Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach

Data-driven deep learning provides efficient algorithms for parameter identification of epidemiology models. Unlike the constant parameters, the complexity of identifying time-varying parameters is largely increased. In this paper, a variant of physics-informed neural network is adopted to identify the time-varying parameters of the Susceptible-Infectious-Recovered-Deceased model for the spread of COVID-19 by fitting daily reported cases. The learned parameters are verified by utilizing an ordinary differential equation solver to compute the corresponding solutions of this compartmental model. The effective reproduction number based on these parameters is calculated. Long Short-Term Memory neural network is employed to predict the future weekly time-varying parameters. The numerical simulations demonstrate that PINN combined with LSTM yields accurate and effective results.

Automated summary

This paper employs a variant of physics-informed neural network to identify time-varying parameters of the COVID-19 transmission model, and uses Long Short-Term Memory neural network to predict future parameter changes. The accuracy and effectiveness of parameter learning are validated through computing model solutions and effective reproduction numbers. The numerical simulations show that the combination of PINN and LSTM produces accurate and effective results.