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Let’s talk about #masks (A Thread)
The masks people are wearing are permeable to viral particles. The spaces between threads > width of a single virus. This is not contested among scientists. It’s common knowledge. BUT
The masks people are wearing are permeable to viral particles. The spaces between threads > width of a single virus. This is not contested among scientists. It’s common knowledge. BUT
Studies have suggested that even fabric masks partially block the movement of small particles. See here: https://pubs.acs.org/doi/10.1021/acsnano.0c03252
This means that even a permeable mask can be useful to reducing transmission of #COVID19
This means that even a permeable mask can be useful to reducing transmission of #COVID19
The aim of everyone wearing masks is NOT to prevent or eliminate transmission of #coronavirus. It is to reduce and slow transmission. And it works.
Consider: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177146/
And: https://medicalxpress.com/news/2020-05-hamster-masks-coronavirus-scientists.amp
Consider: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7177146/
And: https://medicalxpress.com/news/2020-05-hamster-masks-coronavirus-scientists.amp
In fact! If everyone were to wear a mask while in public, transmission could go down by up to 80%
Model here: http://dek.ai/masksim/
But wait!! There’s more!!
Model here: http://dek.ai/masksim/
But wait!! There’s more!!
The reason for this is that the majority of viral particles spreading infection with coronavirus are found in large droplets. While the mask won’t stop individual particles of viral material from passing through, it will stop or slow droplets of moisture containing the virus.
This could have a very small effect on an individual level and still VASTLY reduce transmission. That’s because the infection rate is an exponential value that grows based on what epidemiologists call “R0.”
R0 refers to the number of individuals any one given infected person is expected to pass the virus to and infect. R0 is dependent on a number of factors— transmissibility, population density, population mixing, infectious period etc.
https://bit.ly/2TLlxPM
https://bit.ly/2TLlxPM
Imagine the three following scenarios for R0. These are represented mathematically, but in the graphic you can visualize what is being described. Credit: https://labblog.uofmhealth.org/rounds/how-scientists-quantify-intensity-of-an-outbreak-like-covid-19
R0 = 2
1 -> (1+2 = 3) -> (3 + (2*2) = 7) -> (7+ (4*2) = 15) -> (15 + 16 = 31) -> (31 + 32 = 63)
Over 5 iterations, we’ve gone from 1 person to 63 people.
R0 = 4
1 -> 5 -> 21 -> 85 -> 341 -> 1365
Over 5 iterations, we’ve gone from 1 person to 1,365 people.
1 -> (1+2 = 3) -> (3 + (2*2) = 7) -> (7+ (4*2) = 15) -> (15 + 16 = 31) -> (31 + 32 = 63)
Over 5 iterations, we’ve gone from 1 person to 63 people.
R0 = 4
1 -> 5 -> 21 -> 85 -> 341 -> 1365
Over 5 iterations, we’ve gone from 1 person to 1,365 people.
R0 = 6
1 -> (1 + 6 = 7) -> (7 + 36 = 43) -> (43 + 216 = 259) -> (259 + 1296 = 1555) -> (1555 + 7776 = 9331)
Over 5 iterations, we’ve gone from 1 person to 9,331 people.
A 3-fold increase in R0 results in a greater than 100-fold increase in total infected individuals very quick
1 -> (1 + 6 = 7) -> (7 + 36 = 43) -> (43 + 216 = 259) -> (259 + 1296 = 1555) -> (1555 + 7776 = 9331)
Over 5 iterations, we’ve gone from 1 person to 9,331 people.
A 3-fold increase in R0 results in a greater than 100-fold increase in total infected individuals very quick
As you can see, even when we’re only talking about one single starting individual, small changes in R0 quickly result in a ripple effect that would have massive implications on a population scale.
Even reducing R0 by 1 or 2 is HUGE.
Even reducing R0 by 1 or 2 is HUGE.
