DOI: 10.5061/dryad.4j0zpc8j1
title: Data from: Spatial modeling of sociodemographic risk for COVID-19 mortality

description: 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 global
 Moran’s I test statistic. Results: Multivariate regression results for all
 regions across three time windows suggest that existing measures
 of social vulnerability for disaster preparedness (SVI) are predictive of
 a higher degree of mortality from COVID-19. In comparison, GWRF
 models provide a more robust evaluation of feature importance and
 prediction, exposing the value of local features for prediction, such as
 obesity, which is obscured by coarse-grained analysis. Spatial
 autocorrelation indicates positive spatial clustering,with a progression
 from positively clustered low deaths for liberal counties (cold spots) to
 positively clustered high deaths for conservative counties (hot spots).
 Conclusion: GWRF results indicate that a more nuanced modeling strategy is
 useful for determining spatial variation versus regional modeling
 approaches which may not capture feature clustering along border
 areas. Spatially explicit modeling approaches, such as GWRF, provide a
 more robust feature importance assessment of sociodemographic
 risk factors in predicting COVID-19 mortality.
description: 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.
description: 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.
description: ## ## 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 used in the Supplemental.Rmd. \- html - contains regression output
 tables for each United States Health and Human Services regions. Each of
 the tables is embedded in the Supplemental.Rmd. \- region_pngs - contains
 .png images of regional Health and Human Services maps. **## Data
 Descriptions** Within the data folder,there are a number of datasets. Each
 dataset is described below, with key field names and usage.
 sevenday_combined.csv: This csv contains daily cases and deaths for all
 United States counties, for each day from January 2020 to April 2022.
 Given the large size of this .csv, it is split into TWO zip files, and is
 automatically merged together and loaded as the Supplemental.Rmd is run.
 Column descriptions: \- County: county name \- state: state name \- size:
 population count (raw population number) \- fips: fips number (five digit
 unique county ID) \- cuml.cases: cumulative cases per county (counts) \-
 past.cases: past cases per county (counts) \- new.cases: new cases per
 county (counts) \- daily.cases: cases for that day, per county \- act.pt:
 frequency calculations (not used) \- act.low: frequency calculations (not
 used) \- act.hi: frequency calculations (not used) \- freq.act: frequency
 calculations (not used) \- freq.act.low: frequency calculations (not used)
 \- freq.act.hi: frequency calculations (not used) \- one_act: frequency
 calculations (not used) \- one.act.low: frequency calculations (not used)
 \- one.act.hi: frequency calculations (not used) \- trans.pt: frequency
 calculations (not used) \- trans.pt.low: frequency calculations (not used)
 \- trans.pt.hi: frequency calculations (not used) \- freq.trans_low:
 frequency calculations (not used) \- freq.trans_hi: frequency calculations
 (not used) \- one.trans: frequency calculations (not used) \-
 one.trans_low: frequency calculations (not used) \- one.trans_hi:
 frequency calculations (not used) \- county2: lowercase county name \-
 date: date (YYYY-MM-DD) deaths_nationwide_cumulative.csv: This csv
 provides daily deaths for all United States counties, as well as
 cumulative deaths. Column descriptions \- date: date (YYYY-MM-DD) \- FIPS:
 fips number (five digit unique county ID) \- deaths: number of cumulative
 deaths (raw count) \- daily_deaths: number of daily deaths for a specific
 date (raw count) vaccinationrate.csv: This .csv provides vaccination rates
 as collected by the Centers for Disease Control and Prevention (CDC).
