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Retrospective analysis of equity-based optimization for COVID-19 vaccine allocation
Erin Stafford1, Dobromir Dimitrov1,2, Rachel Ceballos3,4
1Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
PNAS Nexus
|September 11, 2023
Summary
COVID-19 vaccine allocation strategies reveal a trade-off between reducing disease burden and minimizing racial inequities. Increasing vaccine supply is key to optimizing both, especially for marginalized communities.
Area of Science:
- Epidemiology
- Mathematical Modeling
- Public Health Policy
Background:
- Marginalized racial and ethnic groups experienced disproportionate COVID-19 impacts in the US.
- Understanding and addressing these health disparities is crucial for effective public health interventions.
Purpose of the Study:
- To develop and analyze race-conscious mathematical models for SARS-CoV-2 transmission.
- To evaluate counterfactual COVID-19 vaccination strategies aimed at minimizing disease burden and racial inequity.
Main Methods:
- Constructed an age- and race-stratified mathematical model of SARS-CoV-2 transmission.
- Fitted the model to 2020 Oregon data, stratifying by non-Hispanic White persons and BIPOC groups.
- Analyzed hypothetical vaccination strategies in early 2021 with limited vaccine supply.
Main Results:
- A trade-off exists between minimizing overall disease burden and racial inequity with low vaccine coverage (10%).
- Prioritizing disease burden favored older groups; prioritizing equity favored younger BIPOC groups.
- Increased vaccine supply (20-30% coverage) significantly lessened the trade-off, enabling optimization of both equity and mortality.
Conclusions:
- Mathematical modeling provides a framework for quantifying and minimizing racial inequity in public health interventions.
- Vaccine allocation strategies must consider both disease burden and equity, with supply being a critical factor.
- A race-conscious approach is essential for addressing health disparities in current and future pandemics.
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