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

Nov 11
1/ Deutschland, die Energienarren der Welt: Thread.

Hier ist die Preiskurve (31 Tage, stündlich). Kaufe teuer, verkaufe billig. Bottom Nailers (oder auch Narren). Angeblich importieren sie, weil es billiger ist? Nein. Die Sonne scheint eben nicht nachts. Image
2/ Quelle: Agora Energiewende – de facto der Familienbetrieb der Grünen. Man sieht sofort, was los ist: Deutschland, auf einem Irrweg, in bestem Stil echter Narren. Verkaufen billig, kaufen teuer, alles im Namen der „Rettung“. Klar, wer nachts Sonne braucht, zahlt eben drauf. Image
3/ Jetzt wierholen wir zusammen, wie echte Hofnarren:

„Importieren ist billiger“
„Schweden hat versagt“
„Lauterbach rettet Leben“
@roberthabeck for Chancellor. Ab hier anders. Image
Read 5 tweets
Oct 7
1/ The use of the BI (bigness index) to classify rural/urban areas is flawed. Landsat-derived GHSL BU (Global Human Settlement Layer Built-Up) data shows the rural curve (in green 🟩) consistently trailing the urbanized GHSL BU data (10% BU = typically for small towns 🟧). Image
2/ The Bigness Index (BI) is not only a poor parameter but outdated. Even Kabul Airport shows up with BI=0.

Shown below: GHCN stations labeled BI=0, highlighting those with 2% 🟠 and 10% 🔴 built-up areas in 2020 (from the EU-GHSL).

Image
3/ Analyzing further: nearly half of the stations are classified as rural (BI=0). This is complete nonsense, as the GHSL built-up percentages 2020 for these stations clearly indicate. Nearly all are, in fact, urban—which explains why they see no difference to officially urban. Image
Read 32 tweets
Oct 1
1/ Climate stripes - the origins.

Can we do better than Ed Hawkins? Yes, we can.

The perfect smoothed climate stripe, maximizing color pivot for ultimate fear manipulation.

Perfection—better than the original. Or in other words: how to manipulate your mind. Image
2/ Here’s how it’s done:

1 Pick a highly urban(izing) place: Stockholm.
2 Calculate anomaly.
3 Maximize range.
4 Apply LOESS for max color pivot (=mindfuck)

Note: UHI in Stockholm is ~2°C. Never mind that—the goal is to manipulate your minds.


Image
3/ Now let's try GOTHENBURG. Hold on a second... what's happening? It looks like we've accidently landed in the US Midwest—in the middle of nowhere, where hockey sticks don't flourish. Nice flatliner we have here, just like CHAMA.

Image
Read 4 tweets
Sep 10
They're trolling / insulting. The request was clear: compare ERA5 2km / @meteoblue with @AEMET_CValencia sensor at an hourly level. If they match at night, cloudy days, winter, but the sensor shows higher T in summer clear skies / no wind / day 👉 sensor is heat-biased. So? Go.
Thanks, @meteoblue. Normal conversation can be so easy. If the Spanish gentlemen would now provide access to their hourly station dataset, we can overlay it with the fine-grid ERA5 2km hourly product and see what's going on. Does that sound like a way forward @AEMET_CValencia ?

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@meteoblue @AEMET_CValencia He clearly doesn’t understand their response nor my request. At this stage, I just want him to provide THE HOURLY DATA. What the answer actually means is that the 30 km cell is more representative of the region’s climate—yes, it’s better than the station. Well done @ChGefaell 👍.

Image
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Read 10 tweets
Sep 3
1/ Such places have no credibility for accurate bias free measurements. It's the opposite of a stable environment and per default a diesel powered urban expedition place. We see how the melting starts around the airport and the town.

How to measure? 👉 open.substack.com/pub/orwell2024…


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2/ Here we see another example (Alaska). Russian high-lat regions are among the worst. It's a deception to take measurements from such places and claim that you've done 'science' while actually just picking up dirt. Why not Everest dirt basecamp next?
3/ It escalated quickly. Similar to @BMcNoldy from Miami, master's student @Daaanvdb also used airport data instead of professional equipment, like what's available at @UNISvalbard.

Let's do better and use proper data from a better looking station.


Image
Read 19 tweets
Jun 27
1/ As mentioned, Europe is too urbanized for climate measurements. Shown below is just the UHI effect. As mentioned, ANY type of urban landscape altering increases surface temperatures as well. The Netherlands and Benelux regions are all fully biased and unfit for climate science
Image
2/ As mentioned previously, North Sweden is the most credible place for climate measurements due to its development, peace, and ability to capture high-quality data. Besides Sweden, only the US provides reliable historic data. All other regions are not credible and biased today. Image
3/ Source: YCEO Surface Urban Heat Islands: Spatially-Averaged Daytime and Nighttime Intensity for Annual, Summer, and Winter.

It's from 2003. Now it's even more urbanized = worse.

developers.google.com/earth-engine/d…
Read 12 tweets

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