1/P

Interesting paper below co-authored by John Ioannidis, published in a journal Ioannidis was editor-in-chief of for a decade.

It makes some interesting points, but also illustrates the dangers of under-estimating COVID-19.



ncbi.nlm.nih.gov/pmc/articles/P… Image
2/P

Let's start with 2 concepts:
- infection fatality rate (IFR)
- herd immunity

The 'herd immunity threshold' is the proportion of people who need to be immune to infection in order for the pandemic to not accelerate, even if we lived life like usual.

3/P

Proteins known as antibodies (+ the immune cells that make them, such as B cells / plasma cells) are crucial for preventing re-infection, + thus to getting herd immunity.

Seroprevalence studies measure how many people have increased antibodies levels
Image
4/P

Seroprevalence studies can also be used for estimating the number of infected people, since antibody levels increase after infection.

That can then be used to estimate IFR, which is the proportion of SARS-CoV-2-infected people who die of COVID-19.

institutefordiseasemodeling.github.io/nCoV-public/an… Image
5/P

So over-estimating the proportion of people with increased antibody levels (i.e. seroprevalence) is dangerous for at least 2 reasons:

1) under-estimating IFR by over-estimating the number of infected people
2) over-estimating how close society is to herd immunity
6/P

Early in the pandemic, Ioannidis under-estimated the risk of IFR by giving unrealistic under-estimates of both IFR and the number people SARS-CoV-2 would infect.

See @JHowardBrainMD for more emphasis on this.



statnews.com/2020/03/17/a-f… Image
7/P

He also gave a scenario he did not espouse, with an IFR of ~1% and a higher herd immunity threshold that allowed for ~60% of people to be infected.

He later moved on to over-estimating infection rates to under-estimate IFR.



statnews.com/2020/03/17/a-f… Image
8/P

And Ioannidis peddled the debunked idea that the herd immunity threshold (HIT) was low.

So his trifecta is:

1) under-estimate IFR,
2) over-estimate how close herd immunity is by over-estimating infection rates and, 3) under-estimating HIT

Image
9/P

That brings us to his newer paper. He + his co-authors show previously infected people are at lower risk of infection than people who were not previously infected.

Makes sense, since antibodies increasing after infection + help prevent re-infection.

ncbi.nlm.nih.gov/pmc/articles/P… Image
10/P

I'm skeptical of the claim that immune protection following infection is as good as immune protection following vaccination.

But that's not the main focus of this thread. So folks can go elsewhere for discussion of that:

11/P

Their paper over-estimates seroprevalence in India, similar to Ioannidis' fellow Stanford COVID-19 contrarian Jay Bhattacharya.

60% seroprevalence would also be incompatible with Ioannidis' previous speculation on low HIT.



ncbi.nlm.nih.gov/pmc/articles/P… Image
12/P

Since at least May 2020, I + others warned of the risk of saying HIT is low:
archive.is/Xjyec#selectio…


But contrarians like claiming HIT is low to say policies they dislike are not needed.

Here's how that turned out for India:
ourworldindata.org/explorers/coro… Image
13/P

But that's India. How about Austria, the country's Ioannidis paper was about?

Here's a perspective to contrast with Ioannidis:

IFR is high, HIT is low, + infection rates in 2020 were low. So letting SARS-CoV-2 infect more people is dangerous.

Image
14/P

Ioannidis instead gives IFR estimates of ~0.3% - ~0.4%, with his early speculation of HIT being low and a substantial number of people infected.





onlinelibrary.wiley.com/doi/10.1111/ec… Image
15/P

Results from the ski resort to town of Ischgl, Austria seems to support his claim, with ~42% of people infected with a low IFR.

But that IFR estimate is non-robust, as the article notes, especially in combination with the low population.

medrxiv.org/content/10.110… Image
16/P

Results in Vienna are more robust.

Calculating IFR as Ioannidis does (deaths 1 week after the study's mid-point), gives an IFR of ~0.8%, with ~1% infected.

Not what Ioannidis predicted.

ncbi.nlm.nih.gov/pmc/articles/P…

covid19-dashboard.ages.at/dashboard_Tod.…

nature.com/articles/s4159… Image
17/P

Similar pattern in Austria overall months later, with an IFR of ~0.5% and ~5% seroprevalence.
(with Ioannidis' method under-estimating IFR by not including a long enough lag)



covid19.who.int/region/euro/co…

statistik.at/web_de/presse/… Image
18/P

And as with India, it was ridiculous to suggest Austria achieved herd immunity by February 2021.

Austria's cases/day and COVID-19 deaths/day increased, which would not happen with herd immunity.



covid19.who.int/region/euro/co… Image
19/P

So Austria + India illustrate the danger of under-estimating COVID-19 in the way John Ioannidis, Jay Bhattacharya, + their ilk did.

