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Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters its limitations in the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets’ initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. Furthermore, there is a noticeable inconsistency around these collected data amongst many reported studies. Hence, this study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Hence, we will also briefly review the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. Moreover, the relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures will also be reviewed. Eventually, we scrutinize the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis to pave a pathway toward future studies ensuing these identified gaps and limitations.

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