15/ We analyze these outbreaks together, with a model of airborne transmission in a shared room. Based on the Wells-Riley model (pioneers who demonstrated tuberculosis as the first airborne disease):
Step 1) Infected person exhales virus (E in drawing). Low amount if breathing, more if talking, much more if shouting, singing loudly, or exercising
17/ Step 2) Virus-laden aerosols mix in the room air
(We are NOT considering close-proximity transmission, since it can't infect a lot of people and lead to a large superspreading event -- Need > 15 min with each person per @CDCgov, just no time in the events analyzed)
18/ Step 3) Virus in the air can:
- go outside w/ ventilation (F_out, speed of ventilation varies hugely across locations)
- be filtered in the room (not shown in this drawing)
- lose infectivity (typically ~1 hr)
- deposit to the ground (tens of min to many hours)
19/ A level of airborne virus is established in the room. Depends on:
- how much virus is being exhaled
- how big the room is
- how quickly is the virus being removed
Analogous to height of water in a sink, depends on flow from faucet, size of sink, and size of drain:
20/ For those interested in more details and more references, the model is freely-available in spreadsheet format at tinyurl.com/covid-estimator
Useful for those familiar with spreadsheets and quantitative stuff. [Often confusing for those not familiar with those tools]
21/ Multiple very similar models have been published (I'll add a list at the end), they all do the same thing.
But one complexity is that the result depends on many parameters, hard to represent / summarize
22/ This paper makes 2 advances: the first is that we condensed ALL the factors that affect infection into a single number:
- The relative infection risk parameter (Hr)
23/ Where:
- r_e: changes in virus emission (e.g. talking, singing)
- r_b: changes in breathing rate ( changes virus inhaled, e.g. exercise)
- f_e and f_i: for how many aerosols escape masks going out of the infected (e) and being inhaled by the susceptible (i) (=1 if no mask)
24/ - D: duration of the event
- V: volume of the room
- lamba_0: ventilation rate
- lambda_cle: air cleaning (e.g. HEPA filter, #corsirosentalbox, UV disinfection)
- See paper for more details
25/ The model predicts that the attack rate (fraction of people present that were infected) should depend on Hr only, for a GIVEN disease.
Does that work? It does for COVID-19!
Red line is model, datapoints are outbreaks
[Ignore mitigation arrows for now]
26/ Agreement is not perfect, but these are outbreaks taken from the literature, with imperfect data about activities etc.
Personally I was astounded when I saw this (@ZheP_AtmChem did a lot of the actual work)
@ZheP_AtmChem 27/ Now, if these superspreading events were caused by e.g. surface transmission, or something else than airborne tr., THERE IS NO REASON why the model would be able to reproduce data
So this confirms that superspreading events are dominated by shared room airborne transmission
@ZheP_AtmChem 28/ Why don't we have more events? This is a SORE point.
Bc most investigations done by epidemiologists who do not understand ventilation. They don't report size of the room or ventilation rate.
I've reached out to epi investigators of other outbreaks, most don't even reply.
29/ Now, as Kurt Lewin said, "there is nothing more practical than a good theory". Once the model works, we can use it to:
- analyze other outbreaks
- understand the risk of other events
- quantitatively evaluate mitigation measures
- compare with other diseases
- etc.
30/ For example, in this table we estimate the risk of transmission of different situations, for the omicron variant with a 1% infection rate (approx. now in UK and US)
31/ The table shows the probability of infection for an individual that participates in an event.
- There may or may NOT be someone infected there:
e.g. if 10 people present and 1% of population infected, then 90% of the time no one is infected, 10% of the time 1 infected
32/ The rest of the factors are the ones that we have discussed already:
- low vs high occupancy (more people, more change someone infected)
- ventilation rate (outdoors vs indoors good vs indoors poor). Note that poor ventilation is the NORM, not the exception, even in USA
33/ - Wearing masks or not (masks remove exhaled virus and prevent inhalation of virus from the air)
- Vocalization: much more virus exhaled if talking, shouting, exercise
- Contact time: more virus accumulates in the air, and occupants breathe more air over longer time
34/ This table updates a similar, but QUALITATIVE table published by @trishgreenhalgh, Lydia Bourouiba, and colleagues in a paper in @bmj_latest in 2020, that most of you have probably seen.
We collaborated w/ them using model for new QUANTITATIVE table
35/ Another application of the model is to evaluate the impact of mitigation measures.
