Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model

摘要

Many countries are currently dealing with the COVID-19 epidemic and are searching for anexit strategy such that life in society can return to normal. To support this search, computa tional models are used to predict the spread of the virus and to assess the efficacy of policymeasures before actual implementation. The model output has to be interpreted carefullythough, as computational models are subject to uncertainties. These can stem from, e.g.,limited knowledge about input parameters values or from the intrinsic stochastic nature ofsome computational models. They lead to uncertainties in the model predictions, raising thequestion what distribution of values the model produces for key indicators of the severity ofthe epidemic. Here we show how to tackle this question using techniques for uncertaintyquantification and sensitivity analysis. We assess the uncertainties and sensitivities of fourexit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, IntermittentLockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases inintensive care (IC), and the total number of IC patient-days in excess of IC bed capacity.Our results show that uncertainties not directly related to the exit strategies are secondary,although they should still be considered in comprehensive analysis intended to inform policymakers. The sensitivity analysis discloses the crucial role of the intervention uptake by thepopulation and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region inthe model parameter space where the key indicators of the model stay within safe bounds,whereas this region is larger for the other exit strategies.