Chise @sailorrooscout is a very popular account tweeting positive news about vaccines.
Unfortunately, she usually omits all caveats balanced reviews of the studies contain.
I therefore recommend everyone to read her tweets with a big grain of salt. Here's a case study thread.
Today, Chise tweeted the following positive news.
Before I looking at her thread in detail, let's first have a look at what the underlying study says. I will use the excellent @nytimes article referenced by Chise as my source: nytimes.com/2021/05/26/hea…
The headline contains a really good, balanced summary:
"Immunity to the Coronavirus May Persist for Years, Scientists Find
Important immune cells survive in the bone marrow of people who were infected with the virus or were inoculated against it, new research suggests."
Chise's first tweet is much less clear, swapping out accuracy for optimism.
Note that the NYT writes: "may persist for years".
Chise turns that into a categorical claim: "immunity lasts AT LEAST a year"
The rest of her short thread paraphrases two paragraphs from the article.
But what are the caveats? Chise mentions none. Even though the article does. So let's have a look what Chise omitted other than the word "may".
The study suggests that for most people with previous infections one dose of a vaccine will be enough.
However, the article mentions that there were still some people who did not have a robust response after infection and should still get two doses (in theory).
The good news is that B cells keep maturing and last for a long time. It seems that repetitive infections with other coronaviruses occur mostly not because of waning immunity but because these viruses mutate and there escape existing immunity.
Note: this is exactly what seems to happen with Sars-CoV-2. Vaccinations still work well against B.1.617.2, for instance, but the effectiveness is somewhat reduced.
So while immunity to a particular virus may last for years, the virus may mutate and therefore evade.
It appears as though immunity after infection with the real virus is better compared with vaccination alone. Not surprising, after all, the vaccines often just contain a single part of the virus and often don't remain in the body for as long.
Chive left out this very important caveat:
5 out of 19 patients did NOT have detectable B cells in their bone marrow, despite having been infected previously. Despite what Chise's optimism may make one think, immunity is never going to be perfect for everyone.
B cells kept maturing, working better against variants over time. But it's important to note that neutralising activity against variants like B.1.351 was still less than for the wildtype.
The last paragraph is very important: it warns against generalising the study's results to people who were only vaccinated and had not previously been infected.
Those are my takeaways from the article. It's good news, but I don't feel Chise communicates the results well. What do you think now that you've seen both presentations? What's the feeling you get when you read Chise's threads? Is it just me who feels they are overly optimistic?
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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/