Leveraging Dynamics Informed Neural Networks for Predictive Modeling of COVID-19 Spread: a Hybrid SEIRV-DNNs Approach

Overview

Journal Sci Rep

Date 2025 Jan 15

PMID 39814760

Authors

Cheng Cheng

Elayaraja Aruchunan

Muhamad Hifzhudin Noor Aziz

Affiliations

Soon will be listed here.

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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