Physics-informed neural networks integrating compartmental model for analyzing COVID-19 transmission dynamics

Xiao Ning, Yongyue Wei, Feng Chen

Keywords: Compartmental models, COVID-19 transmission, Physics-informed neural networks, Forward-inverse problem

TL;DR: A novel method that combines mathematical modeling and neural networks modeling to efficiently address the complexity of infectious disease transmission dynamics in real-world scenarios.

Abstract: Modelling and predicting the behaviour of infectious diseases is essential for early warning and evaluating the most effective interventions to prevent significant harm. Compartmental models produce a system of ordinary differential equations (ODEs) that are renowned for simulating the transmission dynamics of infectious diseases. However, the parameters in compartmental models are often unknown, and they can even change over time in the real world, making them difficult to determine. This paper proposes an advanced artificial intelligence approach based on physics-informed neural networks (PINNs) to estimate time-varying parameters from given data for the compartmental model. Our proposed PINNs approach captures the complex dynamics of COVID-19 by integrating a modified Susceptible-Exposed-Infectious-Recovered-Death (SEIRD) compartmental model with deep neural networks. The experimental findings on synthesized data have demonstrated that our method robustly and accurately learns the dynamics and forecasts future states. Moreover, as more data becomes available, our proposed PINNs approach can be successfully extended to other regions and infectious diseases.