The COVID-19 Pandemic Vulnerability Index (PVI) Dashboard: Monitoring County-Level Vulnerability Using Visualization, Statistical Modeling, and Machine Learning

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From: Environmental Health Perspectives(Vol. 129, Issue 1)
Publisher: National Institute of Environmental Health Sciences
Document Type: Report
Length: 1,345 words
Lexile Measure: 1700L

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Introduction

Expert groups have coalesced around a roadmap to address the current COVID-19 pandemic centered on social distancing, monitoring case counts and health care capacity, and, eventually, moving to pharmaceutical interventions. However, responsibility for navigating the pandemic response falls largely on state and local officials. To make equitable decisions on allocating resources, caring for vulnerable subpopulations, and implementing local- and state-level interventions, access to current pandemic data and key vulnerabilities at the community level are essential (National Academies of Sciences, Engineering, and Medicine 2020). Although numerous predictive models and interactive monitoring applications have been developed using pandemicrelated data sets (Wynants et al. 2020), their capacity to aid in dynamic, community-level decision-making is limited. We developed the interactive COVID-19 Pandemic Vulnerability Index (PVI) Dashboard (https://covid19pvi.niehs.nih.gov/) to address this need by presenting a visual synthesis of dynamic information at the county level to monitor disease trajectories, communicate local vulnerabilities, forecast key outcomes, and guide informed responses (Figure 1).

Methods

The current PVI model integrates multiple data streams into an overall score derived from 12 key indicators--including wellestablished, general vulnerability factors for public health, plus emerging factors relevant to the pandemic--distributed across four domains: current infection rates, baseline population concentration, current interventions, and health and environmental vulnerabilities. The PVI profiles translate numerical results into visual representations, with each vulnerability factor represented as a component slice of a radar chart (Figure 2). The PVI profile for each county is calculated using the Toxicological Prioritization Index (ToxPi) framework for data integration within a geospatial context (Marvel et al. 2018; Bhandari et al. 2020). Data sources in the current model (version 11.2.1) include the Social Vulnerability Index (SVI) of the Centers for Disease Control and Prevention (CDC) for emergency response and hazard mitigation planning (Horney et al. 2017), testing rates from the COVID Tracking Project (Atlantic Monthly Group 2020), social distancing metrics from mobile device data (https://www. unacast.com/covid19/social-distancing-scoreboard), and dynamic measures of disease spread and case numbers (https://usafacts.org/issues/coronavirus/). Methodological details concerning the integration of data streams--plus the complete, daily time series of...

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Gale Document Number: GALE|A649251146