Temporal Disparities in COVID-19: U.S. Insights with OLS, GWR, and Random Forest
This study explores economic-demographic disparities in COVID-19 infections across varied population densities in the United States, employing OLS, GWR, and random forest models on zip code-level data from four regions. Significant disparities were found, with high-risk areas among disadvantaged groups showing varying infection risks across pandemic periods. Results inform smarter public health planning, guiding comprehensive resource allocation during crises.
Abstract
Although studies have previously investigated the spatial factors of
COVID-19, most of them were conducted at alow resolution and
chose to limit their study areas to high-density urbanized regions.
Hence, this study aims toinvestigate the economic-demographic
disparities in COVID-19 infections and their spatial-temporal patterns
inareas with different population densities in the United States. In
particular, we examined the relationships betweendemographic and
economic factors and COVID-19 density using ordinary least squares,
geographically weightedregression analyses, and random forest based
on zip code-level data of four regions in the United States. Ourresults
indicated that the demographic and economic disparities are significant.
Moreover, several areas with disadvantaged groups were found to be
at high risk of COVID19 infection, and their infection risk changed at
different pandemic periods. The findings of this study can contribute to
the planning of public health services, suchas the adoption of smarter
and comprehensive policies for allocating economic recovery resources
and vaccinesduring a public health crisis.
Team
Yefu Chen Junfeng Jiao Amin Azimian
Authors’ contributions
Yefu Chen: Conceptualization, Methodology, Formal Analysis,
Software, Writing - original draft, Writing - review & editing.
Junfeng Jiao: Project administration, Funding acquisition,
Resources, Writing - review & editing.
Amin Azimian: Data Collection, Software, Writing - review
& editing.
The author(s) read and approved the final manuscript.
Publisher’s Note
The authors would like to acknowledge the funding
supports from NSF (1952193 and 2133302), UT Good
Systems, and USDOT.