ArticleScientific reports2025
Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Stage-aware deep learning with adversarial data synthesis enables prospective season-scale A/H1N1 influenza forecasting in China.Infectious Disease Modelling · 2027Article
- Dynamic properties of an SARS-CoV-2 epidemic model via stochastic PINNs.Infectious Disease Modelling · 2026Article
- Bridging Traditional Modeling and Artificial Intelligence in Measles Epidemiology: Methods, Applications, and Future Directions-A Narrative Review.Journal of clinical medicine · 2026Review
- Hybrid modeling and forecasting of COVID-19: integrating SEAIQHRD and GPR for improved predictions.BMC infectious diseases · 2026Article
- Optimizing breast cancer treatment strategies through fractional-order dynamics: A computational modeling approach.PloS one · 2026Article
- Automated model discovery based on COVID-19 epidemiological data from Thuringia, Germany.PloS one · 2026Article
- Modeling and analysis of a delayed fractional order COVID-19 SEIHRM model with media coverage in Malaysia.Scientific reports · 2025Article
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3 authors.
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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.
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