2/ It can be very confusing to estimate how risky different activities are, depending of size of space, duration, number of people, vocalization, intensity of breathing, ventilation, air cleaning, masks + their quality + fit.
We combine it all into a single parameter, rigorously
3/ We'll explain the parameter(s) later, but first let me convince you that it works.
The key is Fig. 1 b:
- X-axis is risk parameter in log. scale. MUCH riskier to right, MUCH less risky to left
- Y-axis is attack rate (% of people present infected in outbreaks)
4/ Red points are COVID-19 outbreaks from the literature (e.g. Skagit choir, Berlin choir, Chinese restaurant and buses, in aircraft, slaughterhouse, schools etc.)
Red line is airborne transmission model.
The points follow the model ==> OUTBREAKS DOMINATED BY AIRBORNE
5/ If the outbreaks were dominated by fomites or large droplets, they would vary in different ways. No reason why they would correlated with airborne risk.
We used all the outbreaks we could find that had all the needed info. Ventilation and room size rarely reported!
6/ At low values of the risk parameter Hr, the outbreaks tend to be above the model.
Most likely because we are only detecting the outlier events, otherwise we just wouldn't see them. Still they follow well.
7/ Interestingly, there are a few outbreaks for measles (black), tuberculosis (blue) that can be added to the graph. Both airborne. COVID-19 clearly MORE TRANSMISSIBLE than TB and less than measles.
COVID-19 FITS WELL AMONG AIRBORNE DISEASES.
8/ Hopefully this will help stop the nonsense that COVID-19 can't be airborne because it is less contagious than measles.
It is. And it is more contagious than TB, which can ONLY transmit through airborne. Although strong TB outbreak can be similar to COVID-19.
9/ Also of interest: we added the Moser et al. (1979) influenza outbreak: in an airplane, 3 hrs stuck in tarmac w/o any ventilation, person coughing constantly.
A little less transmissible than COVID-19 in that case.
10/ We see that when Hr < 0.001 h2 m-3, we don't see outbreaks. Therefore we should keep activities below that value during the pandemic.
11/ This allows us to combine volume of space, duration, number of people, vocalization, intensity of breathing, ventilation, air cleaning, masks + their quality + fit rationally and quantitatively to estimate the risk of a situation.
12/ If you want to calculate the risk parameter for your situation, you can do so with the COVID-19 Aerosol Transmission Estimator, freely available online.
(Modify sheet "Master-choir" -- will transplant to the others)
14/ We now provide a quantitative version of the table. We worked in collaboration with @trishgreenhalgh (@UniofOxford) and Lydia Bourouiba (@MIT) to update it.
Table in the paper is below. Parameters used shown in the Supp. Info. of the paper.
15/ In addition, we provide an interactive table in the COVID-19 Aerosol Transmission Estimator (tinyurl.com/covid-estimator), so that people can modify the table for conditions of their interest (sheet "Risk Table")
16/ We also show the effect of different mitigations of common activities (school, restaurant, choir)...
We see that the risk of superspreading is strongly reduced with mitigations. Big outbreaks needed to have several problems: low ventilation, strong vocalization, no masks...
17/ But now we can finally quantify when mitigations are ENOUGH (or not enough, or perhaps too much).
Hard to make choirs safe, require A LOT of mitigations. But easy for a library (quiet) and not too difficult for a school.
18/ There is related work in the literature, such as a preprint by @KriegelMartin of Germany (we use data for several of the outbreaks he reports).
Also shows that airborne model works (graph below). Does not derive a simple combined parameter.
22/ But yet with thousands of COVID-19 outbreaks that have been documented, and many investigated, not so many report ventilation rate and size of space, which are critical
I emailed authors of papers on several outbreaks, offered to help them measure ventilation. No one replied
23/ This work was an outgrowth of what I have learned working with the "Group of 36 Scientists" that Lidia Morawska assembled right after this enormous, historical error from @WHO :
24/ We talked to @WHO, they didn't listen. But we've kept working on many papers (e.g. "letter of 239 scientists" last July: academic.oup.com/cid/article/71…)
25/ This paper on " How can airborne transmission of COVID-19 indoors be minimised?"
