Airborne Infection Risk
Calculator
DRAFT PRELIMINARY
User’s Manual for
Version 3.0 Beta
April 2021
Alex Mikszewski

The City University of New York
CIUS Building Performance Lab
New York, New York, USA
alexander.mikszewski@hdr.qut.edu.au

Giorgio Buonanno, Luca Stabile, and Antonio Pacitto
University of Cassino and Southern Lazio
Department of Civil and Mechanical Engineering
Cassino, Frosinone, Italy

Lidia Morawska

Queensland University of Technology
School of Earth and Atmospheric Sciences
International Laboratory for Air Quality and Heath (ILAQH)
Brisbane, Queensland, Australia

DISCLAIMER
The Airborne Infection Risk Calculator (AIRC) is made available on an as-is basis without guarantee orwarranty of any kind, express or implied. Neither the authors nor reviewers accept any liability resulting
from the use of AIRC or its documentation. This is a preliminary decision-support tool that will berevised as the science surrounding airborne transmission of SARS-CoV-2 and other pathogens advances.
Implementation of AIRC and interpretation of its calculations are the sole responsibility of the user.
VERSION 3.0 BETA ACKNOWLEDGEMENT
The authors thank Dr. Ivan Lunati at the Laboratory for Multiscale Studies in Building Physics, Empa Swiss Federal Laboratories for Materials Science and Technology, Switzerland, for his quantitative
review of Version 2.1 of the tool.
VERSION HISTORY
Initial Release Version 1.0 – July 9, 2020
Version 2.0 – September 9, 2020.
• Added stationary exposure conditions (SEC) model with complete solution of the infection risk
equations (AIRC SEC).
• Changed default values by activity level to 66th and 90th percentile instead of 85th percentile for
the Version 1.0 modeling sheet, now termed the AIRC transitional exposure conditions (TEC)model. The 66th percentile values are used for probability of infection estimates and the 90th
percentile values are used for room occupancy calculations.
• Changed Version 1.0 terminology of “individual infection risk” estimates to “probability of
infection” estimates for the AIRC TEC model (see new Glossary).
• Added ability to model any number of infectious occupants present at time zero.
• Added ability to specify two separate custom emission rates and inhalation rates for different
occupants for the AIRC TEC model.
Version 2.1 – October 6, 2020.
• Added the option to use a fixed, or certain, quanta emission rate (ERq) for the SEC model, which
can be entered on the “SEC Calculations” tab and then selected from the dropdown activity lists
on the “AIRC SEC” tab.
• Corrected the PI(ERq) plot on the activity #1, t1 graph on the “SEC Method” tab.
Version 3.0 – April 29, 2021.
• Updated SARS-CoV-2 ERq distributions and included SARS-CoV-2 variant multiplier and four
additional pathogens based on our pre-print: https://doi.org/10.1101/2021.01.26.21250580 .
• Included Revent calculations and secondary transmission histograms based on a specified
occupancy for the SEC model.
• Updated the SEC model to include stochastic ERq distributions for up to 10 infectious occupants.
• Changed default values for the TEC model to the 75th percentile (adjustable).
• Changed the maximum occupancy calculation from using the 90th percentile ERq value to 1/R(SEC) and 1/PI (TEC).
• Corrected the SEC integration calculation to account for unequal spacing of the log10(ERq) values.

2Updated Glossary of Key AIRC Terms
AIRC Stationary Exposure Conditions (SEC) Model: A constant emission source and exposure model thatconsiders the full range of possible quanta emission rates for a selected respiratory activity and theirrespective probabilities of occurrence with no time limit. Infectious and susceptible occupants aremodeled as being together in perfect coincidence.
AIRC Transitional Exposure Conditions (TEC) Model: An update to the model provided in AIRC 1.0, wheretransitional exposure scenarios of both infectious and susceptible persons coming and going can bemodeled for a total exposure period of up to 8 hours.
Room Volume (V): The volume of the room or space being modeled, within which air can be assumed tobe reasonably well mixed.
Air Exchange Rate (AER): The rate at which air in the room or space is replaced with fresh air throughmechanical or natural means, measured in the number of air changes per hour.
Susceptible Occupant A: A susceptible person who can enter and leave the room in the AIRC TEC modelat any points in time.
Continuous Occupant: A susceptible person who is in the room in the AIRC TEC model for the full lengthof the simulation.
Infectious Occupants at Time Zero: An infectious person or group of persons who are initially present in
the room in the AIRC TEC model, and who all must leave the room together at the same time.
Infectious Occupant A: An infectious person who can enter and leave the room in the AIRC TEC model at
any points in time.
Quanta Emission Rate (ERq): A quantum is the dose of airborne droplet nuclei required to cause infectionin 63% of susceptible persons. The quanta emission rate is the number of quanta released into the air
per unit time as a function of infectious occupant expiratory activities, respiratory parameters, andactivity levels.
Probability of Infection (PI): The percent chance of infection of an exposed susceptible occupantreceiving a calculated dose of quanta generated by a fixed, or certain, quanta emission rate.
Infection Risk (R): The percent chance of infection of an exposed susceptible occupant taking into
account all possible ERq values for a certain activity as defined by a probability density function.
Event Reproduction Number (Revent): The average number of secondary cases (C) expected to result froman infectious occupants at an event (can be more than one in the AIRC tool), calculated as the product of
the infection risk (R) and the number of exposed susceptible persons (S) at the event. AIRC calculates
the maximum number of occupants to maintain Revent below 1 as 1/R for the SEC model, and 1/PI for the
TEC model. The term Revent replaces the term ‘basic reproduction number, R0’ in prior versions of AIRC.

3

VERSION 3.0 BETA PREFACE & NEW EXAMPLESThis Preface briefly documents changes to the AIRC tool included in the Version 3.0 Beta release. A
comprehensive update to the User Manual will be posted once the Beta review is complete. An
annotated version of the AIRC 2.1 User Manual is included as an appendix to this document, which will
ultimately be updated and combined with this Preface.
The Version 3.0 edition of AIRC includes updates to the quanta emission rate (ERq) distributions for
SARS-CoV-2, and includes ERq distributions for four other pathogens in addition to SARS-CoV-2 as
documented in https://www.medrxiv.org/content/10.1101/2021.01.26.21250580v1 :••••Seasonal influenza virus (“flu”);
Human rhinovirus (“HRV”);
Measles virus (“MeV”); andMycobacterium tuberculosis, including a distribution based on the bacillary load of active,
untreated cases (“TB”), and that after approximately two weeks of treatment (“TB OT”).

All ERq distributions are lognormal and parameters can be found on the ‘ERq’ tab of the workbook. In
addition, an ERq multiplier is provided to account for increased transmissivity of SARS-CoV-2 variants(“CoV-2 (V)”), with a default suggested value of 2.0.
An additional upgrade for the Version 3.0 Beta edition is the inclusion of ERq distributions for up to 10
infectious occupants in the SEC version created using Monte Carlo simulation (e.g. randomly sampling
the ERq distribution up to 10 times and summing the results). For over-dispersed pathogens such as
SARS-CoV-2, TB, and MeV this provides a more accurate quantification of the higher expectedcumulative emission from multiple infected occupants in a shared airspace, as opposed to using thesimple product of the number of infecteds times the ERq for one infected occupant. Note that custom,user-defined ERq distributions or fixed ERq values still use the simple multiplication approach. The
stochastic effect of multiple infecteds is shown on the box-whisker plot on the following page for the
resting, oral breathing ERq distribution for TB (log10 average and standard deviation of -0.21 and 1.3,
respectively).

4

1E+3

1E+2

Total ERq (quanta h-1)

1E+1

1E+0

1E-1

1E-2

1E-3

1E-4
1

2

3

4

5

Number of Infected Occupants

6

In this plot the box spans the interquartile range, the whiskers extend from the 5th to 95th percentile
values, and the median is denoted by the horizontal line in the box. The median ERq for one infectedoccupant is 0.62 quanta per hour (h-1), but this rises over an order of magnitude to 9.7 quanta h-1 withthree infected occupants sharing the airspace and rises to 40 quanta h-1 with 6 infected occupants. The
above plot clearly demonstrates the challenge presented by congregate living and working settings (e.g.
care homes, abattoirs, prisons), where a single superspreading event (SSE) can initiate a massive
outbreak due to the high cumulative airborne emission expected from the second generation. It also
shows why the simultaneous introduction of multiple infecteds into such a micro-environment is much
more likely to cause an explosive airborne outbreak than introducing a single case.
Another important upgrade in Version 3.0 Beta is the inclusion of histograms showing the probability
distribution of secondary cases for the SEC model. Revent (see Glossary) is the expected number of
secondary cases arising on average from this distribution. These histograms help visualize the effect ofover-dispersion in airborne contagion and assess the probability of SSEs. For example, the histogram on
the following page for a high emitting activity shows a probability of at least 7 secondary cases ofapproximately 50%, approximately twice as likely as the probability of <1 secondary case:5

Alternatively, a low emitting activity (resting, oral breathing) histogram is presented below:

Our calculation of Revent is based on a numerical integration approximating the average value that would
result from performing the calculation using a Monte Carlo simulation randomly drawing from thelognormal ERq distribution and calculating PI. An unrestricted Monte Carlo simulation would result in aslightly higher Revent value due to increased draws from extreme percentile values. The histograms areuseful because they present the probability of specific secondary transmission outcomes independent of
the averaged, over-dispersed Revent calculation (for example, the probability of <1 secondary case).
An important consideration with the inclusion of additional pathogens is that the airborne inactivation
rate will vary between pathogens with markedly different behavior depending on ambient relative
humidity (RH). Subsequent versions of this tool may automate this, but in the interim we havedeveloped the below table which may be of assistance to users. Overall, the data are consistent withthe general observation that viruses with lipid envelopes (influenza, coronaviruses, respiratory syncytial
6virus [RSV] and measles) remain active for longer in air with lower RH, versus the converse for viruses
with non-lipid envelopes (adenovirus, rhinovirus, coxsackievirus). Please note values should beconsidered approximate because in some cases they were digitized from graphs.
Literature Summary of Viral Inactivation Rates in Aerosol (hr-1)
Relative
Humidity
20%
30%
40%
50%
60%
70%
80%

SARS-CoV-2(Dabish et al.,
2020)
0.07
0.26
0.64
1.00
1.39
1.73
--

Measles
Influenza(De Jong and (Yang and Marr,
Winkler, 1964)
2011)
0.48
0.5
0.53
0.8
0.70
1.0
1.56
1.3
4.01
1.6
6.70
1.8
5.66
2.1

EMC virus(De Jong etal., 1975)
15.4
14.8
14.8
10.8
1.06
0.16
0.05

Adenovirus 4
RSV(Miller and(Rechsteiner andArtenstein, 1967) Winkler, 1969)
3.0
1.2
-2.5
-1.4
2.2
1.8
-1.6
-3.4
0.9
5.1

Table References:
Dabisch P, Schuit M, Herzog A, et al. The influence of temperature, humidity, and simulated sunlight on
the infectivity of SARS-CoV-2 in aerosols, Aerosol Science and Technology. 2021;55(2):142-153.
De Jong JG, Winkler KC. Survival of measles virus in air. Nature. 1964;201:1054-1055.
doi:10.1038/2011054a0
Yang W, Marr LC. Dynamics of airborne influenza A viruses indoors and dependence on humidity. PLoS
One. 2011;6(6):e21481. doi:10.1371/journal.pone.0021481
De Jong JG, Harmsen M, Trouwborst T. Factors in the inactivation of Encephalomyocarditis virus inaerosols. Infect Immun. 1975;12(1):29-35. doi:10.1128/IAI.12.1.29-35.1975
Miller WS, Artenstein MS. Aerosol stability of three acute respiratory disease viruses. Proc Soc Exp Biol
Med. 1967 May;125(1):222-7. doi: 10.3181/00379727-125-32054. PMID: 4290945.
Rechsteiner J, Winkler KC. Inactivation of respiratory syncytial virus in aerosol. Journal of General
Virology, 1969;5:405-410.

AIRC 3.0 EXAMPLES
Four new examples are provided in this section to highlight the inclusion of the four additional
pathogens (TB, MeV, HRV, and flu) and to compare tool output to the seminal works on airborne
contagion. Emphasis is placed on comparing model predictions to the proportion of infected occupant
introductions that failed to reproduce infection (e.g. < 1 secondary case). Please note to better reflect
the methodology used in seminal works, the inactivation rate and particle deposition rate terms areomitted from the examples. Their inclusion would further increase the proportion of zeros in eachexample.

