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Abstract

Seasonal influenza is a significant public health concern, causing widespread illness, hospitalizations, and deaths annually. Accurate forecasting of influenza activity is critical for effective resource allocation, vaccination campaigns, and public health preparedness. This paper proposes a novel approach to enhance seasonal influenza prediction using advanced time series machine learning models. We introduce a hybrid framework that combines traditional epidemiological data with machine learning techniques, including Long Short-Term Memory (LSTM) networks, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Prophet. The proposed model is evaluated on historical influenza-like illness (ILI) data, demonstrating superior performance in predicting influenza trends compared to existing methods. This research contributes to the growing field of computational epidemiology by providing a robust and scalable solution for influenza forecasting.