The COVID-19 epidemic in rural U.S. counties

I’ve been working on the COVID-19 epidemic in rural U.S. counties, and have now posted a paper on SocArXiv, here: https://osf.io/preprints/socarxiv/pnqrd/. Here’s the abstract, then some figures below:

Having first reached epidemic proportions in coastal metropolitan areas, COVID-19 has spread around the country. Reported case rates vary across counties from zero to 125 per thousand population (around a state prison in the rural county of Trousdale, Tennessee). Overall, rural counties are underrepresented relative to their share of the population, but a growing proportion of all daily cases and deaths have been reported in rural counties. This analysis uses daily reports for all counties to present the trends and distribution of COVID-19 cases and deaths in rural counties, from late March to May 16, 2020. I describe the relationship between population density and case rates in rural and non-rural counties. Then I focus on noteworthy outbreaks linked to prisons, meat and poultry plants, and nursing homes, many of which are linked to high concentrations of Hispanic, American Indian, and Black populations. The growing epidemic in rural counties is apparently driven by outbreaks concentrated in these institutional settings, which are conducive to transmission. The impact of the epidemic in rural areas may be heightening due to their weaker health infrastructure and more vulnerable populations, especially due to age, socioeconomic status, and health conditions. As a result, the epidemic may contribute to the ongoing decline of health, economic, and social conditions in rural areas.

Here are COVID-19 cases in rural counties across the country. Note that the South, Mid-Atlantic, Michigan, and New England have the most (fewer in West and upper Midwest). When you look at cases per capita, you see the concentration in the South and isolated others.

F1 rural county cases maps

COVID is still underrepresented in rural counties, but their share of the national burden is increasing, as they keep adding more than 2,000 cases and just under 100 deaths per day.

F2 new cases and deaths

Transmission dynamics are different in rural counties. They show a weaker relationship between pop density and cases. This suggests to me that there are more idiosyncratic factors at work (prisons, meat plants, nursing homes), which are high concentrations of vulnerable people.

F3 population density and cases

These are the rural outbreak cases I identified, for which I could find obvious epidemic centers in institutions: Prisons, meatpacking and poultry plants, and nursing homes. These 28 select counties account for 15% of the rural burden.

F4 rural county selected cases

In addition to the institutional concentration, these outbreak cases also show distinct overrepresentation of Hispanic, American Indian, and Black populations. Here are some of the outbreak cases plotted against minority concentrations.

F5 rural county minority scatters

And here’s a table of those selected cases:

crt2

Lots more to be done, obviously. It’s a strong limitation to be restricted to case and death counts at the county level. Someone could go get lists of prisons and meatpacking plants and nursing homes and run them through this, etc. But I wanted to raise this issue substantively. By posting the paper on SocArXiv, without peer review, I’m offering it up for comment and criticism. Also, I’m sharing the code (which links to the data, all public): osf.io/wd2n6/. Messy but usable.

A related thought on writing a paper about COVID19 right now: The lit review is daunting. There are thousands of papers, most on preprint servers. Is this bad? No. I use various tools to decide what’s reliable to learn from. If it’s outside my area, I’m more likely to rely on peer-reviewed journals, or those that are widely citied or reported. But the vast quantity available still helps me see what people are working on, what terms, and types of data they use. I learned a tremendous amount. Much respect to the thousands of researchers who are doing what they can to respond to this global crisis.

COVID-19 graphs, with data and code

Updated March 25.

Although I’m not an expert on pandemic analysis, I am naturally following the COVID-19 data as best I can. And because I always understand data better when I make the figures myself, I’ve been making and looking at COVID-19 trend data, and sharing it as I go.

The figures below are the latest I made as of March 18 25 29, but you can click on the images to link to the current version. The figures, as well as data files and code, are in an Open Science Framework project, here: osf.io/wd2n6/, under CC0 license (free to use for any purpose). The project updates automatically as I go, but these figures won’t (because this is an old fashioned blog).

First, across countries:

country cases and deaths

For this one, to put the diverse US in perspective, in included US states in addition to selected countries. These are deaths.

countries and states since 10 deaths

State cases and deaths, per capita:

state cases and death rates bar

Finally, one with commentary: The first month, in numbers and Trump’s winning words:

Microsoft PowerPoint - first month of winning coronavirus.pptx