COVID-19 PVI Dashboard
You can find the source data for the application at the COVID-19 PVI Data GitHub page
COVID-19 Pandemic Vulnerability Index (PVI)
Details of Current Model (11.2)
The specific datasets (“components”) comprising the current PVI model are given in Table 1. Each component was assigned to an indicator (“data slice”) as part of four major domains: Infection Rate, Population Concentration, Intervention Measures, and Health & Environment. Table 1 includes a description of each component, the rationale for its inclusion, and a link to the public data source. Details of statistical methods used to build and assess the PVI are given below.
|Data Domain (% weight)
Data Slice (% weight)
|Infection Rate (24%)|
|Transmissible Cases (20%)|
|Daily||Population size divided by cases from the last 14 days. Because of the 14-day incubation period, the cases identified in that time period are the most likely to be transmissible. This metric is the number of such “contagious” individuals relative to the population, so a greater number indicates more likely continued spread of disease.||USA Facts|
|Disease Spread (4%)|
|Daily||Fraction of total cases that are from the last 14 days (one incubation period). Because COVID-19 is thought to have an incubation period of about 14 days, only a sustained decline in new infections over 2 weeks is sufficient to signal reduction in disease spread. This metric is always between 0 and 1, with values near 1 during exponential growth phase, and declining linearly to zero over 14 days if there are no new infections.||USA Facts|
|Population Concentration (16%)|
|Population Mobility (8%)|
|Daytime Population Density||Static||Estimated daytime population. Greater daytime population density is expected to increase the spread of infection because more people are in closer proximity to each other.||The field “DPOPDENSCY” (2019 Daytime Pop Density) from ESRI demographics analysis of American Community Survey data
2018 CDC Social Vulnerability Index (adjunct variable)
|Baseline Traffic||Static||Average traffic volume per meter of major roadways in the county from 2018 EPA EJSCREEN. Greater traffic volume is expected to increase the spread of infection due to more people moving and interaction.||2020 County Health Rankings|
|Residential Density (8%)|
|Residential Density||Static||Integrates data from the 2014-2018 ACS on families in multi-unit structures, mobile homes, over-crowding (more people than rooms), being without a vehicle, and persons in institutionalized group quarters. All of these variables are associated with greater residential density, which is expected to increase the spread of infection because more people are in closer proximity to each other.||2018 CDC Social Vulnerability Index (SVI Housing Type & Transportation Theme)|
|Intervention Measures (16%)|
|Social Distancing (8%)|
|Daily||Unacast social distancing scoreboard grade is assigned by looking at the change in overall distance travelled and the change in nonessential visits relative to baseline (previous year), based on cell phone mobility data. The grade is converted to a numerical score, with higher values being less social distancing (worse score) is expected to increase the spread of infection because more people are interacting with other.||Unacast|
|Daily||Population divided by tests performed (currently only state-wide statistics are available). This is the inverse of the tests per population, so greater numbers indicate less testing. Lower testing rates mean it is more likely that infections are undetected, so would be expected to increase the spread of infection.||The COVID tracking project|
|Health & Environment (44%)|
|Population Demographics (8%)|
|% Black||Static||Percentage of population who self-identify as Black or African American.||2018 Census Population Estimates from CHR (County Health Rankings and Roadmaps)|
|% Native||Static||Percentage of population who self-identify as American Indian or Alaska Native.||2018 Census Population Estimates from CHR (County Health Rankings and Roadmaps)|
|Air Pollution (8%)|
|Static||Average daily density of fine particulate matter in micrograms per cubic meter (PM2.5) from 2014 Environmental Public Health Tracking Network. Air pollution has been associated with more severe outcomes from COVID-19 infection.||Air Pollution-Particulate Matter from CHR (County Health Rankings and Roadmaps)|
|Age Distribution (8%)|
|% age 65 and over||Static||Aged 65 or Older from 2014-2018 ACS. Older ages have been associated with more severe outcomes from COVID-19 infection.||2018 CDC Social Vulnerability Index|
|Premature death||Static||Years of potential life lost before age 75 per 100,000 population (age-adjusted) based on 2016-2018 National Center for Health Statistics - Mortality Files. This is a broad measure of health, and a proxy for cardiovascular and pulmonary diseases that have been associated with more severe outcomes from COVID-19 infection.||2020 County Health Rankings|
