Reopening California: Seeking robust, non-Dominated COVID-19 exit strategies

Abstract

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty.

Publication
PLOS ONE
Pedro Nascimento de Lima
Pedro Nascimento de Lima

I’m a Ph.D. Candidate in Policy Analysis working at the intersection of Simulation Modeling (ABM, Systems Dynamics, Microsim), Policy Analysis and Decision Making Under Deep Uncertainty.

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