7

TB EXAMPLEAn ERq of 1.25 quanta h-1 is often cited for a TB patient on treatment, based on Riley et al. (1962), but
this represents a cumulative average emission rate over 2 years produced from a ward with 5 patients at
a time, with the most infectious laryngeal case producing 60 quanta h-1. This is similar to the Riley et al.
(1959) report of the earlier 2-year study that found a cumulative ERq of 0.62-0.83 quanta h- from 6
patients on the ward.
Of epidemiological significance is the proportion of zeros that can be derived from these studies,representing the probability a TB patient failed to infect any guinea pigs. For the two-year period
documented in the 1962 paper, 130 total patients occupied the ward, 107 of which were sputum smear
positive. 63 guinea pigs were infected from the smear positive group only, and the patient responsiblefor infection was identified for 50 of the 63 guinea pigs. Combining Tables 4 and 5 of Riley et al. (1962)shows that of 50 guinea pigs with identified infectors, 43 (86%) were caused by 10 of 67 initiallyuntreated patients (15%), and the remaining 7 (14%) were caused by 2 of 40 (5%) treated patients. For
simplicity, we combine these two groups to produce the following secondary case distribution (of guineapigs) from the 107 sputum smear positive patients using Table 3 of Riley et al. (1962):••••

95/107 (89%) patients infected zero guinea pigs;
5/107 (4.7%) patients infected 1 guinea pig;
2/107 (1.9%) patients infected 2 guinea pigs; and5/107 (4.7%) patients infected 5 or more guinea pigs.

The guinea pig reproduction number for this group was 50 infections/107 patients = 0.47.
The human to guinea pig transmission studies of Riley et al. were remarkably reproduced by Escombe etal. (2007 and 2008), involving 97 pulmonary TB patients with HIV co-infection, 66 of whom were
sputum-culture positive. Over 505 days, 292 guinea pigs were exposed to ward air, and 135 developed
culture-positive TB infections. Of these, spoligotyping results were available for 125 guinea pigs, andcould be traced to 12 different patients. Thus we can assume the following distribution for the 125
identified infections from the group of 66 (Escombe et al., 2008):
•
•
•
•
•

54/66 (82%) patients infected zero guinea pigs;
7/66 (10%) patients infected 1 guinea pig;
3/66 (4.5%) patients infected 2 guinea pigs;
1/66 (1.5%) patients infected 4 guinea pigs; and1/66 (1.5%) patients infected 108 guinea pigs.

The guinea pig reproduction number for this group was 125 infections/66 patients = 1.9, which is
obviously highly over-dispersed due to the extreme infectiousness of the one patient (see Meslew et al.
[2019] for epidemiological quantification of TB over-dispersion).
To illustrate the utility of the provided ERq distributions in the AIRC tool, we can simulate these two
experiments using some simplifying assumptions within the SEC framework as follows:8

Riley et al. (1962) experiment – TB OT ERq distribution (TB on treatment)
•
•
•
•
•

One infectious occupant with activity level of resting, oral breathing;
14 day exposure period (two weeks on the ward);
Room volume of 1 m3 and AER of 390 h-1 (to achieve the reported flow rate at steady-state of
390 m3 h-1, or 230 cubic feet per minute);
Inhalation rate of 0.0095 m3 h1 for the guinea pigs; and120 exposed guinea pigs

Escombe et al. (2008) experiment – TB ERq distribution (Active, untreated TB)
•
•
•
•
•

One infectious occupant with activity level of resting, oral breathing;
14 day exposure period (two weeks on the ward);
Room volume of 1 m3 and AER of 1,680 h-1 (to achieve the reported flow rate at steady-state of
1,680 m3 h-1, or 28 cubic meters per minute);
Inhalation rate of 0.0095 m3 h1 for the guinea pigs; and80 exposed guinea pigs (the median monthly chamber occupancy).

The SEC input screen for this scenario is below:

The Revent screen is as follows:9For a two-week period, the TB OT distribution predicts an 89% probability of less than 1 guinea pig
infection for the Riley et al. (1962) airflow rate with 120 exposed guinea pigs, whereas the TB
distribution predicts a 79% likelihood of less than 1 guinea pig infection for the Escombe et al. (2008)
airflow rate with 80 exposed guinea pigs, showing good agreement with the experimental data in termsof the proportion of zeros.
Instead of modeling a single patient, we can model six patients on the ward together by changing thenumber of infectious occupants to 6. The same two screens are as follows:10With six patients on a ward the probability of <1 guinea pig infections over a two week period issubstantially lower, as expected. The median ERq for the six-patient TB resting and oral breathing
scenario is 40 quanta h-1, consistent with the value of 34 quanta h-1 calculated for the cumulativeemission from a 6-bed ward with masks in use in the most recent guinea pig transmission experimentsof Dharmadhikari et al. (2012). The cumulative ERq of 138 quanta h-1 for the unmasked 6-patient cohort
occurs at the 76th percentile value of the 6-person resting, oral breathing distribution, which isapproximately the value that we define as the default ERq for the TEC version of the AIRC tool
(Dharmadhikari et al., 2012).
TB Example References:
Riley RL, Mills CC, O’Grady F, et al. Infectiousness of air from a tuberculosis ward. Ultraviolet irradiationof infected air: comparative infectiousness of different patients. Am Rev Respir Dis 1962; 85:511-525.
Riley RL, Mills CC, Myka W, et al. Aerial dissemination of pulmonary tuberculosis a two-year study ofcontagion in a tuberculosis ward. American Journal of Epidemiology 1959; 70(2):185-196.
Escombe AR, Oeser C, Gilman RH, et al. The detection of airborne transmission of tuberculosis from HIVinfected patients, using an in vivo air sampling model. Clin Infect Dis. 2007;44(10):1349-1357.
Escombe AR, Moore DAJ, Gilman RH, et al. The infectiousness of tuberculosis patients coinfected with
HIV. PLOS Medicine 2008; 5(9):e188.
Dharmadhikari AS, Mphahlele M, Stoltz A, et al. Surgical face masks worn by patients with multidrugresistant tuberculosis: impact on infectivity of air on a hospital ward. American Journal of Respiratory
and Critical Care Medicine 2012; 185(10): 1104–1109.
Melsew YA, Gambhir M, Cheng AC, et al. The role of super-spreading events in Mycobacterium
tuberculosis transmission: evidence from contact tracing. BMC Infect Dis. 2019;19(1):244. Published2019 Mar 12. doi:10.1186/s12879-019-3870-1

11

MeV EXAMPLE
Riley et al. (1962) also documents the ERq for the average child with measles to be 18 quanta h-1. This isbased on a calculated probability of infection of 11% for a three-day prodromal exposure in a wellventilated classroom reported in Wells (1955). We can reproduce the 18 quanta h-1 calculation based onPage 196 of Wells (1955) using a steady-state quanta concentration of ~0.014 quanta m-3 (from 2,500
cubic feet per infective unit) and an average room ventilation rate of ~1,300 m3 h-1 (from 30 cubic feetper minute per pupil times 25 pupils, or 750,000 cubic feet divided by three 5.5-hour days).
This is a total average emission rate from multiple years of data with over 100 introductions of individual
measles cases into irradiated and unirradiated classrooms. The 11% probability of infection comes from
Table VIII of Wells (1955) Pages 180-181, obtained by dividing 87 secondary cases by 791 susceptibles
exposed for the unirradiated classrooms. Table VIII is further described on Pages 245-246 with respect
to the proportion of zeroes, or the percent of measles introductions that failed to reproduce infection(43% in unirradiated classrooms, 75% in irradiated classrooms).
Using Table VIII, we created a secondary case distribution using the “single exposure” category for theunirradiated classrooms with 6-10 susceptibles (the most populated group). Adding two classrooms
with 6-10 susceptibles and non-functional UV lights to this group (one with 3 infections, the other with7), and we get the following distributions of secondary cases from 29 measles-infected student
introductions:
•
•
•
•
•

13/29 (45%) infected zero classmates;
9/29 (31%) infected 1 classmate;
3/29 (10%) infected 2 classmates;
3/29 (10%) infected 3 classmates; and1/29 (1.5%) infected 7 classmates.

To evaluate this scenario in the SEC model, we use a classrooms size of 198 m3 (7,000 cubic feet from
Wells [1943]), an air exchange rate of 6.5 h-1 calculated from the parameters presented in the firstparagraph of this example, and the resting, oral breathing distribution for a 16.5 hour exposure (3 days).
We can also simulate the irradiated rooms by increasing the air exchange rate to 65 h-1 based on theorder of magnitude increase in sanitary ventilation estimated by Wells (1955). The SEC input screen andRevent results screen (with 9 susceptible occupants) are on the following page:

12

The results of the data set and the AIRC model are fairly consistent in terms of the individual risk andexpected number of secondary cases for the unirradiated classrooms (1.7 in AIRC versus 1.1 indicated by
the data [31 infections/29 introductions, also consistent with an 11% infection risk with 9 susceptible
occupants]); however, the secondary case distribution is more over-dispersed in the model than
indicated by the Wells data. In other words, the model calculates a higher proportion of zeros and ahigher probability that nearly all susceptibles in the class will become infected.
MeV Example References:Wells WF. Airborne Contagion and Air Hygiene. Cambridge, Mass.: Harvard University Press; 1955:423.
Wells WF. Air disinfection in day schools. American Journal of Public Health 1943; 33:1436-1443.

13

HRV EXAMPLE
Airborne transmission of HRV colds was conclusively demonstrated by Dick et al. (1987) through a seriesof transmission trials involving 12-hour poker games. There were three poker games played with 8
infected subjects and 12 susceptible subjects each, which yielded 5, 6, and 12 secondary cases for an
overall attack rate of 61%, or 7.7 secondary cases per poker game. As an AIRC example we can evaluate
a 12-hour poker game in the SEC model based on a room volume of 92 m3 and air exchange rates of 0.3
h-1 and 3.0 h-1 (used by Rudnick and Milton [2003] to model this experiment) at the resting, oral
breathing and resting, speaking activity levels for HRV. The SEC input and Revent output screens arepresented below:

14

The resting, breathing ERq distribution appears sufficient to approximately reproduce the attack rate at
the low ventilation rate, whereas vocalization appears necessary at the higher ventilation rate (to be
expected playing poker). The “donor” poker players were also symptomatic, so periodic coughing could
also account for the additional emissions at a higher ventilation rate.
HRV Example References:
Dick EC, Jennings LC, Mink KA, Wartgow CD, Inhorn SL. Aerosol transmission of rhinovirus colds. J Infect
Dis 1987;156(3):442-8.
Rudnick SN, Milton DK. Risk of indoor airborne infection transmission estimated from carbon dioxide
concentration. Indoor Air 2003;13:237–45.

15

FLU EXAMPLE
To illustrate an AIRC example for seasonal influenza, we constructed a scenario resembling that of thehuman challenge transmission trial documented in Nguyen-Van-Tam et al. (2020), with related
quantitative analysis documented in Bueno de Mesquita et al. (2020). Our scenario consists of a 69 m3
room shared by 3 infected occupants and 10 susceptible occupants for a total of 60 hours. Risk isevaluated at air exchange rates of 0.3 h-1 and 3.0 h-1, as with the HRV scenario. Input and results tabs
are presented below:

The results indicate low secondary transmission risk for the room with an air exchange rate of 3 h-1, with
a 79% probability of <1 secondary case and overall infection risk of 6.8%. This is generally consistent
with the findings of Nguyen-Van-Tam et al. [2020], in which only 1 transmission trial resulted in a single
secondary transmission. Decreasing the ventilation rate by an order of magnitude significantly increases
infection risk and reduces the probability of <1 secondary case to 19%, showing the importance ofventilation at reducing infection risk for long exposures to relatively low emission rates.

16

Flu Example References:Nguyen-Van-Tam JS, Killingley B, Enstone J, et al. Minimal transmission in an influenza A (H3N2) humanchallenge-transmission model within a controlled exposure environment. PLoS Pathog.
2020;16(7):e1008704.
Bueno de Mesquita PJ, Noakes CJ, Milton DK. Quantitative aerobiologic analysis of an influenza humanchallenge‐transmission trial. Indoor Air 2020; 00:1– 10.

17

Appendix: Annotated V2.1 Manual

Airborne Infection Risk
Calculator
User’s Manual
Version 2.1[TO BE UPDATED COMPREHENSIVELY
AFTER BETA REVIEW OF VERSION 3.0
IS COMPLETE – SEE NOTES ADDEDTHROUGHOUT]

October 2020
Alex Mikszewski
The City University of New York
CIUS Building Performance Lab
New York, New York, USA

Giorgio Buonanno, Luca Stabile, and Antonio Pacitto
University of Cassino and Southern Lazio
Department of Civil and Mechanical Engineering
Cassino, Frosinone, Italy

Lidia Morawska
Queensland University of Technology
School of Earth and Atmospheric Sciences
International Laboratory for Air Quality and Heath (ILAQH)
Brisbane, Queensland, Australia

DISCLAIMER
The Airborne Infection Risk Calculator (AIRC) is made available on an as-is basis without guarantee orwarranty of any kind, express or implied. Neither the authors nor reviewers accept any liability resulting
from the use of AIRC or its documentation. This is a preliminary decision-support tool that will berevised as the science surrounding airborne transmission of SARS-CoV-2 and other pathogens advances.
Implementation of AIRC and interpretation of its calculations are the sole responsibility of the user.

