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. Copyright © 2021 by the authors.
CitationCao, K.-H., Damien, P., Woo, C.-K., & Zarnikau, J. (2021). A Bayes decision rule to assist policymakers during a pandemic. Healthcare, 9(8). Retrieved from https://doi.org/10.3390/healthcare9081023
- Bayesian inference
- Mortality rates
- Net benefit
- Sensitivity analysis