A Data Driven Approach for Prioritizing COVID-19 Vaccinations in the Midwestern United States

Area of Science:

• Public Health
• Geographic Information Systems (GIS)
• Epidemiology

Background:

• Social Vulnerability Indices (SVI) are increasingly used to guide COVID-19 vaccine prioritization.
• Assessing the alignment of SVI with actual pandemic outcomes, like mortality, is crucial for effective resource allocation.

Purpose of the Study:

• To evaluate the effectiveness of standard Social Vulnerability Indices in prioritizing COVID-19 vaccinations.
• To propose an improved methodology for vaccine distribution that accounts for both vulnerability and mortality.

Main Methods:

• Developed a methodology to assess and derive vulnerability indices, treating them as classifiers.
• Applied this methodology to analyze SVI rankings against COVID-19 mortality rates in Midwestern states.

Main Results:

• Standard SVI rankings risk prioritizing low-mortality areas while deprioritizing high-mortality regions.
• This highlights a potential mismatch between SVI-based prioritization and actual pandemic impact.

Conclusions:

• A two-dimensional approach is proposed to optimize vaccine distribution.
• This method aims to equitably address both high-vulnerability areas and those severely affected by COVID-19 mortality.