Patterns of Social Vulnerability -- An Interactive Dashboard to Explore Risks to Public Health on the US County Level

TL;DR

This paper identifies patterns of social vulnerability across US counties and introduces an interactive dashboard to explore these patterns, aiding policymakers in assessing risks and resilience related to public health emergencies like COVID-19.

Contribution

It presents a novel methodology for grouping US counties based on social vulnerability patterns and offers an interactive tool for exploration and analysis.

Findings

Identified distinct social vulnerability patterns with high predictive power for COVID-19 impact.

Demonstrated the dashboard's utility in analyzing community resilience and risk.

Showed that certain vulnerability combinations correlate strongly with pandemic outcomes.

Abstract

Social vulnerability is the susceptibility of a community to be adversely impacted by natural hazards and public health emergencies, such as drought, earthquakes, flooding, virus outbreaks, and the like. Climate change is at the root of many recent natural hazards while the COVID-19 pandemic is still an active threat. Social vulnerability also refers to resilience, or the ability to recover from such adverse events. To gauge the many aspects of social vulnerability the US Center of Disease Control (CDC) has subdivided social vulnerabilities into distinct themes, such as socioeconomic status, household composition, and others. Knowing a community's social vulnerabilities can help policymakers and responders to recognize risks to community health, prepare for possible hazards, or recover from disasters. In this paper we study social vulnerabilities on the US county level and present research that suggests that there are certain combinations, or patterns, of social vulnerability indicators into which US counties can be grouped. We then present an interactive dashboard that allows analysts to explore these patterns in various ways. We demonstrate our methodology using COVID-19 death rate as the hazard and show that the patterns we identified have high predictive capabilities of the pandemic's local impact.