Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks
This work addresses epidemic prediction for public health, but it is incremental as it applies an existing PINN method to a new domain with compartmental models.
The authors tackled the problem of modeling COVID-19 spread by developing a physics-informed neural network (PINN) combined with an SIR model, demonstrating its effectiveness on synthetic data and accurate prediction of virus trends in Germany.
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.