https://t.co/xvP6tVlIaE
Quite a surprise. Ferguson isn’t qualified to comment on such things. He lacks understanding of basic biological concepts, partly explaining why his predictions are often so extreme.
I’ll get to London & here immunity. But those wavering about the evidence about lockdown in spring really will benefit from cogitating on this graph.
What this means is that almost all of U.K. experienced what’s called unmitigated...
Back to London. Three months ago, I wrote a detailed article which contained several predictions.
https://t.co/b0rT5Lq9HI
SAGE’s errors unfortunately have been compounded & their mistaken narrative is gradually destroying
More from Yardley Yeadon
Ignore ‘cases’, look instead only at excess deaths (per M Levitt’s tweet). Does that look characteristic of an epidemic? It’s completely diff from spring or any winter flu outbreak.
London:
Can anyone explain why there is no ‘2nd wave’ of excess deaths in London, without invoking herd immunity?
It’s not lockdown. See NW England:
This is the largest #SecondaryRipple (which I predicted).
https://t.co/b0rT5Lq9HI
Now check the 3 predictions I made months ago. They’ve all happened. Compare predictions from SAGE’s statements: they’re all wrong.
Even neutrals at this point might ask themselves “if he’s been right on all predictions, maybe he’s correct now?”
I’ve been saying since the Lighthouse Labs got up & running that I’m deeply sceptical about the trustworthiness of their ‘cases’ data. I showed how, at low virus prevalence, the PCR mass testing data was throwing out potentially 90% positives being
https://t.co/t4qQN4rH0u
I got ‘fact checked’ a LOT over that statement. This paper just published, about precisely that time period I speculated about. Turns out that high-80s% of Dr Healy’s positives by PCR were FALSE. This alone is sufficient in my view to throw severe doubt...
More from Science
1/
I've recently come across a disinformation around evidence relating to school closures and community transmission that's been platformed prominently. This arises from flawed understanding of the data that underlies this evidence, and the methodologies used in these studies. pic.twitter.com/VM7cVKghgj
— Deepti Gurdasani (@dgurdasani1) February 1, 2021
The paper does NOT evaluate the effect of school closures. Instead it conflates all ‘educational settings' into a single category, which includes universities.
2/
The paper primarily evaluates data from March and April 2020. The article is not particularly clear about this limitation, but the information can be found in the hefty supplementary material.
3/
The authors applied four different regression methods (some fancier than others) to the same data. The outcomes of the different regression models are correlated (enough to reach statistical significance), but they vary a lot. (heat map on the right below).
4/
The effect of individual interventions is extremely difficult to disentangle as the authors stress themselves. There is a very large number of interventions considered and the model was run on 49 countries and 26 US States (and not >200 countries).
5/
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