Hervorragend systematischen Daten aus BaWü zeigen: Mutanten-Anteil bereits bei 12% - und das im Schnitt über die letzten 7 Meldetage.
Die 6% aus dem @rki_de-Bericht sollte man schnell als veraltet abheften, lieber hierauf schauen.
Transparente Berechnung: docs.google.com/spreadsheets/d…
Auch die Entwicklung der Zahl der Mutantenfälle wächst exponentiell (also nicht nur der Anteil).
Trotz Rückgang der Fallzahlen steigen die Zahlen der Mutanten - wie man das bei einem R von ca. 0,9 des Wildtyps auch erwarten würde. R der Mutanten dann bei ca. 1,3. 2/
Warum sind die BaWü-Zahlen so wertvoll? Weil dort alle Fälle auf Mutationen untersucht werden. Sehr weise von @RegierungBW um einen besseren Überblick über die Lage zu bekommen - und nicht von veralteten, punktuellen @rki_de-Zahlen abhängig zu sein. swr.de/swraktuell/bad…
Warum steigende Zahl der Mutanten-Fälle ein großes Problem ist, zeigt dieser Artikel aus der @SZ
Nur, dass der Anteil der Mutante in Realität schon höher ist als auf dem Bild (also mehr Rot).
Wichtig: Annahme der Grafik ist, dass KEINE LOCKERUNGEN kommen.
Hier die gleiche Grafik mit den neuesten Daten, wurden gerade eben von @MSI_BW veröffentlicht.
Jeden Tag werden die Zahlen belastbarer, mit geringeren statistischen und systematischen Fehlern. stm.baden-wuerttemberg.de/fileadmin/reda…
Es gibt neue Daten, Wachstum flacht ab, vermutlich wegen Nachholeffekt, es wurden ursprünglich mehr Proben getestet, als an einem Tag reinkommen sollte, deshalb Überhöhung.
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/
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/
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/
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).
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/
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.
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/
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/