  A physics-informed neural network (PINN) embedded with the
susceptible-infected-removed (SIR) model is devised to understand the temporal
evolution dynamics of infectious diseases. Firstly, the effectiveness of this
approach is demonstrated on synthetic data as generated from the numerical
solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model.
Then, the method is applied to COVID-19 data reported for Germany and shows
that it can accurately identify and predict virus spread trends. The results
indicate that an incomplete physics-informed model can approach more
complicated dynamics efficiently. Thus, the present work demonstrates the high
potential of using machine learning methods, e.g., PINNs, to study and predict
epidemic dynamics in combination with compartmental models.
