Simulation and application of COVID-19 compartment model using physics-informed neural network

Then, we implement the physics-informed neural network (PiNN) on both simulated and real-world data. The PiNN model enables robust analysis of the dynamic spread, prediction, and parameter optimization of the COVID-19 compartmental models. The models exhibit relative root mean square error (RRMSE) of < 4% for all components and provide incubation, death, and recovery rates of γ = 0.0130, λ = 0.0001, and ρ = 0.0037, respectively, for the first 310 days of the epidemic in the US with RRMSE of < 0.35% for all components. To further improve the model performance, temporally varying parameters can be included, such as vaccination, transmission, and incubation rates. Our implementation highlights PiNN as a reliable candidate approach for forecasting real-world data and can be applied to other compartmental model variants of interest.