COVID-19 Pandemic Vulnerability

National Institute Of Environmental Health Sciences

Investigators

Linked publications & trials

Abstract

In a collaborative effort with investigators at North Carolina State University (Drs. David Reif, Skylar Marvel, Fred Wright, Yihui Zhou, and Kuncheng Song), and Texas A&M (Drs. Weihsueh Chiu and Ivan Rusyn), we have developed a dashboard related to the COVD-19 Pandemic. Defeating the COVID-19 pandemic requires well-informed, data-driven decisions at all levels of government, from federal and state agencies to county health departments. Numerous datasets are being collected in response to the pandemic, enabling the development of predictive models and interactive monitoring applications. However, this multitude of data streamsfrom disease incidence to personal mobility and comorbiditiesis overwhelming to navigate, difficult to integrate, and challenging to communicate. Synthesizing these disparate data is crucial for decision-makers, particularly at the state and local levels, to prioritize resources efficiently, identify and address key vulnerabilities, and evaluate and implement effective interventions. To address this situation, we developed a COVID-19 Pandemic Vulnerability Index (PVI) Dashboard (https://covid19pvi.niehs.nih.gov/) for interactive monitoring that features a county-level Scorecard to visualize key vulnerability drivers, historical trend data, and quantitative predictions to support decision making at a local level. We assembled U.S. county- and state-level datasets into 12 key indicators across four major domains: current infection rates (infection prevalence, rate of increase), baseline population concentration (daytime density/traffic, residential density), current interventions (social distancing, testing rates), and health and environmental vulnerabilities (susceptible populations, air pollution, age distribution, comorbidities, health disparities, and hospital beds). These 12 indicators (some of which combine multiple datasets) are integrated at the county level into an overall PVI score, employing methods previously used for geospatial prioritization and profiling. The individual data streams comprising these indicators measure either well-established, general vulnerability factors for public health disasters or emerging factors relevant to the COVID-19 pandemic. In developing the PVI, we performed rigorous statistical modeling of the underlying data to augment confidence in responsive actions, enable quantitative analysis and monitoring, and provide short-term predictions of cases and deaths. Our modeling efforts contextualize factors such as racial differences with corrections for socioeconomic factors, health resource allocation, and co-morbidities, plus highlighting place-based risks and resource deficits that might explain spatial distributions. Specifically, three types of modeling efforts were performed and are regularly updated. First, epidemiological modeling on cumulative case- and death-related outcomes provides insights into the epidemiology of the pandemic. Second, dynamic time-dependent modeling provides similar outcome estimates as national-level models, but with county-level resolution. Finally, a Bayesian machine learning approach provides data-driven, short-term forecasts. As the Dashboard has evolved, there are a growing number of use cases and collaborations. We are collaborating with the CDC to compare vulnerability indices, and with FEMA on tracking case positivity in major testing efforts. This project involves research on human coronavirus, novel coronavirus, COVID-19, Severe Acute Respiratory Syndrome coronavirus disease, SARS coronavirus, SARS-coronavirus-2, SARS-cov-2, SARS-cov2, SARS-related coronavirus 2, Severe acute respiratory syndrome coronavirus 2, SARS-Associated Coronavirus, SARS-cov, or SARS-Related Coronavirus.