Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation
W. Zane Billings, Yang Ge, Amanda L. Skarlupka, Savannah L. Miller, Hayley Hemme, Murphy John, Natalie E. Dean, Sarah Cobey, Benjamin J. Cowling, Ye Shen, Ted M. Ross, Andreas Handel
Abstract
Influenza continuously evolves to escape population immunity, which makes formulating a vaccine challenging. Antigenic differences between vaccine strains and circulating strains can affect vaccine effectiveness (VE). Quantifying the antigenic difference between vaccine strains and circulating strains can aid interpretation of VE, and several antigenic distance metrics have been discussed in the literature. Here, we compare how the predicted breadth of vaccine-induced antibody response varies when different metrics are used to calculate antigenic distance.
Introduction
Influenza viruses constantly evolve over time. As host immunity induces selective pressure, new influenza strains accumulate mutations, a phenomenon called antigenic drift [1–6]. As mutations accumulate, antigenic drift leads to vaccine escape [7–9]. Seasonal influenza vaccines are formulated based on the strains that are expected to circulate, but imperfect matches occur between selected vaccine strains and circulating strains in some years, and vaccine effectiveness (VE) varies annually [10].
Methods
Study ethics
Study participants in the UGAFluVac study were enrolled into the study with written informed consent at their respective study site. The study procedures, informed consent, and data collection documents were previously reviewed by the University of Georgia Institutional Review Board (IRB), and by WCG IRB. We only used deidentified data from UGAFluVac, and our study was determined to be not human research and exempt from review by the University of Georgia IRB.
Results
Data description
Our dataset included 54,101 pairs of pre-vaccination and post-vaccination HAI titer measurements drawn from 677 individuals who contributed 1,163 person-years to the study across three different study sites. The contributions of paired measurements, person-years, and unique participants from each study site are shown in Table 1.
Discussion
We computed multiple antigenic distance metrics on the same set of influenza strains. Using immunogenicity data from a human cohort, we were able to compare cartographic data to sequence-based, biophysical, and temporal antigenic distance measures that have been used before for analyzing vaccine breadth.
Acknowledgments
We thank William Michael Landau (Eli Lilly and Company, Indianapolis, IN, USA) and Eric R. Scott (University of Arizona, Tucson, AZ, USA) for their generous help with computational issues and pipeline development. Additionally, we thank Michael A. Carlock (Cleveland Clinic Florida Research & Innovation Center, Port St. Lucie, FL, USA) for assistance with obtaining data. This study was supported in part by resources and technical expertise from the Georgia Advanced Computing Resource Center, a partnership between the University of Georgia’s Office of the Vice President for Research and Office of the Vice President for Information Technology.
Citation: Billings WZ, Ge Y, Skarlupka AL, Miller SL, Hemme H, John M, et al. (2025) Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation. PLoS Comput Biol 21(11): e1013720. https://doi.org/10.1371/journal.pcbi.1013720
Editor: Roger Dimitri Kouyos, University of Zurich, SWITZERLAND
Received: July 7, 2025; Accepted: November 7, 2025; Published: November 14, 2025
Copyright: © 2025 Billings et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Our dataset and code are archived on GitHub (https://github.com/ahgroup/billings-comp-agdist-public) and Zenodo (https://doi.org/10.5281/zenodo.15522148).
Funding: The following authors received partial funding for this work. NED received partial funding from NIH contract(s)/grant(s) R01-AI139761. TMR received partial funding from the Georgia Research Alliance as an Eminent Scholar. AH received partial funding from NIH contract(s)/grant(s) U01AI150747, R01AI170116, and 75N93019C00052. YS received partial funding from NIH contract(s)/grant(s) R35GM146612, R01AI170116, and 75N93019C00052. SC received partial funding from NIH contract(s)/grant(s) R01AI170116. All other authors declare no funding for this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: BJC has consulted for AstraZeneca, Fosun Pharma, GlaxoSmithKline, Haleon, Moderna, Novavax, Pfizer, Roche, and Sanofi Pasteur. None of these companies were involved in the formulation of the study or the decision to publish or conduct the study. All other authors declare no potential conflicts of interest.