ACKNOWLEDGEMENTS
The authors thank the following individuals for their review, comments, and support on AIRC:
Prof. Jose L. Jimenez, Ph.D
Dept. of Chemistry and CIRES
University of Colorado-Boulder
For additional information and resources on SARS-CoV-2 infection risk modeling, the authors referreaders to a modeling tool developed by Prof. Jimenez and available at:https://cires.colorado.edu/news/covid-19-airborne-transmission-tool-available
Piet Jacobs, MSc
TNO – Delft, NL
Neven Kresic, Ph.D
Geosyntec Consultants, Washington, D.C.
Honey Berk, MSc
Managing Director, CIUS Building Performance Lab
New York, New York
Jane Gajwani, PE
New York City Department of Environmental Protection
Director of the Office of Energy and Resource Recovery
Mackenzie Kinard, CEM, CBCP
Energy Project Manager
The New York Public Library
Benedetto Schiraldi
Senior Energy Manager
New York City Department of Sanitation
Rebecca Isacowitz, Daniel Donovan, and Steven Caputo
New York City Department of Citywide Administrative Services

18

Table of Contents
VERSION HISTORY ......................................................................................................................................... 3
GLOSSARY...................................................................................................................................................... 4
SECTION I: INTRODUCTION ........................................................................................................................... 5
SECTION II: AIRC CONCEPTUAL MODEL ........................................................................................................ 7
SECTION III: THE INFECTION RISK MODEL .................................................................................................. 10
SECTION IV: AIRC DATA ENTRY & RESULTS ................................................................................................ 13
SECTION V: EXAMPLE APPLICATIONS ......................................................................................................... 25
1.
2.
3.
4.

Squash ..................................................................................................................................... 25
Seafood Market ....................................................................................................................... 28
Abattoir 2.0.............................................................................................................................. 30
Hospital Waiting Area.............................................................................................................. 32

SECTION VI: REFERENCES ........................................................................................................................... 34

VERSION HISTORY
Initial Release Version 1.0 – July 9, 2020
Version 2.0 – September 9, 2020.
• Added stationary exposure conditions (SEC) model with complete solution of the infection risk
equations (AIRC SEC).
• Changed default values by activity level to 66th and 90th percentile instead of 85th percentile for
the Version 1.0 modeling sheet, now termed the AIRC transitional exposure conditions (TEC)model. The 66th percentile values are used for probability of infection estimates and the 90th
percentile values are used for room occupancy calculations.
• Changed Version 1.0 terminology of “individual infection risk” estimates to “probability of
infection” estimates for the AIRC TEC model (see new Glossary).
• Added ability to model any number of infectious occupants present at time zero.
• Added ability to specify two separate custom emission rates and inhalation rates for different
occupants for the AIRC TEC model.
Version 2.1 – October 6, 2020.
• Added the option to use a fixed, or certain, quanta emission rate (ERq) for the SEC model, which
can be entered on the “SEC Calculations” tab and then selected from the dropdown activity lists
on the “AIRC SEC” tab.
• Corrected the PI(ERq) plot on the activity #1, t1 graph on the “SEC Method” tab.

19

SECTION 1. INTRODUCTION
The Airborne Infection Risk Calculator (AIRC) is an airborne contagion modeling tool programmed in
Microsoft Excel and designed to assist facility managers, building engineers, and public and occupational
health professionals in prospectively evaluating individual infection and community transmission risks
associated with specific indoor environments. AIRC can help users address two primary questions
related to the risks associated with occupying an indoor space when community transmission of an
infectious airborne pathogen, such as SARS-CoV-2, is occurring:
1. What is the potential infection risk associated with varying lengths of stay in the space?
2. What number of occupants helps maintain a basic reproduction number (R0) less than one toprevent the exposure from further contributing to disease spread in the population?
AIRC is directly based on the novel risk modeling approach developed for SARS-CoV-2 by Buonanno et al.
(2020a) and Buonanno et al. (2020b). As stated by Buonanno et al. (2020a):“This approach, based on the principle of conservation of mass, represents a tool to connect the medicalarea, concerned with the concentration of the virus in the mouth, to the engineering area, dedicated tothe simulation of the virus dispersion in the environment.”
While AIRC and its underlying methods were created in direct response to the SARS-CoV-2 pandemic,
the hope is that AIRC becomes a useful risk management tool for other airborne pathogens such as
influenza, tuberculosis, and rhinovirus. The foundation for AIRC was provided by quantification of thequanta emission rate data of SARS-CoV-2 as a function of different respiratory activities, respiratory
parameters, and activity levels. A quantum is the dose of airborne droplet nuclei required to cause
infection in 63% of susceptible persons (Buonanno et al., 2020a). AIRC applies this quanta emission rate
in an acknowledged infection risk model to simulate the individual infection risk associated withcustomized exposure scenarios, and the average number of infected people resulting from this scenario,
i.e. R0 (the basic reproduction number). [CHANGED TO EVENT REPRODUCTION NUMBER]
New to Version 2.0, AIRC has two different risk modeling frameworks, both contained within the same
workbook:#1) AIRC Stationary Exposure Conditions (SEC) – a constant emission source and exposure
model that considers the full range of possible quanta emission rates for a selected respiratory
activity and their respective probabilities of occurrence. The risk equations are completely
solved for three (3) different user-defined exposure times without a time limit.
#2) AIRC Transitional Exposure Conditions (TEC) – an update to the model provided in AIRC 1.0,
where transitional exposure scenarios of both infectious and susceptible persons coming andgoing can be modeled for a total exposure period of up to 8 hours.
For both model types, the viral emission rate for each infectious individual, and the inhalation rate forsusceptible individuals, can be selected based on a list of activities, and the user can specify thedimensions of the occupied indoor space and an infectious viral removal rate term accounting for threemechanisms: particle deposition, viral inactivation, and site-specific ventilation rate. Data input and20results presentation were simplified to facilitate ease of use by non-quantitative professionals, while
also providing some flexibility for more advanced users.
Limitations
The primarily limitation of AIRC is its adoption of a completely mixed box model approach to simplify
extremely complex indoor fluid dynamics processes. The result of this simplification is a viral exposure
concentration that is uniform across the room, instead of a three-dimensional, spatially variable plumewith higher exposure concentrations closer to the source of the viral emissions. Additional limitations
include the adoption of uniform values representing particle deposition and viral inactivation rates that
do not vary according to site-specific environmental conditions, and the maximum TEC simulation lengthof 8 hours. Secondary engineering and administrative controls, such as air filtration, UV disinfection,and mask-wearing, are not explicitly included in AIRC, but more advanced users can adjust input
parameters to account for these interventions. Additional discussion of AIRC concepts and theirlimitations are provided in Section II, with specifics surrounding the useful scale of AIRC applications.
Lastly, a major epidemiological limitation is the requirement to specify the number of infectious
occupants in a space and their constant emissions-generating activity, rather than adopting aprobabilistic approach taking into considering the overall prevalence of the virus in the community andthe likelihood of infectious person occupancy times and activities. Additionally, the selected doseresponse model does not consider variation in host sensitivity to the pathogen of interest, for example
immunity from prior exposure or vaccination.
AIRC is a risk screening tool that approximates more complicated processes occurring in reality. As such,conservative assumptions should be used for input parameters, with special attention given to thequanta emission rate and room air exchange rate. More sophisticated numerical models should beapplied for situations where high-resolution, spatially representative results are required, or where
needed for detailed design of secondary engineering controls in high-risk, complex settings. For CFD
examples, see Vuorinen et al. (2020), Hosotani et al. (2013), and Chen et al. (2012).
Target Users
The target users of AIRC are building managers, engineering consultants, and public, occupational, andenvironmental health scientists. Users should be proficient in Microsoft Excel and have a basic
understanding of building systems and indoor air quality. Users would also benefit from a basic
understanding of human health risk assessment (see https://www.epa.gov/risk/conducting-humanhealth-risk-assessment for an overview). More generally, the target users are the technicalprofessionals working to minimize the risk of airborne disease transmission by implementing the fivestep framework outlined by Morawska et al. (2020):
1. Use engineering controls to reduce the risk of airborne infection;
2. Use existing systems to increase ventilation rates (outdoor air change rate) and enhance
ventilation effectiveness;
3. Eliminate air-recirculation within ventilation systems so as to just supply fresh (outdoor) air;
4. Supplement ventilation with filtration systems to capture airborne microdroplets; and5. Avoid over-crowding

21

SECTION II: AIRC CONCEPTUAL MODEL
To understand the airborne transmission pathway, it is helpful to advance the conceptual model
presented in Morawska and Cao (2020), Morawska (2006), and Li et al. (2005). For purposes of AIRC,“airborne transmission” refers to inhalation of airborne droplet nuclei, or aerosols, at separation
distances that can be greater than 2 meters away from an infectious emission. This conceptual model
represents the concentration of virus-laden small droplets as a plume, where expired viral content isdiluted immediately upon expiration and as it travels in the air carried by the air flow. As a result, theconcentration of the virus does not increase uniformly in the interior environment of the enclosed space
but is found at higher concentrations closer to the infectious subject.
Unfortunately, due to the complexity of indoor computational fluid dynamics (CFD), modeling thisspatiotemporal plume presents a challenge to the broader public and occupational health community.
As with other environmental contaminants in air and water, it is helpful instead to simplify the spatial
component of fate and transport and use a completely mixed box model approach for risk calculation
purposes. AIRC adopts this completely mixed box modeling approach, directly following the process
outlined in Buonanno et al. (2020a) and Buonanno et al. (2020b). The emission of virus-laden small
droplets is assumed to be instantaneously and completely mixed into a box representing an enclosed
indoor environment or room, creating a time-dependent exposure concentration to susceptible
occupants inside the box. A conceptual representation of airborne transport and the box-model
simplification for AIRC is presented as Figure 1.

22

Figure 1: Small Droplet Transport and AIRC Box Model Approach Modified from Morawska and Milton (2020)

Figure 1 (A) illustrates the creation of an infectious droplet plume spreading throughout a poorly
ventilated room. Concentrations are higher closer to the emission source and decay moving further
away and will reach a pseudo steady-state profile in time if conditions are held approximately constant.
Figure 1 (B) shows the corresponding completely mixed box model for this scenario, where theconcentration in time becomes uniform across the room, and susceptible individuals are therefore
exposed to the same concentration regardless of their position in the room. Differences in exposure risk
between susceptible occupants in the room is therefore reduced to a function of exposure duration
rather than spatial location. Figure 1 (C) shows the conceptual effect of increasing the ventilation rate in
the room. The plume is reduced in intensity and extent, and the susceptible occupant is exposed tolower viral concentrations and consequentially has reduced infection risk. Figure 1 (D) illustrates thecompletely mixed box model approach for the room with improved ventilation. The susceptible
occupant is exposed to a lower concentration and thus has a lower probability of infection for the same
exposure time.
With the box model simplification, it becomes straightforward for AIRC to calculate changes in room
concentration over time. Depending on the strength and duration of the emission rate and theventilation rate in the room, the viral droplet concentration profile versus time will assume a predictable
curve shape. Three common concentration curves are presented in Figure 2.

23

Figure 2: Common concentration versus time curves for different contaminant source scenarios on a linear scale,based on concept presented in NEEC (2015). Figure 2 (A) represents the concentration profile in a room with aconstant emission source and constant ventilation rate, showing how the concentration approaches a steady-state
asymptote. Figure 2 (B) presents a scenario where the same emission is eliminated and concentrations decay
accordingly due to constant ventilation. Figure 2 (C) shows a more dynamic scenario where there are two
separate, non-overlapping emission periods, with the second period resulting in higher concentration either due toa higher emission rate or a lower ventilation rate.

With its simplifying assumptions, the accuracy and utility of AIRC becomes a question of scale. In
general, the smaller the enclosed space and the more completely mixed the air, the more the results willapproximate reality. An upper limit to the appropriate room size for AIRC cannot be definitively
provided at this time, but applications to indoor spaces that are thousands of square meters in area withcomplex HVAC zoning are unlikely to produce useful risk predictions. Alternatively, a room ofapproximately 500 square meters or less comprising a single HVAC zone is more likely to be a goodcandidate for AIRC application. A practical way to accommodate larger buildings or spaces with complex
zoning is to divide the area into sub-zones, each represented by an AIRC model. The process isconceptually illustrated in Figure 3, which depicts a multi-zone modeling approach to characterize theMarch 2003 outbreak of SARS-CoV-1 in Ward 8A at the Prince of Wales Hospital in Hong Kong (Li et al.,2005 and Xiao et al., 2017).

