Ensemble forecasting of influenza activity and assessing its year-round dynamical characteristics during and post-COVID-19 pandemic periods in a sub-tropical location

Influenza forecasting in (sub-)tropical regions remains understudied due to year-round, irregular transmission patterns. Further, the variation in seasonality and transmission characteristic of influenza in post-COVID-19 pandemic could be attributed to various drivers to quantify for better understanding. To address this issue, this study introduced an ensemble forecasting approach that incorporates varied dataset lengths to forecast influenza activity in Hong Kong, integrating multi-stream surveillance data, including absolute humidity, temperature, ozone, and school closures/holidays. We applied temporal cross-validation to evaluate forecasting performance for short- and long term separately across different training-sets and model variants, ultimately constructing ensemble forecasts weighted by individual model performance. The optimal ensemble model could forecast the 2019/20 winter influenza season onwards and evaluate the impact of COVID-19 public health and social measures (PHSMs). We further extended the framework to forecast influenza in post-pandemic period since March 2023, accounting for the impact of cessation of PHSMs and COVID-19-induced cross-protection/competition in population susceptibility. Forecasts showed two peaks in 2019/20 season, which could account for 95.2% (95% prediction interval (PI): 89.1%, 98.3%) reduction in attack rate for COVID-19 PHSMs. The post-pandemic forecasts indicated changes in influenza transmission dynamics and seasonality, highlighting the need to consider factors such as population immunity and co-circulation with COVID-19 in future influenza forecasts. This study emphasizes the importance of incorporating diverse factors for better influenza forecasts in (sub-)tropical regions. The proposed framework offers a scalable tool for forecasting other respiratory virus transmissions, supporting healthcare agencies in managing future infection burdens and enhancing preparedness. Author summaryReliable and proactive forecasts of influenza activity and timing of epidemic outcomes enable public health officials to plan targeted responses. However, unlike temperate locations, the irregular seasonality of influenza in tropical/subtropical locations leads to highly variable forecasting patterns when models use varying lengths of historical data, reducing the robustness of forecasts. By leveraging multi-stream surveillance data in Hong Kong, we developed a mechanistic model-based ensemble forecasting framework that integrate potential combinations of data and models for short-, medium-, and long-term forecasts of influenza outcomes. Beyond methodological advancement, this framework has broader implications in assessing the impact of COVID-19-related interventions on influenza dynamics during pandemic and evaluating potential co-circulation risk of respiratory viruses including influenza and COVID-19 in post-pandemic era.