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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2507.19515 |
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| _version_ | 1866915410365906944 |
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| author | Agyemang, Edmund F. Rodrigo, Hansapani Agbenyeavu, Vincent |
| author_facet | Agyemang, Edmund F. Rodrigo, Hansapani Agbenyeavu, Vincent |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19515 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comparative Analysis of Traditional and Deep Learning Time Series Architectures for Influenza A Infectious Disease Forecasting Agyemang, Edmund F. Rodrigo, Hansapani Agbenyeavu, Vincent Machine Learning Computers and Society 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. |
| title | A Comparative Analysis of Traditional and Deep Learning Time Series Architectures for Influenza A Infectious Disease Forecasting |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2507.19515 |