Identification Problem for Parameter Identification in a Pollution‐Driven SEIRS Model of RSV Dynamics

ABSTRACT Environmental pollution affects respiratory disease transmission. A pollution‐driven (‐Susceptible, ‐Exposed, ‐Infected, ‐Recovered, ‐Susceptible) model is developed to study respiratory syncytial virus transmission by incorporating a pollution‐dependent term that quantifies the impact of air quality on infection dynamics. Model parameters are estimated through the method of variational imbedding and the least squares method, and the system is solved numerically using the Crank–Nicolson scheme with Picard iterations. Numerical simulation confirms that higher pollution levels significantly intensify respiratory syncytial virus transmission dynamics. The model is validated against empirical respiratory syncytial virus case data from Shanghai, demonstrating its capability to accurately replicate observed outbreak patterns.