{"ID":2892940,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19515","arxiv_id":"2507.19515","title":"A Comparative Analysis of Traditional and Deep Learning Time Series Architectures for Influenza A Infectious Disease Forecasting","abstract":"Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improvements in sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional and deep learning models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and Exponential Smoothing(ETS) models with six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU, and Transformer. The results reveal a clear superiority of all the deep learning models, especially the state-of-the-art Transformer with respective average testing MSE and MAE of 0.0433 \\pm 0.0020 and 0.1126 \\pm 0.0016 for capturing the temporal complexities associated with Influenza A data, outperforming well known traditional baseline ARIMA and ETS models. These findings of this study provide evidence that state-of-the-art deep learning architectures can enhance predictive modeling for infectious diseases and indicate a more general trend toward using deep learning methods to enhance public health forecasting and intervention planning strategies. Future work should focus on how these models can be incorporated into real-time forecasting and preparedness systems at an epidemic level, and integrated into existing surveillance systems.","short_abstract":"Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improvements in sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional and deep learning models to predict Influ...","url_abs":"https://arxiv.org/abs/2507.19515","url_pdf":"https://arxiv.org/pdf/2507.19515v1","authors":"[\"Edmund F. Agyemang\",\"Hansapani Rodrigo\",\"Vincent Agbenyeavu\"]","published":"2025-07-18T03:20:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CY\"]","methods":"[\"Transformer\"]","has_code":false}