{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:58:21Z","timestamp":1788742701651,"version":"build-2803163510"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1013720","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000}}],"reference-count":132,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01-AI139761"],"award-info":[{"award-number":["R01-AI139761"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008065","name":"Georgia Research Alliance","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008065","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01AI150747, R01AI170116, and 75N93019C00052."],"award-info":[{"award-number":["U01AI150747, R01AI170116, and 75N93019C00052."]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM146612, R01AI170116, and 75N93019C00052"],"award-info":[{"award-number":["R35GM146612, R01AI170116, and 75N93019C00052"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analyzed data from a seasonal influenza vaccine cohort that collected serum samples from 2013\/14 \u2013 2017\/18 at three study sites. The data include pre- and post-vaccination HAI titers to the vaccine strains and a panel of heterologous strains. We used that data to calculate four different antigenic distance measures between assay strains and vaccine strains: difference in year of isolation (temporal),\n                      <jats:italic>p<\/jats:italic>\n                      -Epitope (sequence), Grantham\u2019s distance (biophysical), and antigenic cartography distance (serological). We analyzed agreement between the four metrics using Spearman\u2019s correlation and intraclass correlation. We then fit Bayesian generalized additive mixed-effects models to predict the effect of antigenic distance on post-vaccination titer after controlling for confounders and analyzed the pairwise difference in predictions between metrics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The four antigenic distance metrics had low or moderate correlation for influenza subtypes A(H1N1), B\/Victoria, and B\/Yamagata. A(H3N2) distances were highly correlated. We found that after accounting for pre-vaccination titer, study site, and repeated measurements across individuals, the predicted post-vaccination titers conditional on antigenic distance and subtype were nearly identical across antigenic distance metrics, with A(H3N2) showing the only notable deviation between metrics, despite higher agreement for that subtype.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Despite moderate correlation among metrics, we found that different antigenic distance metrics generated similar predictions about breadth of vaccine response. Costly titer assays for antigenic cartography may not be needed when simpler sequence-based metrics suffice for quantifying vaccine breadth.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pcbi.1013720","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T18:45:17Z","timestamp":1763145917000},"page":"e1013720","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Different antigenic distance metrics generate similar predictions of influenza vaccine response breadth despite moderate correlation"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0184-6134","authenticated-orcid":true,"given":"W. 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