24

Figure 3: Simulated infectious aerosol distribution using a complex CFD model (A) and a simplified multi-zone
approach (B), modified from Xiao et al., 2017. Predicted aerosol concentrations for each approach are overlaid on
top of the reported SARS-CoV-1 attack rate in the zone. The index patient was located in the top left zone.

Figure 3 (A) presents a more realistic spatial representation of aerosol distribution, characterized by aplume emanating from the index patient. However, the multi-zone approach on Figure 3 (B) is likely
sufficient to calculate infection risk, especially where conservative assumptions are used. Wagner et al.
(2009) takes a similar approach by implementing a Sequential Box Model (SBM) that incorporates air
exchange between zones.
Retrospective assessments of the AIRC risk modeling approach are provided in Buonanno et al. (2020b),
simulating the outbreaks of SARS-CoV-2 at a restaurant in Guangzhou, China and a choir rehearsal in
Skagit, WA, USA. The ability of the AIRC approach to reasonably reproduce these airborne“superspreading events” indicate its validity for risk screening purposes, especially when considering theurgent and time-critical need for quantitative tools to inform decision making during the SARS-CoV-2
pandemic. Furthermore, as noted in Buonanno et al. (2020a), in epidemic modeling quantifying
community transmission, it is impossible to specify the geometries, the ventilation, and the locations of
all infectious sources in each microenvironment. Therefore, adopting the completely mixed box model
approach is generally more reasonable than hypothesizing about myriad complex environments because
results must be interpreted on a statistical basis (Sze To and Chao, 2010).

25

SECTION III: THE INFECTION RISK MODEL
The detailed modeling approach implemented in AIRC and described in this section directly follows from
Buonanno et al. (2020a) and Buonanno et al. (2020b).
The model used to quantify airborne infection risk in AIRC was developed by Gammaitoni and Nucci(Gammaitoni and Nucci, 1997), and was successfully applied in previous papers estimating the infection
risk due to different diseases (e.g. influenza, SARS, tuberculosis, rhinovirus) in various settings such as
airplanes (Wagner et al., 2009), cars (Knibbs et al., 2011), and hospitals. The model calculates thequanta concentration (n) in an indoor environment over time, subject to a constant quanta emission
rate and removal rate. As a reminder, a quantum is the dose of airborne droplet nuclei required tocause infection in 63% of susceptible persons (Buonanno et al., 2020a). The full equation for n(t),including an initial concentration term (n0), is presented below:𝑛𝑛(𝑡𝑡) (𝐸𝐸𝑅𝑅𝑞𝑞 ⦁𝐼𝐼
𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞−𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 ⦁𝑡𝑡(1 − 𝑒𝑒 −𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼⦁𝑡𝑡 )
)
=
𝑛𝑛
𝑒𝑒
+
0
𝑚𝑚3
𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼⦁𝑉𝑉

where IVRR (hr-1) represents the total infectious viral removal rate, I is the number of infectious subjects,
V is the volume of the indoor air environment, and ERq is the abovementioned quanta emission rate(quanta/hr) characteristic of the specific disease/virus under investigation. The IVRR term is the sum of
three contributions, all expressed in hr-1: the air exchange rate (AER) via ventilation (typically measured
in the number of air changes per hour), the particle deposition rate on surfaces (k, e.g. via gravitational
settling), and the viral inactivation rate (λ) (Yang and Marr, 2011). Details on specification of thesethree parameters are provided in Section IV.
In addition to the constant ERq and IVRR values, it is assumed that the latent period of the disease islonger than the time scale of the model, and the droplets are instantaneously and evenly distributed in
the room, using the box model approach described in Section II (Gammaitoni and Nucci, 1997). Onceagain, the latter represents a key assumption for the application of the model as it considers that the air
is well-mixed within the modelled space. The risk associated with an exposure is dependent on the doseof quanta and duration of exposure, as well as the probability of occurrence of this exposure condition.
The dose of quanta (Dq) received by a susceptible subject can be obtained by integrating the calculated
quanta concentration over the total exposure time (T), as follows:

where IR is the inhalation rate of the exposed subject (m3/hr) which is a function of the subject’s activity
level. To determine the probability of infection (PI, %) of exposed susceptible occupants for a fixed ERq,a simplified exponential dose-response model is used as presented below:

26

The probability of infection assumes different values based on the selected quanta emission rate. As
such it is defined as a conditional probability for a discrete quanta emission rate, or PI(ERq). To evaluate
the individual risk (R) of an exposed person for a given exposure scenario, the probability of occurrenceof each ERq value (PERq), which is defined by an ERq probability density functions (pdfERq), must also be
evaluated. Since the probability of infection (PI(ERq)) and the probability of occurrence PERq are
independent events, the individual risk for a given ERq, R(ERq), can be evaluated as the product of thetwo terms:
𝑅𝑅�𝐸𝐸𝐸𝐸𝑞𝑞 �(%) = 𝑃𝑃𝐼𝐼 �𝐸𝐸𝐸𝐸𝑞𝑞 � ∙ 𝑃𝑃𝐸𝐸𝐸𝐸𝐸𝐸

where PI(ERq) is the conditional probability of the infection, given a certain ERq, and PERq represents therelative frequency of the specific ERq value. The individual risk (R) of an exposed person, considering thefull spectrum of possible ERq values within a defined distribution, can then be calculated by integrating
the pdfR for all possible ERq values, i.e. summing up the R(ERq) values as follows:
𝑅𝑅 (%) = � 𝑅𝑅�𝐸𝐸𝐸𝐸𝑞𝑞 �𝑑𝑑𝐸𝐸𝐸𝐸𝑞𝑞 = � �𝑃𝑃𝐼𝐼 �𝐸𝐸𝐸𝐸𝑞𝑞 � ∙ 𝑃𝑃𝐸𝐸𝐸𝐸𝐸𝐸 �𝑑𝑑𝐸𝐸𝐸𝐸𝑞𝑞
𝐸𝐸𝐸𝐸𝑞𝑞

𝐸𝐸𝐸𝐸𝑞𝑞The individual risk R also represents the ratio between the number of infection cases (C) and thenumber of exposed susceptible individuals (S) for a given exposure scenario and taking into account all
possible ERq values for the infectious subject under investigation (Buonanno et al., 2020b). In
retrospective analyses of documented outbreaks, the known C/S ratio is typically defined as the “attackrate”.
New to Version 2.0, AIRC includes a stationary exposure conditions model (“AIRC SEC”) that fully
implements the above method to calculate individual risk taking into account all possible ERq values foran assumed infectious occupant activity. However, where the room ventilation, volume, subject
activity, etc. are treated as constant values, Buonanno et al. (2020b) shows that a simplified estimate of
R can be calculated by using the ERq value assumed with a probability of occurrence PERq=1 (i.e.
considered as a certain emission) which induces a PI(ERq) equal to the risk R as shown through the full
integration analysis. This certain emission value is calculated to be the 66th percentile ERq value.
Therefore, when the 66th percentile ERq value is used, the individual risk can be assumed toapproximately equal to the probability of infection. AIRC therefore provides the 66th percentile values
for a range of respiratory activities and recommends these values to be used in risk calculations for the
AIRC Version 1.0 update with transitional exposure conditions, now termed “AIRC TEC” (Buonanno et al.,2020b). [CHANGED TO 75th PERCENTILE {ADJUSTABLE} IN VERSION 3.0 BETA FOR NEW EMISSION
DISTRIBUTION WITH INCREASED OVERDISPERSION] The key distinction between AIRC SEC and AIRC TEC
is the type of exposure scenario being evaluated. AIRC SEC assumes perfect coincidence in the presenceof the two subjects as they enter together and leave the environment together. AIRC TEC allows
consideration of more dynamic scenarios where infectious and susceptible occupants can come and go
and different times, and susceptible occupants can be exposed to residual viral droplets in the air.
When the exact same scenario is evaluated using AIRC SEC and AIRC TEC, the results will be nearly
identical for short exposures (<4 hours), but for longer exposures (e.g. 8 hours) the results may beslightly different due to the 66th percentile approximation adopted in AIRC TEC. [CONSISTENCY
BETWEEN SEC AND TEC WILL VARY BY PATHOGEN, THE TEC PERCENTILE IS ADJUSTABLE]27

On a population level, the basic reproduction number R0, representing the number of susceptible people
infected after the exposure time, can be determined by multiplying the probability of infection (PI, %) by
the number of exposed individuals. For purposes of AIRC, however, reproductive effects are calculateddifferently to provide the maximum number of occupants that may keep the R0 below 1 for the scenario
in question. In this way the user can obtain a potential occupancy or crowding index that considers theneed to reduce community transmission. [CHANGED TERMINOLOGY TO EVENT REPRODUCTION
NUMBER]
This maximum occupancy calculation uses the following equation, rounded down to the nearest integer:
𝑀𝑀𝑀𝑀𝑀𝑀. 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑓𝑓𝑓𝑓𝑓𝑓 𝑅𝑅0 (𝑡𝑡) < 1 =1
𝑃𝑃𝐼𝐼 (𝑡𝑡)

For both AIRC SEC and AIRC TEC, the PI value corresponding to the 90th percentile ERq value is used as
the maximum R(ERq) values occur in the narrow range of 90th-95th percentile for the scenarios evaluatedby Buonanno et al. (2020b). [WITH THE IMPROVED EMISSION DISTRIBUTION THE MAXIMUMOCCUPANCY IS NOW CALCULATED AS 1/R FOR THE SEC VERSION AND 1/PI FOR THE TEC VERSION]

28

SECTION IV: AIRC DATA ENTRY & RESULTS
There are two tabs for the user to enter data in AIRC and view model output, the first tab entitled “AIRC
SEC” (new to Version 2.0) and the second tab entitled “AIRC TEC”. These two modeling sheets arecompletely independent. If a user is only interested in one approach, the other one can be ignored. For
both sheets the user must enter a value into all cells with white fill and black text. All cells with black fill
and white text are calculated by AIRC and are locked to the user. Cells with gray fill are informational
and are not used by AIRC. In addition, drop-down lists in cells with yellow fill are used on both tabs to
select the desired activities for infectious and susceptible occupants. Based on the assumption thatusers are most concerned with SARS-CoV-2, quanta emission rates associated with the selected
activities are for SARS-CoV-2. These values are defined on the “SEC Calculations” tab and the “TEC ERq
IR” tabs for the two different modeling approaches.
A description of each tab in the AIRC 2.0 workbook is provided below. All tabs associated with AIRC SEC
are shaded blue, all tabs associated with AIRC TEC are shaded yellow.
AIRC SEC Tabs
AIRC SEC Tab 1 Name: AIRC SEC
Description: This tab contains model input and results for the constant source and exposure, termed“stationary exposure conditions” model (SEC) new to AIRC Version 2.0. The individual infection risk andmaximum room occupancy is calculated for six different selectable infectious occupant activities forthree different exposure lengths. In addition, a sensitivity analysis on the air exchange rate parameter ispresented for one infectious occupant activity where the user can enter five different air exchange rates
to see how model results change.
Model Input Parameters:
1. Room Dimensions
The room dimension parameters for the user to enter are the floor plan area (in m2) and the ceilingheight of the occupied indoor space to be modeled (in m). The product of the area and ceiling height is
the room volume (m3).
2. Infectious Viral Removal Rate
As defined in Section III, the Infectious Viral Removal Rate (IVRR) is the sum of the air exchange rate
(AER) via ventilation (also termed the number of air changes per hour), the particle deposition rate on
surfaces (k, e.g. via gravitational settling), and the viral inactivation rate (λ). Entry details on these threeparameters are presented below.

29

Parameter

Air Exchange Rate (AER)

Unitshour (hr-1)

Recommended Values

Use site-specific measured values, or design or estimated values if
field measurements are unavailable. For natural ventilation(infiltration only), values of 0.2 – 0.5 hr-1 are recommended. For the
opening of doors and windows on one side of a room, values of 1.0– 5.0 hr-1 are suggested. It is noted that ventilation rates throughwindows are highly site-specific and the user is cautioned against
making assumptions on the higher end of this provided range.

Notes/Estimation
Methods

The AER is the most important site-specific parameter in themodel and the largest contributor to virus removal. Therefore,site-specific measurements or design values are the best sources
for parameter input. AER can be simply calculated as the total
fresh outdoor airflow (OA) divided by the volume of the room.
Recirculated airflow should not be included in the AER calculation
as it represents fresh airflow only.