I don't expect most of their fans to learn from this, though. Ideology trumps facts for them


Image
20/P

So Ioannidis just went to Austria to talk about IFR, without admitting he was wrong.

Oh well. Maybe he'll find more people who don't know his long history of being wrong on COVID-19. 🤷‍♂️

20:16 - 25:20:

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More from @AtomsksSanakan

2 Jul
1/H

One of the leading SARS-CoV-2 lab conspiracy theorists from DRASTIC, @TheSeeker268, has a new article. I want to focus on its discussion of miners since it shows how ridiculous conspiracists can be.





theweek.in/theweek/cover/… Image
2/H

Like many other aspects of lab leak conspiracy theories, the miners point was debunked for a year or more. But conspiracists peddle it anyway, hoping people are uninformed, or paranoid, or... enough to fall for it.



The point:
Image
3/H

An obvious problem:
SARS-CoV-2 almost certainly did not come from RaTG13 (a.k.a. BtCov/4991), the mentioned coronavirus from the mine.

This has been known for at least a year.


Image
Read 14 tweets
30 Jun
PaperOfTheDay

Contrarians should admit they were wrong about IFR in Sweden





"Multianalyte serology in home-sampled blood enables an unbiased assessment of the immune response against SARS-CoV-2"
nature.com/articles/s4146…
Context:

Public Health Agency of Sweden:
"Globally, it is estimated that 0.5–1 percent of those who are infected with COVID-19 die"
web.archive.org/web/2020103000…

"point estimate of the IFR [in Stockholm] is 0.6%, with a 95% confidence interval of 0.4–1.1%"
folkhalsomyndigheten.se/contentassets/…
Stockholm:
"peak [IFR] was 1.34% (95%CI[0.90, 1.89]), and would extrapolate to 0.61% (95%CI[0.41, 0.86] [...])"
medrxiv.org/content/10.110…

Sweden:
"IFR of 0.74 %"
medrxiv.org/content/10.110…

Sweden:
"estimated to 0.40; 0.40; 0.37; 0.28 and 0.27"
medrxiv.org/content/10.110…
Read 4 tweets
29 Jun
1/G

On other threads I criticized estimates of COVID-19's fatality. Here I'll highlight the best estimate I've seen:
0.9% from Neil Ferguson's team at Imperial College.

It's being falsely criticized again.
(h/t @thereal_truther)



tabletmag.com/sections/news/… Image
2/G

Ideologues often criticize the 0.9% estimate in order to downplay the severity of COVID-19 + evade policies they dislike. John Ioannidis resorted to that

judithcurry.com/2020/04/01/imp…
cato.org/blog/how-one-m…
freopp.org/jay-bhattachar…
reason.com/2021/06/22/the…

Image
3/G

In March 2020, Ferguson's team applied work from Verity et al. on China, to Great Britain (GB).

That led to an estimate of 0.9% of SARS-CoV-2-infected people dying of COVID-19; i.e. 0.9% infection fatality rate (IFR).



spiral.imperial.ac.uk:8443/bitstream/1004… Image
Read 13 tweets
28 Jun
1/U

Sometimes John Ioannidis just makes me laugh. 😀

In the slide below, Ioannidis discusses age-specific IFR (infection fatality rate), i.e. what proportion of SARS-CoV-2-infected people die of the disease COVID-19 at various ages.

22:23 - 23:18:
3/U

Ioannidis says his Axfors estimates mostly agree with O'Driscoll:

from 23:04


Yet experts noted for around year that his Axfors estimate is a low outlier.

So what's going on here?

publichealthontario.ca/-/media/docume…
Read 12 tweets
28 Jun
2/T

I + others argued with lab conspiracists for over a year. We saw their arguments + addressed them in detail; that's how we know they're nonsense.

I'll thus often link to threads that explain points in detail, so I don't have to rehash it all here.

3/T

You often need serology (i.e. antibody) studies to tell who's been infected, since many infections are missed otherwise.

Those studies show more prior infections with SARS-like viruses.




archive.is/hFqmR#selectio…
Read 21 tweets
27 Jun
1/F

SARS-CoV-2 lab conspiracy theorists are again misrepresenting scientific fields they have not bothered to try to understand.

This time they're applying their paranoid distortions to immunology. So that deserves a thread.



Image
2/F

When SARS-CoV-2 infects a person, the person's immune system increases production of proteins known as antibodies that bind to SARS-CoV-2.

So if SARS-CoV-2 escaped from the WIV by infecting staff, then that would show up in antibody tests.

Yet...
who.int/docs/default-s… Image
3/F

Conspiracists don't like that result, so they abuse an antibody study I discussed awhile back.

That study estimates ~4% of Wuhan had increased antibody levels; i.e. ~4% seroprevalence, so ~4% of people previously infected.



thelancet.com/journals/lanwp… Image
Read 11 tweets

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