Cheng, Cheng and Aruchunan, Elayaraja and Aziz, Muhamad Hifzhudin Noor (2025) Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach. Scientific Reports, 15 (1). p. 2043. ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-025-85440-1.
Full text not available from this repository.Abstract
A dynamics informed neural networks (DINNs) incorporating the susceptible-exposed-infectious-recovered-vaccinated (SEIRV) model was developed to enhance the understanding of the temporal evolution dynamics of infectious diseases. This work integrates differential equations with deep neural networks to predict time-varying parameters in the SEIRV model. Experimental results based on reported data from China between January 1, and December 1, 2022, demonstrate that the proposed dynamics informed neural networks (DINNs) method can accurately learn the dynamics and predict future states. Our proposed hybrid SEIRV-DNNs model can also be applied to other infectious diseases such as influenza and dengue, with some modifications to the compartments and parameters in the model to accommodate the related control measures. This approach will facilitate improving predictive modeling and optimizing public health intervention strategies.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | COVID-19; Transmission dynamics; Neural networks; DINNs |
| Subjects: | Q Science > QA Mathematics R Medicine > RA Public aspects of medicine |
| Divisions: | Faculty of Science > Institute of Mathematical Sciences Faculty of Business and Economics > Department of Decision Science |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Mar 2025 01:35 |
| Last Modified: | 14 Mar 2025 01:35 |
| URI: | http://eprints.um.edu.my/id/eprint/47712 |
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