From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases
The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, in...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/43511/ http://umpir.ump.edu.my/id/eprint/43511/1/s13040-024-00396-8.pdf |
| _version_ | 1848826894372306944 |
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| author | Xu, Deren Chan, Weng Howe Habibollah, Haron Nies, Hui Wen Moorthy, Kohbalan |
| author_facet | Xu, Deren Chan, Weng Howe Habibollah, Haron Nies, Hui Wen Moorthy, Kohbalan |
| author_sort | Xu, Deren |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools. |
| first_indexed | 2025-11-15T03:52:04Z |
| format | Article |
| id | ump-43511 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:52:04Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-435112025-01-13T01:07:56Z http://umpir.ump.edu.my/id/eprint/43511/ From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases Xu, Deren Chan, Weng Howe Habibollah, Haron Nies, Hui Wen Moorthy, Kohbalan Q Science (General) R Medicine (General) The outbreak of emerging infectious diseases poses significant challenges to global public health. Accurate early forecasting is crucial for effective resource allocation and emergency response planning. This study aims to develop a comprehensive predictive model for emerging infectious diseases, integrating the blending framework, transfer learning, incremental learning, and the biological feature Rt to increase prediction accuracy and practicality. By transferring features from a COVID-19 dataset to a monkeypox dataset and introducing dynamically updated incremental learning techniques, the model's predictive capability in data-scarce scenarios was significantly improved. The research findings demonstrate that the blending framework performs exceptionally well in short-term (7-day) predictions. Furthermore, the combination of transfer learning and incremental learning techniques significantly enhanced the adaptability and precision, with a 91.41% improvement in the RMSE and an 89.13% improvement in the MAE. In particular, the inclusion of the Rt feature enabled the model to more accurately reflect the dynamics of disease spread, further improving the RMSE by 1.91% and the MAE by 2.17%. This study underscores the significant application potential of multimodel fusion and real-time data updates in infectious disease prediction, offering new theoretical perspectives and technical support. This research not only enriches the theoretical foundation of infectious disease prediction models but also provides reliable technical support for public health emergency responses. Future research should continue to explore integrating data from multiple sources and enhancing model generalization capabilities to further enhance the practicality and reliability of predictive tools. Springer Nature 2024 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/43511/1/s13040-024-00396-8.pdf Xu, Deren and Chan, Weng Howe and Habibollah, Haron and Nies, Hui Wen and Moorthy, Kohbalan (2024) From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases. BioData Mining, 17 (1). pp. 1-25. ISSN 1756-0381. (Published) https://doi.org/10.1186/s13040-024-00396-8 10.1186/s13040-024-00396-8 |
| spellingShingle | Q Science (General) R Medicine (General) Xu, Deren Chan, Weng Howe Habibollah, Haron Nies, Hui Wen Moorthy, Kohbalan From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| title | From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| title_full | From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| title_fullStr | From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| title_full_unstemmed | From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| title_short | From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| title_sort | from covid-19 to monkeypox: a novel predictive model for emerging infectious diseases |
| topic | Q Science (General) R Medicine (General) |
| url | http://umpir.ump.edu.my/id/eprint/43511/ http://umpir.ump.edu.my/id/eprint/43511/ http://umpir.ump.edu.my/id/eprint/43511/ http://umpir.ump.edu.my/id/eprint/43511/1/s13040-024-00396-8.pdf |