Gilles Demaneuf Profile picture
Aug 12, 2021 7 tweets 3 min read Read on X
Looks likely China may soon learn what happens when you abuse the trust and patience of Peter Embarek:

"Maybe someone is not at all interested in finding out what the truth is"

translate.google.com/translate?sl=a…
Case in point: the (non)search for patient zero:
And as #DRASTIC has kept highlighting:

According to Peter Embarek, he is hardly the world's very first infected - that is, patient zero.

- He may have just gotten it one day in the subway or on a bus or in a store, says Peter Embarek.
Good point made about the doping tests at the Wuhan military games:
And again the compromise:

- If it had been categorized differently, it would probably have required a week more, maybe some more discussion and arguments for and against, and I did not think it was worth it, says Peter Embarek.
Anyway, many thanks to @Peterfoodsafety for discussing this with TV2.

He had a very difficult job to do, and the compromise the team reached actually left them worse off.

But the failure goes back to the negotiation of the ToRs. Maybe he could tell us about that too.

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More from @gdemaneuf

May 8
Daszak did 4 months of detention in 1986 for stealing a TV set, a hi-fi, a statue and some other items, so that he could indulge in his alcohol fuelled ‘fun’ at other people’s expense.

This fraud later managed to get hold of 100s millions of US taxpayers money.
Someone saw through him very early:

“Judge Lloyd-Jones told Daszak that he had been given more chances than most and had abused other people trust.”
Daszak was such a precocious character:

“He is being maintained by the State at a cost of GBP 1,500 per year, and this is the way he repays the state”.
Read 8 tweets
Apr 4
Another retraction for Robert Garry.

I may be losing track, but it is at least his third retraction.
There is also on expression of concern for one of his papers.
@thackerpd @KatherineEban @emilyakopp
At this stage that should raise alarm bells all around.

Next one should be Proximal Origin.
Read 4 tweets
Mar 13
Here is an important reminder to the Kindergarten epidemiologists who aim to compare themselves to John Snow.

Epidemiology 101:
John Snow never considered his map as proving anything. He relied on fortuitous control groups and cases reviews to establish causality
@mvankerkhoveImage
See for instance this image and extract from a recent paper:

Confirmation of the centrality of the Huanan market among early COVID-19 cases
Reply to Stoyan and Chiu (2024)
arxiv.org/pdf/2403.05859…

Image
Image
John Snow was not a colourist of maps, sorry.

I know that popular culture has transformed the Broad Street map into a meme, but that is totally wrong and can only hurt the discipline.
@RichardKock6 @JamieMetzl Image
Read 16 tweets
Mar 12
1/10 Good Judgment superforecast on COVID-19 Origins:
#DRASTIC Image
2/10 Final probabilities of a research-related accident: Image
3/10 Final probabilities of zoonosis: Image
Read 10 tweets
Feb 16
1/5 It is difficult to be more mistaken than Robert Garry below, when discussing a supposed essential finding of Worobey et al:

@TheJohnSudworth @MichaelWorobey @hfeldwisch Image
2/5 As a matter of fact, that pattern is exactly the one expected if proximity to the market was used as a criteria when identifying cases (as is amply recorded).

Going further, there is no easy way to explain that pattern otherwise.

Here is the mathematically correct version:Image
3/5 This was first pointed out by @mbw61567742.

Here is my explanation in simple words:
Read 5 tweets
Feb 3
1/26 My comments about this just published poling of experts, examining their opinions on the plausible origins of Covid-19.

There is a lot to unpack. Much more than I have seen so far in reductive tweets.

So here it is.

@RogerPielkeJr @BallouxFrancois
2/26 First, a key limitation:

Polling must have been done before Oct 2023, so before:
- Key Science erratum for Pekar et al (invalidated their model)
- Peer reviewed paper showing key statistical flaw in Worobey et al
- DEFUSE draft showing planned work at P2 in China and more Image
3/26 Then we need cumulative numbers to express the results in a natural way:

- For 19% of experts, a research accident is at least 50% likely
- For 44.6% of experts, a research accident is at least 20% likely
- For 61.3% of experts, a research accident is at least 10% likely Image
Read 26 tweets

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