Epidemic Responses Under Uncertainty
Michael Barnett (),
Greg Buchak () and
Constantine Yannelis ()
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Michael Barnett: Arizona State University - W.P. Carey School of Business
Greg Buchak: Stanford University - Graduate School of Business
Constantine Yannelis: University of Chicago - Booth School of Business
No 2020-72, Working Papers from Becker Friedman Institute for Research In Economics
We examine how policymakers should react to a pandemic when there is significant uncertainty regarding key parameters relating to the disease. In particular, this paper explores how optimal mitigation policies change when incorporating uncertainty regarding the Case Fatality Rate (CFR) and the Basic Reproduction Rate (R0) into a macroeconomic SIR model in a robust control framework. This paper finds that optimal policy under parameter uncertainty generates an asymmetric optimal mitigation response across different scenarios: when the diseaseâ€™s severity is initially underestimated the planner increases mitigation to nearly approximate the optimal response based on the true model, and when the diseaseâ€™s severity is initially overestimated the planner maintains lower mitigation as if there is no uncertainty in order to limit excess economic costs.
Keywords: COVID-19; coronavirus; model uncertainty; dynamic general equilibrium (search for similar items in EconPapers)
JEL-codes: E1 H0 I1 (search for similar items in EconPapers)
Pages: 51 pages
New Economics Papers: this item is included in nep-dge and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:bfi:wpaper:2020-72
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