Optimal Case Detection and Social Distancing Policies to Suppress COVID-19

Résumé

This paper shows that an optimal combination of case detection and social distancing allows for quick eradication of COVID-19, ending the current pandemic at low additional human and economic costs. The first contribution is theoretical, and the results hold for any infectious disease, e.g., a future pandemic. I derive the optimal suppression policy as a simple function of observable sufficient statistics, making it easily implementable. I prove that optimal social distancing is the strongest when an outbreak is detected, and then gradually relaxed. The total cost and time of suppression depend crucially on the efficiency of the detection technology. The second contribution is quantitative. I find that, if Italy adopts digital contact tracing, total suppression costs only 0.8 % of annual GDP, the economy is back close to normal after 1.7 months, and the virus causes 3,500 additional casualties. The human and economic costs of the optimal suppression policy are by an order of magnitude lower than the costs of optimal mitigation policies.