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
…when it comes back, it will come back faster and harder to the side you don’t want it on as well. so plan B could make the next peak higher… but also later (which could be good or bad news, depending on timing relative to the winter flu peak & other NHS pressures).
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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)
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:
And in the (un-averaged) detail for schoolkids, that trend looks *really* good.
Switching to look at cases (not growth), we can start to see the peak in the 10-14s emerging:
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:
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).
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.
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
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
I’m concerned that we haven’t quite got our heads round what the dynamics of the long-term endemic state for covid will feel like, and that some of the intuitions we built up in the epidemic phase won’t age well. I know I struggle with this at times. So, I built a model 😉 1/n
This time it’s just a “toy” model. Which means it’s not designed to forecast the endemic state in an accurate way – we don’t really have the data to do that yet. It’s just designed to illustrate the shapes and patterns that might arise. I hope it helps you, as it did me. 2/n
It’s just a very basic SIR model, for those of you familiar with that, and it covers a single sheet of Excel. In fact you can see it here – there’s more cells further down, but they’re just a copy of the earlier cells, extended further out into time. 3/n