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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

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

Figure 1. The dashboard displays a U.S. map and a PVI scorecard for an example county using model 11.2.

In Figure 1, within the map of the U.S., you see blue shading that corresponds to the overall PVI for each county. There are many options within the Dashboard to further explore the underlying sources of vulnerability. You’ll see that the Rank option is on by default, but can be toggled on and off. There are additional options to see more information. The “Slices” option shows special pie charts, known as a radar charts, which visually summarize a county's overall vulnerability to 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. This chart simultaneously shows the magnitude and source(s) of vulnerability.

There are also options to show cumulative case and death counts across the country.

zoom button screenshot Use the box in the lower right corner and mouse pointer to zoom on any part of the U.S. County and 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 model 11.2 generated on July 15, 2020 for the county of St. Francis, Arkansas, 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 July 15, 2020 using model 11.2.

On the left side of Figure 2 is a PVI score distribution. Counties with the lowest vulnerability to COVID-19 appear on the left, while counties with highest vulnerability are on the right. Black vertical lines show where Adams County and St. Francis county are located within the distribution. Radar plots are shown above with information across the 12 slices comprising their PVI score.

On the right 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. Health and Environmental variables are shown in the purple slices 7-12.

As the pandemic has progressed, the PVI model has updated. Most recently, the data has become available for vaccine rates at the local level, and the Dashboard now includes an updated model to include vaccination as an intervention. It is a user option to view the PVI model with or without vaccination included. The PVI model data is available from March 2020, and the model that is PVI + vaccine is available from January 21, 2021. The models can be selected using the option below:

PVI + Vaccine Model

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.

Change Map Menu Option

Additionally, you can change the map itself through the Change Map Menu option. The default dark gray map can be changed to one of several options, including satellite images, topology maps, etc.

Covid-19 Legend

PVI Model Legend

Within Legend, you can adjust the size and opacity of the graphic displayed on the map.

PVI Model Filter

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.

PVI Model Clusters

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 optimal 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 Summary

The Setting Summary option allows the user to save and/or retrieve settings from previous Dashboard navigation, to automate the re-evaluation of previous visualizations and filtering.

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 webpage.

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

Select the PVI model of interest with the corresponding radio button for either PVI Model (11.2) or PVI with Vaccine Model (12.4).

PVI Model Layers

Clicking on any county in the map will populate the county display box (lower left corner) and county-specific info panels along the bottom of the page. Within the display box, the 3-day average number of new Cases and Deaths and number of declining days (Days in the past two weeks where the number of new cases/deaths has declined or remain unchanged relative to the day before) are given. Clicking the magnifier icon will center and zoom to the map to the selected county.

The cumulative data date displayed for observed 'Cases' and 'Deaths' can be scrolled back to March 15, 2020. More options for adjusting this display layer are available under the 'Covid-19 Legend' menu option.

Daily Change

The observed number of Cases and Deaths for the selected county is shown for the preceding 14 days.

Daily Change

The predicted number of Cases and Deaths for a 14-day forecast horizon is plotted as a line within a shaded confidence interval.

A preprint describing the Dashboard and the underlying methods is available at

Project Team

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


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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