The case trends remain hard to read, but with a bit of age-group case ratio analysis, I think we can see a bit further into the fog. Let’s start with the overview: cases still falling in the under-20s and over-60s, but not the 20-60s. But that hides some important detail. 🧵 Image
Looking first at the older (60+) age groups., we can see some very steep (and welcome) declines in the 75+, a gentle decline in the 70-74s, and gentle increases in the 60-69s. Exactly what you might expect if we were giving boosters to the most elderly first, for example. Image
So – good news overall. But also a reminder that if we leave the 60-69s unboosted for too long, we might see those case rates drift upwards, which would be very unhelpful.
Checking into the next group down (30-60), we also see some divergence, with the 50-somethings drifting downward, but the other ages (perhaps more influenced by high case rates in the youngsters?) still heading upwards. Could be worse, but not ideal for sure. Image
And finally in the under-30s, again we see different patterns. 20-29s are growing slowly, 10-19s are still falling fast (although less precipitately than a few days ago) and 0-9s are nudging back into growth – more strongly in the pre-school 0-4 group than in 5-9s. Image
It is perhaps instructive to look at the raw case data for the 10-14s: here we can see clearly the “missing” peak at the start of half term (due in large part to less LFD testing), but also that cases have been helpfully re-set to a new, lower level, even as testing resumes. Image
(note that last data-point for specimen date Monday 1st November is very incomplete, and will get revised upwards in the next couple of days – but probably shouldn’t go much above the 6,000 level).
It’s worth noting that we’re currently benefiting from the real reduced rate of child-to-child infection during half term, but that the growth trend is being artificially boosted by the resumption of testing. Whereas within a few days, those effects will swap around:
…we’ll lose the benefit of half term (as kids are already back at school infecting each other, which will show up from around Friday’s specimen date), but will also lose the testing distortion, as we start to compare days on a more like-for-like basis (from Sunday onwards).
So, turning on the crystal ball, what do I think? This is inevitably speculative, but my gut feel suggests that when the dust settles, we should see the 10-19s resume a gentle downward trend, as they started to do just before half term, and helped by a few more vaccinations.
But I wouldn’t be surprised to see the 0-9s return to growth, which might start a debate about whether, and if so how, we need to take further action to control the spread in primary schools / nurseries etc.
At the other end of the spectrum, we should see continued falls in the 75+ under the influence of boosters, which should also soon drive a stronger trend in the 70-74s and then, in due course, in the 60-69s – let’s hope the latter group won’t have risen too much before then.
The hardest groups to call are the 20-60s, as there isn’t such an obvious driver for cases to fall, or rise. I wouldn’t be surprised to see the 50-59s starting to drift up over the coming weeks, as immunity waning starts to impact, before boosters arrive.
The 30-50s will be heavily influenced by trends in kids, so perhaps we might see 30-39s rise while 40-49s fall away? (if I’ve got my forecasts right on the 0-9s and 10-19s). And the 20-29s will be mostly driven by their own behaviour – will we see more spread as winter deepens?
So, still more questions than answers, but maybe a few pointers on likely direction. Overall, I’d expect cases to fall, but not plummet. As always, admissions will be the key metric driving policy, and they’re still heading up, although that should turn in the next week. /end

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More from @JamesWard73

30 Oct
There been something bugging me for a while about how my model (and others) works. And I think I’ve finally pinned it down. It’s subtle and technical, but I think might turn out to be surprisingly important. And it’s all to do with the “leakiness” of the immunity model. 🧵
The difference between a “leaky” vaccine and an “all-or-nothing” vaccine has been well-covered by others – my favourite explanation is in this thread:
As it happens I’m not particularly happy with the “leaky” terminology – it could mean a number of different things, and so could be misinterpreted. I prefer to think about this in terms of the variability of the vaccine effectiveness (VE) at individual level.
Read 25 tweets
29 Oct
I’ve been thinking a bit about optimism and pessimism, and how that might affect our views on whether additional restrictions (such as the government’s “Plan B”) should be imposed. It’s not quite as simple as it might first appear. Let me explain…. 🧵
The first thing to note is that optimism and pessimism can operate on different timescales. So you might think that things are going to get worse over the next couple of months, but that 2022 will be a more positive experience, due to boosters and improved treatments.
Or you might be relatively optimistic about the next couple of months, but concerned about the potential for a resurgence in early 2022 as we reach the depths of midwinter, and as waning immunity hits the populations in their 40s and 50s who may not have had a booster yet.
Read 12 tweets
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
Read 16 tweets
27 Oct
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)
Read 15 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

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