1/

Many COVID-19 contrarians, including those behind the Great Barrington Declaration, *still* cite John Ioannidis' inaccurate estimate of SARS-CoV-2's fatality rate.

So let's go over how atrocious Ioannidis' paper is.

https://twitter.com/gbdeclaration/status/1340921089023733761

https://web.archive.org/web/20201118093302/https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
2/

Background:

When a virus infects u, your body increases production of proteins known as antibodies, which are usually specific to that virus.

So measuring antibodies lets u estimate who was infected, and from that the infection fatality rate (IFR).

https://institutefordiseasemodeling.github.io/nCoV-public/analyses/first_adjusted_mortality_estimates_and_risk_assessment/2019-nCoV-preliminary_age_and_time_adjusted_mortality_rates_and_pandemic_risk_assessment.html
3/

Ioannidis uses antibody (a.k.a. seroprevalence) studies to estimate the number of people infected with the virus SARS-CoV-2. He then calculates IFR by dividing the number of COVID-19 deaths by the number of infected people.

Ioannidis does this badly:
https://www.medrxiv.org/content/10.1101/2020.05.13.20101253v3#comment-5152947055
4/

It's no secret in the scientific community that Ioannidis' paper is poor. Some examples:

https://rapidreviewscovid19.mitpress.mit.edu/pub/p6tto8hl/release/1
https://rapidreviewscovid19.mitpress.mit.edu/pub/6z09ucch/release/2
https://rapidreviewscovid19.mitpress.mit.edu/pub/k2pkhufi/release/1

https://hildabastian.net/index.php/91 
https://twitter.com/AVG_Joseph96/status/1283234509639618560

https://twitter.com/GidMK/status/1316511734115385344

https://web.archive.org/web/20201217003708/https://www.publichealthontario.ca/-/media/documents/ncov/research/2020/12/synopsis-ioannidis-studies-covid-19-infection-fatality-rates.pdf?la=en
5/

Ioannidis' paper gives a median IFR estimate of 0.27%, which he corrects to 0.23%. That's a low outlier.

"[..] 0.79% (95% credible interval, 0.68–0.92%) [...], with a median range of 0.24–1.49%"
https://nature.com/articles/s41586-020-2918-0

https://twitter.com/AtomsksSanakan/status/1336442679689965570

https://web.archive.org/web/20201217003708/https://www.publichealthontario.ca/-/media/documents/ncov/research/2020/12/synopsis-ioannidis-studies-covid-19-infection-fatality-rates.pdf?la=en
6/

Beware when folks cherry-pick Ioannidis' outlier of ~0.23% as if its credible.

Public Health Agency of Sweden:
"Globally, it is estimated that 0.5–1 percent of those who are infected with COVID-19 die"
https://web.archive.org/web/20201030003921/https://folkhalsomyndigheten.se/the-public-health-agency-of-sweden/communicable-disease-control/covid-19/

https://twitter.com/GidMK/status/1285020775892709377

https://www.sciencedirect.com/science/article/pii/S1201971220321809
7/

Also beware of people who try to mislead you into thinking the World Health Organization agrees with Ioanndis' low IFR estimate, or with his claims that SARS-CoV-2 has an IFR similar to that of seasonal influenza.

https://twitter.com/AtomsksSanakan/status/1290693682602156046

October 12:
https://who.int/publications/m/item/covid-19-virtual-press-conference-transcript---12-october-2020
8/

The points above have been made before. So what will this thread add?

Well, I'll go through each of the 61 studies Ioannidis cites to support his IFR estimate, + show they don't adequately support his estimate.

A numbered list of the studies:
https://web.archive.org/web/20201118093302/https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
8/The points above have been made before. So what will this thread add?Well, I'll go through each of the 61 studies Ioannidis cites to support his IFR estimate, + show they don't adequately support his estimate.A numbered list of the studies: https://web.archive.org/web/20201118093302/https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
8/The points above have been made before. So what will this thread add?Well, I'll go through each of the 61 studies Ioannidis cites to support his IFR estimate, + show they don't adequately support his estimate.A numbered list of the studies: https://web.archive.org/web/20201118093302/https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
9/

Antibody studies can use a *representative* sample of the population to accurately estimate the number of infections. Otherwise, one could over-estimate the number of infections + under-estimate IFR.

Scientists know how to get representative samples.

https://www.sciencedirect.com/science/article/pii/S1201971220321809
10/

Re: "Scientists know how to get representative samples."

For instance:

https://link.springer.com/article/10.1007/s10654-020-00698-1
https://wwwnc.cdc.gov/eid/article/26/9/20-1840_article
https://link.springer.com/article/10.1007/s11357-020-00253-6

https://www.cdc.gov/nceh/casper/overview.htm
http://web.archive.org/web/20201016231417/https://apps.who.int/iris/bitstream/handle/10665/331656/WHO-2019-nCoV-Seroepidemiology-2020.1-eng.pdf?sequence=1&isAllowed=y

https://sci-hub.se/https://www.jclinepi.com/article/S0895-4356(12)00079-0/fulltext

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454696/
11/

Ioannidis mostly cites studies that use non-representative sampling. That causes him to over-estimate seroprevalence + thus under-estimate IFR.

