Thread by @plaforscience

THIS IS VERY IMPORTANT!

We finally get CONFIRMATION that our estimate of Fake Epidemic Creation thru detection of OLD PCR+, non infective, noted as cases, IS CORRECT.

We get access to a new data set, number of + for test technique, that validate our method's values.

Thread:

You can find the model in the link.

We wanted to know the share of PCR+ that were OLD non infectious, using Ct data; and thus finding true Epidemic.

For model development we needed to calculate the number of test that were official positive by type.

https://twitter.com/plaforscience/status/1378825333261135880?s=19

The premise is that any test MUST find same TRUE positivity. Then you can develope equations:
PCR+=(True+)+(False+)+(Old+)
ANTIGEN+=(True+)+(False+)

Solving, we get the number of test by type that MODEL PREDICTS must have been officially positive.

Highlighted in foresee graph:

We DIDN'T KNOW THAT NUMBER

It was a consequence of our predicted different positivities for PCR&Antigen
The number RELIED on true official Ct values, that we used 2 allocate the Old+ proportion in PCR+

Without OLD infections there should be very similar PCR&Antigen positivities

Now, the confirm.
We get new data series, INCLUDING the OFFICIAL NUMBER OF POSITIVES THRU TEST TYPE.

It was thread's 1st image, here softened and zoomed.

We have THIS data for Regions.
We can check our prediction with the official measured data, and thus, our model's estimates

And here our predicted (red) Vs official (green) PCR&Antigen positives.

If our OLD non infectious estimates weren't RIGHT, this curves won't look any similar.

It implies PCR overPositivity IS directly related with Ct, whose true infection limits are the scientifically known.

We also checked Predicted Vs Observed positivities for test type.
Of course, as they're direct function of positives thru test done, our prediction for test type Positivity is accurate.

Again, IT SHOULDN'T.

It's a proof of OLD PCR+ modifying positivity.

Here, the OFFICIAL positivity for test type. Discounting the little, and similar, share of False+ they MUST be constantly similar, catching the prevalence in tested universe.

In the proportion graph you can notice it varies STRONGLY over time.
PCR Positivity is up to 80% higher!

Our Ct study assigned that difference in positivities as a function of known Ct values.

Reality fitting PRECISELY in the same proportions means our model is right.

Only an average of ~60% Spain's PCR+ IS INFECTIVE.
The other 40% is old infections: FAKE.

We're REALLY proud.

Data set is:

For Positives for test type:
cnecovid.isciii.es/covid19/#docum…

For Test performed by type:
mscbs.gob.es/profesionales/…

For Madrid Ct info:
comunidad.madrid/noticias/2021/…