Early Detection and Forecasting of Influenza Epidemics Using a Hybrid ARIMA-GRU Model

Early diagnosis and accurate epidemic prediction are essential in limiting the public health impact of influenza epidemics because intervention on time can effectively curb both the spread of the disease and the strain on health services. Standard ARIMA models have proven their usefulness in short-term forecasting, particularly in stable contexts, but the fact that they cannot keep up with the complex and non-linear dynamics of disease spread makes them less capable of dealing with rapid-evolving outbreaks. This is especially the case when outbreaks are characterized by complicated seasonal trends and irregular peaks which are challenging for ARIMA to predict by itself. To fill this deficit, this study presents a hybrid model that marries ARIMA’s statistical strength in dealing with short-term trends and the high-powered deep learning strengths of Gated Recurrent Units (GRU) that specialize in detecting long-term dependencies and non-linear relationships in data. The WHO Flu Net dataset, a trusted source of influenza surveillance, forms the foundation of training the model, with careful preprocessing operations conducted to normalize the data and eliminate any missing values, providing high-quality input to the model to make precise predictions. By combining ARIMA’s linear prediction strengths with GRU’s sophisticated pattern detection, the hybrid model delivers a powerful solution that is better than both regular ARIMA and other machine learning models, as evidenced by lower error rates on test metrics like MAE, RMSE and MAPE. The experimental findings validate that the ARIMA-GRU model not only enhances predictive performance but also increases the model’s sensitivity to subtle trends, making it a valuable asset for early detection systems in public health. In the future, the incorporation of real-time environmental information such as temperature, humidity, and mobility patterns may further enhance the model’s accuracy and responsiveness, providing more robust forecasting. Also, integrating healthcare infrastructure-related data, i.e., hospital capacity and availability of medical resources, would aid in developing a more complete epidemic management process. In total, the ARIMA-GRU hybridization is an effective and novel strategy for enhancing influenza surveillance, outbreak detection at the early stage, and epidemic control operations.