Utilizing virus genomic surveillance to predict vaccine effectiveness

Area of Science:

• Virology
• Immunology
• Genomics

Background:

• mRNA vaccines for SARS-CoV-2 have undergone multiple updates since 2020 to target evolving variants.
• Genomic surveillance aids in understanding pathogen diversity but has limitations in evaluating vaccine effectiveness against new strains.
• A need exists for a framework to predict vaccine protection levels based on genomic data and emerging variants.

Purpose of the Study:

• To characterize the relationship between vaccine effectiveness (VE) and sequence-based genetic distance.
• To establish a framework for predicting near real-time changes in vaccine protection using virus surveillance data.
• To inform future vaccine updates by leveraging genomic sequences and pathogen features.

Main Methods:

• Analysis of 10,156 whole genome sequences of SARS-CoV-2 cases in Connecticut (April 2021-July 2024).
• Assessment of genetic distance (amino acid substitutions in the spike gene) correlation with VE.
• Development of a Bayesian time-varying model using over 1 million test-negative controls to assess weekly VE, adjusted for demographic and vaccination factors.
• Meta-regression to explore the VE-amino acid distance relationship over time and predict protection against emerging variants.

Main Results:

• A negative correlation was observed between spike gene amino acid distance and VE over time.
• Increased amino acid distance correlated with sharp VE declines during variant emergence and gradual declines with within-variant changes.
• Each 10 amino acid increase in spike gene distance predicted a 15.4% reduction in VE.
• For the 2023/24 vaccine, a rise in spike distance from 12.25 to 30.23 predicted a 43.4% drop in VE based on sequence data alone.

Conclusions:

• A framework was developed to quantify the impact of new SARS-CoV-2 variants on VE.
• The study leverages spike amino acid distance to inform future vaccine updates using genomic surveillance data.
• This framework can serve as a near-real-time surveillance tool for inferring population-level protection and guiding vaccine update decisions.