  In the absence of pharmaceutical interventions to curb the spread of
COVID-19, countries relied on a number of nonpharmaceutical interventions to
fight the first wave of the pandemic. The most prevalent one has been
stay-at-home orders, whose the goal is to limit the physical contact between
people, which consequently will reduce the number of secondary infections
generated. In this work, we use a detailed set of mobility data to evaluate the
impact that these interventions had on alleviating the spread of the virus in
the US as measured through the COVID-19-related deaths. To establish this
impact, we use the notion of Granger causality between two time-series. We show
that there is a unidirectional Granger causality, from the median percentage of
time spent daily at home to the daily number of COVID-19-related deaths with a
lag of 2 weeks. We further analyze the mobility patterns at the census block
level to identify which parts of the population might encounter difficulties in
adhering and complying with social distancing measures. This information is
important, since it can consequently drive interventions that aim at helping
these parts of the population.
