AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods
Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting CO...
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| Format: | Article |
| Language: | English |
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phrp
2024
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| Online Access: | http://eprints.uthm.edu.my/11988/1/J17628_d5ee904c566c9ac581ade7e4d6439964.pdf |
| _version_ | 1824446609213095936 |
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| author | Tariq, Muhammad Usman Ismail, Shuhaida |
| author_facet | Tariq, Muhammad Usman Ismail, Shuhaida |
| author_sort | Tariq, Muhammad Usman |
| collection | UTHM |
| description | Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making.
Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network
(CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models.
Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset.
Conclusion: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The
RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors |
| first_indexed | 2025-02-19T01:17:52Z |
| format | Article |
| id | uthm.eprints-11988 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2025-02-19T01:17:52Z |
| publishDate | 2024 |
| publisher | phrp |
| record_format | dspace |
| spelling | uthm.eprints-119882025-01-21T07:00:45Z http://eprints.uthm.edu.my/11988/ AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods Tariq, Muhammad Usman Ismail, Shuhaida QA Mathematics Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation’s public health authorities in informed decision-making. Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models. Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset. Conclusion: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors phrp 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11988/1/J17628_d5ee904c566c9ac581ade7e4d6439964.pdf Tariq, Muhammad Usman and Ismail, Shuhaida (2024) AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods. Osong Public Health Res Perspect. pp. 1-22. ISSN 2233-6052 https://doi.org/10.24171/j.phrp.2023.0287 |
| spellingShingle | QA Mathematics Tariq, Muhammad Usman Ismail, Shuhaida AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods |
| title | AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods |
| title_full | AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods |
| title_fullStr | AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods |
| title_full_unstemmed | AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods |
| title_short | AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods |
| title_sort | ai powered covid 19 forecasting a comprehensive comparison of advanced deep learning methods |
| topic | QA Mathematics |
| url | http://eprints.uthm.edu.my/11988/1/J17628_d5ee904c566c9ac581ade7e4d6439964.pdf |
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