4 ms·
> Because he's being generous: the 11% difference was on a baseline seroprevalence of 0.76%. Wait a second. > In other words: you'd have to get 1300 people to
by Ginden 5y ago
> Because he's being generous: the 11% difference was on a baseline seroprevalence of 0.76%.
Wait a second.
> In other words: you'd have to get 1300 people to wear masks to prevent one seropositive (in case you're wondering, the confidence interval on that value overlaps zero.)
You are assuming 0.76% prevalence. This is, I assume, in specific point of time, but somehow, you extend this to long-term intervention.
If you spin COVID-19 "but only 0.5% of population is infected _right now_", any intervention will seem extremely ineffective. "Oh, you want to vaccinate 300 millions of people to prevent 2000 deaths" (without saying it's 2000 deaths _per day_).
- timr 5y ago> You are assuming 0.76% prevalence. I am not. I'm using the numbers from the paper to illustrate a point. Even if seroprevalence was as high as 6% at any given time (which is about as high as it ever was in NYC, for example), an 11% difference is 0.66%, or 1 case per 152 people wearing masks. And that's in a fully unvaccinated population. We're simply not talking about huge differences here. Certainly nothing close to a statistically meaningful difference in R0.