Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent, treatment dose trajectory, and generalize a dose-response relationship to a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes using data compiled from sources including Unacast, the United States Census Bureau, and the County Health Rankings and Roadmaps Program. We rejected a primary analysis null hypothesis that stated the social distancing from April 27, 2020, to June 28, 2020, had no effect on the COVID-19-related death toll from June 29, 2020, to August 2, 2020 (p-value < 0.001), and found that it took more reduction in mobility to prevent exponential growth in case numbers for non-rural counties compared to rural counties.
翻译:社会失常被公认为是防治新冠状病毒的有效公共卫生政策,但极端社会失常需要花费成本,也不清楚实现公共健康影响需要多少社会失常才能产生公共健康影响。在本篇文章中,我们开发了一个基于设计的框架,以推断社会失常与COVID-19(COVID-19)之间与死亡人数和病例数有关的剂量反应关系。我们首先讨论如何将观测数据与基于时间、持续治疗剂量的观察数据嵌入一个近似随机的实验中,并开发一种基于随机化的程序,以测试结构化的剂量反应关系是否适合数据。然后,我们推广设计和测试程序,以适应时间依赖的治疗剂量轨迹,并将剂量反应关系概括到长期性环境。最后,我们运用拟议的设计和测试程序来调查在美国分阶段重新研究公共健康结果方面出现社会失常的影响,使用从Unacastard、美国人口普查局、州卫生分级和路线图方案等来源收集的数据。我们拒绝了一个无效的假设,即从2020年4月27日至2020年8月28日至2020年10月2018日的士比相比,从2020年10月28日的士比的士兰-2020年的士比。