Simulation and application of COVID-19 compartment model using physics-informed neural network
This work addresses the need for more accurate pandemic forecasting for public health decision-makers, though it is incremental as it builds on existing compartment models and PiNN methods.
The authors tackled the problem of forecasting COVID-19 spread by introducing SVEIDR compartment models with age and vaccination structures and implementing a physics-informed neural network (PiNN) for analysis and prediction, achieving relative root mean square errors of <4% for all components and providing specific rates like incubation at 0.0130 with RRMSE <0.35% for the first 310 days in the US.
COVID-19 pandemic has had a disruptive and irreversible impact globally, yet traditional epidemiological modeling approaches such as the susceptible-infected-recovered (SIR) model have exhibited limited effectiveness in forecasting of the up-to-date pandemic situation. In this work, susceptible-vaccinated-exposed-infected-dead-recovered (SVEIDR) model and its variants -- aged and vaccination-structured SVEIDR models -- are introduced to encode the effect of social contact for different age groups and vaccination status. 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.