SOCIAL DISTANCING AND COVID-19: RANDOMIZATION INFERENCE FOR A STRUCTURED DOSE-RESPONSE RELATIONSHIP

Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme forms of social distanc-ing, like isolation and quarantine, have 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 test the causal null hypothesis and make inference about the dose-response relationship between reduction in social mobility and COVID-19 related public health outcomes. We first dis-cuss 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 a longitudinal setting and apply them to investigate the effect of social distancing during the first phased reopening in the United States on public health outcomes using data compiled from Unacast^™, the United States Census Bu-reau, 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 num-bers for nonrural counties compared to rural counties.