13 Sep, 21 tweets, 7 min read
Must Read Deep Dive - Calculating Real Infection Rates from AB Studies

I found this incredible data from NYC AB testing

They've been reporting weekly AB testing results since April 11th (2 mil tests)

This graph has significant ramifications for how we understand COVID-19

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Too bad we can't see data from a couple of weeks earlier, but we know NYC was at least 61%!

The first thing we see is the rapid seroreversion in April

This confirms that ABs don't last for the majority of the population

Depending on when you test, results can vary widely

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But the most significant aspect is that eventually, you get to a "Stable AB%" - For NYC this is 20%

This number tells us how many people have long-lasting ABs

Working backward, we can use other data to try to predict a maximum value for Stable AB% if 100% are infected

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I call the maximum "Stable AB Max"

To calculate this we need to know three variables
-% Symptomatic/% Asymptomatic
-% Symptomatic with long-lasting ABs
-% Asymptomatic with long-lasting ABs

I touched on these variables in my last deep dive on testing:

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Last week I said Asymptomatic/Mild were 90-95% but to be precise we need to put the Mild with the symptomatic cases because that how studies on this have been performed

The 95% asymp levels seen in prisons, factories and plants don't account for this

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So what do the scientists tell us?

The study with the largest detected % of pure asymptomatics was 80%

Why use the largest %?

Cause seroreversion happens fast - this study probably got lucky and tested on a perfect day

@BallouxFrancois says 70-80%

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gov.uk/government/pub…
20% Symp/80% Asymp

What is the percentage of symptomatic cases that maintain long-lasting Antibodies?

It's between 88-92% according to all the AB studies I've seen

I'll use a conservative estimate of 92%

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Finally, the hardest variable is what % of asymptomatic cases retain long term ABs?

We know it's 80-20 on infected

We also know that the lowest % of asymps ever found in an AB study was 27%

So, infected asymps start 4 times larger than symps, but are only 1/4 in Stable AB%

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A little bit of math gives us 8.5%

If we run the number we see that the Stable AB Max is 25.2%

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Finally:

Divide NYC's Stable AB% by the Stable AB Max

20% / 25.2%

NYC: 79.3% infected

Added a few more cities for comparison (assuming that those are correct Stable AB% for each)

You can argue Stockholm is too low. I chose conservative estimates

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Here I run the model with 70-30 and 75-25 splits to see how the numbers adjust

I believe 80-20 is the correct split

NYC is definitely at herd immunity!

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Side Note: This virus will infect just about everybody

By infected I mean a productive infection that releases at least trace amounts of RNA for detection by PCR

We know this because we've seen prisons with 88% PCR positive inmates

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reuters.com/article/us-hea…
So the question is: how can Wuhan have only 12% infected?

If you lockdown after the hospitals begin to fill it's already too late

Seems impossible/illogical -only 12% infected

They should have at least 80%

How do you get to 80% infected based on 3% Stable AB%?

97% Asymps

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As I've said before, pre-existing immunity is higher in East Asia/South East Asia/Oceana

There's no other way to explain this discrepancy

Wuhan was not social distancing or wearing masks when the virus hit them

All measures were too late

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Why more immunity?

The thread below explains,but replace eating bats with close contact/handling wild animals via wet markets/bush meat

Dr. Wolfgang Wodarg says coronavirus often jump wild animal to human (&back), but they're harmless-we don't notice

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Getting back on track, what does this new information about real infection rates tell us?

Some cities are at or near herd immunity with as little as 10% detectable via current measures if they are at "Stable AB %"

Outside of major urban areas, it could be as little as 6%

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The IFRs are much lower and in the range of what John Ioannidis had predicted ~.25%

NYC and Bergamo are worst-case scenarios - Factors: health of population, no medical knowledge of disease, older populations not protected

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medrxiv.org/content/10.110…
Here's the whole graph + Calculations in one frame

NYC was at least 61% infected, and it's clear it was decreasing from a much higher %

Note: this data started coming in at the absolute peak of the pandemic in NYC

From peak it takes about 3 months to reach Stable AB%

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I shared this data with @federicolois who's working on an epidemiological model to predict dynamics

His number of total infected are very similar to mine, and he's doing this only with statistical modeling

His model says lockdowns kill more:

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Regarding a second wave, I'm still not sure

Some cities are already at herd immunity

Maybe a small one

What is clear is that the best thing ever is for students to get back to school, colleges to return to normality, and young healthy people to get on with their lives

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And that's it

Sorry, it was so long!

