Optimal Covid-19 Quarantine and Testing Policies

Abstract

We study quantitatively the optimality of quarantine and testing policies; and whether they are complements or substitutes. We extend the epidemiological susceptible-exposed-infectious-recovered model to incorporate an information friction. Our main finding is that testing is a cost-efficient substitute for lockdowns, rendering them almost unnecessary. By identifying carriers, testing contains the spread of the virus without reducing output, although the implementation requires widespread massive testing. As a byproduct, we show that two distinct optimal lockdown policy types arise: suppression, intended to eliminate the virus, and mitigation, concerned about flattening the curve. The choice between them is determined by a ‘hope-for-the-cure’ effect, arising due to either an expected vaccine or the belief that the virus can be eliminated. Conditional on the policy type, the intensity and duration of the intervention is invariant to both the trade-off between lives and output and the aversion to GDP variations: the optimal intervention path depends mostly on the virus dynamics.