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:
a) The impact of prior infection (which may be higher in the unvaccinated group)
b) Correlation between vaccination and willingness to be tested
c) Behavioural impacts e.g. vaccination makes people less cautious, or higher-risk occupations more likely to get vaccinated ...
d) The treatment of “unlinked” cases (which may be more likely to be unvaccinated)
e) The calculation of how many people are unvaccinated, which depends on having an accurate estimate of how many people there are in each age group.
Just taking that last point: the PHE analysis uses the NIMS system to estimate the unvaccinated population. But we know that system can be inaccurate e.g. due to people not telling their GP when they emigrate / move to a different area. So instead let’s use the ONS 2020…
…estimates of population in each age group to estimate the number of unvaccinated (this is a bit rough & ready, because I can’t match the dates precisely, but it’s close enough to illustrate the point). We now get a very different graph for case rates by vaccine status:
As you can see, we no longer have case rates being higher in the vaccinated groups (except maybe in the 80+). I’m not saying the ONS numbers are definitively right either, they will also be distorted by other effects, but in this case I suspect this is a bit closer to the truth.
So long story short, please don’t get panicked by the case rates in Table 4, there’s many reasons why they don’t give the full picture. But this data is still a very powerful resource, and will get more powerful as we get more releases and can track trends over time.
The data also tells us something important about hospitalisations and deaths. We can calculate hospitalisation rates and death rates per case from this, which are unaffected by the denominators issue. Here's the graph for hospitalisations:
and for deaths. Even if you do get infected after a vaccine, your chances of going to hospital or dying are very much reduced if you’ve had the vaccine. (note the hospital data for under-18s will be distorted as the only u18s to have 2 doses are clinically vulnerable).
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 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.
Three points as a postscript to the @boardmasters saga: 1. My final estimate of the number of reported covid cases from infections at Boardmasters is just under 9,000 (note this is restricted to the 15-19yo age group, as that’s where there’s a clear signal at regional level) 🧵
2. While the Boardmasters spike was artificially increasing reported weekly case ratios for England last week, it is now artificially depressing them (as last week’s spike is now the baseline we’re measuring against) – note the adjusted orange line is now above the blue.
Without the spike, the case ratio for England would have been almost exactly flat for specimen dates 17th-20th Aug, suggesting an underlying trend with R = 1. So if people tell you English cases are falling, they may be literally correct, but I’d not celebrate too fast.
I’ve been promising to update my comparison of how the July model projections are going. So here it is. As you can see, the model (orange line) did quite well in predicting admissions for the first couple of weeks (in mid/late July), but missed the sudden decline in cases… 🧵
…that happened after the Euros, and so the actuals fell below the projection (good news). More recently, the gap has been closing, but that’s for the (bad) reason that hospitalisations have started to rise again, while my model was projecting a gentle decline.
A couple of notes on the other models shown here: 1. I think we may have done LSHTM a slight favour by selecting their non-waning model for comparison, when their (more conservative) waning model was probably more their real central case.