Hope you enjoyed

If you liked it please RT the first tweet in the thread

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# More from @gummibear737

25 Sep
Did you know that ICTV-CSG which is the authority on classifying and naming new viruses initially concluded that SARS-CoV-2 was NOT a novel virus?

Ralph Baric would know, he’s an expert

But, this figure didn’t make it to final publication☹️

biorxiv.org/content/10.110…

1/3
And then there’s this article from 2008 which makes reference to SARS-CoV1, SARS-CoV2, SARS-CoV3 which are made from SARS-CoV: Accession number AY864806

Don’t look at 1,2,3 - That’s just what they call different samples in the study

Look at AY864806

2/3
researchgate.net/publication/55…
AY864806 took me to this article - it appears there were 115 genomes for SARS-CoV - each with an accession number

Maybe an expert can explain this for me

Question: Why have all these accession numbers disappeared?

pdf.sciencedirectassets.com/273517/1-s2.0-…

3/3
23 Sep
Remember when I said that SARS-CoV-2 is far more widespread than realized?

46.8% AB+ in Japan

“C-19 infection may have spread widely across the general population of Tokyo despite the very low fatality rate”

H/T @AlexBerenson

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medrxiv.org/content/10.110…
Basically they started of with a group of 600 asymptomatics

Started off at 6% and it grew to 47% seroprevalence

This is a very big deal

Alex breaks it down very well here:

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Remember that time I said that there was higher pre-existing immunity in East Asia, South East Asia and Oceana?

Remember when I said mask wearing and mitigation could not account for the major differences?

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22 Sep
Asymptomatic Deep-Dive

This NYT article revealed that high Ct PCR was identifying mostly non-infectious positives (trace viral RNA)

The upside to supersensitive PCR is that it also tells us a lot about asymptomatics

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nytimes.com/2020/08/29/hea…
First, the prisons

Case 1) Marion Correctional Institution: 95% asymptomatic of 2,028+

Case 2) In 4 state prison systems - Arkansas, North Carolina, Ohio and Virginia: 96% asymptomatics of 3,277+

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reuters.com/article/us-hea…
3) Neuse Correctional Institution: 98% asymptomatic of 444+

Yes, some were presymptomatic at time of testing, but they didn’t significantly change result

DRC: “disputed any notion that DRC is “walking back” its claim”

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ohiocapitaljournal.com/2020/06/17/ohi…
21 Sep
Throughout, the Covid-19 Pandemic, we’ve had scientists willing to stand up for science

I admire and recognize these brave souls

They are searching for truth despite a scientific narrative which is not based on science

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During this pandemic:

Many epidemiologists of high reputation were ignored
- @SunetraGupta of Oxford

Many epidemiologists with sterling records had their reputations attacked(ex:hack reporters from BuzzFeedNews 🙄)
- John Ioaniddis of Stanford
- Jay Bhattacharya of Stanford

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Many epidemiologists of renown were villified
- Anders Tegnell of Sweden

Many epidemiologists of note found their research questioned due to the possibility of its affects on public health policy(or the public’s receptiveness to said health policy)
- @mgmgomes1 of Glasgow

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18 Sep
Let me explain why anybody who is for lockdowns is using grandma as a human shield

I’ve repeatedly linked to this thread by @federicolois, but I want to explain it is my own words

People are not seeing the sheer hypocracy and cowardness of the “every life counts” bullshit

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By locking down, you are ensuring that the 20 year old grandson has the same chance of getting the virus as his 80 year old grandma

Despite being about 400x less likely to die, we prefer they each have an equal chance of getting the virus

Don’t wanna infect grandma afterall!

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But it doesn’t work that way

The grandson is going to be careful

But that doesn’t mean grandma isn’t going to be infected by someone else - example: grandson’s friend who mows grandma’s lawn

The longer the virus circulates the greater the chance to infect grandma

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17 Sep
Professor Neil Ferguson Deep Dive

"Professor Lockdown", as he's nicknamed in some newspapers, is back in the public space and offering his scientific opinions on public health policy

As such, let's take a look at Neil and his most important work on pandemics - including C19

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Who is Neil Ferguson?

He was the Imperial College epidemiologist who presented the doomsday scenario to U.K. leaders

He predicted that 550K would die in UK and 2.2M in the US if lockdown measures were not undertaken promptly

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Lockdowns were never a part of any rational pandemic playbook

As I tweeted: "Lockdown is not a strategy, it’s a panic move"

Yet governments accepted his predictive modeling without criticism

How did Ferguson manage to convince the world to shut down?

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