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Your Environment. Your Health.

COVID-19 Pandemic Vulnerability Index Quick Start Guide

COVID-19 Pandemic Vulnerability Index (PVI)

A collaborative effort developed this COVID-19 Pandemic Vulnerability Index (PVI) Dashboard. Scientists from NIEHS, North Carolina State University and Texas A&M University contributed to the effort.

Population-level data is a powerful resource for understanding how the virus is spreading and which communities are at risk. However, interpreting that information is challenging. The data visualization in this dashboard offers an effective means of communicating data to scientists, policy makers, and the public.

This dashboard creates risk profiles, called PVI scorecards, for every county in the United States. It is continuously updated with the latest data. The PVI summarizes and visualizes overall risk in a special version of a pie chart, called a radar chart, where different data sources make up pieces of the pie.

Instructions below tell you how to navigate the COVID-19 PVI Dashboard.

Dashboard Map

screenshot of the dashboard

Figure 1. The dashboard displays a U.S. map and a PVI Scorecard for an example county.

In Figure 1, within the map of the U.S., you'll see special pie charts, known as a radar charts, which visually summarize a county's overall vulnerability COVID-19.

Each slice of these radar charts, or PVI profiles, represents a different data source, described below. The information is combined to generate a PVI score for each county.

zoom button screenshot  Use the box in the lower right corner and mouse pointer to zoom on any part of the U.S. County border lines will appear. Clicking on a county or PVI profile will cause a county scorecard to appear within the map pane.

You can click on and get data about any county, whether or not a PVI profile already appears. Default display settings show only radar charts for the top 250 counties by overall PVI score. You can adjust the number of charts displayed or specify counties to display in the Quick Filter box in the upper left corner.

PVI Scorecards

Using the county of St. Francis, Arkansas, as an example, this section explains PVI Scorecards, which appear on the left side of the screen.

screenshot from dashboard of PVI scorecards

Figure 2. PVI Scorecard example for St. Francis county, Arkansas.

On the left side of Figure 2, the PVI Scorecard for St. Francis shows data sources and their corresponding colors. Infection rates, depicted in red slices, are labeled 1 and 2. Intervention rates, noted in blue slices 5 and 6, are highly variable and are updated daily. Population concentration and density are fixed values describing general demographic information, and these are shown in green slices 3 and 4, and health care vulnerabilities are shown in purple slices 7, 8, 9, and 10.

The right side of Figure 2 has a score distribution, which shows where the St. Francis PVI Score ranks across all counties. Counties with the lowest vulnerability to COVID-19 spread appear on the left, while counties with highest vulnerability are on the right. A black vertical line shows the location of St. Francis county within the distribution.

screenshot of the score ranges for relative vulnerability

Figure 3. Relative Vulnerability.

As shown in Figure 3, the fill within the slice indicates risk level within that metric category. Indication of the highest relative vulnerability (score = 1) is a slice filled all the way to the outer ring of the profile.

Slice coloring is used to indicate categories of different data streams. Details of the data sources are described on the Details page. The overall quantification and size of the PVI represents an overall vulnerability scorecard. Note that the PVI value is based on ranks, such that these values are relative and not tied to a unit.

Navigating the Dashboard

The advanced menu options in the upper-left corner,screenshot of advanced menu, allow you to search the map and customize the display.

screenshot of the layers function PVI  With the Layers option, you can toggle between the PVI model or the COVID-19 cases layer.

profile function screenshot  Within Profiles, you can adjust the size and opacity of the PVI graphic displayed on the map.

filters function screenshot  With the Filters options, you can create complex queries, or subsets, of information, and you can:

  • Limit the number of scorecards displayed
  • Change how those are ordered
  • Specify counties to display by name
  • Specify clusters of similar PVI profiles
  • Restrict ranges for a slice or overall scores.

The default order is listed by the overall PVI, but it can also be ranked based on a data slice.

In the Specify Names box, you can list specific counties of interest using one per line while the Specify Clusters option will provide check boxes to show PVI profiles that look like one another or form a cluster of counties.

Many Filters options work together, so be aware of selections made across options.

Counties can be grouped together, or clustered, based on PVI similarity. Cluster membership is noted on the Scorecard for each county. Two types of clustering are available: hierarchical, or HClust, and KMeans.

Hierarchical clustering, as the name suggests, is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then, the two nearest clusters are merged into the same cluster. This algorithm terminates when there is only a single cluster left. Alternatively, KMeans clustering begins with a set number of clusters and finds the maximal grouping of counties into each of these clusters.

By clicking on the cluster numbers, the map will display only the counties within that cluster. It allows you to view counties with similar vulnerability profiles.

settings function screenshot  The Settings tab is for advanced GIS users who want to know specific settings. It can also be used to share or save analyses.

toggle function screenshot  Toggle options allow you to remove the three panels shown from the display to maximize the map for higher resolution viewing.

Details of the current PVI, including model documentation, data sources, and performance evaluation, are available on the Details page.

Source Data links to the GitHub repository of all daily PVI data files.

Project Team

NIEHS: John House and Alison Motsinger-Reif
NC State University: Skylar Marvel, Fred Wright, and David Reif
Texas A&M University: Weihsueh Chiu and Ivan Rusyn

Contact

Contact Alison A. Motsinger-Reif, Ph.D., chief of the NIEHS Biostatistics and Computational Biology Branch, with questions about this application.

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