|Smoking||Static||Percentage of adults who are current smokers from 2017 Behavioral Risk Factor Surveillance System. Smoking has been associated with more severe outcomes from COVID-19 infection, and also causes cardiovascular and pulmonary disease.||2020 County Health Rankings|
|Diabetes||Static||Percentage of adults aged 20 and above with diagnosed diabetes from 2016 United States Diabetes Surveillance System. Diabetes has been associated with more severe outcomes from COVID-19 infection.||2020 County Health Rankings|
|Obesity||Static||Percentage of the adult population (age 20 and older) that reports a body mass index (BMI) greater than or equal to 30 kg/m2. Obesity has been associated with more severe outcomes from COVID-19 infection.||2020 County Health Rankings|
|Health Disparities (8%)|
|Uninsured||Static||Percentage uninsured in the total civilian noninstitutionalized population estimate, 2014- 2018 ACS. Individuals without insurance are more likely to be undercounted in infection statistics, and may have more severe outcomes due to lack of treatment.||2018 CDC Social Vulnerability Index (adjunct variable)|
|SVI Socioeconomic Status||Static||Integrates data from 2014-2018 ACS on percent below poverty, percent unemployed (historical), income, and percent without a high school diploma. Lower SES are more likely to be undercounted in infection statistics, and may have more severe outcomes due to lack of treatment.||2018 CDC Social Vulnerability Index (SVI Socioeconomic Status score)|
|Hospital Beds (4%)|
|Static||Summation of hospital beds for hospitals with “OPEN” status and “GENERAL MEDIAL AND SURGICAL” description.||Homeland Infrastructure Foundation-Level Data (HIFLD)|
The time series of all data underlying our current model are available at github.com/COVID19PVI/data. The software used to generate PVI scores and profiles from these data is freely-available at toxpi.org.
The summarization and communication goals of the PVI and the corresponding scorecards are human-centric and designed to convey and distill high-dimensional complex data. As such, a combination of quantitative modeling and prior knowledge on risk drivers were used to inform apportionment of data into slices.
In order to gauge the association of daily PVI versus observed death outcomes, we assessed the rank-correlation between overall PVI and the key vulnerability-related outcome metrics of Cumulative Deaths (Figure 1A), Population Adjusted Cumulative Deaths (Figure 1B), and the Deaths from Cases Percentage (Figure 1C). The Spearman Rho values for PVI (from March 15 through June 28) versus outcomes 1, 7, 14, 21, and 28 days ahead of a given day are displayed. All daily rank-correlation estimates were highly significant (all p-values < 1e-14). The mean Rho values typically increase with a longer time horizon (blue text on Figures 1A,1B,1C).
While our general PVI model communicates an integrated concept of vulnerability, purpose-built forecasting models are used to predict case and death outcomes. Predictive modeling for both COVID-19 cases and mortality presents several unique challenges. Unlike other airborne viruses (e.g., influenza), testing methods and best medical practices for this contagion are quickly evolving. Tests were initially limited to those presenting severe symptoms, testing practices differed by geographic region, and testing has recently become much more widely available. As a result, detected cases per population and deaths per case are difficult to compare across time or region. To accurately predict future cases and mortality, it is necessary to account for the fluid nature of the data.
Accordingly, we developed a Bayesian spatiotemporal random-effects model that jointly describes the log-observed and log-death counts to build local forecasts. Log-observed cases for a given day are predicted using known covariates (e.g., population density, social distancing metrics), a spatiotemporal random-effect smoothing component, and the time-weighted average number of cases for these counts. This smoothed time-weighted average is related to a Euler approximation of a differential equation; it provides modeling flexibility while approximating potential mechanistic models of disease spread. The smoothed case estimates are used in a similar spatiotemporal model predicting future log-death counts based on a geometric mean estimate of the estimated number of observed cases for the previous seven days as well as the other data streams. The resulting county level predictions and corresponding confidence intervals are shown.
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