Studies with non-representative sampling may be useful for other purposes, but not for estimating population-wide IFR.
12/

For instance, blood donor studies:

- under-sample older people, a population more likely to die of COVID-19
- include people who go outside to donate blood, + are thus more likely to interact with people and get infected

https://twitter.com/AtomsksSanakan/status/1321093394475765760

https://link.springer.com/article/10.1007/s10654-020-00698-1
13/

So one can exclude the following blood donor studies from Ioannidis' list of 61 studies in part 8/:

9 studies:
11, 14, 17, 29, 33, 35, 44, 46, 61

https://twitter.com/GidMK/status/1316514628613009409

https://twitter.com/AtomsksSanakan/status/1321081788517752832
14/

Studies of leftover samples from hospital visitors are non-representative. For instance:

- one can get infected at a hospital (nosocomial infection)
- some visit hospitals due to concern they're infected with SARS-CoV-2

https://twitter.com/DiseaseEcology/status/1285696445241229319

https://link.springer.com/article/10.1007/s10654-020-00698-1
15/

So one can exclude the following 'residual sera / hospital visitors' studies from Ioannidis' list of 61 studies in part 8/:

13 studies:
2, 9, 23, 27, 30, 31, 36, 39, 40, 42, 47, 59, 60

https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa1804/6029296?login=true

https://academic.oup.com/cid/advance-article/doi/10.1093/cid/ciaa1868/6041690?login=true
16/

Volunteers who hear antibody testing is occurring, but were not targeted for testing by random selection, may ask to be tested because they think they're infected (ex: they have symptoms, known prior exposure to an infected person, etc.).

https://www.uoflnews.com/post/uofltoday/co-immunity-project-shows-covid-19-infection-rate-in-jefferson-county-increased-tenfold-since-september/
17/

A similar problem occurs for studies with non-randomized (non-probabilistic) sampling steps after initial randomization.

Besides studies in part 18/, many of the other studies Ioannidis cites use non-targeted volunteers.

https://twitter.com/AtomsksSanakan/status/1336482738925416453

https://link.springer.com/article/10.1007/s10654-020-00698-1
18/

So one can exclude the following 'non-targeted volunteers / non-probabilistic step' studies from Ioannidis' list of 61 studies in part 8/:

10 studies:
12, 13, 15, 34, 38, 48, 50-52, 54

https://rapidreviewscovid19.mitpress.mit.edu/pub/p6tto8hl/release/1
19/

People in schools, workplaces, shoppers, etc. are not necessarily representative of the general population. For instance, they're socially interacting in a closed setting, increasing their risk of infection.

https://twitter.com/magnusnordborg/status/1309048404220276738

https://twitter.com/GidMK/status/1309028316389801989
20/

So one can exclude the following 'workplaces / schools / shops' studies from Ioannidis' list of 61 studies in part 8/:

6 studies:
10, 16, 19, 20, 22, 58

https://twitter.com/AtomsksSanakan/status/1333565239703588866

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454696/
21/

So out of 61 studies Ioannidis cites in part 8/, only 23 have at least decently representative sampling:

1, 3-5, 7, 8, 18, 21, 24-26, 28, 32, 37, 41, 43, 45, 49, 53, 55-57

(note: part 13/ should include 10 studies, not 9, since paper #6 is also blood donor study).
22/

But papers 4, 5, 7, + 8 should be dropped from IFR calculations, since they sample regions included in paper 3.

That leaves 19 papers.

Sampling the same region multiple times unfairly skews the results, as per collinearity.

https://twitter.com/GidMK/status/1283232054646173696

https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
23/

Paper 18 should be dropped, since it implies only ~365 infections, which is much too small a number to derive a robust IFR estimate.

It's telling that Ioannidis tried to infer an IFR of 0.00% from that.

https://wwwnc.cdc.gov/eid/article/26/11/20-2736_article
24/

Re: "It's telling that Ioannidis tried to infer an IFR of 0.00% from that."

Ioannidis states it's risky to draw inferences from a sample size of less than 500... but doesn't let that stop him from using ~365 infections to infer a low IFR.

https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
25/

Paper 37 (Karachi, Pakistan) should also be dropped, because Ioannidis uses an unsound method of getting the COVID-19 deaths he applies in his IFR calculation.

https://www.medrxiv.org/content/10.1101/2020.07.28.20163451v3
https://www.aku.edu/news/Pages/News_Details.aspx?nid=NEWS-002322

https://twitter.com/FyezahJehan/status/1251911813605851141

https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
26/

That leaves 16 studies:
1, 3, 21, 24-26, 28, 32, 41, 43, 45, 49, 53, 55-57

(note: part 21/ should read say "only 22", not "only 23")

That's barely a quarter of the 61 studies Ioannidis said supported his IFR estimate. But at least the studies are now of better quality.
27/

Yet problems remain.

