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Journal of Data Science


The Effects of County-Level Socioeconomic and Healthcare Factors on Controlling COVID-19 in the Southern and Southeastern United States
Volume 22, Issue 4 (2024), pp. 631–646
Pub. online: 5 September 2023      Type: Data Science In Action      Open accessOpen Access
Received
30 November 2022
Accepted
10 July 2023
Published
5 September 2023

Abstract

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.

Supplementary material

 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.

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

Keywords
American Community Survey interactive system LASSO regression R shiny

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