JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1111 10.6339/23-JDS1111 Data Science in Action The Effects of County-Level Socioeconomic and Healthcare Factors on Controlling COVID-19 in the Southern and Southeastern United States BarthJackson1 ChengGuanqing1 WilliamsWebb1 ZhangMing1 https://orcid.org/0000-0003-4685-2199 NgHon Keung Tonytng@bentley.edu2∗ Department of Statistical Science, Southern Methodist University, Dallas, Texas 75275, USA Department of Mathematical Sciences, Bentley University, Waltham, Massachusetts 02452, USA Corresponding author. Email: tng@bentley.edu. 2024592023224631646 Supplementary Material

S1. Code

To ensure the reproducibility of the results presented in this manuscript, the following supplementary materials are provided at the GitHub archive https://github.com/chriszhangm/ASA-Data-Expo-2021:

Data_clean.R: The R code for data cleaning;

modeling.R: The R functions to show results in our paper and R shiny website.

app.R: The R code to run the R shiny website;

full_data.csv: full data set includes two response variables (score_infection, score_death) and socioeconomic and healthcare factors.

counties_prj.csv & states_SE.csv: Two datasets for producing geographic graphs in the R shiny website.

301120221072023 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

This paper aims to determine the effects of socioeconomic and healthcare factors on the performance of controlling COVID-19 in both the Southern and Southeastern United States. This analysis will provide government agencies with information to determine what communities need additional COVID-19 assistance, to identify counties that effectively control COVID-19, and to apply effective strategies on a broader scale. The statistical analysis uses data from 328 counties with a population of more than 65,000 from 13 states. We define a new response variable by considering infection and mortality rates to capture how well each county controls COVID-19. We collect 14 factors from the 2019 American Community Survey Single-Year Estimates and obtain county-level infection and mortality rates from USAfacts.org. We use the least absolute shrinkage and selection operator (LASSO) regression to fit a multiple linear regression model and develop an interactive system programmed in R shiny to deliver all results. The interactive system at https://asa-competition-smu.shinyapps.io/COVID19/ provides many options for users to explore our data, models, and results.

 American Community Survey interactive system LASSO regression R shiny References Abedi V, Olulana O, Avula V, Chaudhary D, Khan A, Shahjouei S, et al. (2021). Racial, economic, and health inequality and COVID-19 infection in the United States. Journal of Racial and Ethnic Health Disparities, 8: 732–742. https://doi.org/10.1007/s40615-020-00833-4 Bilinski A, Emanuel EJ (2020). COVID-19 and excess all-cause mortality in the US and 18 comparison countries. JAMA: The Journal of the American Medical Association, 324(20): 2100–2102. https://doi.org/10.1001/jama.2020.20717 Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al. (2022). shiny: Web Application Framework for R. R package version 1.7.3.9001. Cheng KJG, Sun Y, Monnat SM (2020). COVID-19 death rates are higher in rural counties with larger shares of blacks and hispanics. The Journal of Rural Health, 36(4): 602–608. https://doi.org/10.1111/jrh.12511 Clouston SA, Natale G, Link BG (2021). Socioeconomic inequalities in the spread of coronavirus-19 in the United States: A examination of the emergence of social inequalities. Social Science & Medicine, 268: 113554. https://doi.org/10.1016/j.socscimed.2020.113554 Desmet K, Wacziarg R (2022). JUE insight: Understanding spatial variation in COVID-19 across the United States. Journal of Urban Economics, 127: 103332. https://doi.org/10.1016/j.jue.2021.103332 Doti JL (2021). Examining the impact of socioeconomic variables on COVID-19 death rates at the state level. Journal of Bioeconomics, 23(1): 15–53. https://doi.org/10.1007/s10818-021-09309-9 Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B (2011). A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8(1), Article 3. Friedman J, Hastie T, Tibshirani R (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1): 1–22. https://doi.org/10.18637/jss.v033.i01 Haeder SF, Gollust SE (2020). From poor to worse: Health policy and politics scholars assessment of the U.S. COVID-19 response and its implications. World Medical and Health Policy, 12(4): 454–481. https://doi.org/10.1002/wmh3.371 Hastie T, Tibshirani R, Friedman J (2009). The Elements of Statistical Learning, chapter 7, 241–243. Springer, New York, twelve edition. Karmakar M, Lantz PM, Tipirneni R (2021). Association of social and demographic factors with COVID-19 incidence and death rates in the us. JAMA Network Open, 4(1): e2036462. https://doi.org/10.1001/jamanetworkopen.2020.36462 McLaren J (2021). Racial disparity in COVID-19 deaths: Seeking economic roots with census data. The B.E. Journal of Economic Analysis & Policy, 21(3): 897–919. https://doi.org/10.1515/bejeap-2020-0371 Mollalo A, Vahedi B, Rivera KM (2020). GIS-based spatial modeling of COVID-19 incidence rate in the continental United States. Science of the Total Environment, 728: 138884. https://doi.org/10.1016/j.scitotenv.2020.138884 R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Tibshirani R (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1): 267–288. Ward MD, Gleditsch KS (2018). Spatial Regression Models, volume 155. Sage Publications. Woolf SH, Chapman DA, Lee JH (2021). COVID-19 as the leading cause of death in the United States. JAMA: The Journal of the American Medical Association, 325(2): 123–124. https://doi.org/10.1001/jama.2020.24865