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
Influenza is a highly transmissible respiratory virus that can cause devastating pandemics. The 2009 influenza pandemic, caused by a strain of A(H1N1) virus, resulted in millions of cases and hundreds of thousands of deaths worldwide. In the following years, many regions experienced waves driven by environmental conditions, waning immunity, and other factors. More dramatically, several locations saw unpredicted large resurgent outbreaks, yet the mechanisms behind these severe resurgences remain poorly understood. Despite an extensive literature of epidemic models aimed at explaining and predicting H1N1 dynamics, mathematical frameworks specifically designed to address why these large outbreaks recur years after an initial epidemic are still lacking. Here we address this gap by proposing a mechanistic model with time-varying rates of infection, loss of immunity, and vaccination. In particular, we explore two mathematical functions to model dynamic loss of immunity, capturing how viral evolution may reduce the duration of immune protection. Numerical simulations show that the model reproduces key features of H1N1 case data, and that delayed resurgence is associated with longer immunity periods and higher vaccine efficacy. We also fit the model to data from Brazil, Turkey, Iran, New Zealand, the United States, Colombia, South Africa, and Croatia, demonstrating its ability to capture resurgence patterns. Fitted infection rates exhibited a wide range of temporal profiles, from regular annual cycles to higher-frequency oscillations and gradual changes, suggesting that transmission is driven by factors beyond seasonal influences. The model also inferred dynamic loss-of-immunity rates, representing different effects of antigenic drift. To our knowledge, this is the first mechanistic modeling study specifically aimed at investigating large resurgent H1N1 epidemics across multiple locations. Our findings highlight the need for context-specific models and surveillance systems that account for the complex interplay among environmental drivers, population immunity, and viral mutation driving H1N1 dynamics.
C. Kottegoda, C. Codeço, C. Struchiner et al.· Infectious Disease Modelling· 0 citations
Simple Summary African swine fever (ASF) continues to pose a serious challenge to pig production in the Philippines. This study provides a comprehensive characterization of ASF epidemic dynamics and examines how ASF spread over time and across regions using national laboratory-confirmed surveillance data and complementary temporal analytical methods. 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. Disease occurrence varied across regions, with earlier activity observed in Luzon and later outbreaks reported in the Visayas and Mindanao. ASF cases were more frequently detected during the later months of the year, particularly from September to November. Over time, the pattern of transmission appeared to shift from large, synchronized outbreaks to smaller, more localized and persistent disease activity. These findings highlight the importance of continuous surveillance, timely outbreak investigation, and adaptable control strategies to support ASF prevention and control in the Philippines.
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
Background: Seasonal influenza, commonly known as flu, is an acute respiratory, highly contagious illness caused by influenza viruses. A clear understanding of influenza seasonality is crucial for guiding prevention and treatment strategies, including decisions on vaccination timing to prevent outbreaks. While well documented in temperate regions, data on influenza epidemiology in tropical areas, particularly sub-Saharan Africa, remain limited. We described the types, subtypes and positivity rate of seasonal influenza in Uganda during 2019-2023. Methods: We abstracted data from the National Influenza database on positive seasonal influenza cases confirmed by Polymerase Chain Reaction. The cases were disaggregated by age group, sex, region, month and year of reporting. Using Microsoft excel, we calculated the influenza positivity rate and disaggregated it by strain, sex, age, region and time. Test positivity rate was computed as the number of positive cases as a percentage of the total samples tested. Results: Among 17,957 individuals tested, the overall positivity rate for seasonal influenza was 5% (936 cases). Positivity was higher among males compared to females (7% vs. 4%), with children aged 5-9 years having the highest positivity rate (16%), while individuals aged 50-54 years had the lowest (1%). The median positivity rate was 4%, with a range of 1-16%. Regionally, the central region reported a positivity rate of 5%, with rates across all regions ranging from 5% to 8%. Over time, there was a gradual decline in positivity rates, decreasing from 16.5% in 2019 to 5.3% in 2023. Seasonal influenza exhibited bimodal peaks, with the primary peak occurring between March and May and a secondary peak from October to December. Influenza A was the predominant strain, accounting for 70% of seasonal influenza cases (669/936). Among the Influenza A subtypes, H3N2 was most common, representing 63% of cases (425/669). Conclusions: 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. Given Influenza A's high genetic variability and potential for novel strain emergence, monitoring circulating strains, informing vaccine development, and implementing targeted interventions for high-risk groups and regions are critical to controlling and preventing outbreaks.
M. Nankya, N. Owor, J. Kayiwa et al.· medRxiv· 0 citations
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