The news from today’s case data continues to be good, but because I can only do undiluted positivity for so long before providing some balance, I’m going to give you the good news quickly, and then give you four reasons to avoid premature celebrations.
First, the good news: case growth in school-age kids continues to decline, and even more quickly in the last day or two. (note: this latest cliff-edge is undoubtedly exaggerated by the impact of people doing fewer LFD tests last weekend, as half term started for most kids)
In particular we can see the 10-14s now clearly over a peak: (best to look at that orange dotted line, which is a 7-day centred average, for an indication of the trend)
And cases in their parents’ age groups have now started to fall as well.
Looking at the age groups together, we can see continuing declines in growth (but not yet falling cases) in the 20-40s and in the 60-80s.
Now reasons for caution:
1) As noted, half term is definitely exaggerating the rate of fall in cases. And while I don’t think that has been a big factor over the last week, it’s possible that early half terms and year group closures have had some effect in pushing cases down.
2) While cases in under-20s are definitely falling, cases in 80+ seem roughly stable, and they’re still growing in 60-80s. We’d expect that to flow through to admissions and deaths – and while England hospitalisations seem to have plateaued, that may just be noise in the data.
Given the current level of pressure in the NHS (ably documented by @ShaunLintern and others), even small additional loads from extra covid admissions are highly unwelcome.
Note that stable cases in the 80+ suggests the combination of boosters + infections is roughly balancing the impact of waning immunity in this group - if we want cases and admissions to fall sharply, which we do, then we need to materially speed up the booster rollout programme.
3) Looking at that age group chart again, we can see examples in the past (e.g. in early September) when growth fell, but just caused a plateau in cases, and then resumed an upwards trend (growing cases).
We don’t want that to happen here – and while I don’t think this is the most likely scenario, I can’t completely rule it out e.g. if after half term we get some colder/wetter weather, and some more relaxed behaviour as overall case rates fall.
4) Even if, as I think is more likely, cases do continue to fall (including in older age groups) for some weeks after half term, we’re not necessarily out of the woods quite yet. In fact, the lower the cases fall in November/December, the more worried I become about Jan & Feb.
This is because, if cases fall below their equilibrium level (whatever that is), we will be building up immunity debt, and there is then a significant risk of a resurgence in early 2022 if boosters don’t come through quickly enough to stop it.
So if cases do crash before Christmas, I’ll be the guy reminding you not to put up banners on aircraft carriers and declare this thing “over”. Sorry to inject a note of caution into a good-news day- I just don’t want us to get carried away by a few green figures on the dashboard.
/end

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More from @JamesWard73

28 Oct
It’s been a while since we’ve had a set of SPI-M models and it’s taken me a few days to read through them properly. Having done so, I have a few thoughts – as usual I’ve found 10 key messages to take away. For reference, the papers are all here: gov.uk/government/col… 🧵
In terms of overall messages, I would agree with everything that @BristOliver said here: . So I’ll focus my own comments more in the detail of how the modelling has been done, and its implications.
1) In general, the models are fairly optimistic about the course of the next 2 months, but after that they vary a lot – with some scenarios showing significant peaks in 2022 (at any time from January through to June) and others with no big resurgence Image
Read 16 tweets
26 Oct
interesting article with some great quotes from @AdamJKucharski and @BristOliver. but unusually I disagree with @TomChivers on two counts:
1) I don’t think we can or should make our policy responses into an algorithm (it’s too complex a system, with strong human factors) and…
2) although “plan B” may feel low-cost to some, if we’re about to enter a downswing of cases (which seems quite likely, although not yet guaranteed) then implementing it now is as likely to make the next peak of hospitalisations higher, as it is lower.
this may seem counter-intuitive, but as the dynamics become more endemic-like, so they behave more like a pendulum. and if the pendulum is already moving in the direction you want it to, giving it a shove in that direction will certainly get you there faster (and for longer) but
Read 4 tweets
25 Oct
I usually like to mix good news and bad news (“this.. but also that”). But I can’t find any bad news in today’s dashboard data, so here goes with a happy half-term round-up. Case growth continuing to fall, and now even more steeply in the under-20s: Image
And in the (un-averaged) detail for schoolkids, that trend looks *really* good. Image
Switching to look at cases (not growth), we can start to see the peak in the 10-14s emerging: Image
Read 12 tweets
23 Oct
A bit of a mixed bag in today’s case data. The good news is in children, where the trend of recent days (stalling growth) continues: Image
And if we look into the detail, we can see weekly growth rates heading very slightly negative in all three school-age groups (although that last day will get revised up a bit, it does suggest we’re about at the peak of cases). Image
I note that (as @BristOliver mentions here) there’s a risk that the Immensa problems in the South West are now causing the growth to appear lower than the true underlying trend – so we should watch this closely over the coming days.
Read 6 tweets
10 Oct
I’m not liking the trend in over-60s case rates much at all – see chart of growth rates below. This will inevitably feed through into hospitalisations and deaths over the coming weeks, so boosters for this age group may be arriving not a moment too soon. 1/4
The more I look at this, the more I think I was wrong to nudge down the waning rates in the last model iteration. If I left the waning for vaccine effectiveness vs. infection at 7% a month (instead of reducing to 5% per month) I got this scenario: 2/4
Interestingly, if I take those assumptions and fit to the very latest data, I get a slightly different projection – which has the odd feature of a long, bumpy plateau through the winter. NB I’m not offering this as a forecast with any sense of confidence; 3/4
Read 4 tweets
3 Oct
Are you ready for Part 2 of the endemic “toy” model thread? I thought so. To pick up the story, we started yesterday (see thread below) with a non-seasonal model, and explored the effects of adding in controls and vaccines. Now we need to explore the impact of seasonality. 1/n
To start with, let’s see what happens if we inject some mild seasonality into the equilibrium model – here with a +10% variation in R during winter, and -10% in summer. (so about 20% overall peak-to-trough variation). With this, we get gentle oscillations in cases: 2/n
Now let’s dial up the seasonality a bit – to 20% either way. Unsurprisingly, the waves get bigger. 3/n
Read 31 tweets

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