文章基本信息

标题：A Bayes Decision Rule to Assist Policymakers during a Pandemic
本地全文：下载
作者：Kang-Hua Cao ; Paul Damien ; Chi-Keung Woo 等
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2021
卷号：9
期号：8
DOI：10.3390/healthcare9081023
语种：English
出版社：MDPI Publishing
摘要：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.
关键词：enBayesian inference;decisions;employment;mortality rates;net benefit;sensitivity analysis