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 journalJournal articlepeer-review

    1 Citation (Scopus)

    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

    Fingerprint

    Dive into the research topics of 'A Bayes Decision Rule to Assist Policymakers during a Pandemic'. Together they form a unique fingerprint.

    Cite this