A statistical framework for assessing the effectiveness of filtration and ventilation in preventing indoor airborne viral transmission using high-fidelity simulations

The risk of airborne viral contagion in indoor spaces can be greatly mitigated or aggravated by the quality of the ventilation system. Theoretical models have been used extensively to predict the spread of infectious diseases in such settings. However, it is reasonable to expect that a perfectly well-mixed state predicted by those models cannot be achieved at any realistic level of ventilation. This work evaluates the theory with results from large eddy simulations of flow in a canonical room of dimensions 10m × 3.2m × 10m with an overhead air conditioning unit.

The robustness of the theory is examined by analyzing the concentration of the viral load at the room level and for specific distances between a source and a sink. The statistics help us in determining the accuracy of the theory at a room-scale and quantifying the deviation of the simulation from the theory depending on the separation distance between the source and the sink. Beyond this current setting, the effect of a filtration system in alleviating viral infectivity is also explored using a similar statistical framework.

The statistics from the simulations indicate that the theory is extremely accurate in predicting the decay rate and mode of exit of airborne viral nuclei. However, the source-to-sink distance-specific results show that the theory can be too lenient and too restrictive for small and long separation distances respectively. Based on these results, we provide a simple multiplicative correction factor, which quantifies the deviation of the simulation from the theory, that depends on ACH, expiration activity, filtration efficiency, and separation distance. Using this correction factor, we provide corrected guidelines for safe exposure time as a function of occupancy for chosen indoor settings.