Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics. (May 2023)

• Record Type: Journal Article
• Authors: Ning, Xiao; Jia, Linlin; Wei, Yongyue; Li, Xi-An; Chen, Feng
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: 34A34 -- 68T07; Compartmental models -- Deep neural networks -- Parameter estimation -- Runge–Kutta method -- COVID-19; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106693
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Differential equations-based epidemic compartmental models and deep neural networks-based artificial intelligence (AI) models are powerful tools for analyzing and fighting the transmission of COVID-19. However, the capability of compartmental models is limited by the challenges of parameter estimation, while AI models fail to discover the evolutionary pattern of COVID-19 and lack explainability. This paper aims to provide a novel method (called Epi-DNNs) by integrating compartmental models and deep neural networks (DNNs) to model the complex dynamics of COVID-19. In the proposed Epi-DNNs method, the neural network is designed to express the unknown parameters in the compartmental model and the Runge–Kutta method is implemented to solve the ordinary differential equations (ODEs) so as to give the values of the ODEs at a given time. Specifically, the discrepancy between predictions and observations is incorporated into the loss function, then the defined loss is minimized and applied to identify the best-fitted parameters governing the compartmental model. Furthermore, we verify the performance of Epi-DNNs on the real-world reported COVID-19 data on the Omicron epidemic in Shanghai covering February 25 to May 27, 2022. The experimental findings on the synthesized data have revealed its effectiveness in COVID-19 transmission modeling. Moreover, the inferred parameters from the proposed Epi-DNNs method yield a predictive compartmental model, which can serve to forecast future dynamics. Highlights: Compartmental models with time-varying parameters capture COVID-19 dynamics well. Modeling COVID-19 dynamics by coupling the compartmental model and neural networks. Using neural networks to express time-varying parameters in compartmental models. Applying Fourier transformation to reduce the stochastic and noise of real-world data. Analyzing the effect of intervention policies and providing predictions.