Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We study targeted lockdowns in a multigroup SIR model where infection, hospitalization, and fatality rates vary between groups—in particular between the "young," the "middle-aged," and the "old." Our model enables a tractable quantitative analysis of optimal policy. For baseline parameter values for the COVID-19 pandemic applied to the US, we find that optimal policies differentially targeting risk/age groups significantly outperform optimal uniform policies and most of the gains can be realized by having stricter protective measures such as lockdowns on the more vulnerable, old group. Intuitively, a strict and long lockdown for the old both reduces infections and enables less strict lockdowns for the lower-risk groups.