If actual total and outdoor airflows cannot be measured directly,
the percent OA delivered to a space is commonly estimated using
air handler carbon dioxide (CO2) concentrations in parts per million(ppm) as follows:

Note that the above calculation is also routinely performed using
temperatures instead of CO2 concentrations, but that it is lessaccurate, especially when temperature differences are small.
Another common method of estimating ventilation rate is using“rule of thumb” values per person based on steady-state CO2
concentrations achieved in offices and classrooms with ASHRAE
62n default occupancy rates, as follows (NEEC, 2015):
Zone CO2
(ppm)
2,500
1,400
1,000
750

Outside Air(CFM perperson)
5
10
15
30
30

Outside Air(Liters (L)/sper person)
2.4
4.7
6.9
14

Outside Air(m3/hr perperson)
8.5
17
25
51

AIRC follows the risk minimization framework outlined by
Morawska et al. (2020), in which enhanced ventilation is theprimary reliable and readily available line of defense against
airborne transmission in indoor air environments. Secondary
measures where ventilation alone may be insufficient, such asenhanced filtration, ultraviolet germicidal irradiation (UVGI),
and/or room humidification, are not explicitly included in AIRC.
Advanced users of AIRC can include filtration in the model byadding “equivalent” air exchanges to the AER term.
Azimi and Stephens (2013) provide a comprehensive review ofinfectious droplet nuclei filtration efficiencies (see Figure 4). To
incorporate filtration removal in AIRC, the user can calculate theequivalent AER in hr-1 as follows:

where Qrecirculated is the airflow rate recirculated from the space
through the filter, ƞfilter is the infectious droplet removal efficiencyof the filter, and V is the volume of the room.
The recirculated airflow plus the fresh outdoor airflow will equal
the total airflow rate of the air handler. Remember that the air
exchange rate is calculated using only the fresh outdoor airflow
rate, and if air is recirculated the AER will be reduced. Example
Application 3 presents how this calculation is performed for an air
handler serving an office space.

Parameter

Particle Deposition Rate (k) (SARS-CoV-2)

Units

hr-1

Recommended Value

0.24

Notes/Estimation
Methods

The recommended value is from Buonanno et al. (2020a), whichcalculated the deposition rate as the ratio between the settling
velocity of super-micrometric particles (roughly 1.0 × 10−4 m s−1 asmeasured by Chatoutsidou and Lazaridis [2019]) and the height ofthe emission source (1.5 m). A site-specific computational fluid
dynamics (CFD) model may be needed to quantify this parametermore accurately.

31

Parameter

Viral Inactivation Rate (λ) (SARS-CoV-2)

Units

hr-1

Recommended Value

0.63

Notes/Estimation
Methods

The recommended viral inactivation rate is calculated from the
SARS-CoV-2 half-life (1.1 hr) detected by van Doremalen et al.
(2020) as follows:

Users should follow the evolving literature on SARS-CoV-2 with aview towards refinement of this parameter. Fears et al. (2020)
reports retained infectivity and virion integrity of SARS-CoV-2 forup to 16 hours in respirable-sized aerosols. For a resource to help
incorporate UV treatment or relative humidity into this parameter,
the user is referred to: https://www.dhs.gov/science-andtechnology/sars-airborne-calculator (Schuit et al., 2020).

Figure 4: Infectious droplet nuclei filtration efficiency (ƞfilter) as a function of HVAC filter MERV rating, using theminimum reported values from Azimi and Stephens (2013).

32

3. Number of Infectious Occupants
New to Version 2.0, the user can model infectious occupants as a group of multiple individuals in theroom for the same defined period of time. For AIRC SEC, the infected group is in the room for the fulllength of the simulation with the susceptible individuals. Any positive integer may be entered for thisvalue. [NOW SELECTABLE FOR UP TO 10 INFECTIOUS OCCUPANTS]
4. Initial Quanta Concentration
The initial quanta concentration term, in quanta/m3, has been provided in the event the user wants tomodel a scenario where residual viral emissions are present in indoor air at time zero. This is a usefulfunction where the user wants to begin a new simulation using the final quanta concentration of aprevious simulation. If the indoor air environment is expected to be free of airborne virus at the start ofthe simulation, the user should enter zero for this parameter.
5. Exposure Time
AIRC SEC calculates infection risk and maximum occupancy for three different exposure times that theuser can enter in hours (with no time limit).
6. Susceptible Inhalation Rate
The user can select from a list of pre-determined activity levels (resting, standing, light exercise, heavy
exercise) to quantify the inhalation rate of susceptible occupants in the model. If a user would like toenter a custom inhalation rate value, this may be done on the SEC Calculations tab.
7. Infectious Occupant Activities
The user can select six (6) different pre-determined activity levels from the list provided in Table 1 toquantify the viral emission rate (ERq) of infectious occupants of the room, from Buonanno et al. (2020b).
Table 1: Selectable Infectious Occupant Activities and Associated ERq Values[SUPERCEDED – SEE ‘ERQ’ TAB OF VERSION 3.0 BETA FOR UPDATED DISTRIBUTIONS FOR SARS-COV-2
AND OTHER PATHOGENS]
The results represent six different model realizations for the same scenario, not different infectious
occupants doing different things in the same room. This can be conceptualized as a sensitivity analysis
on the selected ERq distribution for the model. If a user would like to enter a custom ERq distribution ora fixed ERq value for use in the model, this may be done on the SEC Calculations tab.
8. AER Sensitivity Analysis
To help the user assess how model results change based on air exchange rate, the user may enter fivedifferent AER values that will re-calculate results for infectious occupant activity #6 only.
9. Model Results
This section presents the model results as the individual infection risk for exposed susceptible occupants
for the three different exposure times, and as the estimated maximum number of occupants to maintaina reproductive number less than one for the modeled scenario. In total the results reflect 33 different
33model runs: 6 different infectious occupant activities at 3 different exposure times for the primary AER(18 runs), and 5 different AERs at 3 different exposure times for infectious occupant activity #6 (15 runs).
AIRC SEC Tab 2 Name: SEC Graphs
Description: This tab contains graphical results of the 33 different simulations run for AIRC SEC for theuser’s input parameters. Results are presented in four (4) different graphs. The top graphs present
infection risk as a function of the quanta emission rate at the fixed user-entered AER, and as a functionof AER using infectious occupant activity #6. The bottom graphs simply present the estimated maximum
room occupancy instead of the infection risk. Graphs will update automatically as user’s change input
parameters, including titles.
AIRC SEC Tab 3 Name: SEC Method [CHANGED TO REVENT CALCULATION PAGE WITH DEFINEDOCCUPANCY AND SECONDARY TRANSMISSION PROBABILITY HISTOGRAMS]AIRC SEC Tab 4 Name: SEC Calculations
Description: This tab contains the calculations for the stationary exposure conditions implementation ofAIRC. Advanced users can enter a fixed (certain) ERq value, custom ERq probability distributions andinhalation rates for use in the model. [A CUSTOM SARS-COV-2 VARIANT MULTIPLIER CAN ALSO BE
ENTERED ON THIS TAB]
AIRC TEC Tabs
AIRC TEC Tab 1 Name: AIRC TEC
Description: This tab contains the updated version of the Version 1.0 AIRC model with transitional
exposure conditions. New to Version 2.0, the user selects the infectious occupant and susceptible
occupant activities directly on the input and results tab, now named AIRC TEC. For this version of themodel, the total time of occupancy, in minutes, represents the maximum duration of continuous
occupancy of the modeled room by any person. The maximum simulation length supported by AIRC is
480 minutes (8 hours), therefore, the maximum value for this parameter is 480. This parameter must beentered as an integer value. AIRC calculates the continuous occupancy risk and maximum room
occupancy based on the total time of occupancy entered by the user. The total time of occupancy value
entered by the user should be greater than the time of exit values for Infectious Occupant #1, Infectious
Occupant #2, and Susceptible Occupant A.
To define an exposure scenario, the user enters the time of entry and time of exit for infectious
occupants and one susceptible occupant. All times are to be entered in minutes and represent minutes
after the start of the simulation. Any number of non-zero infectious occupants can be assumed to bepresent at the start of the simulation (time zero), but all of these occupants must leave the room at thesame time. [FOR VERSION 3.0 BETA UP TO 10 CAN BE SELECTED AS WITH SEC] Infectious Occupant A
can be omitted from the model by selecting “No” from the drop-down list next to the “Include in
Model?” field. The values for quanta emission rate (ERq) for each infectious occupant, and the value forinhalation rate (IR) for Susceptible Occupant A and a continuous occupant of the space are pulled fromthe TEC ERq IR Tab.

34

The results section reports calculated risk values of interest for Susceptible Occupant A and for othersusceptible persons who occupy the space for the entirety of the simulation (i.e. the total time ofoccupancy parameter). The following values are reported:
•

•

•

•

•

Modeled Exposure Time: For Susceptible Occupant A this will be the time of exit minus the timeof entry in minutes. For continuous occupants this will be equal to the total time of occupancy
parameter.
Probability of Infection: The probability of infection, PI (%), represents an estimate of thepercent chance of infection for exposure to the quanta concentration profile integrated acrossthe modeled exposure time for a certain, known emission rate. When the 66th percentile valueof ERq is used, the probability of infection is assumed to be approximately equal to the infection
risk taking into account all possible ERq values for the infectious subject under investigation.
However, since this is an approximation and a user may enter a custom value for ERq, AIRC TEC
uses the probability of infection term instead of individual risk.
Exposure Time for 0.1% PI: This value is the exposure time in minutes associated with a 0.1%
probability of infection, or a threshold of 10-3. If the exposure time is less than this value, PI isestimated to be less than 0.1%, and vice versa. The quanta concentration profile for Susceptible
Occupant A will be different from that of the continuous occupants; hence, calculated exposure
times may be different. If Susceptible Occupant A or the continuous occupants do not exceed
the 0.1% PI threshold during the simulation, AIRC presents a result of greater than (“>”) themodeled exposure time. If a user enters extreme value parameters such as a very high ERq
combined with a very small room volume, a #N/A value may result, meaning that the PI
threshold is already exceeded at the first time step of 1 minute.
Exposure Time for 1.0% PI: This value has the same characteristics as the above parameter but
uses a higher PI threshold of 1% (or 10-2). The calculated exposure time for 1% PI will be higher
than that of 0.1% PI. The decision of what threshold to use for risk management purposes is up
to the user and should depend on the type of occupancy and characteristics of the inhabitants
and should appreciate the screening-level intent of the tool. Calculated exposure times
associated with other risk thresholds can be evaluated on the Model Graph.
Maximum Room Occupancy for R0 < 1: [CHANGED TO REVENT] This parameter represents the
maximum number of occupants allowable in the room for the exposure time and quantaconcentration profile of the designated scenario (e.g. Susceptible Person A or continuous
occupancy) in order to keep the basic reproduction number (R0) below 1 for that exposure. In
other words, more than one person may be expected to become infected if the occupancy
increases beyond this number. Conceptually this is easier to understand for the continuous
occupancy calculation as it represents the allowable number of occupants who will be present
for the entire simulation. For Susceptible Occupant A, the maximum room occupancy
calculation applies to a cohort of persons sharing the same exact exposure profile as Susceptible
Occupant A (i.e. entering and leaving at the same time). If a user is interested in the calculated
R0 value for a pre-determined number of occupants, it can be obtained by dividing the predetermined occupancy by the maximum room occupancy calculated by AIRC. It is reiterated
that the maximum room occupancy is for a specific exposure scenario up to a maximum of 8hours (480 minutes). If this exposure scenario is expected to occur repeatedly, such as daily, theuser may want to reduce the calculated occupancy further as R0 would be expected to exceed 1
over time. Alternatively, these cases could be evaluated as long-term exposures using the AIRC
35

