million Americans, or ~23.5% of the U.S. population, and there...

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Detalles Bibliográficos
Autores principales: Chen, Cynthia, Shen, Shiqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202658/
https://www.ncbi.nlm.nih.gov/pubmed/35721376
http://dx.doi.org/10.14218/ERHM.2022.00025
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author Chen, Cynthia
Shen, Shiqian
author_facet Chen, Cynthia
Shen, Shiqian
author_sort Chen, Cynthia
collection PubMed
description The governors of New Jersey, New York, California, Connecticut, Delaware and Oregon announced early in the week of February 7 that select mask mandates in their states would end in two to six weeks. These states together account for 77.9 million Americans, or ~23.5% of the U.S. population, and therefore these changes in policy could have a significant impact on the U.S. economy, as well as education and healthcare systems in each state. As counts of COVID-19 cases, hospitalizations and deaths decrease, mask mandates should be reassessed. We propose that a data-driven, dynamic and flexible approach may help lift mask mandates safely and facilitate a smooth transition to post-pandemic normalcy.
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spelling pubmed-92026582022-06-16 A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal Chen, Cynthia Shen, Shiqian Explor Res Hypothesis Med Article The governors of New Jersey, New York, California, Connecticut, Delaware and Oregon announced early in the week of February 7 that select mask mandates in their states would end in two to six weeks. These states together account for 77.9 million Americans, or ~23.5% of the U.S. population, and therefore these changes in policy could have a significant impact on the U.S. economy, as well as education and healthcare systems in each state. As counts of COVID-19 cases, hospitalizations and deaths decrease, mask mandates should be reassessed. We propose that a data-driven, dynamic and flexible approach may help lift mask mandates safely and facilitate a smooth transition to post-pandemic normalcy. 2022-06 2022-02-25 /pmc/articles/PMC9202658/ /pubmed/35721376 http://dx.doi.org/10.14218/ERHM.2022.00025 Text en https://creativecommons.org/licenses/by-nc/4.0/This article has been published under the terms of Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/) , which permits noncommercial unrestricted use, distribution, and reproduction in any medium, provided that the following statement is provided.
spellingShingle Article
Chen, Cynthia
Shen, Shiqian
A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal
title A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal
title_full A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal
title_fullStr A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal
title_full_unstemmed A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal
title_short A Data-driven, Dynamic and Flexible Approach to Safely Lifting Mask Mandate: A Proposal
title_sort data-driven, dynamic and flexible approach to safely lifting mask mandate: a proposal
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202658/
https://www.ncbi.nlm.nih.gov/pubmed/35721376
http://dx.doi.org/10.14218/ERHM.2022.00025
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