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Updated: Oct 12, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Spatial Clustering of County-Level COVID-19 Rates in the U.S
Marcus R Andrews1, Kosuke Tamura2, Janae N Best1
1Department of Health Behavior and Health Education, School of Public Health, University of Michigan, 1450 Washington Heights, Ann Arbor, MI 48109, USA.
Abstract:
Despite the widespread prevalence of cases associated with the coronavirus disease 2019 (COVID-19) pandemic, little is known about the spatial clustering of COVID-19 in the United States. Data on COVID-19 cases were used to identify U.S. counties that have both high and low COVID-19 incident proportions and clusters. Our results suggest that there are a variety of sociodemographic variables that are associated with the severity of COVID-19 county-level incident proportions. As the pandemic evolved, communities of color were disproportionately impacted. Subsequently, it shifted from communities of color and metropolitan areas to rural areas in the U.S. Our final period showed limited differences in county characteristics, suggesting that COVID-19 infections were more widespread. The findings might address the systemic barriers and health disparities that may result in high incident proportions of COVID-19 clusters.
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