tetano
Editor, Senior Moderator
Commun Health
. 2026;1(1):16.
doi: 10.1038/s44528-026-00016-3. Epub 2026 Aug 5.
Influenza A/H3N2 epidemiology in England during the 2025 to 2026 season: a mathematical modelling study
James A Hay[SUP] 1 [/SUP], Punya Alahakoon[SUP] #[/SUP][SUP] 1 [/SUP], Alexander Greenshields-Watson[SUP] #[/SUP][SUP] 1 [/SUP], Michelle Kendall[SUP] 1 [/SUP], Mahan Ghafari[SUP] 1 2 [/SUP], Chris Wymant[SUP] 1 [/SUP], Robert Hinch[SUP] 1 [/SUP], Luca Ferretti[SUP] 1 [/SUP], Jasmina Panovska-Griffiths[SUP] 1 3 4 [/SUP], Christophe Fraser[SUP] 1 [/SUP]
Affiliations
Background: 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.
Methods: 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.
Results: 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 R [SUB]0[/SUB] , or an earlier seed date, though it is not possible to distinguish the relative importance of these mechanisms from these data alone.
Conclusions: 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.
Keywords: Computational biology and bioinformatics; Diseases; Immunology; Microbiology.
. 2026;1(1):16.
doi: 10.1038/s44528-026-00016-3. Epub 2026 Aug 5.
Influenza A/H3N2 epidemiology in England during the 2025 to 2026 season: a mathematical modelling study
James A Hay[SUP] 1 [/SUP], Punya Alahakoon[SUP] #[/SUP][SUP] 1 [/SUP], Alexander Greenshields-Watson[SUP] #[/SUP][SUP] 1 [/SUP], Michelle Kendall[SUP] 1 [/SUP], Mahan Ghafari[SUP] 1 2 [/SUP], Chris Wymant[SUP] 1 [/SUP], Robert Hinch[SUP] 1 [/SUP], Luca Ferretti[SUP] 1 [/SUP], Jasmina Panovska-Griffiths[SUP] 1 3 4 [/SUP], Christophe Fraser[SUP] 1 [/SUP]
Affiliations
- PMID: 42564072
- PMCID: PMC13441895
- DOI: 10.1038/s44528-026-00016-3
Background: 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.
Methods: 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.
Results: 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 R [SUB]0[/SUB] , or an earlier seed date, though it is not possible to distinguish the relative importance of these mechanisms from these data alone.
Conclusions: 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.
Keywords: Computational biology and bioinformatics; Diseases; Immunology; Microbiology.