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.
3. More recently, case ratios for 21st-22nd Aug have gone back above 1 -and note the ratio for 23rd Aug is likely to increase as late-reported cases come in. This *could* be a sign of secondary infections from Boardmasters, but it’s hard to be sure without contact tracing data.
It was inevitable there would be some onward transmission – e.g. as reported here by @TonyNewsCamera. And if R is generally around 1, then our default assumption should be to expect roughly one secondary case for each primary case.
– so if we’re keeping the ratio down to 20-30%, that’s still much better than it might have been. At the moment I haven’t got a clear handle on the age breakdown of those secondary cases, because it gets a bit lost in the noise when you go into more detail.
Of course, those secondary cases may lead to further cases, and so on – and so it’s essentially unknowable how many cases would have been avoided if the Boardmasters spike had never happened. But please note that it’s not all bad news, and it’s also very hard to know how many...
…cases will be avoided in the future because those 9,000 teenagers got infected and so will now interrupt transmission chains. As I’ve shown in the past (see thread below), a well-timed spike of infections in low-risk groups, with limited onward...
…transmission, can *in some circumstances* reduce the total number of infections and deaths in the long run. I’m not saying that necessarily will happen, just pointing out that the long-term effects could be either positive or negative, and it’s hard to work out which. /end
forgot to mention the regional breakdown of the estimated ~9k infections at Boardmasters, which is: 5k from South West, 2.3k from South East, and about 500 from each of London, West Mids and East of England. handfuls elsewhere. NB analysis does not include Wales, only England.

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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
24 Aug
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.
Read 13 tweets

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