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

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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

coronavirus, data, aerosol transmission

COVID-19 Aerosol Transmission Estimator File --> Make a Copy OR Download to Excel (Click GREEN links below if don't see option)

Developed by: Prof. Jose L Jimenez & Dr. Zhe Peng, Dept. of Chem. & CIRES, Univ. Colorado-Boulder Shortcut: https://tinyurl.com/covid-estimator

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: https://tinyurl.com/FAQ-aerosols 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: English Version Version en español

Subscribing to email list for tool: https://groups.google.com/forum/#!forum/covid-estimator

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 Greenhalgh, 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 3.6.8 21-Mar-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. [email protected]

Scientific Approach

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 https://doi.org/10.1111/ina.12751

Original Wells-Riley model: https://academic.oup.com/aje/article-abstract/107/5/421/58522

Buonnano et al. (2020a) https://www.sciencedirect.com/science/article/pii/S0160412020312800

Buonnano et al. (2020b) https://doi.org/10.1016/j.envint.2020.106112

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 https://doi.org/10.1111/ina.12751

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

Tags Coronavirus, Data, Aerosol transmission
Type Google Sheet
Published 22/11/2023, 08:36:39


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