COVID-19 Airborne Transmission Estimator
If you don't see download option, click link just below
Prof. Jose L Jimenez, Dept. of Chem. and CIRES, Univ. of Colorado-Boulder
Short description of this tool:
Using input or feedback from:
Linsey Marr, Shelly Miller, Giorgio Buonnano, Lidia Morawska, Don Milton, Julian Tang, Jarek Kurnitski, Xavier Querol, Matthew McQueen,
|Charles Stanier, Joel Eaves, Alfred Trukenmueller, Ty Newell, Greg Blonder, Andrew Maynard, Nathan Skinner, Clark Vangilder, Roger Olsen|
(only listing the most important here, many others have contributed feedback as well over email and Twitter. Thanks a lot to everyone!)
(Any mistakes are my own)
|Version & date||3.0.11||14-Jul-20|
How to use the estimator
|This online version will be kept up-to-date. We can't alllow people to make changes to the online version, as otherwise people would overwrite each other's changes|
People interested in using the model should download an Excel version from File --> Download
(Ignore Excel Errors in some versions, it still works fine)
The online model will continue to be updated, so you may want to re-download the file later on, if you continue to use it, to get the latest updates
See the version log at the bottom of this sheet for a brief description of the updates
Inputs and Outputs
Inputs are colored in yellow.
These are the cells you should change to explore different cases.
Descriptions and intermediate calculations are not colored. Do not overwrite the calculations or you will break the estimator.
Outputs are colored in blue.
These are the final results of the model for each case. Do not overwrite them or you will break the estimator.
Note that in some cases, the case in a sheet assumes that an infected person is present (e.g. in the classroom). While in other cases we use the prevalence of the disease in the population as
an input on the calculations. They can be converted easily, but pay attention to what each specific sheet is doing.
All sheets are self-contained, except for the University case
For the University case
Approximately scaled for a large University in the Western US for the Fall 2020 semester
First, results are calculated for a typical classroom ("Classroom Sheet"), assuming either one student or the professor are infected
Assumes enhanced social distancing and masks in place
Classroom size does not matter much, since students will scale with it
Then, results are scaled to the whole campus ("Campus Sheet"), taking into account the probability of infection in the population
What we are trying to estimate
The propagation of COVID-19 by airborne transmission ONLY
The model is based on a standard model of airborne disease transmission, the Wells-Riley model. It is calibrated to COVID-19 per recent literature on quanta emission rate
This is NOT an epidemiological model, rather it takes input from such models for the average rate of infection for a given location and time period
This model does NOT include droplet or contact / fomite transmission, and assumes that 6 ft / 2 m social distancing is respected. Otherwise higher transmission will result
This model does NOT include transmission to the people present, when they are in locations other than the one analyzed here
The model can easily be adapted to other situations, such as offices, shops etc.
Simplicity and uncertainties
The model is kept simple so that it can be understood and changed easily. The goal is to get the order-of-magnitude of the effects quickly, and to explore the trends.
Several parameters are uncertain, and have been estimated based on current knowledge. Alternative estimates can be entered to explore their effect in the results.
More complex and realistic models can be built, however the parametric uncertainty may still dominate the total uncertainty
Parameters based on new research can be incorporated as they become available. Pls send them my way
Disclaimer: this model is our best scientific estimate, based on the information currently available. It is provided in the hope that it will be useful to others, based on us
receiving a large number of requests for this type of information. We trust most the relative risk estimates (when changing parameters such as the type
of mask worn) of two runs of the model. We also trust the order-of-magnitude of the risk estimates, if the inputs are correct. The exact numerical results
for a given case have more uncertainty and also have to be interpreted statistically. (I.e. if 1000 classrooms or 1000 buses did this, that would be the
average number of transmission cases. Any one event may have much fewer or many more transmission cases.)
Suggestions and improvements
Please email me for any suggestions for improvements, additional input data etc.
The model is based on standard airborne transmission models (Wells-Riley type models), as formulated in Miller et al. 2020, and references therein
Miller et al. Skagit Choir Outbreak
Original Wells-Riley model:
Buonnano et al. (2020a)
Buonnano et al. (2020b)
Key parameters, sources, and uncertainties
The most uncertain parameter is the quanta emission rates for SARS-CoV-2
See FAQ sheet for the definition of quanta
|970 q / h|
This is from the Miller et al. choir superspreading case
This value is at the high end of the Buonnano et al. values provided below, consistent with this being a superspreading event
which was likely influenced by a very high emission rate of quanta from the specific index case
We do not think that this very high value should be applied to all situations, as that would overestimate the infection risk.
Buonnano et al. (2020a, b) provides a range of estimates. Recommended values by the author are:
|Paper 1||Paper 2|
For a professor delivering a lecture:4.4, 21, and 134 for oral breathing, speaking and aloud speaking (or singing)
For a student sitting on a lecture: 4, 16, 97 for oral breathing, speaking and aloud speaking (or singing)
For a more general set of activities, provided by the same author, based on their 2nd paper:
Resting – Oral breathing = 2.0 quanta/h
Resting – Speaking = 9.4 quanta/h
Resting – Loudly speaking = 60.5 quanta/h
Standing – Oral breathing = 2.3 quanta/h
Standing – Speaking = 11.4 quanta/h
Standing – Loudly speaking = 65.1 quanta/h
Light exercise – Oral breathing = 5.6 quanta/h
Light exercise – Speaking = 26.3 quanta/h
Light exercise – Loudly speaking = 170 quanta/h
Heavy exercise – Oral breathing = 13.5 quanta/h
Heavy exercise – Speaking = 63.1 quanta/h
Heavy exercise – Loudly speaking = 408 quanta/h
For children as a first approximation I would reduce these numbers proportionally to body mass.
For comparison, values for measles can be over 5500 q h-1 (Riley et al. above). So COVID-19 is much less transmissible through the air than measles, but it
can still be transmitted through aerosols under the right circumstances (indoors, lower ventilation, crowding, longer duration, activities that favor
higher emission rates of respiratory aerosols such as singing, talking, aerobic exercise etc.) If you are curious, change the quantum emission rate
to 5500 to see what measles would do, if it encountered a susceptible population with its high infectivity.
Values in m3 h-1 from Buonnano et al. 2020a, averaged for males and females (https://www.sciencedirect.com/science/article/pii/S0160412020312800)
Based on https://journals.lww.com/epidem/Citation/1995/03000/132_MEASUREMENT_OF_BREATHING_RATE_AND_VOLUME_IN.162.aspx