Epidemic Responses Under Uncertainty

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 disease’s severity is initially underestimated the planner increases mitigation to nearly approximate the optimal response based on the true model, and when the disease’s severity is initially overestimated the planner maintains lower mitigation as if there is no uncertainty in order to limit excess economic costs.