SEC model. As the reproduction number scales linearly with occupancy, reducing the AIRC
calculated occupancy by one-half corresponds to an estimated R0 of 0.5.
AIRC TEC Tab 2 Name: TEC ERq IR
Description: This tab provides the ERq and IR rates used in the AIRC TEC model in quanta/hr and m3/hr,
respectively, associated with the selectable activity levels. Two reference value tables are provided, one
for infectious occupant quanta emission rates, and one for susceptible occupant inhalation rates.
Summary tables are provided that show what values are used in the model based on the user’s
selections. If Infectious Occupant A is not included in the model as indicated on the AIRC TEC tab, its
displayed value will be greyed out. If the user would like to use a custom value for IR or ERq, the user
can enter values on this tab. Separate custom emission rates can be entered and selected for the groupof infectious occupants present at time zero, and for Infectious Occupant A if included in the model.
All provided values of ERq in the lookup tables are specific to the SARS-CoV-2 virus and represent the 66th
and 90th percentile value of a lognormally distributed data set (base 10), as reported in Buonanno et al.
(2020b). The 66th percentile value is provided because the total individual risk is expected to beapproximately equal to the probability of infection at this “certain emission” ERq value (Buonanno et al.,2020b). Hence the 66th percentile values are used for calculation of the probability of infection. The
90th percentile value is used for calculation of the maximum room occupancy, as the maximum R(ERq)values occur in the narrow range of 90th-95th percentile for the scenarios evaluated by Buonanno et al.
(2020b). [CHANGED TO 75TH PERCENTILE VALUE AS DEFAULT WHICH CAN BE ADJUSTED BY THE USER –THIS VALUE IS ALSO USED TO CALCULATE THE MAXIMUM ROOM OCCUPANCY]
All values of IR for the susceptible occupant are also provided from Buonanno et al. (2020a). As with
ERq, a custom entry option is provided for IR for advanced users, and different IR values can be specified
for Susceptible Occupant A and a continuous occupant of the space.
AIRC TEC also calculates the exposure scenario probability of infection for four additional fixed quantaemission rates on TEC ERq Analysis tab. In Version 1.0, these emission rates were labeled to be
associated with other pathogens. Since all other parameters are held constant, Version 2.0 reframes
this as an ERq sensitivity analysis. However, the user may relabel the headers corresponding to thealternative ERq (e.g. ERq #2) if the goal is to characterize a specific pathogen. [DIFFERENT PATHOGENSARE NOW SELECTABLE ON THIS PAGE, USING EITHER THE 75th OR USER-DEFINED PERCENTILE VALUE]

36

For reference, a range of literature values for ERq for different pathogens is presented in Figure 5,
alongside the range of SARS-CoV-2 values reported in Buonanno et al. (2020b). Quanta emission rates
used in similar risk modeling analyses are presented in Table 2 and Table 3, the latter table fortuberculosis only based on Internal Clinical Guidelines Team (UK) (2016). [TO BE UPDATED]

Figure 5: Range of published quanta emission rates for six pathogens. Range for rhinovirus from Rudnick and
Milton (2003) and for SARS-CoV-1 from Azimi and Stephens (2013). For SARS-CoV-2, the range and 66th percentile
values for different activities are presented from Buonanno et al. (2020b). See Tables 2 and 3 for influenza,
measles and tuberculosis (TB) references (chart excludes estimates from aerosol-producing procedures for TB).

37

Table 2: Quanta Emission Rates from Published Risk Modeling Studies [TO BE UPDATED]
Pathogen

Influenza

Modeled or Calculated ERq(Quanta/hr)
0.029 – 0.17
95th % = 53, max = 630
A (H1N1) = 9; A (H3N2) = 7
B = 7; p-H1N1 = 21
CA04 = 15.9
Rg-WH359 = 10.6
67
50-128Measleslog10(ERq): average = 2.78,
stand. dev = 0.415
1,925 (elementary school);
2,765 (high school)
8,640
61

SARS-CoV-2
970

Modeling Application

Reference

University of Maryland HumanChallenge Transmission Trial
Derived Emission Rates from
Elementary School Modeling
Influenza Aerosol Experiments andAnalytical System for Ferrets
Derived Emission Rate from School
Influenza Surveillance Reports
Assessment of Airborne Influenza
Transmission on an Airplane
Analysis of Measles Epidemics in
Elementary Schools
Multi-Zone Transient Wells-Riley
Modeling Study
Airborne Transmission in a Physician's
OfficeGuangzhou Restaurant
Superspreading Event
Skagit Valley Chorale
Superspreading Event

Bueno de Mesquita et al.
(2020), Yan et al. (2018)
Cheng and Liao (2013)
Zhou et al. (2018)
Liao et al. (2005)
Wagner et al. (2009), after
Rudnick and Milton (2003)
Riley (1980)
Azimi et al. (2020)
Remington et al. (1985)
Buonanno et al. (2020b)
Miller et al. (2020)

Table 3: Tuberculosis Quanta Emission Rates from Published Risk Modeling Studies [TO BE UPDATED]
Calculated ERq(Quanta/hr)

Modeling Application

Controlled Experimentwith Guinea Pigs

Reference

1.25

TB patient on treatmentx

Riley et al. (1962)

1.8 – 226
(average of 37)

x

Escombe et al. (2008)

34 (masks worn)
138 (no masks)

Drug susceptible and MDR-TB,
HIV co-infected patients
Untreated TB case causing
outbreak in an office
MDR-TB patients with mixed HIV
status60

Laryngeal case of TB

108

Outbreak from an infectious
MDR-TB case on a long flight

Ko et al. (2004)

250; 360

Bronchoscopy-related outbreak

Nardell et al. (1991), Gammaitoni& Nucci (1997)

2,280

Outbreak related to jet-irrigationof an abscess

Gammaitoni & Nucci (1997)

5,400

Autopsy outbreakGammaitoni & Nucci (1997)

30,840Intubation-related outbreakGammaitoni & Nucci (1997)

12.7

Nardell et al. (1991)

38

x

Dharmadhikari et al. (2012)

x

Riley et al. (1962)As shown in Figure 5, the quanta emission rate for SARS-CoV-2 is highly variable depending on theemitting activity of the infectious individual. The emissions rate for oral breathing at rest is comparable
to the reported range for rhinovirus, whereas the emissions rate for speaking loudly during heavy
activity (for example, singing), falls within the range of the highly infectious measles virus. This may
explain why SARS-CoV-2 superspreading events have been reported at nightclubs, choir practices andother settings where people are speaking loudly or singing at high activity levels in enclosed spaces. On
a population level, Dai and Zhao (2020) estimated the quantum generation rate of SARS-CoV-2 at 14-48
per hour using a reproductive number-based curve fitting approach. Dai and Zhao (2020) also report an
ERq range of 6-140 quanta/hr for Middle East Respiratory Syndrome (MERS) virus, but the original
source of this estimate could not be verified. To derive an independent estimate of ERq for MERS andother viruses, AIRC can be used as an inverse model to back-calculate the quantum generation rate for aknown airborne transmission scenario with a measured attack rate.
With respect to mask wearing, Wood et al. (2018) found that at 2 meters from the source, both surgicaland N95 masks reduced aerosols containing viable Pseudomonas aeruginosa in the droplet nuclei size
range by over 90% during voluntary coughing in people with Cystic Fibrosis. The findings wereconsistent with Driessche et al. (2015), which found an 86% reduction in environmental detection of
airborne Pseudomonas aeruginosa concentration during mask wearing compared with the referencegroup (coughing without a surgical mask) in a controlled laboratory model. While mask wearing
therefore has the potential to be a highly effective measure reducing the quanta emission rate of an
infectious subject, masks may be worn improperly or intermittently in indoor environments. Therefore,
the user is advised against using custom quanta emission rates where the provided percentile values are
significantly reduced to account for mask wearing.
The Institute for Health Metrics and Evaluation (IHME) at the University of Washington performed a
meta-analysis of peer-reviewed scientific studies and medRxiv pre-prints to assess mask efficacy, theresults of which suggest a reduction in infection for mask wearers by at least one-third (33%) compared
to control groups (IHME, 2020). Therefore, reducing the calculated risk value by a factor of 33% or lessto account for effective mask wearing of both infectious and susceptible occupants may be a reasonably
defensible approach.
AIRC TEC Tabs 3, 4 Names: TEC Graph 2 hr, 8 hr
Description: To help the user visualize how model calculations change in time based on the enteredparameters, a graph of model results is presented including the following data series:••••

The calculated quanta concentration in the room in quanta/m3;
The probability of infection for Susceptible Occupant A;
The probability of infection for a person continuously occupying the space; andThe calculated maximum occupancy to maintain R0 <1 for continuous occupants of the space(e.g. the number of occupants allowable for a cohort entering at time zero).

As the user may create scenarios of varying time scales, two graphs are provided: one limited to 120
minutes and the second displaying results for the maximum 480 minutes. The graphs are the same andtwo versions are only provided to facilitate data visualization for times of interest.

39

AIRC TEC Tabs 5-7 Names: TEC ERq Analysis and ERq Graph 2 hr, 8 hr
Description: These tabs in AIRC summarize the results of the TEC exposure scenario modeling using four
different user-entered quanta emission rates. On TEC ERq Analysis tab, the user can input any quantaemission rate for both the infectious occupants initially occupying the space, and Infectious Occupant A.
Probabilities of infection and exposure time thresholds for Susceptible Occupant A and a continuous
occupant are also presented on this tab. Probability of infection graphs for continuous occupancy arepresented on the ERq Graph tabs. These graphics are provided to establish a frame of reference for themodel results and facilitate use of AIRC for deterministic sensitivity analysis for his or her original
simulation, as all other parameters remain the same.
AIRC TEC Tabs 8, 9 Names: Calculations 66 and Calculations 90 [NOW JUST ‘CALCULATIONS 75’]
Description: These tabs of AIRC allows users to see the Excel formulas used to implement the TEC
modeling approach. Users can click on individual cells to see formulas and cell references and can adjust
formatting if a time step is of interest. The implementation approach is straightforward, and theformulas can be copied and pasted into a different spreadsheet program as needed. The “66” tab uses
the 66th percentile ERq values to calculate the probability of infection, while the “90” tab uses the 90th
percentile ERq values to calculate the maximum occupancy for R0 < 1.

40

SECTION V: EXAMPLE APPLICATIONS [TO BE UPDATED, RISK ESTIMATESWILL BE HIGHER IN VERSION 3.0 BETA WITH ALL INPUT PARAMETERS
THE SAME, DIFFERENCES WILL BE LESS FOR OCCUPANCY CALCULATIONSAS VERSION 2.0 CONSERVATIVELY USED THE 90TH PERCENTILE ERq]
Four example applications are provided in this manual for AIRC 2.0 to assist the user in the process ofcreating a conceptual exposure model, entering input data, and visualizing and interpreting results.
Note that with the changes to the provided quanta emission rates and risk calculation, model output
with AIRC 2.0 will not exactly match the example applications described in the AIRC 1.0 User Manual.
However, the risk modeling concepts remain the same.
1. Squash
Scenario Description: Brlek et al. (2020) present a retrospective analysis of a cluster of five COVID-19
cases linked to playing squash at a sports facility in Slovenia. The authors conclude that viraltransmission may have occurred through aerosol inhalation as all subjects shared the exact same poorly
ventilated squash hall, and the aerosol emission rate for the infectious index case would have been highfor strenuous physical activity. While indirect transmission in the locker room or other parts of the
facility through fomites (e.g. doorknobs), large respiratory droplets, or aerosols could not be ruled out,
for purposes of this illustrative example we assume that all transmission occurred through aerosols
within the squash hall.
The sequence of events within the shared squash hall and the timeline for AIRC TEC modeling is
described below (Brlek et al., 2020). There were three independent squash matches and therefore
three separate AIRC TEC scenarios are necessary. Susceptible occupant A represents the susceptible
squash players in each of the three successive matches and AIRC models:
•

•

•

•

The index case (infectious occupant at time zero) played squash with a susceptible occupant for
one hour in the squash hall. This match and exposure period for susceptible occupant A spans
0-60 minutes in the AIRC model;
Two different susceptible persons started playing squash in the same squash hall 45 minutes
after the prior match ended and played for 45 minutes. This match and exposure period forsusceptible occupant A spans 105-150 minutes in the second AIRC model;
Two different susceptible persons started playing squash just after the prior match ended andalso played for 45 minutes. This match and exposure period for susceptible occupant A spans
150-195 minutes in the third AIRC model;
All 5 susceptible persons were infected with COVID-19 (100% attack rate for shared occupantsof the squash hall). Of the five employees at the facility, none developed COVID-19 symptoms
and the one employee who was tested received a negative result.

