Though urban, well-traveled areas like New York City are receiving the lion’s share of media attention for coronavirus outbreaks, we are concerned about a number of rapidly forming COVID clusters throughout the country in New Orleans, Detroit, Colorado, and throughout the South, including Arkansas, Tennessee, Mississippi and Georgia.

Using county-level data rather than state-level allows us to zero in on information that may be “hidden” inside larger pictures. County-level visualizations show a dramatically more detailed pandemic landscape, as using aggregate data alone can miss local hotspots of surging COVID cases.

When considering hotspots using the total number of cases, population-dense areas emerge as multiple cores of the pandemic, including NYC, New Jersey, Boston, Seattle, and central Colorado. But we also found concerning clusters in New Orleans, Miami, and Chicago. Recent news reports from the area show Louisiana health officials quoting that the state is averaging a death every two hours from the virus, as of Monday. 

As of March 23rd, significant clusters of counties with population adjustment are found in Detroit (Michigan), New Orleans (Louisiana), Little Rock and Pine Bluff (Arkansas), Greenville (Mississippi), Atlanta and Albany (Georgia), and Columbia (South Carolina). While high case numbers in population-dense, high-travel areas like NYC, New Jersey, and Seattle are of course a focus of concern, local hotspots in areas with limited hospital infrastructure can be easily overlooked without nation-wide county-level analysis and spatial statistics. 

Two large COVID clusters cover most of Arkansas (Faulkner, Independence, Stone, Van Buren, Grant, Jefferson, Lincoln, and Cleveland counties), and nearby areas in Mississippi. With low numbers of deaths in these clusters and a rapid growth rate of newly confirmed cases, Arkansas is likely highly vulnerable for intensive cases in the coming weeks. Albany and Atlanta in Georgia have demonstrated such vulnerability already and may be further along in the pandemic as several hospitals have already begun to reach stress points; ICU beds are already at capacity in many places. New Orleans and surrounding counties are another area of major concern, all identified as clusters. In Detroit, hospitals are already almost at capacity, and may be spreading faster there than anywhere else in the country because of extremely high local vulnerability.

Hospital infrastructure in areas outside of mega-cities will be less comprehensive but also support smaller populations. With fewer people, even if there are fewer numbers of cases, there are corresponding fewer beds, ventilators, and so forth. These areas may also have less power and bargaining ability to negotiate more products and services required for the pandemic response.

The Center for Spatial Data Science at the University of Chicago has been developing more refined, county-level data visualizations and analytics to better identify and track COVID hot spots as they develop and change on a daily basis. The CSDS COVID Analytics team is co-led by Marynia Kolak, PhD (Assistant Director of Health Informatics), Xun Li, PhD (Assistant Director of Data Science), and  Qinyun Lin, PhD (Postdoctoral Scholar).

County cluster hot spots(red) using confirmed cases of COVID-19.

County cluster hot spots(red) using population-weighted adjustment of COVID-19 cases.

Methodology

To get early access to county level data, our team leveraged crowdsourced county-level estimates from the 1point3acres tracker, a dataset generated by a team of volunteers led by an Uber technical lead since the beginning of the epidemic. (The dataset is validated daily against state health department estimates.) The CSDS team recently joined a regional effort to amplify impact with UW-Madison’s COVID Data Science team, led by Brian Yangell, to further validate these county estimates against two other independent sources.  

The CSDS team identifies hot spots two ways: using the total number of confirmed cases, and then by adjusting for population. Because of the infectious nature of COVID, high numbers of cases anywhere will be of concern. At the same time, identifying areas that have a disproportionately high number of cases within the population is needed to locate areas hardest hit by the pandemic. The team further differentiates hot spot clusters and outliers: clusters are counties that have a high number of cases, and are surrounded by counties with a high number of cases. Outliers are areas that have a high number of cases within the county and fewer cases in neighboring counties, highlighting an emerging risk or priority for containment.

The CSDS team is rapidly developing a map application using county-level data and more refined interactions for data visualization and analysis with the goal of providing research-quality data and analytics for the public, planners, and health professionals alike. The final application will facilitate interactive exploration of cases, a spatio-temporal slider, and animation to view choropleth and local clustering maps change over time, at both state and county level. The app is made using Libgeoda is a lightweight C/C++ library that acts as a wrapper to the core features of GeoDa, an open source spatial analysis software developed by the Center. For this web map, it’s customized and compiled to WebAssembly, a format supported by most modern web browsers, and bound to the geo-visualization module via Javascript, which is implemented using deck.gl, d3.js and Mapbox. The Github for our project is public and available at github.com/GeoDaCenter/covid. A public release of the application is expected by the end of the week and will continue to be updated with new data and features on a regular basis.


COVID-19 county clusters are pervasive through the NOLA region of Louisiana.


In Georgia, Atlanta and Albany are surrounded by multi-county cluster COVID hot spots