Abstract
Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, 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 of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown 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 in intensive 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 policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population 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 in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.
Highlights
Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal
Our results show that uncertainties not directly related to the exit strategies are secondary, they should still be considered in comprehensive analysis intended to inform policy makers
Since this study is of conceptual nature and does not aim to model real-world scenarios as accurately as possible, we simulate a population of 1 million individuals and we focus on the first year after the implementation of a strategy
Summary
Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. In absence of an effective curative treatment and, until recently, of an effective vaccine, non-pharmaceutical interventions have been used to keep case numbers as low as possible. In the past there have been numerous other epidemics during which government actions were required to protect the population. When an infectious disease outbreak occurs, governments rely on computational models to predict the spread of the disease and to explore the potential impact of interventions [2,3,4]. Computational models enable decision makers in government and public health institutions to assess the efficacy of policy measures before actual implementation
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