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. 2020 Sep;36(4):591-601.
doi: 10.1111/jrh.12486. Epub 2020 Jun 30.

Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates

Rajib Paul  1 , Ahmed A Arif  1 , Oluwaseun Adeyemi  1 , Subhanwita Ghosh  1 , Dan Han  2
Affiliations

Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates

Rajib Paul et al. J Rural Health. 2020 Sep.

Abstract

Purpose: There are growing signs that the COVID-19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county-level change in prevalence rates of COVID-19 by rural-urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates.

Methods: We used crowdsourced data on COVID-19 and linked them to county-level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R-studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis-Ord local statistics.

Findings: In the rural counties, the mean prevalence of COVID-19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID-19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID-19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25-49 years had increased COVID-19 prevalence rates.

Conclusions: Our findings show a rapid spread of COVID-19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID-19 prevalence.

Keywords: Bayesian influence; disease hotspots; geographic disparity; pandemic; respiratory disease.

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Figures

Figure 1
Figure 1
Median Prevalence Trend of COVID‐19 Infection From the Observed Data Before Denoising. The triangles represent urban median prevalence rates and the circles represent rural median prevalence rates.
Figure 2
Figure 2
Estimated (Denoised) Prevalence Rates From Fitted Spatiotemporal Model for (a) Rural and (b) Urban Counties. The black lines indicate median prevalence rates. Gray lines represent prevalence curves for 2,107 rural and 1,001 urban counties. Square root of rates are plotted for better comparison. The red line in plot (a) denotes the prevalence for Plaquemines Parish, Louisiana. The red line in plot (b) denotes the prevalence for New York City and the green line indicates the prevalence plot for New Orleans, Louisiana.
Figure 3
Figure 3
Estimated COVID‐19 (Denoised) Prevalence per 100,000 Population From the Fitted Spatiotemporal Model: April 3 to April 22, 2020.
Figure 4
Figure 4
Hotspots of COVID‐19 Estimated (Denoised) Prevalence: April 3 to April 22, 2020.
Figure 5
Figure 5
Significant Increase or Decrease of Percentage Change in Prevalence Over a 14‐Day Period.
See this image and copyright information in PMC

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