A Bayes Decision Rule to Assist Policymakers during a Pandemic

Kang Hua Cao, Paul Damien*, Chi Keung Woo, Jay Zarnikau

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number1023
Number of pages20
JournalHealthcare (Switzerland)
Volume9
Issue number8
DOIs
Publication statusPublished - 9 Aug 2021

Scopus Subject Areas

  • Health Informatics
  • Health Policy
  • Health Information Management
  • Leadership and Management

User-Defined Keywords

  • Bayesian inference
  • Decisions
  • Employment
  • Mortality rates
  • Net benefit
  • Sensitivity analysis

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