COVID-19 Aerosol Transmission Estimator
File --> Make a Copy OR Download to Excel (Click GREEN links below if don't see option)
Prof. Jose L Jimenez & Dr. Zhe Peng, Dept. of Chem. & CIRES, Univ. Colorado-Boulder
Short description of this tool in CIRES Press Release:
Simplified version of this tool by Nat Geographic
Direct copy in Google Drive (as Google Sheet)
|For more info:|
Other languages: https://tinyurl.com/preguntas-espanol
Direct download into Excel
Model described in Peng et al. (2022). Includes peer-reviewed research in Miller et al. (2021) and Peng & Jimenez (2021)
Come back for new versions
5 min. read on aerosol evidence:
Patterns of transmission
Extensive discussion in my Twitter Threads
Recorded webinar on this tool:
1. Description & Tour (watch first)
|2. Q&A session|
3. Short intro by A. Mishra
Informacion en espanol / castellano:
1. Descripcion y demonstracion
|2. Entrevista PF|
3. Entrevista HA
El Pais Simulation based on this:
Version en español
Subscribing to email list for tool:
|Feedback to improve this tool:||http://tinyurl.com/estimator-feedback|
|Using extensive input and feedback from many people|
(But any mistakes are my own):
|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, Alex Mikszewski, Prasad Kasibhatla, Joe Bruce, Paul Dabisch, Yumi Roth, Andrew Persily, Susan Masten, Sebastien Tixier, Amber Kraver, Howard Chong, John Fay, Dustin Poppendieck, Jim Bagrowski, Gary Chaulklin, Richard Meehan, Jarrell Wenger, Alex Huffman, Bertrand Waucquez, Elizabeth Goldberg, Trish Greenhlagh, Lydia Bourouiba (only listing the most important here, many others have contributed feedback as well over email and Twitter. Thanks a lot to everyone!)|
|Version & date||3.6.5||14-Jan-22|
What we are trying to estimate
The propagation of COVID-19 by aerosol transmission ONLY
The model is based on a standard model of aerosol 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 can take input from such models for the average rate of infection for a given location and time period. Or it could possibly be used as a sub-component of an epi-model, to estimate aerosol transmission as a function of various parameters|
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 - IMPORTANT, PLEASE READ
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.
|The model is consistent with known superspreading events of COVID-19. It represents the situation in which someone highly infectious is present in the space. Note that many people are much less infectious (e.g. Ma et al., 2020, Clinical Infectious Diseases, https://doi.org/10.1093/cid/ciaa1283), and for those the number of infected people will be too high.|
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 wearing a
mask or not) 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. For example if you obtain a 1% chance of infection, in reality it could be 0.2% or 5%. But it won't be 0.001% or 100%.
Results also have to be interpreted statistically, i.e. the result is the average number of transmission cases, across many realizations of a given event. I.e. if
1000 similar events were conducted, this would be the average probability. Any one event may have much fewer or many more transmission cases.
How to use the estimator
|This online version will be kept up-to-date. We can't allow 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 or make a G Sheets copy with File --> Make a copy
Or you can download an Excel version with the direct link above
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
Most important inputs are colored in orange
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
Suggestions and improvements
Please email me for any suggestions for improvements, additional input data etc.
|The model combines two submodels: |
(1) a standard atmospheric "box model", which assumes that the emissions are completely mixed across a control volume quickly (such as an indoor room or other space). See for example Chapter 3 of the Jacob Atmos. Chem. textbook, and Chapter 21 of the Cooper and Alley Air Pollution Control Engineering Textbook for indoor applications. This is an approximation that allows easy calculation, is approximately correct as long as near-field effects are avoided by social distancing, and is commonly used in air quality modeling.
(2) a standard aerosol infection model (Wells-Riley model), 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.
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.
Relative Quanta Exhalation Rates
Calculated according to Peng et al. (2022) for the estimation of infection risk parameters
Basic quanta exhalation rate
infectious doses (quanta) h-1
Quanta exhalation rate for "Resting - Oral breathing".
This is for the original Wuhan variant
Note that Buonanno et al. estimated a lower value of 2 q h-1. The uncertainty bands of both values overlap. See Peng et al. (2022) for further details
The relative quanta emission rates are based on Buonnano et al. (2020a, b), and personal comm. from the author:
|Paper 1||Paper 2|
|IMPORTANT: The uncertainty of these values is high, probably at factor of 5 or 10. We just don't know enough about this disease yet. Also there are likely superspreaders which are less frequent but may have higher emissions (as in the choir case). Thus don't take abs. probabilities of infection at face value, just look at the order-of-magnitude (i.e. it is of the order of 0.001% or 0.01% or 0.1% or 1% or 10% or approaching 100%?. It is the relative effect of control measures, disease prevalence etc. that is most useful from this estimator, given the current state of knowledge.|
For a more general set of activities, provided by the same author, based on their 2nd paper:
[Note that these values are for the original Wuhan variant -- there is a modifier, described below, for other variants]
Resting – Oral breathing =
All are dimensionless
Resting – Speaking =
Resting – Loudly speaking =