Abstract / Summary
Introduction: In temperate regions, influenza and respiratory syncytial virus (RSV) typically exhibit seasonal epidemics during the winter months, often concurrently with SARS-CoV-2 epidemics. Monitoring past and real-time trends in infection dynamics is important for informing public health situational awareness, including identifying and characterising unusual epidemic activity. However, data used to monitor these trends can exhibit substantial noise including strong day-of-the-week variation in reporting. In addition, case data for influenza can obscure distinct trends in influenza subtypes if not combined with virological data. Aim: By quantifying trends in respiratory virus activity using statistical methods, the Australia-Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) aims to provide government partners with comprehensive epidemic intelligence. Methods: Analyses were performed on daily case time series data for SARS-CoV-2, RSV and influenza across Australia's eight jurisdictions. Past and real-time trends were inferred for each pathogen and each influenza subtype using Bayesian p-spline models. Results: The 2025 Winter Situational Assessment Program provided weekly reports throughout the winter season and during the out-of-season influenza A H3N2 epidemic, driven by the emergence of the novel influenza subclade ('subclade K'). Despite limited subtyping data, our analysis provided an early signal of increasing influenza A H3N2 during a period in which overall influenza cases were decreasing; increases in influenza A H3N2 activity were first detected in July, and by mid-August, increasing H3N2 activity was estimated across all jurisdictions. Discussion: Our analysis highlights the benefits of incorporating statistical methods into real-time epidemic situational assessment and in consolidating reporting across jurisdictions.