  Background: The novel coronavirus, COVID-19, was first detected in the United
States in January 2020. To curb the spread of the disease in mid-March,
different states issued mandatory stay-at-home (SAH) orders. These
nonpharmaceutical interventions were mandated based on prior experiences, such
as the 1918 influenza epidemic. Hence, we decided to study the impact of
restrictions on mobility on reducing COVID-19 transmission. Methods: We
designed an ecological time series study with our exposure variable as Mobility
patterns in the state of Maryland for March- December 2020 and our outcome
variable as the COVID-19 hospitalizations for the same period. We built an
Extreme Gradient Boosting (XGBoost) ensemble machine learning model and
regressed the lagged COVID-19 hospitalizations with Mobility volume for
different regions of Maryland. Results: We found an 18% increase in COVID-19
hospitalizations when mobility was increased by a factor of five, similarly a
43% increase when mobility was further increased by a factor of ten.
Conclusion: The findings of our study demonstrated a positive linear
relationship between mobility and the incidence of COVID-19 cases. These
findings are partially consistent with other studies suggesting the benefits of
mobility restrictions. Although more detailed approach is needed to precisely
understand the benefits and limitations of mobility restrictions as part of a
response to the COVID-19 pandemic.
