ABSTRACT
ObjectivesConsidering three viral transmission routes: fomite contact, aerial transmission by droplets, and aerial transmission by aerosols, the aerial routes have been the focus of debate about the relative role of droplets and aerosols in SARS-CoV-2 infection. We seek to quantify infection risk in an enclosed space via short-range airborne transmission from droplets and long-range risk from aerosols toward focusing public health measures. MethodsData from three published studies were analyzed to predict relative exposure at distances of 1 m and farther, mediated by droplet size divided into two bins: larger than 8 {micro}m and smaller than 75 {micro}m (medium droplets) and smaller than 8 {micro}m (small droplets or aerosols). The results at 1 m from an infectious individual were treated as a boundary condition to model infection risk at greater distance. At all distances, infection risk was treated as the sum of exposure to small and medium droplets. It was assumed that number of virions is proportional to droplet volume. ResultsThe largest infection risk (as exposure to droplet volume) came from medium droplets, close to the infectious individual out to approximately 1 m. Farther away, the largest risk was due to aerosols. For one model, medium droplet exposure disappeared at 1.8 m. ConclusionsPolicy concerning social distancing for meaningful infection reduction relies on droplet exposure as a function of distance, yet within this construct droplet size determines respiratory deposition. This two-fold distance effect can be used to evaluate additional measures such as plexiglass barriers and masking.
ABSTRACT
August 16, 2020 Risk-cost-benefit analysis requires the enumeration of decision alternatives, their associated outcomes, and the quantification of uncertainty. Public and private decision-making surrounding the COVID-19 pandemic must contend with uncertainty about the probability of infection during activities involving groups of people, in order to decide whether that activity is worth undertaking. We propose a deterministic linear model of SARS-CoV-2 infection probability that can produce estimates of relative risk for diverse activities, so long as those activities meet a list of assumptions, including that they do not last longer than one day. We show how the model can be used to inform decisions facing governments and industry, such as opening stadiums or flying on airplanes. We prove that the model is a good approximation of a more refined model in which we assume infections come from a series of independent risks. The linearity assumption makes interpreting and using the model straightforward, and we argue that it does so without significantly diminishing the reliability of the model.
