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.
A data-driven spatio-temporal framework that integrates geospatial, ecological and climatic datasets to explain and forecast the dynamics of H5N1 outbreaks between 2021 and 2024 indicates that H5N1 transmission is structured by ecological drivers and local persistence mechanisms rather than purely seasonal effects.
Mehak Jindal, Samsung Lim, C. MacIntyre· The International Archives o...· 0 citations
This is the first mechanistic modeling study specifically aimed at investigating large resurgent H1N1 epidemics across multiple locations, and highlights the need for context-specific models and surveillance systems that account for the complex interplay among environmental drivers, population immunity, and viral mutat...
C. Kottegoda, C. Codeço, C. Struchiner et al.· Infectious Disease Modelling· 0 citations
The results showed that outbreaks expanded rapidly after 2019, reached a peak in September 2020, and were followed by smaller but recurring waves until 2023, highlighting the importance of continuous surveillance, timely outbreak investigation, and adaptable control strategies to support ASF prevention and control in t...
Samuel Joseph M. Castro, Roderick Salvador, R. Gundran et al.· Animals· 0 citations
A spatiotemporal mechanistic model of infectious disease dynamics developed for the HPAI Modelling Challenge and its implications for forecasting and policy in Australia are described.
M. Theng, Si-Min Lee, Michelle Wille et al.· bioRxiv· 0 citations
These data document a K‐clade‐predominant epidemic with persistent J.2.2.4 co‐circulation, within‐season HA‐NA reassortment, and marked HA1 divergence from the vaccine reference, against a background of fully susceptible antiviral genotypes.
Asif Naeem, Maymunah Hakami, Kadi Megbel Alanazi et al.· Journal of Medical Virology· 0 citations
The declining seasonal influenza positivity rates from 2019 to 2023 and the predominance of Influenza A and H3N2 highlight the need for sustained surveillance in Uganda.
M. Nankya, N. Owor, J. Kayiwa et al.· medRxiv· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.