OK, so here’s a question: why are case rates so persistently high in older age groups in the North of England? For simplicity here I’m just looking at the 50+, and using three super-regions: North inc Yorkshire, Midlands & East, and London & South. 1/4
You might think it’s just reflecting case rates in the whole population. But it’s not: look here at the same graph for the under-50s, it has quite a different pattern, with the North very similar to the Midlands through August (and to the South until the last few weeks). 2/4
One possible explanation is that it reflects lower immunity in the older Northern groups – but the vax rates have been good, and if we look at a longer time horizon, there’s not an obvious difference in attack rates (the January peak is lower, but the autumn one was higher). 3/4
So I’m a bit stumped. Is there a behavioural difference? Or something structural with that population, which is different in the North to elsewhere in England? The effect seems most pronounced in the North East, if that’s any help. Twitter hive mind, you can do this… 4/end
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A few people have asked how we managed to have such a stable period in England during August, with cases apparently stuck around 25k per day, and R pretty close to 1. After all, case rates in an epidemic tend to go up and down in waves, not stay at roughly the same level. 1/10
One possible answer is that the growing immunity from vaccines and infections (which would normally be expected to pull R and cases down) was being offset by waning of the existing stock of immunity, causing case rates to stabilise in a sort of bumpy plateau. 2/10
If that is the case, then August could be seen as an early glimpse of what endemicity might look like, although I don’t think we’re near to finding a long-term equilibrium, and there are several good reasons why August isn’t a good predictor of what that will look like. 3/10
One of the questions I often get in response to my modelling threads (such as last Thursday’s below) is: what does this imply for levels of hospital occupancy with covid? Unfortunately I don’t have a good model of hospital stay dynamics, but helpfully...
…I know a man who does, and @nicfreeman1209 very generously offered to convert my various scenarios for weekly admissions into a corresponding occupancy forecast. So what follows is very much a collaboration on the analysis, but the policy commentary is all mine.
Essentially, Nic’s model uses the known data for hospital admissions and occupancy to estimate a distribution of how long people stay in hospital with covid – here’s some discussion on an earlier version:
Apologies to those of you who’ve been waiting for a model update: I’ve been slowed down a bit by work, start-of-term chaos with the kids, and by trying to organise an U13 girls rugby team. But it’s finally here in its glorious 25-tweet thread detail. Hope you enjoy…. 1/25
The July iteration of my model did an OK job of predicting (at least in “ballpark” terms) the level of hospital admissions over the last couple of months – in fact it’s almost spot-on right now, albeit maybe in the same way that a stopped clock is correct twice a day. 2/25
But that model won’t be a good guide to what happens over the autumn and winter, because it’s missing two significant drivers: waning immunity, and booster vaccinations. So I’ve upgraded the model to include those factors, and am ready to give you the results. 3/25
I know, I’m meant to be doing that modelling thread (it will come later this evening!). But first I got distracted by today’s release of data from @PHE_uk – thanks to the amazing @kallmemeg and the team there. The main point of interest in today’s report has been …
… the release of data on cases, hospitalisations and deaths split by vaccine status. In particular Table 4 of the report has caused some consternation as it suggests case rates are higher in vaccinated groups than unvaccinated, for age groups 40-79.
There are some real reasons why we might expect vaccine effectiveness to have declined – including the impact of delta, and waning of immunity over time. But there are also a number of potential confounders and distortions here, including:
I spent a bit of time yesterday building immunity waning into the model, which is one of those bits of code I hope no-one ever sees (because it’s a massive hack), but it seems to be working OK so 🤷♂️. Now all I need is some numbers to go in the waning rate assumptions! 🧵
And that’s where I could use some help. The way the model works is as follows: as well as the classic compartments for fully susceptible (S) and recovered and immune via prior infection (R), I also have states for immune by vaccination (V) and partially immune (P).
People in state P are able to get infected, and to pass the disease on, but are unable to get severe disease (so won’t be hospitalised, or die). So in terms of waning, I’ve set things up with three different waning processes: R to P, V to P, and P to S, as illustrated below:
England case data is looking moderately encouraging, with case ratios (compared to same day last week) dipping below 1 even on my adjusted curve which removes the Boardmasters spike – see orange line below. Note that last day will be adjusted up, but is unlikely to go over 1. 🧵
As a result, my estimate of R in England also dips below 1 (but only just… to 0.99) for the first time since early August.
I’m conscious that the case rates in over-40s are still growing, which isn’t ideal, and may mean that hospital admissions continue to grow as well. The hope is that, as in previous cycles, it’s the younger groups that move first, and then drag the other ones along.