ORIGINAL RESEARCH article
Front. Public Health
Sec. Infectious Diseases: Epidemiology and Prevention
Volume 13 - 2025 | doi: 10.3389/fpubh.2025.1608360
This article is part of the Research TopicSARS-CoV-2: Virology, Epidemiology, Diagnosis, Pathogenesis and Control, Volume IIView all 10 articles
A Framework for Modeling County-Level COVID-19 Transmission
Provisionally accepted
- 1University of Wisconsin–Stout, Menomonie, United States
- 2Inglemoor High School, Kenmore, United States
- 3Fisk University, Nashville, Tennessee, United States
- 4Murray State University, Murray, Kentucky, United States
- 5Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China
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This study examines COVID-19 transmission across 3,142 U.S. counties using a truncated dataset from March to September 2020. County-level factors include demographics, socioeconomic status, environmental conditions, and mobility patterns. Ordinary Least Squares regression establishes a baseline for analyzing covid-19 confirm case counts for each county. We then use Moran's I to evaluate spatial clustering, prompting Spatial Autoregressive and Spatial Error Models when autocorrelation is significant.Notably, spatial models outperform the Ordinary Least Squares approach-R 2 rises from 0.4849 with Ordinary Least Squares to 0.6846 under Spatial Error Model, while RMSE decreases from 2.0891 to 1.642demonstrating improved fit and more accurate spatial transmission dynamics. A multilevel framework further explores state-level policy variations. Finally, Geographically Weighted Regression captures spatial non-stationarity by mapping local coefficient differences; we visualized temperature, precipitation, and other key variables-revealing precipitation peaks near 110°W in the Southeast and Northeast and strong sensitivity to temperature. This integrated sequence of methods provides a comprehensive lens for studying epidemiological phenomena. While certain findings align with established research, other variables reveal unexpected patterns. The proposed framework offers a robust template for future investigations where spatial dependence and policy heterogeneity warrant close examination.
Keywords: COVID-19, County-level analysis, spatial dependence, multilevel modeling, Geographically weighted regression, moran
Received: 08 Apr 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Bao, Huang, Li, Zhang, Xing, Hou and Ye. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yida Bao, University of Wisconsin–Stout, Menomonie, United States
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