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Significance Seasonality in disease incidence is ubiquitous among infectious diseases. Seasonal drivers include weather variables, such as temperature and humidity, and social factors (e.g., contact patterns). Attempts to make long-term predictions of infectious diseases are hampered by the inability to understand the complex interplay of these factors. Here, we model the dynamics of seasonal influenza based on a high-quality 12-year Israeli dataset. The dynamics are completely explainable by the time evolution of the model equations, the antigenic jumps of the virus, and the climatic forcing, yielding high-correlation fits to the data (r = 0.94). The forecasting ability is greatly increased through the predictability of the system’s transient dynamics, resulting in accurate predictions (r = 0.93) that have not yet been found elsewhere.

Multiannual forecasting of seasonal influenza dynamics reveals climatic and evolutionary drivers