Leveraging dynamics informed neural networks for predictive modeling of COVID-19 spread: a hybrid SEIRV-DNNs approach
Document Type
Article
Publication Date
1-1-2025
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.
Keywords
COVID-19, Transmission dynamics, Neural networks, DINNs
Divisions
MathematicalSciences,deptdecision
Publication Title
Scientific Reports
Volume
15
Issue
1
Publisher
Nature Research
Publisher Location
HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY