  COVID-19 had a strong and disruptive impact on our society, and yet further
analyses on most relevant factors explaining the spread of the pandemic are
needed. Interdisciplinary studies linking epidemiological, mobility,
environmental, and socio-demographic data analysis can help understanding how
historical conditions, concurrent social policies and environmental factors
impacted on the evolution of the pandemic crisis. This work deals with a
regression analysis linking COVID-19 mortality to socio-demographic, mobility,
and environmental data in the US during the first half of 2020, i.e., during
the COVID-19 pandemic first wave. This study can provide very useful insights
about risk factors enhancing mortality rates before non-pharmaceutical
interventions or vaccination campaigns took place. Our cross-sectional
ecological regression analysis demonstrates that, when considering the entire
US area, the socio-demographic variables globally play the most important role
with respect to environmental and mobility variables in describing COVID-19
mortality. Compared to the complete generalized linear model considering all
socio-demographic, mobility, and environmental data, the regression based only
on socio-demographic data provides a better approximation and proves to be a
better explanatory model when compared to the mobility-based and
environmental-based models. However, when looking at single entries within each
of the three groups, we see that the mobility data can become relevant
descriptive predictors at local scale, as in New Jersey where the time spent at
work is one of the most relevant explanatory variables, while environmental
data play contradictory roles.
