Orwell2024🏒 Profile picture
Nov 1, 2021 11 tweets 8 min read Read on X
1/ Expected rate of vaccinated in ICU.

(Correcting here the spotted mistake in the explanation part.)

Here is the formula explained. It depends on vaccination rate and vaccine efficiency.

The special case for 90% vaccinated is shown on the right as function of VE-ICU.
2/ For Austria and NL where 90% of the at risk group is vaccinated we expect 30%-50% of the ICU patients to be vaccinated if assuming a VE-ICU of ~90%.
3/ Let's show the results of the formula in 2D as function of

x) vaccination level
y) VE agains ICU

The resulting rate (calculation on right) of vaccinated in ICU is shown in the cell.

It can be used as look-up table to estimate (roughly) the VE.
4/ Example The Netherlands: With around 90% vaccinated we expect something between 15%-50% vaccinated in ICU depending on the VE-ICU.

RIVM reported around 15% (right). In order for this to work with 90% vaccinated, VE-ICU needs to be above 95%.
5/ VE look up example using UK PHE report week 43

assets.publishing.service.gov.uk/government/upl…

using the data from Table 5:

We look up the expected VE-emergency-care for 4 different age groups with different vaccination levels and resulting emergency care rates.

Result: VE ~ 96% in each case.
6/ Discussion: The relative number of vaxxed-patients in ICU is irrelevant (increasing with vax level).

Only the absolute total reduction of ICU patiens matters (shown below).

1-(𝑟_𝑣𝑎𝑥∙(1−𝑉𝐸_𝐼𝐶𝑈)+𝑟_𝑢𝑛𝑣𝑎𝑥)

Here we have >90% reduction! Nothing more to gain.
7/ Another way to look to the same data:

VE-ICU as function of % vaccinated in ICU. Each line for a different vaccination level.

public.tableau.com/app/profile/or…
8/ Use example: estimate VE-ICU based on latest numbers "percent vaccinated in ICU" from NL (from @BertMulderCWZ ).
9/ Here the link to the article by Prof. Suess in @KURIERat today
10/ Assuming March as the date where most elderly were fully vaccinated in NL, I compared the derived NL VE versus (time) with the Swedish study on waning efficiency.

Result assuming AUG ~ day 150.

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More from @orwell2022

Apr 12
1/ I was told non US GHCN “raw” is adjusted already.

-----TRUE-----

Now I see it. Gosh.

Composite. 2x adjusted. NOAA doesn’t even know where non-US stations are—or what they’re measuring. Their own US data (USCRN) is light-years better. But for “global”? It’s clown-tier level. Image
2/ And here it is—the DOUBLE-adjusted COMPOSITE.
Not raw. I doubted @connolly_s at first—like someone denying their 2nd-hand car is stolen, crash-salvaged, and repainted twice. Turns out he was right.
NOAA’s “global” QCU (non-US): not raw.
Image
Image
3/ Credit where due.
Normally I block on first bad-faith signal.
But intuition said: bait him back.
Let’s see what he hands over.
And he did:
✔ Clown location
✔ 120% urbanized
✔ Composite
✔ Adjusted twice
Thanks for the assist.
Image
Image
Read 5 tweets
Apr 10
1/ The WMO’s temperature station classification study isn’t a glamorous reading —but it’s the bare minimum anyone aggregating climate data should know about every single station. They don’t.

Scandal hiding in plain sight Image
2/ Class 1 is “bare minimum” for climate-grade weather station suitability. One means maybe ok.
met.no/publikasjoner/…
I’ll be counting impressions. I’ll know if you didn’t read.
(you’re allowed to LLM TlDR it.)
Next up: NOAA climate site requirements (HLR). 👇 x.com/orwell2022/sta…
3/ The NOAA HLR system makes WMO classes look gentle.
Most stations? Fail spectacularly.

Here a flow-down from high-level requirements into practical criteria:
orwell2024.substack.com/p/quality-requ
(Use LLMs to TLDR.)
x.com/i/grok/share/u…
Ok, so ready for real fun after boring reading? x.com/orwell2022/sta…
Read 16 tweets
Mar 28
1/ Digging deeper, we find 3 USCRN sites with 2 IDs — a legacy historical one and a USCRN. That’s big. It means we can stitch together long-term time series for 3 “golden” stations. Why haven’t @NOAA or @hausfath done this? Not the “right” narrative result? 🙃 Let’s take a look Image
2/ Here is an example of such a pair. STILLWATER. Note that you can see the wind fence around the precipitation gauge on satellite picture — that round structure.
ncei.noaa.gov/access/crn/pdf…Image
3/ Well, let’s do it. We try. And...

...no hockey stick.

Despite STILLWATER being a growing urban area.
So... where’s the hockey stick? Anyone?
We're told it should be there. But the best data says no. Image
Read 43 tweets
Mar 19
1/ Mr. @hausfath packed multiple fallacies into one graph. We replicate: he used homogenized data. We get the same.

Bottom right shows the raw. His fallacy: claiming that USCRN-ClimDiv agreement in the modern era (where adjustments are ~zero) validates strong past adjustments. Image
Image
3/ His fallacy is blatant bad faith. Measurement validation isn't done by induction. He claims adjustments are valid because USCRN-ClimDiv align from 2008-2024—yet no adjustments were made in that period. Then he asserts past adjustments are proven. Exceptional level of malice. Image
4/ Another fallacy: He cherry-picked 1970—the coldest point in 100 years. He highlights only post-1970 warming in green, hiding earlier trends. But the real scandal? Extreme (false) pre-1970 adjustments, erasing the 1930s warmth with absurd corrections. Image
Read 15 tweets
Mar 19
1/ New tool - let's test with VALENTIA (hourly) overlay: solid agreement. A model (ERA5) is only as good as its ground truth measurements constraints it. We saw good US results before, but obvious heat bias in polar regions—nothing measured to compare with there anyway. Image
2/ Now we match the 1940-2024 range. Note temp vs. anomaly scale—same curve, just shifted. A trick to amplify range. Few notice. Climate stripes? Perfect for manipulation—e.g. add offset (ECMWF) to make it red “=warm"= behavior science (manipulative).
Image
3/ With the 1940–2024 range matched, comparison improves. For a clearer view, monthly temps are shown on top left, yearly in the middle—overlaying ERA5. Not perfect overlay, but ERA5 is A) a cell average (of a weather model) and B) fed by adjusted data. Image
Read 6 tweets
Mar 15
1/ Absolutely my worldview. But I haven’t found a trace of it in temperature measurements. Accuracy doesn’t seem to be a factor at all. Instead, they rely on bizarre software that arbitrarily alters the data. No station audits. No QMS existing. Nothing.
2/ This magic software even adjusts in various directions from day to day—without any explicit justification beyond it doing so. Is the sensor accuracy changing day to day?? No.

This finding by @connolly_s is important and exposes PHA being unrelated to measurement principles.
3/ Here’s clear proof of failure. If the @noaa adjustments were correct, they’d bring raw data closer to the high-quality USCRN reference station (designed bias/error free). Instead, PHA alters the classic (cheap) neighborhood station’s raw data to be wrong—to be false.
Read 7 tweets

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