A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

Feinuo Sun, Stephen A. Matthews, Tse Chuan Yang, Ming Hsiao Hu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Purpose: This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States. Methods: We assemble a county-level data set that contains COVID-19–confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence. Results: The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties. Conclusions: Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Original languageEnglish (US)
JournalAnnals of Epidemiology
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Epidemiology

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