Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach
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...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85440-1 |
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| author | Cheng Cheng Elayaraja Aruchunan Muhamad Hifzhudin Noor Aziz |
| author_facet | Cheng Cheng Elayaraja Aruchunan Muhamad Hifzhudin Noor Aziz |
| author_sort | Cheng Cheng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-325131860d0e448db62cb83d884bd3dd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-325131860d0e448db62cb83d884bd3dd2025-01-19T12:20:29ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-85440-1Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approachCheng Cheng0Elayaraja Aruchunan1Muhamad Hifzhudin Noor Aziz2Institute of Mathematical Sciences, Faculty of Science, Universiti MalayaDepartment of Decision Science, Faculty of Business and Economics, Universiti MalayaInstitute of Mathematical Sciences, Faculty of Science, Universiti MalayaAbstract 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.https://doi.org/10.1038/s41598-025-85440-1COVID-19Transmission dynamicsNeural networksDINNs |
| spellingShingle | Cheng Cheng Elayaraja Aruchunan Muhamad Hifzhudin Noor Aziz Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach Scientific Reports COVID-19 Transmission dynamics Neural networks DINNs |
| title | Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach |
| title_full | Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach |
| title_fullStr | Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach |
| title_full_unstemmed | Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach |
| title_short | Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach |
| title_sort | leveraging dynamics informed neural networks for predictive modeling of covid 19 spread a hybrid seirv dnns approach |
| topic | COVID-19 Transmission dynamics Neural networks DINNs |
| url | https://doi.org/10.1038/s41598-025-85440-1 |
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