[TL-DR: ventilation, air cleaning with filters, air cleaning with UV in some cases. AVOID ionizers (and plasmas, photocatalysis, hydroxils) and foggers & ozonizers]
26/ This paper in @jhieditor Journal of Hospital Infection on "Dismantling myths on the airborne transmission of severe acute respiratory syndrome coronavirus (SARS-CoV-2)"
27/ This paper on the history of droplets and aerosols, and the origin of the ERROR of using 5 microns as the aerosol / droplet boundary, instead of the correct 100 microns.
[TDLR: someone confused deep penetration in lung for TB, with falling to ground]
I was the only author from the group of 36 in this paper, but it contained much I learned from the group.
29/ The paper on the Skagit Choir outbreak, led by @ShellyMBoulder.
The clearest airborne transmission case of the early pandemic IMHO, and what set me on the hunt for simple ways to express infection risk, led to preprint that's focus of this thread.
31/ This latest preprint on the simple estimation or risk also has @ZheP_AtmChem as the first author. Zhe is a remarkable scientist, and was able to push this project fast and address all the tricky details.
33/ Forgot to clarify at the start that this model is for airborne transmission in shared room air. So we assume that the distance is kept, so that short-range airborne transmission (sciencedirect.com/science/articl…) is not important
If distance not kept, risk is higher than we calculate
1/ UPDATES TO COVID-19 AEROSOL TRANSMISSION ESTIMATOR
We have just implemented several updates:
- added increased risk of variants
- made clearer how to enter vaccinated people
- added calculation of infection risk parameters
2/ The risk parameters allow quantitative decisions on which mitigations are needed to avoid outbreaks. See the thread from yesterday on that topic and paper:
3/ We also added a sheet with a quantitative version of the BMJ table (bmj.com/content/370/bm…). See sheet "Risk Table", where you can modify it for your conditions.
Reception area: set at 1 of 3, could not feel any air. Turned to 3, made more noise, started to feel air.
Procedure room: set to 2, I asked to turn to 3, noisier
3/ Not impressed, despite some precautions. I was not too concerned due to vaccination. But there ar other respiratory diseases, they need to do better, I'll let them know.
Repeated problem: HEPA filters viewed as "talisman" by being there. Even if too small and at low setting!
Despite 100 times LOWER dose, monkeys infected by SARS-CoV-2 thru aerosols developed MORE SEVERE respiratory disease and lung pathology (vs. nose/trachea)
2/ This further argues for the importance of aerosols over large droplets, adding an 11th reason to the 10 that we summarized in our recent @TheLancet paper (thelancet.com/journals/lance…)
3/ Note that this has been demonstrated for other diseases such as the flu, for which the intranasal dose needs to be 100000 (one hundred thousand) times larger to lead to the same symptoms, compared to aerosol infection. See: journals.sagepub.com/doi/abs/10.318… onlinelibrary.wiley.com/doi/abs/10.100…
2/ @SAFTehnika, fabricante de los Aranet4, se ha enterado de nuestra campaña, y contribuye donando 51 Aranet4 más!
46 están en EEUU y pueden ir a Latinoamérica y comunidades hispanas (o de bajos recursos en Canadá y EEUU).
5 van a Europa, tratando de llegar a nuevos países
3/ Muchísimas gracias a @SAFTehnika por esta generosa contribución.
Y muchas gracias a @citlanx, distribuidor de Aranet 4 (naltic.com/aranet4-co2.ht…), que hace todo el trabajo de logística y los envíos voluntariamente sin coste.
- @trishgreenhalgh, Prof. of Medicine at Oxford Univ., pioneer of evidence-based medicine (EBM)
- @DFisman, Prof. at Univ. of Toronto, epidemiologist
- @chipatucsd, Prof. of Medicine at UC-San Diego, Chief Editor of journal "Clinical Infectious Diseases"
3/ The rest of the authors:
- @kprather, Prof. UC-San Diego, member of US National Academies of Science and of Engineering, aerosol scientist
- @zeynep, Prof. Univ. of North Carolina, sociologist
- Yours truly, Prof. Univ. of Colorado, Highly Cited Scientist, aerosol scientist
- Hay epidemia de cólera en Londres
- Se piensa que se transmite por el aire
- John Snow investiga y se da cuenta que los casos se concentran alrededor de una bomba de agua
- Quita el asa de bomba de agua, y la epidemia de cólera se para
3/ @DFisman: "Es bueno recordar q prácticamente todos los expertos de salud pública pensaban q John Snow estaba equivocado cuando dijo q el cólera se transmitía por partículas en el agua demasiado pequeñas para verlas
Modelo dominante era q se transmitía por un gas, una miasma"