Directional antigenic drift forecasts influenza vaccine effectiveness and guides strain selection

Abstract

Anticipating antigenic evolution is essential for selecting effective seasonal influenza A/H3N2 vaccine strains. To this end, we integrated data from multiple immunological assays spanning two decades 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 vaccine composition belonged to an earlier cluster than the dominant circulating cluster. We identified geometric features of antigenic space that accounted for three-quarters of the variation in vaccine effectiveness. The direction of each vaccine update relative to recent viral drift predicted effectiveness one season ahead out of sample. In every season, our analyses identified a virus that would have raised predicted effectiveness by 10 percentage points.