  Social distancing and stay-at-home are among the few measures that are known
to be effective in checking the spread of a pandemic such as COVID-19 in a
given population. The patterns of dependency between such measures and their
effects on disease incidence may vary dynamically and across different
populations. We described a new computational framework to measure and compare
the temporal relationships between human mobility and new cases of COVID-19
across more than 150 cities of the United States with relatively high incidence
of the disease. We used a novel application of Optimal Transport for computing
the distance between the normalized patterns induced by bivariate time series
for each pair of cities. Thus, we identified 10 clusters of cities with similar
temporal dependencies, and computed the Wasserstein barycenter to describe the
overall dynamic pattern for each cluster. Finally, we used city-specific
socioeconomic covariates to analyze the composition of each cluster.
