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

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 throug...

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Autores principales: Sun, Feinuo, Matthews, Stephen A., Yang, Tse-Chuan, Hu, Ming-Hsiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386391/
https://www.ncbi.nlm.nih.gov/pubmed/32736059
http://dx.doi.org/10.1016/j.annepidem.2020.07.014
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author Sun, Feinuo
Matthews, Stephen A.
Yang, Tse-Chuan
Hu, Ming-Hsiao
author_facet Sun, Feinuo
Matthews, Stephen A.
Yang, Tse-Chuan
Hu, Ming-Hsiao
author_sort Sun, Feinuo
collection PubMed
description 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.
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spelling pubmed-73863912020-07-29 A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters? Sun, Feinuo Matthews, Stephen A. Yang, Tse-Chuan Hu, Ming-Hsiao Ann Epidemiol Original Article 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. Elsevier Inc. 2020-12 2020-07-28 /pmc/articles/PMC7386391/ /pubmed/32736059 http://dx.doi.org/10.1016/j.annepidem.2020.07.014 Text en © 2020 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Original Article
Sun, Feinuo
Matthews, Stephen A.
Yang, Tse-Chuan
Hu, Ming-Hsiao
A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
title A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
title_full A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
title_fullStr A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
title_full_unstemmed A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
title_short A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
title_sort spatial analysis of the covid-19 period prevalence in u.s. counties through june 28, 2020: where geography matters?
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386391/
https://www.ncbi.nlm.nih.gov/pubmed/32736059
http://dx.doi.org/10.1016/j.annepidem.2020.07.014
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