This one is kind of aimed at @MarkusWinter since he is a biologist
The three different vaccines uses 2, or 3, different ways to create a vaccine.
But when they talk about efficacy of each how are they measuring this ?
Antigen tests ? Or ?
So the neighbor of the house I am staying in right now is a Dr of Pandemic (type) Diseases (not real title but laymans terms) and I asked her the same question. She was good at putting in laymans terms but I didnt get it. Lots of science around chemistry and biology.
the great thing about being in a small town that supports a large research university is I am always not the smartest person in the room and I am always learning something new.
but this really doesnt answer your question Norm. sorry.
I’m sure at some point markus will find his way back here and reply 
or maybe someone else that I’m unaware of that might know ???
You make a double blind study.
10000 people get vaccinated, but only the organizer of the test knows who got the placebo and who got the real thing. Then you wait and see who gets it. So you have to call them each week asking Hilo they feel. Now you could make 20000 people and 3 vaccines each with 5000 people.
See which one causes less infections.
Christian is correct, you would use double-blind studies where neither the patient nor the doctor knows who gets what.
There are different levels: you start with groups of healthy volunteers, then groups that do not include vulnerable people (eg pregnant women, pre-existing conditions, etc), then groups that represent the general population (that’s why testing usually takes years). And then of course you follow along for long-term studies.
It is important that the two groups are matched: same age, gender, race, social level, area (ideally you would use twins living in the same household, but you won’t find many adult ones, so matching it is).
Then you “vaccinate” each group, let’s say 10,000 each.
Group with vaccine: 5 infected
Group with placebo: 100 infected
Hypothesis: the vaccine protects 95% of those infected.
And then you try to falsify the hypothesis: can you explain the result in other ways (eg maybe re-existing conditions in one group, smokers, etc) - although these should be matched in both groups there might be others so you look for any link that might be hiding in the data (like a super-spreader event). Is the difference large enough to not be a statistical fluke? Basically a LOT of data analysis, slicing them in any conceivable way and looking if that could explain the result.
If you can’t falsify the hypothesis after exhaustive trying you accept it as theory (eg a hypothesis with data that back it up).
So next you do the same at the next stage with larger test groups and long-term studies to prevent unintended side effects (like Thalidomide/Contergan that turned out to be REALLY bad for pregnant women at a certain stage of pregnancy when limbs are supposed to grow, but was much later discovered to be excellent for treating leprosy).
In emergency situations you can go straight to deployment, though you would then limit it to people that were represented by the tested group, eg if you haven’t tested vulnerable people you would recommend not vaccinating them - but they would still benefit from others being vaccinated and hopefully not being able to propagate the virus (though that is still one of the open questions: can vaccinated people still pass on the virus? Seems unlikely but we don’t have the data yet).
well duh me of course
here I was trying to think of how they could tell that based on antigen testing etc
my mom even was involved in double blind tests in canada for arthritis meds (turns out it was something like ibuprofen or acetominophen way back in the 1970’s)
The presence of antigen does not say anything about the effectiveness against infection per se, so yes, that train of thought leads into a blind alley.
brick wall … and sore noses
As far as I can see the effectivity is calculated very simply:
162 persons got sick without vaccine
8 persons got sick with vaccine
Effectivity = 100 - (162/8) = 95%