Geometry of antigenic evolution improves influenza vaccine selection

Anticipating antigenic evolution is essential for selecting effective seasonal influenza A/H3N2 vaccine strains. To this end, we integrated hemagglutination-inhibition and neutralization titers spanning 2002 to 2025 into a unified Bayesian antigenic map. The map resolves twelve antigenic clusters advancing in discrete steps, with several clusters co-circulating in most seasons. In 15 of 21 seasons, the WHO-recommended vaccine belonged to an earlier cluster than the dominant circulating cluster. The direction of each vaccine update relative to recent viral drift predicted vaccine effectiveness one season ahead in out-of-sample forecasts. Antigenic distance, the conventional measure of vaccine-virus match, was weakly associated with effectiveness until update direction was accounted for. Retrospectively ranking candidate strains by predicted effectiveness would have selected a strain predicted to outperform the WHO recommendation in every season, raising mean predicted effectiveness by 10 percentage points.