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
But let’s explore the numbers for a second. We know there were around 25k cases per day, and if we make the rough assumption that we find around half of actual infections, that implies around 50k actual infections per day. (feel free to insert your own assumptions here). 4/10
Hence we can estimate there were ~1.5 million infections in August, or 2.5% of the population of England. There were also about 5 million vaccinations in the month– but that’s a mix of first and second doses, neither of which gives 100% protection vs. infection, and some… 5/10
…of those people will already have had decent levels of immunity via prior infection. My model estimates that those 5 million doses are equivalent to about a million people being fully immunised, or 2% of the population. Adding infections, that makes 4-5% newly immune. 6/10
Now if R was stable during August, and we assume (NB big assumption here, quite probably wrong) that other factors such as behavioural caution and weather didn’t change much, that new 4-5% of immunity must have been roughly balanced by a 4-5% loss of immunity via waning. 7/10
My model thinks there was about 70% total immunity at the start of August, so that 4-5% loss is actually a 6-7% waning rate per month. Which is remarkably consistent with various studies (eg PHE khub.net/documents/1359…) suggesting a waning rate of about that level. 8/10
Please note it’s likely that behaviour etc. did change during August, so this calculation is unreliable. But I think it’s interesting that the waning rate implied by PHE is pretty much exactly what you need to explain the behaviour of case rates in England during August. 9/10
Coincidence? Quite possibly. There are hints in other studies (eg nejm.org/doi/full/10.10… ) that the real waning rate is about half that quoted above, and we can’t really tell from this data. Only time, and more data, will tell. 10/end.

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More from @JamesWard73

12 Sep
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:
Read 13 tweets
9 Sep
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
Read 25 tweets
9 Sep
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:
Read 12 tweets
30 Aug
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:
Read 8 tweets
28 Aug
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.
Read 5 tweets
26 Aug
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) 🧵 Image
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. Image
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.
Read 10 tweets

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