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A Bayes Decision Rule to Assist Policymakers during a Pandemic

A new decision rule based on net benefit per capita is proposed and exemplified with the aim of assisting policymakers in deciding whether to lockdown or reopen an economy—fully or partially—amidst a pandemic. Bayesian econometric models using Markov chain Monte Carlo algorithms are used to quantify...

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Autores principales: Cao, Kang-Hua, Damien, Paul, Woo, Chi-Keung, Zarnikau, Jay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391194/
https://www.ncbi.nlm.nih.gov/pubmed/34442160
http://dx.doi.org/10.3390/healthcare9081023
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author Cao, Kang-Hua
Damien, Paul
Woo, Chi-Keung
Zarnikau, Jay
author_facet Cao, Kang-Hua
Damien, Paul
Woo, Chi-Keung
Zarnikau, Jay
author_sort Cao, Kang-Hua
collection PubMed
description A new decision rule based on net benefit per capita is proposed and exemplified with the aim of assisting policymakers in deciding whether to lockdown or reopen an economy—fully or partially—amidst a pandemic. Bayesian econometric models using Markov chain Monte Carlo algorithms are used to quantify this rule, which is illustrated via several sensitivity analyses. While we use COVID-19 data from the United States to demonstrate the ideas, our approach is invariant to the choice of pandemic and/or country. The actions suggested by our decision rule are consistent with the closing and reopening of the economies made by policymakers in Florida, Texas, and New York; these states were selected to exemplify the methodology since they capture the broad spectrum of COVID-19 outcomes in the U.S.
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spelling pubmed-83911942021-08-28 A Bayes Decision Rule to Assist Policymakers during a Pandemic Cao, Kang-Hua Damien, Paul Woo, Chi-Keung Zarnikau, Jay Healthcare (Basel) Article A new decision rule based on net benefit per capita is proposed and exemplified with the aim of assisting policymakers in deciding whether to lockdown or reopen an economy—fully or partially—amidst a pandemic. Bayesian econometric models using Markov chain Monte Carlo algorithms are used to quantify this rule, which is illustrated via several sensitivity analyses. While we use COVID-19 data from the United States to demonstrate the ideas, our approach is invariant to the choice of pandemic and/or country. The actions suggested by our decision rule are consistent with the closing and reopening of the economies made by policymakers in Florida, Texas, and New York; these states were selected to exemplify the methodology since they capture the broad spectrum of COVID-19 outcomes in the U.S. MDPI 2021-08-09 /pmc/articles/PMC8391194/ /pubmed/34442160 http://dx.doi.org/10.3390/healthcare9081023 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cao, Kang-Hua
Damien, Paul
Woo, Chi-Keung
Zarnikau, Jay
A Bayes Decision Rule to Assist Policymakers during a Pandemic
title A Bayes Decision Rule to Assist Policymakers during a Pandemic
title_full A Bayes Decision Rule to Assist Policymakers during a Pandemic
title_fullStr A Bayes Decision Rule to Assist Policymakers during a Pandemic
title_full_unstemmed A Bayes Decision Rule to Assist Policymakers during a Pandemic
title_short A Bayes Decision Rule to Assist Policymakers during a Pandemic
title_sort bayes decision rule to assist policymakers during a pandemic
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391194/
https://www.ncbi.nlm.nih.gov/pubmed/34442160
http://dx.doi.org/10.3390/healthcare9081023
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