Social Distancing and COVID-19: Randomization Inference for a Structured Dose-Response Relationship

TL;DR

This paper develops a novel statistical framework to infer the dose-response relationship between social distancing measures and COVID-19 health outcomes, accounting for time-dependent treatment trajectories.

Contribution

It introduces a design-based, randomization inference approach for structured dose-response analysis in observational COVID-19 data, including longitudinal and time-dependent treatments.

Findings

Social distancing significantly reduced COVID-19 death tolls (p < 0.001).

More mobility reduction was needed in non-rural counties to prevent exponential case growth.

The framework effectively distinguishes causal effects in observational, time-dependent settings.

Abstract

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.