Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics

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.

Keywords: COVID-19; Compartmental models; Deep neural networks; Parameter estimation; Runge–Kutta method.