ArticleAnnals of epidemiology2020
A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?
Article in Annals of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 3 of them syntheses that pooled it.
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Who cites it
75 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Spatio-Temporal COVID-19 Modeling: A Global Systematic Review of Data Integration, Equity, and Lessons for Pandemic Preparedness.International journal of environmental research and public health · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- Built Environment, Community Safety, and Diabetes in the Deep South: A County Level Geospatial Analysis.Journal of racial and ethnic health disparities · 2026Article
- Spatio-temporal modelling of COVID-19 infection and associated risk factors in Dakar, Senegal.PLOS global public health · 2026Article
- An ecological study of COVID-19 outcomes among Florida counties.BMC public health · 2025Article
- Unlocking insights from complex data: Leveraging heat maps for decision-making in LMIC.PloS one · 2025Article
- Community resilience and stroke outcomes in older adults: beyond rural-urban classifications.Frontiers in public health · 2025Article
- The impact of COVID-19 vaccination rate on traffic recovery.Scientific reports · 2024Article
- Spatiotemporal dynamics of epidemiology diseases: mobility based risk and short-term prediction modeling of COVID-19.Frontiers in public health · 2024Article
- Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows.PloS one · 2024Article
- Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19.Journal of public health in Africa · 2023Article
- EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization.Scientific reports · 2023Article
- Unveiling Spatial Associations between COVID-19 Severe Health Index, Racial/Ethnic Composition, and Community Factors in the United States.International journal of environmental research and public health · 2023Article
- Exploration of spatiotemporal heterogeneity and socio-demographic determinants on COVID-19 incidence rates in Sarawak, Malaysia.BMC public health · 2023Article
- Ecosystem degradation and the spread of Covid-19.Environmental monitoring and assessment · 2023Review
- Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies.International journal of health geographics · 2023Article
- Spatial associations of long-term exposure to diesel particulate matter with seasonal and annual mortality due to COVID-19 in the contiguous United States.BMC public health · 2023Article
- Image-guided tunneled peritoneal dialysis catheter placement.Cardiovascular diagnosis and therapy · 2023Review
- Review
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis 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.
methodsWe 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.
resultsThe 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.
conclusionsSpatial 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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