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The novel coronavirus disease (COVID-19) is a highly contagious respiratorydisease that was first detected in Wuhan, China in December 2019, and has sincespread around the globe, claiming more than 69,000 lives by the time thisprotocol is written. It has been widely acknowledged that the most effectivepublic policy to mitigate the pandemic is \emph{social and physicaldistancing}: keeping at least six feet away from people, working from home,closing non-essential businesses, etc. There have been a lot of anecdotalevidences suggesting that social distancing has a causal effect on diseasemitigation; however, few studies have investigated the effect of socialdistancing on disease mitigation in a transparent and statistically-soundmanner.  We propose to perform an optimal non-bipartite matching to pair counties withsimilar observed covariates but vastly different average social distancingscores 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 illnesscollected by Kinsa Inc. two weeks after the intervention period. Although theobserved illness does not directly measure COVID-19, it reflects a real-timeaspect of the pandemic, and unlike confirmed cases, it is much less confoundedby counties' testing capabilities. We also consider observed illness threeweeks after the intervention period as a secondary outcome. We will test aproportional treatment effect using a randomization-based test with covarianceadjustment and conduct a sensitivity analysis.

Protocol for an Observational Study on the Effects of Social Distancing on Influenza-Like Illness and COVID-19