There is growing evidence that the spreading of coronavirus SARS-CoV-2 through aerosols, (i.e., small airborne particles and microdroplets in the size range up to 5 µm in diameter), is a significant transmission pathway of COVID-19 [1
]. Case studies worldwide indicate that SARS-CoV-2 has viable survival rates in the air and it remains airborne for an extended period of several hours [2
]. The European Centre for Disease Prevention and Control (ECDC) states that COVID-19 is mainly transmitted via SARS-CoV-2 virus-containing respiratory droplets (i.e., larger than 5 µm in diameter), while aerosols are implicated in transmission, but the relative roles of droplets and aerosols remain unclear [3
]. Further, the ECDC underscores the high viral load in subjects close to symptom onset, suggesting that COVID-19 patients are relatively contagious at an early stage of infection. Findings for both SARS-CoV-1 and SARS-CoV-2 point to superspreading of aerosols as an important factor in the indoor disease transmission [4
]. This is consistent with the nearly 20 times higher likelihood of indoor compared to outdoor disease spreading [6
]. Many studies on the spread of viruses, including respiratory syncytial virus (RSV), Middle East Respiratory Syndrome coronavirus (MERS-CoV), and influenza, corroborate that viable viruses in aerosols are emitted by infected subjects, and have been detected in their environment. From this, it may be concluded that people inhale the aerosolized viruses which result in infection and disease [7
Zhang and colleagues pose that airborne transmission is highly virulent and represents the dominant route in the spreading of COVID-19 [8
]. They find that the wearing of face masks has been a critical aspect in the outcome of COVID-19 trends in three main areas affected by the pandemic (Wuhan, Italy, New York). Other measures, such as social distancing, appeared to have been insufficient, suggesting an important role of aerosols as they disperse over relatively large distances. Schools represent an environment of special interest with compulsory attendance, and their closure can be societally disruptive. Nonetheless, schools have been closed, which has helped reduce the disease incidence and mortality, especially in the early stages of COVID-19 outbreaks [9
]. SARS-CoV-2 viral loads in infected children appear to be similar to those in adults [10
]. On the other hand, the incidence of COVID-19 under children may be less than among adults [11
]. Transmission of SARS-CoV-2 between children in schools was predicted to be less efficient than influenza, leading to the conclusion that school closures may prevent only a small percentage (<5%) of COVID-19 deaths [15
]. Furthermore, it was indicated that by demonstrating aerosol generation by speaking and coughing, or by recovering viral RNA from the air, the aerosol-based transmission is not proven—infection also depends on the route and duration of exposure, the infection dose and host defenses [16
]. These authors also argue that infection rates and transmission during daily life are difficult to reconcile with aerosol-based transmission.
Apparently, the role of aerosols in the spreading of SARS-CoV-2 is controversial. Here we present a simple, transparent and easily adjustable spreadsheet algorithm to estimate the indoor infection risk from aerosolized viruses, based on adjustable parameters such as room size, number of exposed subjects, inhalation volume, and aerosol production from breathing and vocalization. We apply it to study the role of aerosol transmission of SARS-CoV-2 in indoor locations, based on assumptions about the viral load and infection dose, for example, constrained by data from the literature. We focus on aerosol transmission, hence implicitly assuming that contact infection by larger droplets is either minimal (e.g., through social distancing and hygienic measures), or that these transmission routes should also be considered. Our approach aims to provide insight into the effectiveness of mitigation measures against indoor COVID-19 infection through aerosols.
2. Materials and Methods
To estimate the COVID-19 infection risk from airborne transmission, we developed a spreadsheet model that includes a number of modifiable environmental factors that represent relevant physiological parameters and environmental conditions. The model can be found in the Supplementary Materials
, and is also available as a user-friendly, online calculation tool, for which the parameters are listed in Table 1
. The standard setting represents a case for a 60 m2
large and 3 m high room with 24 susceptible subjects (i.e., a total of 25 subjects), representing a classroom where one pupil is infectious for two days, being present during six hours per day. For simplicity, all subjects are assumed to be equal in terms of breathing, speaking and susceptibility to infection. The two-day period represents that of highest infectiousness of the index subject, after which she/he is assumed to either develop symptoms and stay at home or become substantially less infectious. The spreadsheet parameters can be easily adjusted to account for different environmental conditions and activities. In addition to the classroom setting, we consider three common types of environment, as indicated in Table 2
. To study the impact of measures that can mitigate infection risks, we consider five scenarios (Table 3
). In the next section we evaluate the recent literature to underpin the parameter settings and ranges. We emphasize that continued progress in scientific understanding of COVID-19 may require future adjustments of assumptions and parameter settings.
In the standard case for a room with closed windows, we assume a passive indoor air volume exchange rate of 0.35 hr−1
]. If active blow ventilation with outside air is applied for short periods every hour, the rate increases to 2 hr−1
, while the application of one or more high-volume purifying devices with High-Efficiency Particulate Air (HEPA) filters can reduce the aerosol concentration according to an equivalent exchange rate of 9 hr−1
. Obviously, the exchange rates can vary depending on the capacity of devices. We emphasize that the conditions presented here exemplify a few prominent cases, and the effectiveness of the mitigation measures are illustrations. By modifying the parameter settings in the spreadsheet, a range of conditions, settings and mitigation measures can be simulated. For example, a respiration rate of 10 L/min (Table 1
) is representative for a person between being at rest and performing light activity, whereas calculations for exercising subjects in a fitness center would have to scale this up by a factor of three to five. Further, children respire a smaller volume of air than adults but at a higher frequency. Finally, we assume rapid mixing of the air in the rooms (within minutes) considering the turbulent atmosphere with localized human heat sources and movement, but it imposes the restriction of room sizes up to about 100 m2
. Although our spreadsheet algorithm is simple, it represents the available scientific knowledge of aerosol transmission, and shows how different conditions, parameters and precautionary measures can influence SARS-CoV-2 infection.
While there has been some doubt in the literature about the role of aerosols in the spreading of COVID-19, our results substantiate that airborne transmission in the indoor environment is an important factor. We reiterate that we present typical indoor environments as examples, and deviations for individual locations and conditions that occur in practice are imminent. To assess the risk reduction from the wearing of face masks, we adopted two levels of filtering efficiency, though did not consider the large range of available types. Clearly, improved quality masks, optimal fitting and hygienic discipline will greatly increase the efficiency. Notwithstanding the uncertainties, we are confident that the relative reductions predicted for different mitigation measures such as active ventilation with outside air, the ubiquitous wearing of face masks of intermediate and high quality, and air filtering are robust. Since we only address aerosol transmission in this study, it needs to be emphasized that both surface-contact and droplet transmission pose additional infection risks. Our algorithm for estimating the contribution by aerosols can be found in the Supplementary Material
. It can easily be adapted to other environments and scenarios, under the premise that the room size is not too large (>100 m2
), for which our rapid air mixing assumption would be violated. The algorithm can also be used through a web-based, interactive tool [83
]. Finally, we endorse the reasoning of Kai and colleagues [70
], who conclude that a, “mouth-and-nose lockdown is far more sustainable than a full lockdown, from economic, social, and mental health standpoints”.