2020_06_COVID-19_Airborne_Transmission_Estimator
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COVID-19 Airborne Transmission Estimator
If you don't see download option, click link just below
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Developed by:
Prof. Jose L Jimenez, Dept. of Chem. and CIRES, Univ. of Colorado-Boulder
Shortcut:
https://tinyurl.com/covid-estimator
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Short description of this tool:
https://cires.colorado.edu/news/covid-19-airborne-transmission-tool-available
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Using input or feedback from:
Linsey Marr, Shelly Miller, Giorgio Buonnano, Lidia Morawska, Don Milton, Julian Tang, Jarek Kurnitski, Xavier Querol, Matthew McQueen,
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Charles Stanier, Joel Eaves, Alfred Trukenmueller, Ty Newell, Greg Blonder, Andrew Maynard, Nathan Skinner, Clark Vangilder, Roger Olsen
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Prasad Khasibhatla
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(only listing the most important here, many others have contributed feedback as well over email and Twitter. Thanks a lot to everyone!)
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(Any mistakes are my own)
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Version & date3.0.1114-Jul-20
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How to use the estimator
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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
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People interested in using the model should download an Excel version from File --> Download
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(Ignore Excel Errors in some versions, it still works fine)
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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
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See the version log at the bottom of this sheet for a brief description of the updates
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Inputs and Outputs
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Inputs are colored in yellow.
These are the cells you should change to explore different cases.
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Descriptions and intermediate calculations are not colored. Do not overwrite the calculations or you will break the estimator.
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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.
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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
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an input on the calculations. They can be converted easily, but pay attention to what each specific sheet is doing.
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All sheets are self-contained, except for the University case
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For the University case
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Approximately scaled for a large University in the Western US for the Fall 2020 semester
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First, results are calculated for a typical classroom ("Classroom Sheet"), assuming either one student or the professor are infected
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Assumes enhanced social distancing and masks in place
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Classroom size does not matter much, since students will scale with it
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Then, results are scaled to the whole campus ("Campus Sheet"), taking into account the probability of infection in the population
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What we are trying to estimate
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The propagation of COVID-19 by airborne transmission ONLY
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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
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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
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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
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This model does NOT include transmission to the people present, when they are in locations other than the one analyzed here
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The model can easily be adapted to other situations, such as offices, shops etc.
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Simplicity and uncertainties
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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.
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Several parameters are uncertain, and have been estimated based on current knowledge. Alternative estimates can be entered to explore their effect in the results.
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More complex and realistic models can be built, however the parametric uncertainty may still dominate the total uncertainty
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Parameters based on new research can be incorporated as they become available. Pls send them my way
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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
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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
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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
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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
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average number of transmission cases. Any one event may have much fewer or many more transmission cases.)
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Suggestions and improvements
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Please email me for any suggestions for improvements, additional input data etc.
jose.jimenez@colorado.edu
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Scientific Approach
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The model is based on standard airborne transmission models (Wells-Riley type models), as formulated in Miller et al. 2020, and references therein
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Miller et al. Skagit Choir Outbreak
https://www.medrxiv.org/content/10.1101/2020.06.15.20132027v1
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Original Wells-Riley model:
https://academic.oup.com/aje/article-abstract/107/5/421/58522
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Buonnano et al. (2020a)
https://www.sciencedirect.com/science/article/pii/S0160412020312800
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Buonnano et al. (2020b)
https://www.medrxiv.org/content/10.1101/2020.06.01.20118984v1
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Key parameters, sources, and uncertainties
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The most uncertain parameter is the quanta emission rates for SARS-CoV-2
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See FAQ sheet for the definition of quanta
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970 q / h
This is from the Miller et al. choir superspreading case
https://www.medrxiv.org/content/10.1101/2020.06.15.20132027v1
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This value is at the high end of the Buonnano et al. values provided below, consistent with this being a superspreading event
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which was likely influenced by a very high emission rate of quanta from the specific index case
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We do not think that this very high value should be applied to all situations, as that would overestimate the infection risk.
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Buonnano et al. (2020a, b) provides a range of estimates. Recommended values by the author are:
Paper 1Paper 2
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For a professor delivering a lecture:4.4, 21, and 134 for oral breathing, speaking and aloud speaking (or singing)
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For a student sitting on a lecture: 4, 16, 97 for oral breathing, speaking and aloud speaking (or singing)
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For a more general set of activities, provided by the same author, based on their 2nd paper:
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Resting – Oral breathing = 2.0 quanta/h
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Resting – Speaking = 9.4 quanta/h
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Resting – Loudly speaking = 60.5 quanta/h
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Standing – Oral breathing = 2.3 quanta/h
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Standing – Speaking = 11.4 quanta/h
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Standing – Loudly speaking = 65.1 quanta/h
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Light exercise – Oral breathing = 5.6 quanta/h
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Light exercise – Speaking = 26.3 quanta/h
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Light exercise – Loudly speaking = 170 quanta/h
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Heavy exercise – Oral breathing = 13.5 quanta/h
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Heavy exercise – Speaking = 63.1 quanta/h
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Heavy exercise – Loudly speaking = 408 quanta/h
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For children as a first approximation I would reduce these numbers proportionally to body mass.
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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
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can still be transmitted through aerosols under the right circumstances (indoors, lower ventilation, crowding, longer duration, activities that favor
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higher emission rates of respiratory aerosols such as singing, talking, aerobic exercise etc.) If you are curious, change the quantum emission rate
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to 5500 to see what measles would do, if it encountered a susceptible population with its high infectivity.
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Inhalation Rates
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Values in m3 h-1 from Buonnano et al. 2020a, averaged for males and females (https://www.sciencedirect.com/science/article/pii/S0160412020312800)
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Based on https://journals.lww.com/epidem/Citation/1995/03000/132_MEASUREMENT_OF_BREATHING_RATE_AND_VOLUME_IN.162.aspx
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0.49Resting
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0.54Standing
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1.38Light exercise
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