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COVID-19 had a strong and disruptive impact on our society, and yet furtheranalyses on most relevant factors explaining the spread of the pandemic areneeded. Interdisciplinary studies linking epidemiological, mobility,environmental, and socio-demographic data analysis can help understanding howhistorical conditions, concurrent social policies and environmental factorsimpacted on the evolution of the pandemic crisis. This work deals with aregression analysis linking COVID-19 mortality to socio-demographic, mobility,and environmental data in the US during the first half of 2020, i.e., duringthe COVID-19 pandemic first wave. This study can provide very useful insightsabout risk factors enhancing mortality rates before non-pharmaceuticalinterventions or vaccination campaigns took place. Our cross-sectionalecological regression analysis demonstrates that, when considering the entireUS area, the socio-demographic variables globally play the most important rolewith respect to environmental and mobility variables in describing COVID-19mortality. Compared to the complete generalized linear model considering allsocio-demographic, mobility, and environmental data, the regression based onlyon socio-demographic data provides a better approximation and proves to be abetter explanatory model when compared to the mobility-based andenvironmental-based models. However, when looking at single entries within eachof the three groups, we see that the mobility data can become relevantdescriptive predictors at local scale, as in New Jersey where the time spent atwork is one of the most relevant explanatory variables, while environmentaldata play contradictory roles.

Analysis of COVID-19 first wave in the US based on demographic, mobility, and environmental variables