Brlek et al. (2020) does not provide the room dimensions of the squash hall. However, assuming thedesign followed the World Squash Federation (WSF) standard for a singles-court, we model the room as
having an area of 62.4 m2 and a ceiling height of 5.64 m, for a room volume of 352 m3 (WSF, 2013). As
the squash hall was described as poorly ventilated, we assume an air exchange rate of 0.2 hr-1. An

41

activity level of heavy exercise, loudly speaking is selected for the index case, and inhalation rates ofheavy exercise are selected for all susceptibles.
AIRC TEC Screenshots:

42

Discussion: The model calculates probability of infection estimates of approximately 40% for thesusceptible person who played with the index case from 0-60 minutes, 18% for susceptible persons whoplayed a match in the same hall from 105-150 minutes, and 8.7% for the susceptible persons who playeda match in the same hall from 150-195 minutes. These estimated attack rates were calculated using the66th percentile value for a person speaking loudly while exercising heavily. Since they are lower than theremarkable 100% infection rate observed in this case study, if all infections were related to aerosol
exposure in the shared squash hall it is likely that the index case was a so-called “supershedder” or“superspreader” whose emission rate represents a high percentile value. Indeed, if we use the 95th
percentile value of the ERq distribution for heavy exercise, loudly speaking (1,150 quanta/hr), theprobability of infection estimates increase to approximately 98%, 79%, and 50% for susceptible squash
players in the three different matches. Alternatively, other pathways of transmission may have played arole beyond aerosol inhalation.
These results are consistent with the retrospective modeling presented in Buonanno et al. (2020b),
which estimated emission values occurring in the 92nd-94th percentile range for the well-publicized
COVID-19 superspreading events in the restaurant in Guangzhou and the choir rehearsal in Skagit Valley.
Similarly, Bueno de Mesquita et al. (2020) re-evaluated the well-studied 1977 outbreak of influenza
43

A/H3 in a Boeing 737 airliner and found the estimated emission rate to be consistent with the 95th
percentile value of measurements of RNA shed in exhaled breath fine aerosols by symptomatic influenza
cases described in Yan et al. (2018). Lastly, in his paper presented at the Airborne Contagion conference
held November 7-9, 1979 by the New York Academy of Sciences, Edward C. Riley noted that the often
cited emission rate of approximately 5,600 quanta/hr for measles virus “would be expected only about
one time in one hundred observations” (Riley, 1980). The user can explore how increasing theventilation rate alone reduces the probability of infection for the squash example in both the averagecase and the case of the supershedder.
2. Seafood Market
Scenario Description: This example application is based on a modeling study presented in Zhang et al.
(2020) that quantified the airborne transmission risk of SARS-CoV-2 during the initial phase of theoutbreak at the South China Seafood Market in Wuhan, China in December 2019. A zone model of 650
m2 by 5 m high dimension was used to represent Street No. 7 of the naturally ventilated market. A
dose-response model by Monte Carlo simulation was used to estimate the risk of infection to consumers
spending one hour in the market, and shopkeepers working 12-hour days in the market for the entire
month of December. The model assumed one infected shopkeeper working inside Street No. 7 with aviral emission rate of 50 plaque-forming units (PFU)/hr. This emission rate corresponds toapproximately 0.24 quanta/hr assuming 210 PFU/quanta as in Buonanno et al. (2020b), or the 40th
percentile value of the resting, oral breathing activity level.
For the AIRC TEC representation of this model, an activity level of standing, oral breathing is selected forthe infectious shopkeeper as it is likely that the shopkeeper would be standing for a significant fractionof the day and talking occasionally. Susceptible Occupant A is a consumer spending one hour in themarket in the middle of the day at an activity level of light exercise due to the need to walk through themarket. The continuous occupant of the market is a susceptible shopkeeper with an activity level ofstanding. An air exchange rate of 0.5 hr-1 is assumed to reflect the natural ventilation of the market.
The same parameters are used in the SEC version of the model with a maximum modeled exposure timeof 360 hours (30 days at 12 hours/day) to reflect an entire month worked by the susceptible shopkeeper
in the presence of a fellow shopkeeper infected with SARS-CoV-2.
AIRC TEC Screenshots:

44

AIRC SEC Screenshots:

45

Discussion: The TEC model results indicate a probability of infection of 0.028% for the susceptible
consumer with a one-hour exposure in the market, and the SEC model results indicate an infection riskof 3.6% for the shopkeeper working the entire month. Zhang et al. (2020) estimated the median risks
for these exposures to be 0.00223% and 0.976%, respectively. The approximate order of magnitude
higher AIRC estimate for the one-hour exposure is due to the higher quanta emission rate and higher
inhalation rate of the susceptible consumer. If the ERq is reduced to 0.24 quanta/hr and the inhalation
rate for the susceptible consumer is reduced to 0.6 m3/hr as modeled by Zhang et al. (2020), the AIRC
probability of infection estimate is reduced to 0.0034% for the consumer, showing general consistency
between the two models. We note that the Zhang et al. (2020) zone model used a significantly lower
inactivation rate corresponding to a half-life of 12 hours; however, this is offset by a more complex
multi-surface particle deposition rate approach.
In terms of a threshold value for acceptable risk, Zhang et al. (2020) adopts the available hospital bed
capacity for COVID-19 treatment per capita in Wuhan (1.17 × 10−3, or 0.117%). Using the activity level ofstanding, oral breathing, an air exchange rate of 6 hr-1 would be needed to reduce the shopkeeper’s
infection risk to approximately this threshold level (0.13%) over a 60-hour workweek. If an activity levelof standing and speaking is assumed for the infectious shopkeeper, which is reasonable consideringsome level of customer interaction occurs, the exposure of a susceptible shopkeeper would exceed thisrisk threshold within approximately three hours in the naturally ventilated market.
3. Abattoir 2.0
Scenario Description: This example application is based on a retrospective analysis of thesuperspreading event that preceded the largest SARS-CoV-2 outbreak in any German abattoir,presented in Gunther et al. (2020). The index case worked three shifts while being infected, resulting intransmission to 20 of 78 fellow workers in the same “proximal” work area of 360 m2 by 6.1 m high(25.6% attack rate). There was clear a spatial correlation to the pattern of infection, with workers
stationed within 8 m of the index case experiencing an attack rate of 66%. Gunther et al. (2020) reportsthat cooling air was recirculated through localized ceiling-mounted fans without filtration, and the air
exchange rate for the entire processing plant was less than 1 air change per hour.
This scenario is evaluated using AIRC SEC as the exposure time is long (24 hours, or three 8 hour shifts),
and the index case and workers can be assumed to have a relatively consistent activity for the hours
worked, and therefore relatively constant emission and inhalation rates. The model is evaluated using46

both the larger shared workspace of 360 m2, as well as within the smaller 8 m separation distance where
ceiling recirculating fans may have created a local airflow mixing zone. The air exchange rate is assumed
to be 0.5 hr-1, and the susceptible workers are assumed to breath at a light activity level.
AIRC SEC Screenshots:47

Discussion: The model results indicate the documented attack rate of 25.6% for the larger 360 m2“proximal” work area, and 66% for the smaller 64 m2 work area, corresponds to a quanta emission rate
between the infectious activity levels of standing, loudly speaking and light exercise, loudly speaking fora 24-hour exposure. As the median ERq for these activities are 12.1 and 31.6 quanta/hr, respectively,
this analysis shows that extreme value emission rates are not needed to cause major outbreak events
where ventilation is poor, exposure periods are long, and activity levels are high. This example also
illustrates that while susceptible workers closer to the index case had a higher attack rate, the model
produces representative results regardless of whether the larger or smaller room volume is used. The
completely mixed air assumption appears reasonable for this case. However, the AER sensitivity analysis
shows that the size of the exposure area has a significant impact on the necessary virus removal rate to
mitigate local infection risk. For the smaller modeled work area an AER of 15 hr-1 is insufficient to
reduce infection risk below 1% for an 8-hour shift. This indicates an advanced ventilation system for
infection control and/or additional in-room disinfection may be needed for these types of exposures, inaddition to proper personal protective equipment for workers.
4. Hospital Waiting Area
Scenario Description & Discussion: This example is an AIRC reproduction of a modeling scenariopresented in Beggs et al. (2010) to evaluate the risk of airborne infection in a hypothetical 132 m3
hospital waiting area in the presence of one infectious occupant. This example demonstrates the utilityof the ERq sensitivity analysis feature of AIRC and compares AIRC output to a similar Gammaitoni-Nucci
equation application in literature. As Beggs et al. (2010) conducted a Monte Carlo simulation toevaluate the probability of infection for tuberculosis, measles, and influenza, the AIRC results arecompared to the mean probability values calculated in the study for 30 minutes and 60 minutes of
exposure. A comparison of AIRC output and Beggs et al. (2010) results is presented in Table 3, below,
and shows strong agreement. Minor differences are potentially due to the slightly different inhalation
rate, the integration approach, time step differences, and/or rounding, and are largest for measles.
Input parameters are shown in the screenshots that follow.
Table 3: Comparison of Calculated Probability of Infection, AIRC vs. Beggs et al. (2010)30-Minute Waiting Room Exposure
Disease60-Minute Waiting Room Exposure

AIRC Probabilityof Infection

Beggs et al. (2010)
Mean Probability of
Infection

AIRC Probabilityof Infection

Beggs et al. (2010)
Mean Probability of
Infection

Tuberculosis(ERq #4)

0.34 %

0.34 %

0.89 %

0.87 %

Influenza(ERq #3)

2.66 %

2.62 %

6.83 %

6.62 %

Measles(ERq #2)

14.3 %

13.5 %

33.2 %

30.9 %AIRC TEC Screenshots (on following page):

48

49

SECTION VI: REFERENCES
The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), 2020. COVID(Coronavirus) Preparedness Resources. https://www.ashrae.org/technical-resources/resources.
Accessed June 18, 2020.
Azimi, P., Keshavarz, Z., Cedeno Laurent, J.G. et al., 2020. Estimating the Nationwide Transmission Riskof Measles in US Schools and Impacts of Vaccination and Supplemental Infection Control
Strategies. BMC Infect Dis 20, 497. https://doi.org/10.1186/s12879-020-05200-6
Azimi P., Stephens B., 2013. HVAC filtration for Controlling Infectious Airborne Disease Transmission in
Indoor Environments: Predicting Risk Reductions and Operational Costs. Build Environ.;70:150
160. doi:10.1016/j.buildenv.2013.08.025
Beck, S.H., Castillo, A., Kinney, K.A., Zuniga, A., Mohammad, Z., Lacey, R.E., King, M.D., 2019. Monitoringof Pathogenic Bioaerosols in Beef Slaughter Facilities Based on Air Sampling and Airflow
Modeling. Applied Engineering in Agriculture. 35. 1015-1036. 10.13031/aea.13553.
Beggs, C. B., Shepherd, S. J., Kerr, K. G., 2010. Potential for Airborne Transmission of Infection in the
Waiting Areas of Healthcare Premises: Stochastic Analysis Using a Monte Carlo Model. BMC
Infectious Diseases, 10, 247. https://doi.org/10.1186/1471-2334-10-247
Bertran, K., Balzli, C., Kwon, Y.K., Tumpey, T.M., Clark, A., Swayne, D.E., 2017. Airborne Transmission ofHighly Pathogenic Influenza Virus During Processing of Infected Poultry. Emerg. Infect. Dis. 23,
1806–1814.
Bertran, K., Clark, A., Swayne, D.E., 2018. Mitigation Strategies to Reduce the Generation andTransmission of Airborne Highly Pathogenic Avian Influenza Virus Particles During Processing of
Infected Poultry. Int J Hyg Environ Health. 221(6):893‐900.
Bide, R.W., Armour, S.J., and Yee, E., 1997. Estimation of Human Toxicity from Animal Inhalation
Toxicity Data: 1. Minute Volume-Body Weight Relationships Between Animals and Man. Defense
Research Establishment Suffield (DRES). Suffield Report 673 (Unclassified). Available at:https://apps.dtic.mil/dtic/tr/fulltext/u2/a336351.pdf
Brlek, A., Vidovič, Š., Vuzem, S., Turk, K., Simonović, Z., 2020. Possible Indirect Transmission of COVID-19
at a Squash Court, Slovenia, March 2020: Case Report. Epidemiology and Infection 148, e120, 13.
https://doi.org/10.1017/S0950268820001326
Bueno de Mesquita, P.J., Noakes, C.J., Milton, D.K., 2020. Quantitative Aerobiologic Analysis of an
Influenza Human Challenge‐Transmission Trial. Indoor Air. 2020; 00: 1– 10.
https://doi.org/10.1111/ina.12701

