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DDE: Deep Dynamic Epidemiological Modeling for Infectious Illness Development Forecasting in Multi-level Geographic Entities.
- Source :
-
Journal of healthcare informatics research [J Healthc Inform Res] 2024 May 28; Vol. 8 (3), pp. 478-505. Date of Electronic Publication: 2024 May 28 (Print Publication: 2024). - Publication Year :
- 2024
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Abstract
- Understanding and addressing the dynamics of infectious diseases, such as coronavirus disease 2019, are essential for effectively managing the current situation and developing intervention strategies. Epidemiologists commonly use mathematical models, known as epidemiological equations (EE), to simulate disease spread. However, accurately estimating the parameters of these models can be challenging due to factors like variations in social distancing policies and intervention strategies. In this study, we propose a novel method called deep dynamic epidemiological modeling (DDE) to address these challenges. The DDE method combines the strengths of EE with the capabilities of deep neural networks to improve the accuracy of fitting real-world data. In DDE, we apply neural ordinary differential equations to solve variant-specific equations, ensuring a more precise fit for disease progression in different geographic regions. In the experiment, we tested the performance of the DDE method and other state-of-the-art methods using real-world data from five diverse geographic entities: the USA, Colombia, South Africa, Wuhan in China, and Piedmont in Italy. Compared to the state-of-the-art method, DDE significantly improved accuracy, with an average fitting Pearson coefficient exceeding 0.97 across the five geographic entities. In summary, the DDE method enhances the accuracy of parameter fitting in epidemiological models and provides a foundation for constructing simpler models adaptable to different geographic areas.<br />Competing Interests: Competing InterestsThe authors declare no competing interests.<br /> (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)
Details
- Language :
- English
- ISSN :
- 2509-4971
- Volume :
- 8
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Journal of healthcare informatics research
- Publication Type :
- Academic Journal
- Accession number :
- 39131102
- Full Text :
- https://doi.org/10.1007/s41666-024-00167-4