Details for PVI Maps

Details of Current Models

The specific datasets (“components”) comprising the two current models are given below for each of model 11.2 and 12.4. The main difference between 11.2 and 12.4 is the inclusion of vaccine data (and subsequent re-weighting) in model 12.4. In each model, each component was assigned to an indicator (“data slice”) as part of four major domains: Infection Rate, Population Concentration, Intervention Measures, and Health & Environment. The expanded tables include 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.

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 cumulative death and case outcomes, we assessed the rank-correlation between overall PVI and the key vulnerability-related outcome metrics of Cumulative Deaths (Figure 1A/B) and Cumulative Cases (Figure 1C/D). The Spearman Rho values for PVI (from Jan 13 through May 16) 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 for each time horizon are displayed in blue text.

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.

Figure 1. Spearman correlation (Rho) estimates of daily PVI (by county) vs. Cumulative Deaths (A), Population Adjusted Cumulative Deaths (B), and % Deaths/Cases (C) for 1, 7, 14, 21, or 28 days into the future. Mean Rho values for each time horizon are displayed in blue.

Are You Ready to Get Started?

Visit the COVID-19 Pandemic Vulnerability Index (PVI) Dashboard