I haven't got the links to hand, but I got the data from the ons.gov.uk website and it was covid hospitalisations/deaths vax/no vax on a weekly basis, but each dataset covered the previous 4 weeks (eg report A=wk 37-41, report B=wk 38-42 etc.).
The data was embedded tables in a pdf and I had to manually transpose them to a spreadsheet because they made the data really hard to analyse.
Just remember, figures don't like,
but liars do figure.
Data manipulation is how they lie to the sheep.
If they don't like the data, and they can't manipulate it enough, then they change the definition of words. For instance, a person is no longer considered vaccinated if he hasn't the proper number of boosters.
I used the weekly ONS figures in the UK to show a trend that vaccinated in every age group were dying at a rate of +1% per week.
I stopped working the data a couple of months ago because it was just too scary.
tldr; even with fudged figures you can track trends which can elicit real data.
Can you link the database you used? Wondering if the same approach can be used for US stats.
I haven't got the links to hand, but I got the data from the ons.gov.uk website and it was covid hospitalisations/deaths vax/no vax on a weekly basis, but each dataset covered the previous 4 weeks (eg report A=wk 37-41, report B=wk 38-42 etc.).
The data was embedded tables in a pdf and I had to manually transpose them to a spreadsheet because they made the data really hard to analyse.
Just remember, figures don't like, but liars do figure.
Data manipulation is how they lie to the sheep. If they don't like the data, and they can't manipulate it enough, then they change the definition of words. For instance, a person is no longer considered vaccinated if he hasn't the proper number of boosters.
quite, which is why it's useful to track trends, because week to week they were using the same benchmarks.