What the poster means is that going from 0.79% to 0.04% means that 0.75% were prevented from getting it due to the vaccine. You go from 79 per 10000 to 4 per 10000, meaning you saved 75 per 10000.

But preventing 0.75% of the population from getting it out of the 0.79% who got it implies 94.9% (0.0075/0.0079) of those who _would_ have gotten it were prevented from getting it. That's the relative ratio.

I agree with the poster that the relative number seems more interesting. There are lots of things going on right now (masks, social distancing, etc.) to prevent people from getting COVID. The fact that only 0.79% of the placebo group got it implies a lot about _other_ mitigation factors, and puts a limiting force on how many can be saved by the vaccine (a number less than or equal to 0.79%). Like OP, I wonder if there's any case where the -0.75% number is interesting in and of itself. In fact, if we weren't social distancing, presumably the prevalence of COVID would be much higher and thus the absolute ratio of people prevented from getting COVID would be higher too.

The 95% effectiveness number should (theoretically) be the same no matter what other measures you're taking. It would reduce 0.79% of population from getting it to 0.04% getting it, and would reduce 10% of the population from getting it to 0.5% of the population getting it. I'm totally ignoring that lots of people getting the vaccine will lead to less people getting sick and less people spreading it (herd immunity), but you get the point.

"""There are lots of things going on right now (masks, social distancing, etc.) to prevent people from getting COVID"""

I hope this doesn't come across as snarky, but this seems like it could have a larger impact in the US than elsewhere, since we've (collectively) been doing such an abysmal job of doing masks + distancing.

With absolute numbers of 162 and 8, 94.9% would have an error range of somewhere around 10%, right? So the actual percent would be somewhere in the range of 85-97%?

(No actual math done here, I'm ballparking it based on looking at error ranges with sample size 100 at work; my main point is that I don't think the ~20k enrolled in each group affects this particular error range, so even though "94.9%" is stated precisely the actual data isn't nearly so precise)

Sure, but we also have to consider the r0. For both the placebo and the non-placebo patients, they were generally operating in an environment where essentially ~90% of others effectively had the placebo (no immunity). As the number of people with the vaccine goes up, the number of transmission vectors goes down, further decreasing the spread. So even on the low end of the error bar, the impact here would be huge.