I know I’m meant to be reading the details of the SPI-M papers but there’s only so many coloured curves on a chart you can stare at before they all start blending into one. I’ll re-convene on that tomorrow, but in the meantime I found something interesting in the case data. 1/7
This is plotting the growth rates for the 5-year age groups up to 30 over the last 3 weeks. You can see the explosive growth in the 20-24s and 25-29s following Step 3, and then a significant deceleration (falling growth rates) over the last few days. 2/7
On the other hand, the growth rate in school-aged children (5-9 and 10-14) looks to be resurging, having taken a short break over half term – suggesting that we might have a rocky few weeks ahead in the last few weeks of the school summer term. 3/7
But anyone who says with certainty that they know what will happen next is probably wrong. (it’s a bit like that alleged Richard Feynman quote: if you think you understand quantum mechanics, then you don’t understand quantum mechanics). 4/7
I’m very much with @BallouxFrancois on this: there’s a wide range of outcomes that wouldn’t surprise me, ranging from a new surge to a fizzle-out, or quite possibly a sort of bumpy plateau as different regions and groups rise and fall. 5/7
And that, in a nutshell, is why the government had to delay: because we don’t even have a good handle on the short-term dynamics, let alone what would happen if you tried to layer Step 4 on top. So our funnel of uncertainty is very wide, and contains some very nasty… 6/7
…scenarios in which the NHS is overwhelmed. The job of the next 4 weeks is to let the data settle down, and to do enough vaccinations that those extreme scenarios become highly unlikely. Then we can proceed, not pain-free, but knowing the downside risk is contained. /end
PS with thanks and apologies to @nicfreeman1209 whose age-group growth rate curves I was inspired by, except I got the colours the wrong way round.
I think this deserves an explanation: why is it that countries that have already completed their epidemics with Alpha (or similar variants) might have an easier time with Delta than the UK, which was still in the process of opening up when Delta hit? 1/n
The answer has all to do with “overshoot”. For most epidemiological concepts there is an @AdamJKucharski thread to go with them, and overshoot is no exception, so I’ll leave you to explore here if you’d like a reminder of how this works: 2/n
Now for the purposes of this illustration, let’s assume we have two variants:
A, which has R0 of 4.0 and therefore -in a simple model- a herd immunity threshold (HIT) around 75%,
and B, which is 67% more transmissible than A, and so has R0 of 6.7, and HIT of ~85%. 3/n
I promised that I would come off the fence re. plans for Step 4 by today, and I’ve left it right to the last minute, but now I will: I believe we should be delaying by 4-6 weeks. And if asked to pick a specific date, I’d go with 26 July (5 weeks on from 21 June). 1/n
(this has the marginal presentational benefit of using the government’s standard step length, so Boris can ask us all to take “one more step” on the journey back to normality). 2/n
This would normally be the point for me to launch into a long thread on the reasons why this is the best approach, and all the alternatives are sub-optimal. But I actually need to do some work today, so I’m not going to do that (if I get a chance I’ll add something later). 3/n
@kallmemeg has kindly nominated me to undertake some vaccine efficacy (VE) estimates from the data in Table 6 of the latest PHE Tech Briefing. I know the number of deaths in the “double vaxxed” column has been causing concern, but I think it’s OK. 1/n
My overall conclusions are:
1)The vaccine’s efficacy vs. disease is hard to deduce from this table, as it’s sensitive to the exposure risk assumption, but a rough estimate is consistent with the PHE’s figures in Table 18 (i.e. 33% after 1 dose and 80% after 2 doses) 2/n
2)The VE vs. hospitalisation and death appears robust to different assumptions of exposure risk, and implies that the vaccines are maintaining good protection (~80%) after 1 dose, and very strong protection {>95%) after 2 doses. 3/n
As you can imagine, I’ve been trying for the last few days to work out what’s going on in the case data, and why it is that our models haven’t predicted the take-off in cases over the last week. I think I’m starting to get my head around it, but it’s a work-in-progress. 1/n
In essence I think we’re still thinking too much in terms of national averages, and not quite recognising that this is a wave composed of a number of local/regional outbreaks, which are concentrated in specific age groups. This has a number of implications for modelling. 2/n
In particular I want to focus on the age-group dynamic, because I think this has been under-recognised, and has led to a couple of specific problems with the models. Let’s take a quick look at the growth rate by age group in the most recent case data: 3/n
There’s something a bit odd in that CO-CIN data I tweeted on Monday, showing shorter stays in hospital more recently. The problem is, if I use the full dataset to predict the number of inpatients, I get more than there actually were in wave2. 1/n
Let’s try that to start with, using a model scenario (I’ll bring in real data in a bit). I’m taking the worst scenario from my recent model update, which has very similar peak hospitalisations to the actual peak in Jan 2021. 2/n
I can run that admissions curve through a simple model which combines it with the CO-CIN data for length of stay, and predicts what the inpatients curve would have been. In fact I get two curves: one using the “recent” data (yellow), and one for the whole dataset (blue): 3/n
thanks to the person who pointed me towards this report: assets.publishing.service.gov.uk/government/upl… (you know who you are!) - lots of interesting data. In particular it does support the anecdotal evidence that average length of stay in hospital is shorter in recent admissions: see this chart 1/3
...and compare to the older data in the chart below. So after 10 days, about 75% have been discharged in the recent data, compared to 40% in the older data. (but note deaths are lower in the recent data, which slightly offsets the benefits for hospital bed occupancy). 2/3
what's not entirely clear to me is whether this is just the result of a lower average age of patients being admitted - see below- (and would reverse if more older cases arrived), or whether it says something more fundamental about the progression of covid disease with Delta. 3/3