Thread by @plaforscience

IMPORTANT THREAD

We've developed a way to calculate number of test done by age frame

It allows us to study where POLITICAL intention focus thru test policy

As we advance from sociology, a GUILTY group was needed for Moral Panic creation: the YOUNG

Here, test/hab in age frames

Strongest pressure is for 80+ frame >1 test/hab. It's logical from Epidemic reasons: MUCH more symptomatic, full institutionalised testing.

There's no reason for special search among 15-29 frame, but political: supporting The Fear Narrative. Young Irresponsibles infecting oldies

15-29 have received 0,9 test/hab versus 0,4 for 70ers or ~0,5 for scared 50ers

Why make 2x test in that age frame?
Because you want to FIND, they're to blame

It makes no sense that searching is INVERSE, except 80+, to severity. The more danger for the age, the LESSER tests done

The method appears when we realised we got crossed age segregated data from serological survey and cases/outcomes from RENAVE reports.
There you've PCR cases, statistic national estimated incidence and outcomes, all by age frames.
We also know total # test done.

That was enough!

With real statistical cases by frame we estimate the true expectable positivity. Using it over official cases by PCR, we get the number of test done for age frame, out of the expected positivity.

Calculated test total corresponds with total number of test performed.

It's robust

The rest of kids have been also victims of some blaming, but part of their overpressure responds to the fact they're usually afflicted with little infections, with symptoms compatible to Covid, and therefore, subject of testing. Also new born protocols and school screenings help.

The mechanism used to create this Guilt Machine is made out of indiscriminate AGE RESTRICTED screenings, age biased screenings (universities, high schools, dorm buildings...) and even young population neighborhoods; apart from evenly distributed chance tests: true sick/contacts.

It's satisfying to find math proof for sociological thesis, observed from politic interests and curve behaviours.

Here, the thread on Moral Panic, which goes parallel to this one

We theorised they were blaming a scapegoat, here we show they do, and how.

https://twitter.com/plaforscience/status/1365753142260998149?s=19

We needed some work to harmonize different series but now (being scaled to totals/person-within-age-frame) they're comparable, EVEN at different number of years per age frames, that come from official weird criteria

It allows test comparison with outcomes (ICU/deaths). Let's see

Disproportions are SCANDALOUS of we check the # of test done for en hospitalisation, ICU and death.

For each 15-29 years' hospitalisation, ~800 test were performed, only ~70 test were made for each hosp 60er

1.8 MILLION! test for every 0-4 dead.
Only 50 test for each death 80+

That's 36.000 TIMES MORE among babes, than oldest frame.
As 100K test are performed per each young (15-29) death, it still TWO THOUSAND TIMES MORE, than 80+.

Will you search 2K times more in those NOT dying, than in those in real risk?
Why?

There's no why but Blaming the Young.

Tracing could be an answer. But if we check test performed Vs true Epidemic spread (thru serological survey) we see test overpressure is again UNBALANCED on Young's shoulders, discarding the already explained very old&young not political and Epidemic coherent high testing.

Again, young fraction is tested DOUBLE than mature adults, in terms of the effective number of true infections.

For each real 14-29 infected, ~9 tests were done, only ~4 test were performed for 70ers frame.

Why do they search LESS where it is more dangerous?!
For FEAR NARRATIVE

I haven't seen this kind of age framed test policy analysis. Let me know if u've got similar for other countries

As Twitter is limiting my impressions I wanna ping
@MLevitt_NP2013 @stacey_rudin @ClareCraigPath @NickHudsonCT @FatEmperor ,
as u may be interested in this new method