50

Buhnerkempe M.G., Gostic K., Park M., Ahsan P., Belser J.A., Lloyd-Smith, J.O., 2015. Mapping Influenza
Transmission in the Ferret Model to Transmission in Humans. Elife; 4. doi:10.7554/eLife.07969
Buonanno, G., Stabile, L., Morawska, L., 2020a. Estimation of Airborne Viral Emission: Quanta Emission
Rate of SARS-CoV-2 for Infection Risk Assessment. Environment International.
https://doi.org/10.1101/2020.04.12.20062828.
Buonanno, G., Morawska, L., Stabile, L., 2020b. Quantitative Assessment of the Risk of Airborne
Transmission of SARS-CoV-2 Infection: Prospective and Retrospective Applications. Environment
International. https://doi.org/10.1101/2020.06.01.20118984.
Chadde, S., 2020. Tracking Covid-19’s Impact on Meatpacking Workers and Industry. Midwest Center for
Investigative Reporting. Available fromhttps://investigatemidwest.org/2020/04/16/trackingcovid-19s-impact-on-meatpackingworkers-and-industry/ (Accessed June 27, 2020)
Chatoutsidou, S.E., Lazaridis, M., 2019. Assessment of the Impact of Particulate Dry Deposition on Soilingof Indoor Cultural Heritage Objects Found in Churches and Museums/Libraries. J. Cult. Heritage39, 221–228. https://doi.org/10.1016/j.culher.2019.02.017.
Chen, Q.Y., McDevitt, J.J., and Gupta, J.K. et al., 2012. Infectious Disease Transmission in Airliner Cabins.
Airliner Cabin Environmental Research Report RITE-ACER-CoE-2012-01. Available at:https://www.faa.gov/data_research/research/med_humanfacs/CER/.
Cheng Y.H., Liao, C.M., 2013. Modeling Control Measure Effects to Reduce Indoor Transmission ofPandemic H1N1 2009 Virus. Build Environ; 63:11-9.
Cooley, P., Brown, S., Cajka, J., et al., 2011. The role of Subway Travel in an Influenza Epidemic: A New
York City Simulation. J Urban Health. 88(5):982-995. doi:10.1007/s11524-011-9603-4
Dai, H., Zhao, B., 2020. Association of Infected Probability of COVID-19 with Ventilation Rates in
Confined Spaces: A Wells-Riley Equation Based Investigation. medRxiv 2020.04.21.20072397;
doi: https://doi.org/10.1101/2020.04.21.20072397
Dharmadhikari, A. S., Mphahlele, M., Stoltz, A., Venter, K., Mathebula, R., Masotla, T., Lubbe, W.,
Pagano, M., First, M., Jensen, P. A., van der Walt, M., & Nardell, E. A., 2012. Surgical Face Masks
Worn by Patients with Multidrug-Resistant Tuberculosis: Impact on Infectivity of Air on a
Hospital Ward. American Journal of Respiratory and Critical Care Medicine, 185(10), 1104–1109.
https://doi.org/10.1164/rccm.201107-1190OC
Driessche, K.V., Hens, N., Tilley, P., Quon, B.S., Chilvers, M.A., de Groot R., et al., 2015. Surgical Masks
Reduce Airborne Spread of Pseudomonas aeruginosa in Colonized Patients with Cystic Fibrosis.
Am J Respir Crit Care Med; 192:897–899

51

Escombe, A.R., Moore, D.A.J., Gilman, R.H., Pan, W, Navincopa, M., et al., 2008. The Infectiousness ofTuberculosis Patients Coinfected with HIV. PLOS Medicine 5(9): e188.
https://doi.org/10.1371/journal.pmed.0050188
Fears, S.C., Klimstra, W.B., Duprex, P., Hartman, A., Weaver, S.C., Plante KS, et al., 2020. Persistence of
Severe Acute Respiratory Syndrome Coronavirus 2 in Aerosol Suspensions. Emerg Infect Dis.
https://doi.org/10.3201/eid2609.201806
Gammaitoni, L., Nucci, M.C., 1997. Using a Mathematical Model to Evaluate the Efficacy of TB Control
Measures. Emerg. Infect. Dis. 335–342.
Guenther, T., Czech-Sioli, M., Indenbirken, D., Robitailles, A., Tenhaken, P., Exner, M., Ottinger, M.,
Fischer, N., Grundhoff, A., and Brinkmann, M., 2020. Investigation of a Superspreading Event
Preceding the Largest Meat Processing Plant-Related SARS-Coronavirus 2 Outbreak in Germany.
Available at SSRN: https://ssrn.com/abstract=3654517 or
http://dx.doi.org/10.2139/ssrn.3654517
Hosotani, N., Yamakawa, M., Matsuno, K., 2013. Numerical Simulation for Behavior of Virus in an Indoor
Environment. Jpn. Soc. Mech. Eng. G0401-01. doi: 10.1299/jsmefed.2013
Institute for Health Metrics and Evaluation (IHME), 2020. COVID-19 Projections. Seattle, WA: IHME,
University of Washington, 2020. Available from http://www.healthdata.org/covid/updates
Internal Clinical Guidelines Team (UK), 2016. Tuberculosis: Prevention, Diagnosis, Management andService Organisation. London: National Institute for Health and Care Excellence (UK); (NICE
Guideline, No. 33.) Appendix F, Health economics report (2015 work undertaken by NICE
Internal Clinical Guidelines) Available from: https://www.ncbi.nlm.nih.gov/books/NBK361224/
Knibbs, L.D., Morawska, L., Bell, S.C., Grzybowski, P., 2011. Room Ventilation and the Risk of Airborne
Infection Transmission in 3 Health Care Settings Within a Large Teaching Hospital. Am. J. Infect.
Control 39, 866–872.
Ko, G., Thompson, K.M., Nardell, E.A., 2004. Estimation of Tuberculosis Risk on a Commercial Airliner.
Risk Anal. 24:379–88.
Li, Y., Huang, X., Yu, I., Wong, T., Qian, H., 2005. Role of Air Distribution in SARS Transmission During theLargest Nosocomial Outbreak in Hong Kong. Indoor Air 15, 83–95.
Liao, C-M., Chang, C-F., Liang, H-M., 2005. A Probabilistic Transmission Dynamic Model to Assess Indoor
Airborne Infection Risks. Risk Analysis. 25:1097–1107.
Miller, S., Nazaroff, W., Jimenez, J. et al., 2020. Transmission of SARS-CoV-2 by Inhalation of Respiratory
Aerosol in the Skagit Valley Chorale Superspreading Event. Indoor Air. medRxiv
2020.06.15.20132027; doi: https://doi.org/10.1101/2020.06.15.20132027
52

Morawska, L., Cao, J.,2020. Airborne Transmission of SARS-CoV-2: The World Should Face the Reality.
Environment International 2020, 139, 105730. https://doi.org/10.1016/j.envint.2020.105730.
Morawska, L., Milton, D.K., 2020. It is Time to Address Airborne Transmission of COVID-19. Clinical
Infectious Diseases, ciaa939, https://doi.org/10.1093/cid/ciaa939.
Morawska, L., Tang, J. W., Bahnfleth, W., Bluyssen, P. M., Boerstra, A., Buonanno, G., Cao, J., Dancer, S.,
Floto, A., Franchimon, F., Haworth, C., Hogeling, J., Isaxon, C., Jimenez, J. L., Kurnitski, J., Li, Y.,
Loomans, M., Marks, G., Marr, L. C., Mazzarella, L., … Yao, M. 2020. How can Airborne
Transmission of COVID-19 Indoors be Minimised? Environment international, 142, 105832.
Advance online publication. https://doi.org/10.1016/j.envint.2020.105832
Morawska, L., 2006. Droplet Fate in Indoor Environments, or Can We Prevent the Spread of Infection?
Indoor Air 16, 335–347.
Myatt, T.A., Minegishi T., Allen J.G., MacIntosh, D.L., 2008. Control of Asthma Triggers in Indoor Air with
Air Cleaners: A Modeling Analysis. Environ Health; 7:43.
Nardell, E.A., Keegan, J., Cheney, S.A., Etkind, S.C., 1991. Airborne Infection: Theoretical Limits ofProtection Achievable by Building Ventilation. Am Rev Respir Dis; 144:302-6.
Northwest Energy Efficiency Council (NEEC), 2015. Indoor Environmental Quality. Building Operator
Certification Student Handbook, Edition 1.21. US Department of Energy Award Number DE
EE0003854.
Remington, P.L., Hall, W.N., Davis, I.H., Herald, A., Gunn, R.A., 1985. Airborne Transmission of Measles
in a Physician's Office. JAMA. 253(11):1574–1577. doi:10.1001/jama.1985.03350350068022
Riley, R.L., Mills, C.C., O'Grady, F., Sultan, L.U., Wittstadt, F., Shrivpuri, D.N., 1962. Infectiousness of Air
From a Tuberculosis Ward. Ultraviolet Irradiation of Infected Air: Comparative Infectiousness ofDifferent Patients. Am Rev Respir Dis.; 85:511-25
Riley, E.C., Murphy G., Riley, RL., 1978. Airborne Spread of Measles in a Suburban Elementary School.
Am J Epidemiol; 107:421-32.
Riley, E.C., 1980. The Role of Ventilation in the Spread of Measles in an Elementary School. Airborne
Contagion. Annals of the New York Academy of Sciences; Volume 353, December 5.
Rudnick, S.N., Milton, D.K., 2003. Risk of Indoor Airborne Infection Transmission Estimated from Carbon
Dioxide Concentration. Indoor Air. 13(3):237-245. doi:10.1034/j.1600-0668.2003.00189.x

53

Schuit, M., Ratnesar-Shumate, S., Yolitz, J., et al., 2020. Airborne SARS-CoV-2 is Rapidly Inactivated by
Simulated Sunlight, The Journal of Infectious Diseases, jiaa334,
https://doi.org/10.1093/infdis/jiaa334
Setti, L., Passarini, F., De Gennaro, G., Barbieri, P., Perrone, M. G., Borelli, M., Palmisani, J., Di Gilio, A.,
Piscitelli, P., & Miani, A., 2020. Airborne Transmission Route of COVID-19: Why 2 Meters/6 Feetof Inter-Personal Distance Could Not Be Enough. International Journal of Environmental
Research and Public Health, 17(8), 2932. https://doi.org/10.3390/ijerph17082932
Stabile L., Frattolillo, A., Dell'Isola, M., Massimo, A., Russi A., 2015. Air Permeability of Naturally
Ventilated Italian Classrooms. Energy Procedia, 78, pp. 3150-3155.
Sze To, G.N., Chao, C.Y.H., 2010. Review and Comparison Between the Wells-Riley andDose-Response Approaches to Risk Assessment of Infectious Respiratory Diseases. Indoor
Air 20, 2–16. https://doi.org/10.1111/j.1600-0668.2009.00621.x.
van Doremalen, N., Bushmaker, T., Morris, D.H., Holbrook, M.G., Gamble, A., Williamson, B.N., Tamin,A., Harcourt, J.L., Thornburg, N.J., Gerber, S.I., Lloyd- Smith, J.O., de Wit, E., Munster, V.J., 2020.
Aerosol and Surface Stability of SARSCoV-2 as Compared with SARS-CoV-1. N. Engl. J. Med.
Vuorinen, et al., 2020. Modelling Aerosol Transport and Virus Exposure with Numerical Simulations in
Relation to SARS-CoV-2 Transmission by Inhalation Indoors, arXiv: 2005.05882 [physics.u-dyn].
Wagner, B.G., Coburn, B.J., Blower, S., 2009. Calculating the Potential for Within-Flight Transmission of
Influenza A (H1N1). BMC Med. 7, 81. https://doi.org/10.1186/1741-7015-7-81.
WHO, 2009. Natural Ventilation for Infection Control in Health-Care Settings (https://www.who.int/water_sanitation_health/publications/natural_ventilation/en/) ed^eds:
World Health Organization.
Wood, M.E., Stockwell, R.E., Johnson, G.R., et al., 2018. Face Masks and Cough Etiquette Reduce theCough Aerosol Concentration of Pseudomonas aeruginosa in People with Cystic Fibrosis. Am J
Respir Crit Care Med. 197(3):348-355. doi:10.1164/rccm.201707-1457OC
World Squash Federation (WSF), 2013. Court Specifications.
https://www.worldsquash.org/courtconstruction/ Accessed August 30, 2020.
Xiao, S., Li, Y., Wong, T.-W., Hui, D.S., 2017. Role of Fomites in SARS Transmission During the Largest
Hospital Outbreak in Hong Kong. PLoS ONE.
Yan, J., Grantham, M., Pantelic, J., et al., 2018. Infectious Virus in Exhaled Breath of Symptomatic
Seasonal Influenza Cases From a College Community. Proc Natl Acad Sci USA. 115(5):1081-1086.

54

Yang, W., Marr, L.C., 2011. Dynamics of Airborne Influenza A Viruses Indoors and Dependence on
Humidity. PLoS ONE 6, e21481. https://doi.org/10.1371/journal.pone.0021481.
Zhang, X., Ji, Z., Yue, Y., Liu, H. & Wang, J., 2020. Infection Risk Assessment of COVID-19 Through Aerosol
Transmission: a Case Study of South China Seafood Market. Environmental Science &Technology, doi:10.1021/acs.est.0c02895 (2020).
Zhou, J., Wei, J., Choy, K.T., et al., 2018. Defining the Sizes of Airborne Particles that Mediate Influenza
Transmission in Ferrets. Proc Natl Acad Sci U S A.; 115(10):E2386-E2392.
doi:10.1073/pnas.1716771115

55