  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 (PINN) 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 (LSTM) 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.
