Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.
Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
PLoS Comput Biol. 2021 Sep 17;17(9):e1009355. doi: 10.1371/journal.pcbi.1009355. eCollection 2021 Sep.
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.
许多国家目前正在应对 COVID-19 疫情,并正在寻找一种退出策略,以使社会生活恢复正常。为了支持这一搜索,计算模型被用于预测病毒的传播,并在实际实施之前评估政策措施的效果。不过,模型输出必须谨慎解释,因为计算模型存在不确定性。这些不确定性可能源于输入参数值的知识有限,也可能源于某些计算模型的固有随机性。它们导致模型预测的不确定性,引发了这样的问题:模型对于疫情严重程度的关键指标会产生什么样的分布值。在这里,我们展示如何使用不确定性量化和敏感性分析技术来解决这个问题。我们评估了在具有地理分层的基于代理的传播模型中实施的四种退出策略的不确定性和敏感性。这些退出策略分别称为“平缓曲线”、“接触追踪”、“间歇性封锁”和“分阶段开放”。我们考虑了两种避免灾难性医疗资源过载的退出策略的关键指标:重症监护室(IC)中流行病例的最大数量,以及超过 IC 床位容量的 IC 患者天数的总数。我们的结果表明,与退出策略直接相关的不确定性是次要的,尽管在旨在为决策者提供信息的综合分析中仍应考虑这些不确定性。敏感性分析揭示了人口干预接受率和追踪感染个体能力的关键作用。最后,我们探索了安全操作空间的存在。对于间歇性封锁,我们仅在模型参数空间中找到了一个较小的区域,其中模型的关键指标保持在安全范围内,而对于其他退出策略,这个区域更大。