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

## TL;DR

This study analyzes how socio-demographic, mobility, and environmental factors influenced COVID-19 mortality in the US during the first wave, highlighting the dominant role of socio-demographics and local mobility patterns.

## Contribution

It provides a comprehensive regression analysis linking multiple variables to COVID-19 mortality, emphasizing the importance of socio-demographic factors over mobility and environmental data.

## Key findings

- Socio-demographic variables are the most significant predictors of COVID-19 mortality.
- Socio-demographic model outperforms models based on mobility and environmental data.
- Mobility data can be relevant at local scales, such as in New Jersey.

## Abstract

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.