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Cornelius Roemer Profile picture
@CorneliusRoemer
Mar 20, 2021 • 8 tweets • 4 min read • Read on X
Bei einer Zahl von 99,9 wird jeder Steuerfahnder hellhörig.
@rki_de-Inzidenz ist durch verzögerte Meldung nach unten manipulierbar.
Jeder Tag Verzug von Labor bis RKI verringert die Zeit, die ein Fall in Inzidenz einfließt.
Mit 8T Verzug geht ein Fall nie in die RKI-Inzidenz ein!
Die echte Inzidenzen ist eigentlich 103.
Die @risklayer-Inzidenz ist nicht manipulierbar, weil jeder Fall von Meldung genau 7T in der Inzidenz auftaucht.
Verzögerung in Meldung führt dazu, dass der Fall später eingeht, aber dafür auch später rausfällt.
Das bringt mich auf eine Idee:
Man sollte mal den Calw-Faktor C = Risklayer-Inzidenz/RKI-Inzidenz für jeden Tag berechnen und analysieren, ob es da pattern gibt.
Wird manchmal mehr verfälscht, manchmal weniger? Mehr vor #MPK? Oder immer gleich?
Auf geht's Daten-Journalisten! #ddj
Übrigens ist es nicht das RKI welches manipulieren kann, sondern die (Landes-)Gesundheitsämter. Diese können verzögern.
Das RKI ermöglicht durch die schlechte Berechnungsart jedoch diese Manipulationsmöglichkeit.
Sieh mal einer an:
in Lockerer-Laschet-Land NRW ist die Inzidenz bei 99,6
in Hamburg bei 99,7
Das ist statistisch unwahrscheinlich auffällig. Drei Inzidenzen knapp unter 100, keine knapp drüber.
Da müsste man eine ökonometrische Analyse machen. Bei Manipulation: Diskontinuität! Image
Das ist keine Verschwörungstheorie.
Es wurde bereits klar von Politikern kommuniziert, dass sie kreative Methoden nutzen werden um Inzidenz herunterzurechnen.
Das gab einen Aufschrei, also muss man jetzt etwas vorsichtiger vorgehen.
Siehe Freudenstadt.
Sie mal einer an. Der Ökonometriker @AndreasShrugged hatte den gleichen Gedanken bereits im Dezember.
Zeit das wirklich zu analysieren!
Überraschung!
Die Inzidenz in Baden-Württemberg ist 99,8
Ebenfalls 99,8 die Inzidenz in Freudenstadt.
Seltsame Zufälle gibt's. ImageImage

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More from @CorneliusRoemer

Cornelius Roemer Profile picture
Aug 21, 2023
Here are counts of BA.2.86 and overall sequence submissions to GISAID
Note that the English sample included in week 2023-08-07 likely got expedited, so it may be best to exclude from this analysis. 1/ Image
In my very rough reading, there's not enough data yet to pin down growth advantage. It could be small or non-existent, or it could be sizeable, e.g. doubling every week.
Bear in mind that due to constant antigenic drift, there are always lineages with decent growth advantage. 2/
To really have an impact, BA.2.86 would have to become dominant, outgrowing even the fittest lineages around, e.g. HK.3 (EG.5.1 with S:L455F) or FL.1.5.1 (456L, 478R) which themselves are doubling in share about every two weeks. 3/
Read 14 tweets
Cornelius Roemer Profile picture
Aug 11, 2023
EG.5 (and EG.5.1) has recently got attention due to being highlighted by @UKHSA and @WHO.
EG.5, which is an alias for XBB.1.9.2.5, is a sublineage of XBB characterized in particular by Spike RBD mutation F456L. 1/
EG.5 is one of the fastest growing XBB sublineages, particularly common in China where it appears to be dominant.
As EG.5 has only one RBD difference compared to the upcoming vaccine strain XBB.1.5 vaccine protection is expected to be good. 2/
While the name EG.5 may sound very different from XBB, it is important to know that this is just due to naming - EG.5 is the short form of XBB.1.9.2.5. 3/
Read 8 tweets
Cornelius Roemer Profile picture
Apr 10, 2023
It appears that China has stopped uploading SARS-CoV-2 sequences to GISAID and now shares via its own version of Genbank: Genbase
github.com/yatisht/usher/…
I don't yet fully understand the terms under which China shares the sequences - I assume (and hope_ they are just as open as Genbank.
In that case this is a great development towards having as much SARS-CoV-2 data being free of usage restrictions as possible.
GISAID should still be able to integrate the data in their platform (unless the license prohibits it, if it's Genbank-like then GISAID can pull the data as they've done with Genbank-only published sequences).
Read 6 tweets
Cornelius Roemer Profile picture
Nov 28, 2022
I'm seeing quite a bit of discussion about the potential impact of a big wave in mainland China on variant evolution.
I do not think a big wave in mainland China would have major consequences outside of China. 1/
While China is a big country, it has less than 20% of the global population. The rate at which new variants evolve would only increase slightly as a result of fractionally more infections worldwide. 2/
We have by now seen multiple second generation BA.2 variants evolve independently: BA.4/5 in Southern Africa, BA.2.75 in South Asia, BA.2.3.20 in the Philippines, BS.1 in South East Asia. 3/
Read 9 tweets
Cornelius Roemer Profile picture
Oct 14, 2022
For straightforward problems, @github Copilot really rocks.
Yes, as a Python dev, I could code this up myself in less than a minute
But why spend mental energy on this if it can be auto-generated?
As a beginner, this could have easily taken me a few minutes. Now it's just seconds
And the only unnatural about this example is that I deleted the output generated by copilot before recording.
Everything else is exactly as it was when I did it.
Nothing contrived, very natural usage pattern.
It's hard to think of a better way of solving this problem.
Copilot chose a very pythonic solution.
There's a good chance handwritten solutions by many developers would be less idiomatic and more confusing.
Read 6 tweets
Cornelius Roemer Profile picture
Oct 13, 2022
BQ.1* and XBB have different geographic foci
BQ.1* is mostly in Africa, Europe and North America
XBB in South (East) Asia
3 countries with similar levels worth watching for comparison and potential co-circulation are:
- Japan
- Australia
- South Korea
cov-spectrum.org/explore/World/… 1/ ImageImageImage
BQ.1.1 and XBB have quite a lot of spike differences and seem to have similar growth advantages - this makes them candidates for co-circulation.
We may only be able to know once we see how the variants do in countries that had a wave with the other one. 2/
Here are the mutations that only occur in one of the two variants:
XBB only: S:V83A, S:Y144-, S:H146Q, S:Q183E, S:V213E, S:G339H, S:R346T, S:L368I, S:V445P, S:G446S, S:F486S, S:F490
BQ.1.1 only:
S:H69-, S:V70-, S:V213G, S:G339D, S:K444T, S:L452R, S:F486V 3/
Read 7 tweets

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