That is what calculations of this nature do. I thought it was important to get a rough order of magnitude sense of how many people need to wear a mask in public areas to save one life in the US. I did the most reasonable calculation I could think of given the data available.
I would be very pleased to see other calculations -- for example, embedding our results in a structural model which figured out the long-run impact on deaths given a race between vaccines and natural immunity would be extremely interesting.
But if you're just going to say, "You've made assumptions" as opposed to, "Here is a better calculation that makes more plausible assumptions", then you have not produced a better estimate of a number that a lot of people want to see.
This is a very general problem -- people look at quantitative models and dismiss the results because they don't like the assumptions when they have no quantitative model of their own.
Your intuitive guess is *also* based on assumptions that are equally questionable, just not ones that are clearly articulated.
If you don't like my estimate, give a better one, state your assumptions clearly, and then we can discuss which calculation makes more defensible assumptions.
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It does seem a little sinister when I get threats from "a meeting of the Clans", but hey, at least I'll be "part of world history"
Incidentally, we found that masks reduced COVID symptoms for people under the age of 50 but imprecise zeros for serologically-confirmed COVID.
This could mean that masks are most effective at combatting other respiratory diseases for people under 50, or just that we have less precision when we restrict to serologically confirmed COVID.
The value of masks in places where nearly everyone is vaccinated is clearly lower. Caution is necessary since, in many parts of the world, the vaccines being used have lower efficacy than in the US, meaning that masks likely have value on top of vaccines.
Additionally, masks may prevent breakthrough cases which may eventually spread to unvaccinated people. However, I haven't seen a quantitative calculation of the magnitude of this benefit -- it may be small.
An intuitive way to grasp the effectiveness of masks: extrapolating from our results, every 600 people who wear masks for a year in public areas prevents 1 person from dying of COVID given status quo death rates in the US.
Note that this is *taking into account current vaccination rates in the US*. Despite the availability of vaccines in the US, the weekly death rate is higher than at any point prior to November 2020.
Here is how I arrived at this number. Our study shows that inducing a 30 pp increase in mask-use prevented 35% of COVID cases among the elderly.
The idea that we should patiently educate the aggressively ignorant sounds laudable, but it practically means disengagement. How many who liked the above post consistently attempt to do this? They might try once, but they'll give up because it's too time-consuming.
For those keeping track at home, this is definitely not what a confidence interval is. A 95% CI is a function of the data such that, given the data generating process with an unknown true parameter, the CI constructed in this way will contain the true parameter 95% of the time.
The idea that all values in a 95% CI are equally likely is preposterous. If one were instead constructing Bayesian credibility intervals, you do not need a gaussian prior to rule this out.
In the Bayesian problem, this would represent an absurd corner case where the data was completely uninformative about the underlying parameter within a specified range. I can't imagine how this would be a reasonable model of the situation at hand.
Firstly, our study does not say that masks can only prevent 11% of COVID. Our study says that our intervention -- which raised surgical mask-wearing from 13% to 43% -- prevented 11% of COVID cases, and 35% among age 60+.
To put the point on your own terms -- if you vaccinated 30% of the population, would you prevent 35% of cases age 60+?