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

Abstract

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.

Keywords:

• PINN
• LSTM
• SIRD
• COVID-19
• deep neural network

2010 Mathematics Subject Classifications:

• 65L05
• 65Z05

Acknowledgments

We appreciate the volunteer organization, The COVID Tracking Project, for providing the most complete data about COVID-19 in the US. We are thankful to the referees for their valuable comments and suggestions which has improved the presentation of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).