Table 1.
Parameter values and ranges used in modeling transport of SARS-CoV-2 in indoor scenarios.
Fig 1.
The fraction of individuals infected in simulated office buildings increases with the assumed viral emission rate but can be mitigated by improvements to common HVAC systems.
Lines and points represent the median of 1,000 simulations, while shaded regions encompass the 25th to 75th percentile of results. *MERV 12, MERV 16, and any combination of UVC filtration of 90% and 99% efficiency with any mechanical (MERV-rated) filter produced nearly identical results.
Table 2.
Increasing ACH, filter MERV rating, and the FOA reduce simulated SARS-CoV-2 infections with an emission rate of 3,000 PFU / min.
UVC filtration and in-room air filters also reduce SARS-CoV-2 infections. Percent changes are shown relative to the highlighted baseline for each mitigation strategy (i.e., 0% change).
Fig 2.
In-room filtration unit in the room of the SARS-CoV-2 release (“Release”, orange) and in all rooms of an office building (“All”, green) compared to no in-room filters (“None”, blue). Lines and points represent the median of 1,000 simulations, while shaded regions encompass the 25th to 75th percentile of results.
Fig 3.
The fraction of individuals infected in simulated social gatherings increases with the assumed viral emission rate but can be mitigated by improvements to common HVAC systems.
Lines and points represent the median of 1,000 simulations, while shaded regions encompass the 25th to 75th percentile of results. *MERV 12, MERV 16, and any combination of UVC filtration of 90% and 99% efficiency with any mechanical (MERV-rated) filter produced very similar results.
Table 3.
Increasing ACH, filter MERV rating, and the FOA reduce simulated SARS-CoV-2 infections in the bar/restaurant, wedding reception venue, and nightclub with an emission rate of 3,000 PFU / min.
UVC filtration also reduced SARS-CoV-2 infections. Percent changes are shown relative to the highlighted baseline for each mitigation strategy (i.e., 0% change).
Fig 4.
HVAC systems can facilitate spread in very limited settings, and to a relatively small extent.
When air circulation rates are low and most air is being recirculated instead of pulled from outside, increasing the air exchange rate can increase the number of individuals infected outside the immediate release zone.
Table 4.
Parameter values covering a wide range of possible HVAC configurations.
Fig 5.
Predicted number of infections resulting from a simulated three-day release in apartments connected in a multistory complex.
Horizontal lines indicate the median of 1,000 stochastic realizations, boxes represent the interquartile range, and whiskers extend to the minimum and maximum of all realizations.
Table 5.
Parameter comparisons between Buonanno et al. and this study for a representative restaurant exposure scenario.
Fig 6.
Infection probability as a function of dose for the Wells-Riley quanta of infection model and a Probit model with an ID50 of 240 PFU and Slope of 1.16.
Two translations between quanta and PFU are shown, one using the PFU required to achieve an infection probability of 63% which defines one quanta (1 quanta ≅ 469 PFU) and the other using the quanta required to achieve an infection probability of 50% which defines the ID50 (240 PFU ≅ 0.69 quanta).
Table 6.
Estimated SARS-CoV-2 aerosol emission rates via exhaled breath for a single individual.
Table 7.
Plausible particle size distribution ranges for various emission types.
Table 8.
Estimated aerosol decay rates for SARS-CoV-2 suspensions or simulated droplets.
Fig 7.
Particle distribution by mass for dry MMAD of 4.02 micrometers and a GSD of 2.0 (Section 4.3.1) with mechanical filter bins (Section 4.3.4), respirable particle range (particles that contribute to dose) (Section 4.4), and particle settling rate as calculated by Stokes’ Law (Section 4.3.2).
Table 9.
MERV filter efficiency by particle diameter range for select filters.
Fig 8.
The simulated office building comprises individual offices connected by shared hallways, staircases, and air handling units (AHU).
Table 10.
Office Model Parameters.
Fig 9.
Social gathering building layouts.
Table 11.
Social gathering building sizes and ACH.
Table 12.
Social gathering population size and residence time.