SUC–PINNs: A physics-informed neural networks approach to inverse problems in epidemic models with partial observability
TL;DR
Experiments with synthetic data and COVID-19 surveillance data show that SUC–PINN recovers plausible hidden infection trajectories, yields stable parameter estimates, and provides accurate short-term forecasts, support SUC–PINN as a practical computational approach for inverse modeling and prediction in partially observed epidemic dynamics.
Abstract
Partially observed epidemic systems are difficult to analyze because confirmed cases provide only an indirect view of transmission and hidden infections are usually unobserved. We consider the susceptible–unidentified infected–confirmed (SUC) epidemic model and develop SUC–PINN, a physics-informed neural network framework for estimating hidden epidemic states, transmission and confirmation rates, and the initial unidentified population from confirmed-case time series alone. The method combines confirmed-case observations with SUC residuals and initial-condition constraints, so that the learned trajectories remain tied to the governing dynamics. Experiments with synthetic data and COVID-19 surveillance data from Indonesia, Thailand, India, and the Philippines show that SUC–PINN recovers plausible hidden infection trajectories, yields stable parameter estimates, and provides accurate short-term forecasts. These results support SUC–PINN as a practical computational approach for inverse modeling and prediction in partially observed epidemic dynamics.