Spatial modeling of sociodemographic risk for COVID-19 mortality

Background: In early 2020, the Coronavirus Disease 2019 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19, few have looked at spatiotemporal variation of COVID-19 deaths at refined geographic scales.
Methods: The objective of this analysis is to examine the spatiotemporal variation in COVID-19 deaths with respect to socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Spatial autocorrelation was additionally examined using a local and gl..., The attached zip file contains the full GitHub repository, which includes data, the supplemental code, and an output HTML. The GitHub repository can be additionally viewed at: http://github.com/erichseamon/COVIDriskpaper. A README is provided as part of the repository, which describes each dataset, including all variable names and their unit of measure. All data used to generate the supplemental materials is located in the /data folder., All analyses for these supplemental materials were generated in R version 4.0.5.  In order to appropriately run the supplemental.Rmd, please use this version of R, with associated packages., ## ## Data from: Spatial modeling of sociodemographic risk for COVID-19 mortality

Submitted February 2024 - Revision submitted September 2024

Erich Seamon, Benjamin J. Ridenhour, Craig R. Miller, Jennifer Johnson-Leung

University of Idaho, September 2024

**## Overview:**

The following github repository provides all code and data for generating analyses and supplemental materials for the aforementioned manuscript.

\## How do I regenerate the Supplemental Appendix?

\- If you want to generate the supplemental appendix pdf on your own, you may run the Supplemental.Rmd (also at the root level). All of the folders and datasets support running the Supplemnental.Rmd. The published manuscript from the journal provides the fully compiled Supplemental Appendix pdf.

**## Folder Descriptions**

\- data - folder which contains all data accessed as part of Supplemental.Rmd. Descriptions of datasets are noted below under \"Data Descriptions\".

\- figures - contains .png image files that are ...