Seasonal Influenza Transmission Patterns and Predictive Modeling

Abstract

Seasonal influenza is a recurring public-health burden that peaks in winter and tests outpatient and hospital capacity. Accurately predicting its seasonality and near-term activity is crucial to preventing avoidable hospitalisations and activating and timing season-appropriate preparedness actions. This article distils salient evidence on environmental drivers and data streams for multi-faceted surveillance and operational prediction, as well as modelling families that complement the latter. It overviews laboratory and epidemiologic evidence that absolute humidity modulates viral survival and transmissibility, thus partly explaining influenza’s robust winter seasonality in temperate climates. It then discusses surveillance systems ranging from the global WHO FluNet/GISRS to national dashboards such as CDC FluView and ECDC ERVISS that provide timely, standardised indicators and prediction targets. It subsequently reviews main method families (statistical time-series, mechanistic compartmental models, machine learning) and highlights that multimodel ensembles often outperform single approaches at predicting onset, peak week, and intensity. It also presents case studies from Europe, the U.S. and China that illustrate prediction skill, the importance of climate-aware forcing, and of practical factors such as reporting lags. Generally, robust and interpretable forecasts require well-aligned targets, hybrid/ensemble frameworks, adaptive weighting and drift monitoring, and hold promise to support earlier warnings, intervention scenario analysis, and efficient resource planning.