Utilizing virus genomic surveillance to predict vaccine effectiveness

As new vaccines are being developed for fast-evolving viruses, determining when and how to update them, and what data should inform these decisions, remains a significant challenge. We developed a model to inform these vaccine updates in near real-time and applied it to SARS-CoV-2 by quantifying the relationship between vaccine effectiveness (VE) and genetic distance from mRNA vaccine formulation sequences using 10,156 genomes from Connecticut (April 2021-July 2024) and data from over one million controls, employing a two-stage statistical approach. We showed a strong inverse correlation between spike gene amino acid distance and VE; every 10 amino acid substitutions away from the vaccine sequences resulted in a 15.4% (95% credible intervals (CrI): -2.0%, 34.6%) reduction in VE. Notably, this framework allows us to quantify the anticipated impact of emerging variants on VE, as demonstrated by the predicted 43.4% (95% CrI: -5.7%, 90.1%) drop in VE for the 2023/24 vaccine following the emergence of JN.1 variants based on sequence data alone. By linking amino acid substitutions to VE, this approach leverages genomic surveillance to monitor population-level protection and inform timely vaccine updates. Structured Abstract Background and aimsSince the development of the first vaccines targeting the original SARS-CoV-2 virus sequence in 2020, mRNA-based vaccines have been updated three times: targeting Omicron BA.4/BA.5 in 2022, the XBB lineage in 2023, and the KP.2 variant in 2024. While genomic surveillance has advanced our understanding of pathogen diversity, gaps remain in incorporating genomic information to evaluate vaccine effectiveness (VE) against emerging variants. This study aims to characterize the relationship between VE and sequence-based genetic distance, to establish a framework for predicting near real-time changes in the level of vaccine protection from virus surveillance data. MethodsWe analyzed 10,156 whole genome sequences of SARS-CoV-2 cases from Connecticut, USA, between April 2021 to July 2024. We first assessed how genetic distance, specifically the number of amino acid substitutions in the spike gene between COVID-19 case sequences and the mRNA vaccine formulation sequence(s), correlates with vaccine protection levels. Incorporating data from over 1 million test-negative controls, we developed a Bayesian time-varying model with autoregressive terms to assess VE at a weekly level. The analysis was adjusted for ZIP-code-level income, age, sex, and prior vaccine doses received. We then employed a random effects meta-regression to explore the relationship between VE and amino acid distance over time. Finally, we used the meta-regression model to estimate potential vaccine protection against emerging variants. ResultsWe found that spike gene amino acid distance showed a strong negative correlation with VE over time. Stepwise increases in amino acid distance, aligned with sharp VE declines during variant emergence, while accumulation of within-variant changes was also associated with gradual VE decline. Each 10 amino acid increase in distance in the spike gene corresponds to a predicted 15.4% (95% Credible intervals (CrI): -2.0%, 34.6%) reduction in VE. For the 2023/24 updated vaccine, spike distance rose from 12.25 to 30.23, predicting a 43.4% (95% CrI: -5.7%, 90.1%) drop in VE using sequence information alone. ConclusionOur framework quantifies how the emergence of new variants is expected to affect VE for SARS-CoV-2. By quantifying the relationship between amino acid substitutions and time-varying VE, we leverage intrinsic pathogen features, such as spike amino acid distance, to inform future vaccine updates using genomic sequences. As genomic surveillance data becomes more widely available across pathogens, this framework can serve as a near-real time surveillance tool to infer population-level protection and offers valuable insights for vaccine update decisions.