摘要：SummaryLockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, enmassemitigation has come with substantial socioeconomic costs. In this paper, we demonstrate how individualized policies based on disease status can reduce transmission risk while minimizing impacts on economic outcomes. We design feedback control policies informed by optimal control solutions to modulate interaction rates of individuals based on the epidemic state. We identify personalized interaction rates such that recovered/immune individuals elevate their interactions and susceptible individuals remain at home before returning to pre-lockdown levels. As we show, feedback control policies can yield similar population-wide infection rates to total shutdown but with significantly lower economic costs and with greater robustness to uncertainty compared to optimal control policies. Our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.Graphical abstractDisplay OmittedHighlights•We develop a feedback approach to mitigate COVID-19 and enable safer return-to-work•Feedback policies show greater robustness to uncertainty compared to optimal control•Return-to-work is accelerated by large-scale viral and serological testing•Large-scale testing guides targeted changes in behavior rather than en-masse lockdownsVirology
