The recent outbreak of COVID-19 has renewed interest among researchers in modeling the transmission of airborne viruses and the resultant diseases. A significant research avenue is the modeling of virion count behavior and dynamics of host’s immunse cell response, commonly referred to as Host Cell Dynamics (HCD). Mathematical modeling of HCD is instrumental in predicting the temporal changes in virion concentration within an infected host. This information is vital for establishing patient isolation protocols as part of an overall strategy of containing virus transmission. Determining HCD model parameters necessitates the initial virion concentration at infec- tion onset, in addition to the virion concentration measured experimentally throughout the infection. While post-infection virion concentration is measurable, determining it at onset of infection remains challenging. Our work introduces a framework employing computational fluid-particle dynamics (CFPD) simulations [1] to estimate these critical HCD modeling parameters. Specifically, we utilize Computational Fluid-Particle Dynam- ics (CFPD) simulations to ascertain the initial virion concentration at the infection’s inception in a prospective host. To achieve this, we conduct implicit Large Eddy Simulations (LES) to model the inhala- tion of respiratory particles during interactions involving respiratory events like coughing and speaking, across various environments using an in-house large-scale multi-physics solver known as CUBE[2]. The outcomes of these diverse CFPD simulations are then integrated with the HCD model. This integration facilitates an optimization analysis, aimed at determining the HCD model parameters. These parameters are firmly grounded in real-world measurements and simulations, reflecting actual conditions. This approach not only enhances the precision of the HCD model but also contributes significantly to our understanding of airborne virus transmission dynamics.