Application of elastic net regression for modeling COVID-19 sociodemographic risk factors.

ABSTRACT:

Objectives

COVID-19 has been at the forefront of global concern since its emergence in December of 2019. Determining the social factors that drive case incidence is paramount to mitigating disease spread. We gathered data from the Social Vulnerability Index (SVI) along with Democratic voting percentage to attempt to understand which county-level sociodemographic metrics had a significant correlation with case rate for COVID-19.

Methods

We used elastic net regression due to issues with variable collinearity and model overfitting. Our modelling framework included using the ten Health and Human Services regions as submodels for the two time periods 22 March 2020 to 15 June 2021 (prior to the Delta time period) and 15 June 2021 to 1 November 2021 (the Delta time period).

Resu

SUBMITTER: Moxley TA 

PROVIDER: S-EPMC10817220 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature