An Age-Structured Model for COVID-19 Hospitalization Rate

In this study, an age-structured framework is developed to model post-pandemic COVID-19 hospitalization rates. A partial differential equation (PDE) incorporating age-specific information is first formulated and analyzed. Based on this PDE, a physics-informed neural network (PINN) is constructed and calibrated using real-world data. Through this integration, mathematical rigor is combined with the adaptability of machine learning (ML), resulting in an interpretable approach that leverages physical principles while reducing the opacity typically associated with ML-based models.