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Utilizing virus genomic surveillance to predict vaccine effectiveness.
Jiye Kwon1,2, Ke Li1,2, Joshua L Warren2,3
1Department of Epidemiology of Microbial Diseases, Yale School of Public Health; New Haven, CT, USA.
Medrxiv : the Preprint Server for Health Sciences
|June 30, 2025
Summary
A new model predicts vaccine effectiveness (VE) against SARS-CoV-2 by measuring genetic distance. This helps determine timely vaccine updates for evolving viruses like JN.1, improving public health strategies.
Area of Science:
- Virology
- Immunology
- Genomic Epidemiology
Background:
- Developing updated vaccines for rapidly mutating viruses like SARS-CoV-2 presents challenges in decision-making.
- Real-time data is crucial for assessing vaccine effectiveness (VE) against emerging viral variants.
Purpose of the Study:
- To develop and apply a predictive model for informing near real-time vaccine updates.
- To quantify the relationship between genetic distance in SARS-CoV-2 and VE against mRNA vaccines.
Main Methods:
- A two-stage statistical approach was used, analyzing 10,156 SARS-CoV-2 genomes from Connecticut (April 2021-July 2024) and over one million controls.
- The model quantified the correlation between spike gene amino acid substitutions and VE.
- Genomic surveillance data was integrated to monitor population-level protection.
Main Results:
- A significant inverse correlation was found between spike gene amino acid distance and VE.
- Every 10 amino acid substitutions decreased VE by 15.4% (95% CrI: -2.0%, 34.6%).
- A predicted 43.4% (95% CrI: -5.7%, 90.1%) drop in VE was estimated for the 2023/24 vaccine due to JN.1 variants, based on sequence data alone.
Conclusions:
- The developed framework effectively links viral genetic changes to VE.
- This approach enables proactive monitoring of population immunity and informs timely vaccine strain selection.
- Genomic surveillance is a vital tool for optimizing vaccine strategies against evolving viruses.