Take the example of paper 3 (for Brazil). Months ago when I first saw what Ioannidis did with paper 3, I lost any remaining confidence I had in the credibility of his paper and in his IFR work in general

https://twitter.com/AtomsksSanakan/status/1272361132527955968

https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
28/

Paper 3 stated an IFR of ~1.0%.

If Ioannidis' paper was really a *systematic* review, then he would have used that IFR to start with, as he did for some other studies he cited.

Instead Ioannidis was non-systematic + biased.

https://twitter.com/AVG_Joseph96/status/1283236273558294528

https://www.medrxiv.org/content/10.1101/2020.05.30.20117531v1.full.pdf
29/

To avoid paper 3's larger stated IFR, Ioannidis invented a new calculation based on his misinterpretation of figure 3 from the paper. In doing so, he contradicted paper 3 and used reasoning that makes no sense.

https://twitter.com/AtomsksSanakan/status/1297117232103915522

https://www.who.int/bulletin/online_first/BLT.20.265892.pdf
30/

Suppose a country of 10,001,000 people contains:
- city X: 10,000,000 people
- ten other towns: 100 people per town

Obviously, X predominately determines the country's IFR. But Ioannidis' method gives the same weight to X as to one of the towns. 🤦‍♂️

https://www.medrxiv.org/content/10.1101/2020.05.30.20117531v1.full.pdf
31/

There are other IFR calculation issues. For example, Ioannidis gives an uncorrected IFR of 0.45% for paper 43 for Geneva (see image on the right side in part 8/).

But co-authors of paper 43 later used the paper's data to calculate an IFR of 0.64%:
https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30584-3/fulltext
32/

Addressing those obvious issues for papers 3 and 43 leaves one with the following median IFR from the 16 studies in part 26/:

~0.5%

Similar median IFR @GidMK showed previously, but now with the removal of residual sera studies, collinearity, etc.

https://twitter.com/GidMK/status/1316511787798261760
33/

This median estimate of ~0.5% would likely go up once one corrected other issues with Ioannidis's paper.

Some of these issues include:

- under-estimated COVID-19 deaths via right-censoring

https://twitter.com/GidMK/status/1262960100022648837

https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30584-3/fulltext

https://link.springer.com/article/10.1007/s10654-020-00698-1
34/

- under-estimating deaths b/c of death reporting

https://twitter.com/AtomsksSanakan/status/1312369315342057474

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703539/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7361282/

https://www.ijmr.org.in/article.asp?issn=0971-5916;year=2020;volume=152;issue=1;spage=48;epage=60;aulast=Murhekar
https://www.medrxiv.org/content/10.1101/2020.12.07.20245621v1
https://www.medrxiv.org/content/10.1101/2020.07.29.20164392v2.full
https://www.medrxiv.org/content/10.1101/2020.07.31.20166249v1
https://www.bmj.com/content/370/bmj.m2859

https://www.sciencedirect.com/science/article/pii/S1201971220321809
35/

- over-estimating seroprevalence for papers 21, 28, 49 (Gangelt, Guilan, Los Angeles), and papers 56 + 57 under-estimating seroprevalence by a smaller proportion

https://twitter.com/AtomsksSanakan/status/1332826132341788673

https://twitter.com/AtomsksSanakan/status/1329620151571001344
36/

Take-home messages:

- Ioannidis predominately uses studies with non-representative sampling
- excluding the most non-representative studies doubles his IFR to ~0.5%
- correcting his other mistakes would most likely increase his stated IFR even more https://twitter.com/AtomsksSanakan/status/1341334316673019905
37/

Re: "doubles [Ioannidis'] IFR to ~0.5%"

Ironic.

https://twitter.com/AtomsksSanakan/status/1323074012122124288

"Ioannidis told viewers that the virus has an “infection fatality rate that is in the same ballpark as seasonal influenza.”"
https://buzzfeednews.com/article/stephaniemlee/stanford-coronavirus-neeleman-ioannidis-whistleblower


https://www.who.int/publications/m/item/covid-19-virtual-press-conference-transcript---12-october-2020
38/

The difference between an IFR of ~0.6% and IFR of ~0.2% is 3X less COVID-19 deaths (with the same number of infections).

Ex: the USA now has ~320,000 reported COVID-19 deaths. IFR 3X lower would be ~213,000 more people alive

https://twitter.com/AtomsksSanakan/status/1272672360416579589

39/

Alongside parts 5/ and 6/:
"[IFR in a typical low-income country:] 0.23% (0.14-0.42 [...]) [...].
[In a typical high income country:] 1.15% (0.78-1.79 [...])"
https://web.archive.org/web/20201101030133/https://www.imperial.ac.uk/media/imperial-college/medicine/mrc-gida/2020-10-29-COVID19-Report-34.pdf

From May, before move evidence on IFR:
[ https://mdpi.com/2076-393X/8/2/236/htm]
https://www.sciencedirect.com/science/article/pii/S1074761320301709
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