Efficient Social Distancing for COVID-19: An Integration of Economic Health and Public Health

Kexin Chen, Chi Seng Pun, H. Y. Wong
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Abstract

Social distancing has been the only effective way to contain the spread of an infectious disease prior to the availability of the pharmaceutical treatment. It can lower the infection rate of the disease at the economic cost. A pandemic crisis like COVID-19, however, has posed a dilemma to the policymakers since a long-term restrictive social distancing or even lockdown will keep economic cost rising. This paper investigates an efficient social distancing policy to manage the integrated risk from economic health and public health issues for COVID-19 using a stochastic epidemic modeling with mobility controls. The social distancing is to restrict the community mobility, which was recently accessible with big data analytics. This paper takes advantage of the community mobility data to model the COVID-19 processes and infer the COVID-19 driven economic values from major market index price, which allow us to formulate the search of the efficient social distancing policy as a stochastic control problem. We propose to solve the problem with a deep-learning approach. By applying our framework to the US data, we empirically examine the efficiency of the US social distancing policy and offer recommendations generated from the algorithm.
有效保持社会距离应对COVID-19:经济卫生与公共卫生的结合
在获得药物治疗之前,保持社交距离一直是遏制传染病传播的唯一有效方法。它可以以经济成本降低疾病的感染率。然而,像COVID-19这样的大流行危机给政策制定者带来了两难境地,因为长期的限制性社交距离甚至封锁将使经济成本不断上升。本文利用具有流动性控制的随机流行病模型,研究了一种有效的社会距离政策,以管理COVID-19的经济健康和公共卫生问题的综合风险。保持社交距离是为了限制最近通过大数据分析得到的社区流动性。本文利用社区流动数据对新冠肺炎疫情过程进行建模,并从主要市场指数价格中推断出新冠肺炎疫情驱动的经济价值,从而将有效社会距离政策的寻找作为一个随机控制问题。我们建议用深度学习的方法来解决这个问题。通过将我们的框架应用于美国的数据,我们实证地检验了美国社交距离政策的效率,并提供了由算法生成的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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