Given the fact that we already know that masks have poor performance based on the what I’ve already mentioned, models are pointless for most situations.
If you’re referring to modelling a strategy of maximizing personal (rather than public) protection with a poor performing tool, models could help you do that, but in the case of masks, it will turn out that most strategies are impractical because 1) there will be too many variables to keep track of, 2) some variables will be impossible or hard to obtain, and 3) some variables will be hard to control even with perfect knowledge. With or without a mask, if the distance between people is far enough, infection will be avoided regardless of infectivity due to dilution. If the distance is between two people, you may be able to calculate a minimum safe distance if you know all of the variables. Some of these variables are room size, ventilation, infectivity, mask type, rates of breathing and vocalization, and vaccination status. You’d also need to know if the room was previously occupied and by whom. Some of these these variables will be known but some will not be. You’ll also need to recompute these variables once they change. If you’re dealing with a simple model with two people in which nothing changes, this strategy might work. But real world cases are almost nothing like this. What if you go to another room or another person walks in? Is the ventilation the same? Is the person vaccinated? What kind of mask are they wearing? How many people were in the room before you walked in (aerosols can become suspended for hours even if the people that generated them are no longer around)? Modeling this stuff quickly becomes impractical, and if you can avoid that by wearing a respirator, why bother?
“10%, 1%, 0.1%” was meant to poke fun at the attempt of precisely quantifying the poor performance of masks and is not based on any data.
Given the fact that we already know that masks have poor performance based on the what I’ve already mentioned, models are pointless for most situations.
If you’re referring to modelling a strategy of maximizing personal (rather than public) protection with a poor performing tool, models could help you do that, but in the case of masks, it will turn out that most strategies are impractical because 1) there will be too many variables to keep track of, 2) some variables will be impossible or hard to obtain, and 3) some variables will be hard to control even with perfect knowledge. With or without a mask, if the distance between people is far enough, infection will be avoided regardless of infectivity due to dilution. If the distance is between two people, you may be able to calculate a minimum safe distance if you know all of the variables. Some of these variables are room size, ventilation, infectivity, mask type, rates of breathing and vocalization, and vaccination status. You’d also need to know if the room was previously occupied and by whom. Some of these these variables will be known but some will not be. You’ll also need to recompute these variables once they change. If you’re dealing with a simple model with two people in which nothing changes, this strategy might work. But real world cases are almost nothing like this. What if you go to another room or another person walks in? Is the ventilation the same? Is the person vaccinated? What kind of mask are they wearing? How many people were in the room before you walked in (aerosols can become suspended for hours even if the people that generated them are no longer around)? Modeling this stuff quickly becomes impractical, and if you can avoid that by wearing a respirator, why bother?
“10%, 1%, 0.1%” was meant to poke fun at the attempt of precisely quantifying the poor performance of masks and is not based on any data.
I don’t think further discussion in this thread is likely to prove fruitful.