Indoor airborne transmission of viruses, as underscored by the COVID-19 pandemic, poses a major health risk in confined spaces. Traditional models combining CFD (Computational Fluid Dynamics) simulations with infection-risk calculations are computationally intensive and impractical for rapid evaluation. This study proposes a ventilation-driven exponential decay model to characterize the post-occupancy decline of virus-laden aerosol concentrations. By correlating the model’s decay parameters a and b with the room’s total ventilation flow rate, we derive parametric equations enabling fast and normalized prediction. The model was validated against CFD results in a typical office: quadratic regressions of parameters versus airflow achieved high goodness-of-fit (R^2>0.68), and predicted decay curves closely matched detailed simulations. This parametric model thus allows rapid evaluation of ventilation and air cleaner strategies without additional CFD runs, offering an efficient tool for engineering design and indoor health management.