论文标题
通过非药物干预措施对COVID-19爆发的数据驱动控制：几何编程方法
Data-Driven Control of the COVID-19 Outbreak via Non-Pharmaceutical Interventions: A Geometric Programming Approach
论文作者
论文摘要
在本文中，我们为SARS-COV-2传播的数据驱动模型提出了一个数据驱动的模型，并使用它来设计人类弹性限制的最佳控制策略，以遏制流行病并最大程度地减少与实施非药物干预措施相关的经济成本。我们开发了SEIR流行模型的扩展，该模型捕获了人类流动性变化对疾病传播的影响。我们的数据驱动模型的参数是使用多任务学习方法来学习的，该方法利用了一组区域的死亡人数的两个数据，以及有关每个区域特有的个人移动性模式的手机数据。我们在此数据驱动的模型上提出了一个最佳的控制问题，并通过几何编程提供的可拖动解决方案。该框架的结果是一种基于流动性的干预策略，该战略遏制了流行病的传播，同时遵守产生的经济成本预算。此外，在没有从人类流动数据到经济成本的直接映射到经济成本的情况下，我们提出了一种实用方法，通过该方法，可以选择因过度利用医院资源而导致的经济损失预算来消除过多的死亡。使用来自费城大都市地区的实际数据，通过数值模拟证明了我们的结果。
In this paper we propose a data-driven model for the spread of SARS-CoV-2 and use it to design optimal control strategies of human-mobility restrictions that both curb the epidemic and minimize the economic costs associated with implementing non-pharmaceutical interventions. We develop an extension of the SEIR epidemic model that captures the effects of changes in human mobility on the spread of the disease. The parameters of our data-driven model are learned using a multitask learning approach that leverages both data on the number of deaths across a set of regions, and cellphone data on individuals' mobility patterns specific to each region. We propose an optimal control problem on this data-driven model with a tractable solution provided by geometric programming. The result of this framework is a mobility-based intervention strategy that curbs the spread of the epidemic while obeying a budget on the economic cost incurred. Furthermore, in the absence of a straightforward mapping from human mobility data to economic costs, we propose a practical method by which a budget on economic losses incurred may be chosen to eliminate excess deaths due to over-utilization of hospital resources. Our results are demonstrated with numerical simulations using real data from the Philadelphia metropolitan area.
