COVID Study - Santa Clara

Santa Clara (3,330 sample size), found between 2.49%-4.16% exposed (or 50-80 times higher than expected). CFR=0.125%

Stanford did an antibody scan of Santa Clara (3,330 sample size), and found between 2.49%-4.16% of 1.9 million people had been exposed (or 50-80 times higher than expected). Which would mean 55,000-81,000 people in Santa Clara County have had it (instead of the 1,833 reported), against 69 deaths means an CFR (Death Rate) of 0.125%, or similar to the seasonal Flu.

ℹ️ Info

~ Aristotle Sabouni
Created: 2020-04-27

Because this didn't show what the Flu Klux Klan wanted wrt the Shutdown, they've attacked the authors and this study on technicality, but ignore that there are a dozen other studies that showed similar results.

Attacks[edit | edit source]

This study is a little higher than the others, not in gross numbers but in how far off the the reported numbers versus these are. Most are more in the 10-40 times reported, so 50-80 means that either Santa Clara was especially bad at testing, or these numbers are materially correct, but a little off.

🗒️ Note:
California was some of the slowest at getting adequate testing, and strictest for getting tested. So slightly inflated over NYC which was much more aggresive, makes sense.

People attacking the study complain because of:

False positives: antibody tests have a low false-positive rate of like ≈1%. However, a quirk of low true-posistives on tests means if only 1% of people are positive, then ≈50% of your positives could be false. The fact that the number of positives were 2-4x that, means the error rate is more like 12-20%, which is fairly imprecise -- but when the reported numbers are off by 50-80 fold, you know that the reported numbers are way, way more wrong than this study is.
Not random study: this study was done by getting people to sign up and then randomizing/normalizing after the fact. The problem there is because it spread by social network, the people most likely to sign up were most likely to have had symptoms, so you could be overweighted than the general population (even with corrections). But remember the other size, with 50-80% being asymptomatic, that error might not matter, or might actually UNDER-represent the number of cases. So the confidence is lower -- but we can't be sure which way it will be off, if at all.

However, the results are you are up to 80x off the reported, and this matches many other studies that show the same thing. So even if you assume that the error rate was 20%, that means the study shows that reported numbers were off by 40-60 times reality. That's a BIG screw up in the reported numbers.

What that means is that this study is scientifically valid (and materially correct), even if it isn't scientifically accurate.

A lot of flim-flammers are trying to argue that because it's imprecise (not scientifically accurate) that it's not materially correct. That's a lie.

This shows that the reported numbers are bullshit, and this disease is way wider than our testing had previously caught, and the reported fatality rates, and cases are distorted. The government is incompetent or lying, as are those trying to diminish this study because it shows a truth that they don't want to face.

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COVID Studies
By 04/2020 studies show we are being lied to about the denominator to inflate the death rate and scare people.

🔗 Links

Refutations:

https://twitter.com/nataliexdean/status/1251309217215942656

Tags: COVID Studies