 Column descriptions: \- Date: date (DD/MM/YYYY) \- FIPS: fips number (five
 diget unique county ID) \- MMWR_week: week within the year \-
 Recip_County: county \- Recip_State: state \- Series_Complete_Pop_Pct:
 vaccination rate (percentage) voting_nationwide_liberal.csv: This .csv
 contains voting data from the 2020 general election, using the Biden/Trump
 voter breakdown. The voting percentage refers to the % of people, per
 county, who voted for Biden. Column descriptions: \- FIPS: fips number
 (five diget unique county ID) \- lname: name of candidate \- votes: number
 of votes cast for candidate \- totalvotes: total votes cast for all
 candidates \- state: state where votes were cast \- county: county where
 votes were cast \- pct: democratic voting percentage (percentage)
 age65_over.csv: This .csv contains the population of adults over the age
 of 65, per county (2020 US Census). Column descriptions: \- FIPS: fips
 number (five diget unique county ID) \- Age_over_65 - the raw number of
 adults age 65+ broadband.csv: This .csv contains the number of people per
 county who have access to broadband, by county. Column descriptions: \-
 FIPS: fips number (five digit unique county ID) \- State: state name \-
 County: county name \- broadband_access: percentage of households that
 have broadband access (percentage) countyrankings_refined.csv: This .csv
 file contains county level health rankings that are taken from the
 University of Wisconsin's Population Health Institute. For this
 analysis, we only use a limited number of these variables in our final
 analysis. Column descriptions: \- FIPS: fips number (five diget unique
 county ID) \- State: state name \- County: county name \- Smoking:
 percentage of people who smoke (percentage) \- Obesity: percentage of
 people who are obese (percentage) \- FEI: The food environment index
 combines two measures of food access: the percentage of the population
 that is low-income and has low access to a grocery store, and the
 percentage of the population that did not have access to a reliable source
 of food during the past year (food insecurity) \- Excessive_Drinking:
 percentage of people who excessively drink (percentage) \- Uninsured:
 percentage of adults who are uninsured (percentage) \- Flu_Vaccination:
 percentage of people who have had a flu vaccination (percentage) \-
 HS_graduation: percentage of people who graduated HS (percentage) \-
 Some_College: percentage of people who have some college (percentage) \-
 Unemployed: percentage of people who are unemployed (percentage) \-
 Children_in_Poverty: percentage of children listed as in poverty
 (percentage) \- Income_Ratio: percentage of people who smoke (percentage)
 \- Single_Parent: percentage of people who are single parents (percentage)
 \- Associations: percentage of people who engage in social
 associations/clubs (percentage) \- Violent_Crime: percentage of violent
 crime (percentage) \- Severe_Housing_Problems: percentage of people who
 have housing problems (percentage) \- Diabetes: percentage of people who
 have diabetes (percentage) \- Food_Insecurity: percentage of people who
 are food insecure (percentage) \- Uninsured_Adults: percentage of adults
 who are uninsured (percentage) \- African_Americans: percentage of
 African_Americans (percentage) \- American_Native: percentage of
 American_Native (percentage) \- Asian: percentage of Asian (percentage) \-
 Pacific_Islander: percentage of Pacific_Islander (percentage) \- Hispanic:
 percentage of Hispanic (percentage) \- Non_Hispanic_White: percentage of
 Non_Hispanic_White (percentage) population_density.csv: This .csv contains
 the population counts and density for each county in the United States, as
 derived from US Census 2020 data. Column descriptions: \- FIPS: fips
 number (five diget unique county ID) \- name: county name \- density:
 population density percentage (percentage) svi.csv: This .csv contains
 social vulnerability indices (SVI) at the county level, taken from the
 Centers for Disease Control and Prevention (CDC). Column descriptions: \-
 FIPS: fips number (five diget unique county ID) \- RPL_THEME1: Social
 vulnerability CDC index - Socioeconomic status (percentage) \- RPL_THEME2:
 Social vulnerability CDC index - Household Type (percentage) \-
 RPL_THEME3: Social vulnerability CDC index - Minority Status and Language
 (percentage) \- RPL_THEME4: Social vulnerability CDC index - Housing and
 Transportation (percentage) counties: This folder contains a shapefile and
 associated data for county mapping data - UScounties_conus.shp. states:
 This folder contains a shapefile and associated data for state mapping
 data - states.shp.