I.e. we are told to spend less time, better mask, more ventilation, add HEPA filter. Confusing, one gets tired after doing a few things
How much is enough? How much is risk reduced?
36/ For example here we start with a high risk situation
By using N95 masks (with attention to fit), halving the duration, and improving ventilation and filtration, we get to a situation where the probability of outbreak is quite low.
Can rank benefit per $$ of each measure
37/ In the paper we show similar mitigations for a range of locations, that comply with recent ASHRAE (@ashraenews) guidelines.
Which is typically a BEST case scenario. Older places have less ventilation. And compliance only at construction, often things degrade
38/ For example:
- A lot harder to make choir safe: a lot of virus emission by singing, more breathing
- Much easier to make library safe: much lower virus emission since no talking, sedentary, also inhaling less air
Consistent w/ tons of choir outbreaks, none in libraries TMK
39/ Finally, the model allows comparison of different airborne diseases:
- Pulmonary tuberculosis is ONLY airborne
- Measles superspreading is dominantly airborne
- Flu is also airborne (science.org/doi/10.1126/sc…)
40/ COVID-19 has intermediate transmissibility between tuberculosis and measles
Both are airborne. Which is why the old argument (made by e.g. @WHO experts aricjournal.biomedcentral.com/articles/10.11…) "COVID-19 can't be airborne because it is less contagious than measles" never made any sense
41/ We couldn't find many outbreaks in the literature for other diseases that DID have ventilation rate, room volume, and the other parameters we needed.
But relative trend is clear.
[If you know of other outbreaks w/ that info, email me the papers]
42/ The flu outbreak is the famous case in an airplane that was parked in a tarmac for 3 hours without ventilation, with someone coughing continuously:
43/ IMPORTANT: all of the outbreaks included in the paper are from early in the pandemic (it takes time to do the analysis and publish it), so they are all for the original (Wuhan) variant(s)
44/ We can estimate the impact of the omicron variant, from recent reports of its increased transmissibility. Uncertain, clearly worse, but not like the few historical measles outbreaks yet (subject to update once we have O data):
- Wuhan: central red line
- Omicron: pink line
45/ So I think that's all I wanted to say. Thank you if you made it to here. For more info on the overall airborne transmission thread, you can see this other thread, and papers and threads linked therein:
46/ If you mostly care about what you can do to protect yourself (hopefully it makes a little more sense now after reading about the model -- since if you made it here, you must be quantitatively minded), see this thread and its links:
48/ I kept working on it, but as they say in my native Spain, I was "slower than the horse of the bad guy" in a Western movie.
I asked @ZheP_AtmChem to work on it w/ me. He did enormous amount of careful work to distill the results.
I couldn't believe my eyes how well it worked
49/ Many other scientists also participated (and were very patient with me), most from Lidia Morawska's "group of 36" that met with @WHO on 3-Apr-2020, led the "Letter of the 239 scientists" etc.
CO2 (above ~400 ppm outdoors) indicates the amount of exhaled air (& virus) trapped in a space
Also per recent scientific results by @ukhadds, CO2 helps SARS-CoV stay infectious in air much longer
@united flight boarding, pretty terrible!
2/ This is the trip so far:
-Low outdoors
-Pretty high ~2000 in @RideRTD bus to airport
- ok ~800 at @DENAirport, except restroom ~1500. Not sure why restrooms at this airport are so often poorly ventilated
- Then boarding on @united, ventilation OFF, so huge increase till ON
3/ For details of the recent results on how and why CO2 makes SARS-CoV-2 stay infectious much longer in the air, see this recent thread by @ukhadds
1/ "After four years of fighting about it, @WHO has finally proclaimed that viruses, including the SARS-CoV-2 virus that causes COVID, can be spread through the air"
3/ "Words matter. When people heard that COVID might spread on surfaces, they wasted time wiping down groceries. People who misunderstood airborne spread needlessly wore masks on outdoor walks and veered off sidewalks to avoid their neighbors."
1/ New paper in @ScienceMagazine: "Mandating Indoor Air Quality for Public Buildings"
Explaining current status of indoor air quality standards (in short: bad or non-existent), the huge health benefits that would arise from them & proposing a path forward science.org/doi/10.1126/sc…
2/ "People living in urban & industrialized societies, which are expanding globally, spend more than 90% of time indoors, breathing indoor air (IA)."
"Most countries do NOT have legislated indoor air quality (IAQ) performance standards for public spaces"