Road to recovery: Managing an epidemic☆

https://doi.org/10.1016/j.jmateco.2021.102482Get rights and content

Abstract

Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang–bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs.

Keywords

COVID-19
SIR models
Capacity constraints
Managing an epidemic

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☆

We thank an anonymous referee and the co-editors of this journal for comments and suggestions that have helped us improve the paper. We are also grateful for feedback from and discussion with Eric Budish, Gabriel Carroll, Chris Edmond, Ian Harper, Zi Yang Kang, Paul Milgrom, Martin Souchier, Gary Stoneham and seminar audiences at the COVID-19 Policy Hackathon organized by the Stanford Economic Association and the MIT Undergraduate Economic Association and at the University of Melbourne. Anand Bharadwaj provided excellent research assistance. Financial support by the June and Samuel Hordern Endowment is also gratefully acknowledged.

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