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Influenza A/H3N2 epidemiology in England during the 2025 to 2026 season: a mathematical modelling study

Aug 2026 · Communications Health · Vol 1 · 0 citations · 52 references
Medicine

TL;DR

The 2025/26 influenza season is characterised by early but not unusually rapid growth, and Laboratory evidence for antibody escape does not directly translate to large reductions in population immunity, supporting the need for complementary real-time epidemiological analyses and modelling.

Abstract

England experienced an unusually early and rapid increase in influenza A/H3N2 subclade K infections in 2025/26. Antigenic change and a fast selective sweep raised concerns over a potentially severe season. Building on analysis conducted as the subclade emerged, we aim to compare epidemic dynamics of the 2025/26 season to previous years and to model plausible epidemiological scenarios. We compared peak epidemic growth rates and reproduction numbers across influenza seasons from 2011/12 to 2025/26 using routine surveillance data in England. Weekly epidemic growth rates were estimated using a Gaussian random walk model, and time-varying reproduction numbers using EpiEstim. We also developed an age-stratified transmission model and interactive web tool to explore scenarios varying immune escape, transmissibility, and seed date, using 2022/23 as a baseline season. Peak A/H3N2 growth rates and time-varying reproduction numbers for the 2025/26 season are of similar magnitude but earlier than previous severe seasons. Scenario analyses suggest early trends are compatible with moderate levels of immune escape, a 10% higher R0, or an earlier seed date, though it is not possible to distinguish the relative importance of these mechanisms from these data alone. The 2025/26 influenza season is characterised by early but not unusually rapid growth. Earlier growth does not systematically lead to especially large epidemics due to earlier susceptible depletion combined with a dampening effect from school holidays. Laboratory evidence for antibody escape does not directly translate to large reductions in population immunity, supporting the need for complementary real-time epidemiological analyses and modelling.

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