Quantifying the COVID19 infection risk due to droplet/aerosol inhalation

Abstract

The dose-response model has been widely used for quantifying the risk of infection of airborne diseases like COVID-19. The model has been used in the room-average analysis of infection risk and analysis using passive scalars as a proxy for aerosol transport. However, it has not been employed for risk estimation in numerical simulations of droplet dispersion. In this work, we develop a framework for the evaluation of the probability of infection in droplet dispersion simulations using the dose-response model. We introduce a version of the model that can incorporate the higher transmissibility of variant strains of SARS-CoV2 and the effect of vaccination in evaluating the probability of infection. Numerical simulations of droplet dispersion during speech are carried out to investigate the infection risk over space and time using the model. The advantage of droplet dispersion simulations for risk evaluation is demonstrated through the analysis of the effect of ambient wind, humidity on infection risk, and through a comparison with risk evaluation based on passive scalars as a proxy for aerosol transport.

From microscopic droplets to macroscopic crowds: Crossing the scales in models of short-range respiratory disease transmission, with application to COVID-19

“…Note, however, that (moderate) air currents may also be worth considering indoors, where they are also present [32]. In the context of the COVID-19 pandemic, transport by the wind has alternatively been thought to favor transmission by extending the spatial reach of droplets and inhibit it by quickly dispersing pathogens [14,32,33].…”

“…Numerical studies provide a means to circumvent these limitations; indeed, their replicability enables researchers to test assumptions, investigate the effect of different parameters, relate behaviors to transmission risks and build a mechanistic picture of the risks in such contexts. The COVID-19 pandemic has prompted an unprecedented effort from the fluid mechanics community to probe the transport of respiratory droplets after their emission, in particular using CFD [13,14,30,[47][48][49][50], which has shed light on the sensitivity of this propagation to the environment [13][14][15]. Simulations have thus considered diverse environmental settings, as well as diverse expiratory events, including coughing [13,49,51], sneezing [15,51], speaking [14,30,49,51] and breathing [30].…”

“…The COVID-19 pandemic has prompted an unprecedented effort from the fluid mechanics community to probe the transport of respiratory droplets after their emission, in particular using CFD [13,14,30,[47][48][49][50], which has shed light on the sensitivity of this propagation to the environment [13][14][15]. Simulations have thus considered diverse environmental settings, as well as diverse expiratory events, including coughing [13,49,51], sneezing [15,51], speaking [14,30,49,51] and breathing [30]. Coughs, in particular, have received a lot of attention, but in this paper we put the focus on talking and breathing through the mouth, because we have deemed that direct exposure to coughs (not covered by the emitter's hand and directed towards the receiver's face) is fairly rare and, in addition, talking for 1 minute produces approximately as many droplets (i.e., a few thousand altogether) as one cough [52].…”

“…Leaving aside inhalation, the local concentration of virus in a region of interest may be monitored, as a proxy for the infection risk [15]; the need to simulate the susceptible person at each position that they may occupy is thus bypassed. Time may be involved by comparing the quantity of inhaled virus over time to an infectious dose [14,47,48,50,54,55] via a dose-response model [56,57]. This quantity can be measured in absolute terms, as a number of viral copies, which requires specifying the viral titer in the emitter's respiratory fluids and the minimal infectious dose, or in terms of quanta of infection, if the number of emitted virus is rescaled by the infectious dose [19].…”

“…To assess how the disease may spread in crowds, modeling the emission, transport, and inhalation of respiratory droplets is an appealing option and has been widely used in COVID-19-related studies. However, modeling transmission is a major challenge, owing to the sensitivity of droplet propagation to environmental factors such as temperature, humidity and wind [13][14][15], as well as the uncertainty about the sizes of emitted droplets [16] or the person-to-person variability [17], among others. Moreover, microscopic studies of droplet propagation, supposed to describe the evolution of droplets most accurately, are generally limited to static scenarios involving two people facing each other.…”