Physics-Informed Neural Network-Based Parameter Estimation and Bifurcation Analysis of an Epidemic Model

: This study presents a comprehensive data-driven and bifurcation analysis of an epidemic model to investigate the transmission dynamics and long-term behavior of infectious diseases. The model is analyzed using equilibrium and stability analysis through bifurcation study. Furthermore, a data driven framework is incorporated to estimate model parameters and assess the agreement between the mathematical model and observed epidemic data. Numerical simulations are performed to illustrate the effects of important epidemiological parameters on disease prevalence and population dynamics. The combined results provide insights into the mechanisms responsible for disease persistence, epidemic outbreaks, and transitions between different dynamical regimes. The proposed framework can support reliable epidemic forecasting and help identify effective intervention strategies for controlling infectious diseases