Apart for the amazing confirmation of the existence of OLD PCR+ noted as cases, we can use the official data on cases detected for test type to do some interesting analysis.

1st wave was obviously a PCR issue, not interesting except for little scale due to lower testing.
Zooming in 2nd&3rd Waves we observe a CLEAR disproportion between them, depending on test type.
2nd wave was over 2/3 of 3rd thru PCR, but less than 1/3 thru antigen!!

The MUST be same proportion. Why is there EXTRA PCR+ positivity?!

We've talked about it LONG AGO:
OLD cases.
That shows even clearer when you draw observed positivity for PCR&Antigen.

3rd wave, the winter seasonal expected wave, shows similar positivities, meaning every kind of test founding the same, but 2nd wave is unbalanced, with only a 60% of positivity thru Antigen.
I remark this is official data. No model.

They KNOW using PCR makes the cases grow.
They KNOW it happens because extremely high Ct are finding old cases.
There's no RATIONAL to ignore that facts for epidemic control, it is A POLITICAL manipulation.

It is a FACT.

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More from @plaforscience

11 Apr
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.

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:
Read 10 tweets
4 Apr
We've refined our calculations with Madrid Ct data. We've included pure false positivity, and isolated PCR&Antigen real/official series.

The 2nd wave is showing it's mainly Human made thru test policy, which maintained high proportional levels thru winter: Xmas irresponsibility Image
We can calculate official positive #test for each kind of test, considering Cts

Despite the variable proportion PCR+ are always more important in Epidemic creation, specially weird spikes, not shown in the more natural Antigen+
Guess when is more different?
Yep, Irresponsible! Image
We observed a relation between official positivity and inverted average Ct.

It does mean positivity is contaminated with high test pressure, creating more positivity than real.

We also observe average Ct<28 relates with Epidemic growth, while higher values point descent/plateau Image
Read 5 tweets
4 Apr
I bet you've never seen this graph. I haven't.

It's so interesting: just dividing the test done for the cases found the PREVIOUS week, we can see test pressure is NOT dependent on Epidemic spread BUT political intentions.

It's Madrid data, as we're currently working with.
One usual myth used by trølls and or government, sorry for the redundance, is claiming that is not that rising the number of test increases cases, BUT the raise in case forces increase in test.

It's FALSE.

It's EXACTLY THE OPPOSITE:
More test pressure when lower cases found.
For graph dummies, red line means up to 25 test/case-found are made with low spread, but only 5 during spike.

It should be a straight line, the more u find the more you search, or a Crisis Watch, curve related to Epidemic curve: u search even more when u find.

It's THE OPPOSITE
Read 6 tweets
1 Apr
Twitter is build for noise.
Very often I need to Google my own old tweets, as TL is a dark, chaotic, bottomless pit.

I'm gathering here our team's
main original work, with their link and a little description, as an easy way to find them, both for me and anyone interested.
This is our statistically true daily infection reconstruction.
It uses the time from infection to death curve proportionally for each decease.

Deaths is the less manipulable series, and time curve is not disputed even among covidlievers.

April 2020

Variables affecting Rt in complex seasonality model.

It considers Climate, Previous/crossed Immunity, Biological Balance among respiratory viruses & Herd Immunity, showing seasonal HI threshold creation.

Valid for moderate climate (northern) hemisphere.

Read 8 tweets
31 Mar
The Madrid Region dossier we've been commenting lately is full of proofs of appalling intentions from our gov'ts.

The sheet on universal screenings shows the LACK of test confirmation after a positive.
The protocol includes not this step, and ONE positive is noted as 'Case'
There IS redundant check for NEGATIVES, as we see in the Close Contact protocol sheet.

Antigen negative is double checked thru PCR.
It also shows that EVEN negative test have quarantine consequences.

If every PCR+ mean case, without check, it means accepting ALL FALSE POSITIVES
But the worst antiscientifical manipulation, for me, hides in the footnote

PCR is specially recommended in LOW PREVALENCE SCREENINGS

There's a purely MATHEMATICAL rational for low prevalence suffering high proportion of False Positives, plain, non "covid is new" debatable truth
Read 5 tweets
30 Mar
We have shown the declared Madrid region lie in terms of active infections. Active infections were 40% lower than official Epidemic

It's NOT the only criteria present in reference document.

They also consider infectivity limits:
Ct>29 means NO INFECTIVE

As we can estimate Ct average, w total number of PCR+, we can calculate the number of 'cases' that were infective: according to gov't criteria!

The INFECTIVE Epidemic is not even half the Official.

All official cases were treated as DANGEROUS INFECTIVE.

They KNOW they weren't
Accumulated incidence in 14 days for Infective, hits 600 as max

Official was 1.000
Restrictions we're applied for that high number

We see clearly what we've told, the winter spike, specially so-called 2nd wave was GROSSLY EXAGGERATED thru testing policy catching old infections
Read 4 tweets

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