Recent Working Papers
This paper studies optimal mitigation and testing during a pandemic in the presence of partial information. We develop a stylized dynamic epidemiological model where the true number of infected individuals can only be partially inferred from two noisy signals: hospitalization and positivity rate. An egalitarian planner chooses the level of mitigation and testing, which respectively affect the infection rate and signal noise, at a certain economic cost. We first show that the planner is willing to pay a significant "information premium" to eliminate the uncertainty by extensive testing. However, if testing is prohibitively costly, then a stringent mitigation becomes optimal, as it partially substitutes for testing as an information acquisition device. Such policies were often criticized as excessive at the onset of the COVID-19 pandemic. We argue that this "optimal overreaction" is a result of the extreme costs of policy mistakes---such as high future casualties---and not due to an aversion to risk.