The COVID-19 pandemic has certainly affected all of us over the past year; however, not all regions of the country have been impacted equally. The purpose of our project is to determine how well the impact of COVID-19 across the United States has been predicted by “social vulnerability,” which is defined by the CDC as “potential negative effects on communities caused by external stresses on human health.” The 2018 Social Vulnerability Index (SVI) predicts the level of assistance communities would need after experiencing external stresses.
In order to quantify the impact of COVID-19 at the county level, we utilized three different metrics to create an impact score: COVID-19 case rate, COVID-19 death rate, and the change in unemployment rate from Dec. 2019 to Dec. 2020. This constructed impact score was then compared to the SVI to determine how well social vulnerability predicted COVID-19 impact.
Through mapping our COVID-19 impact score to the SVI, we observed that the SVI captured the actual vulnerability well in some regions of the United States, such as parts of the Southwest, while it failed in other areas, such as Montana and the Dakotas. While counties with high SVI ranks did tend to have slightly larger impact scores, there appeared to be little correlation between the two values overall.
Going forward, our next step with this project will be to determine if we can develop a more precise COVID-19 impact score using additional metrics. We will then create our own model using current U.S. census data to more accurately predict COVID-19 impact in order to determine which counties truly seemed to be the most vulnerable. This experience has been relevant to our future careers as data analysts by allowing us to gain experience working with real-world data in a significant and meaningful field.
Student Authors: Coby Warkentin, Lydia Carter
Faculty Advisor: Dr. Thomas Fisher, Statistics
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