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.
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.
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
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
@mvankerkhove
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…
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
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
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.
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
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