  The novel coronavirus disease (COVID-19) is a highly contagious respiratory
disease that was first detected in Wuhan, China in December 2019, and has since
spread around the globe, claiming more than 69,000 lives by the time this
protocol is written. It has been widely acknowledged that the most effective
public policy to mitigate the pandemic is \emph{social and physical
distancing}: keeping at least six feet away from people, working from home,
closing non-essential businesses, etc. There have been a lot of anecdotal
evidences suggesting that social distancing has a causal effect on disease
mitigation; however, few studies have investigated the effect of social
distancing on disease mitigation in a transparent and statistically-sound
manner.
  We propose to perform an optimal non-bipartite matching to pair counties with
similar observed covariates but vastly different average social distancing
scores during the first week (March 16th through Match 22nd) of President's
\emph{15 Days to Slow the Spread} campaign. We have produced a total of $302$
pairs of two U.S. counties with good covariate balance on a total of $16$
important variables. Our primary outcome will be the average observed illness
collected by Kinsa Inc. two weeks after the intervention period. Although the
observed illness does not directly measure COVID-19, it reflects a real-time
aspect of the pandemic, and unlike confirmed cases, it is much less confounded
by counties' testing capabilities. We also consider observed illness three
weeks after the intervention period as a secondary outcome. We will test a
proportional treatment effect using a randomization-based test with covariance
adjustment and conduct a sensitivity analysis.
