James Ward Profile picture
May 23, 2021 31 tweets 8 min read Read on X
When I tweeted my initial reaction to the PHE data release last night, I promised you some model scenarios to help understand the impact of B.1.617.2 on the roadmap, and in particular whether opening up on 21st June still looked possible. 1/
I’ve done some analysis, and I’ll warn you in advance it’s a bit of a mixed bag: at the more optimistic end of assumptions, things look not too bad. But at the more pessimistic end, we’re back to facing a mid-sized exit wave, which calls the timing of Step 4 into question. 2/
Before that, a quick word on assumptions: while the PHE data last night was very helpful, it still leaves quite a lot of questions unanswered, and some of the data is confusing, or appears to contradict things we thought we knew. See for example: 3/
But there are some lights emerging in the mist. The data on secondary attack rates points to B.1.617.2 spreading 50-60% faster than B.1.1.7. And while this could be distorted by local factors in the hotspots where 617.2 is prevalent, this is starting to feel more solid now. 4/
So let’s take that as our starting point. The variable I want to explore is the *source* of that gain i.e. is it an innate change in transmissibility (R0), or is it a side-effect of escaping immunity (not just vaccines but maybe also prior infection), or a mix of both? 5/
Hence I will consider 3 main scenarios, all with a transmission gain of +55% (B.1.617.2 vs. B.1.1.7). In the first scenario, the gain is all down to increased R0, with no immunity escape. In the second scenario, it’s all escape and no R0 gain. And the third is a 50:50 mix. 6/
The first scenario is quite similar to one I looked at last week, where I showed that even scenarios with 60% higher R0 for the new variant were potentially manageable with the retention of baseline controls after 21st June, and vaccination of teens. 7/
But as the model is always changing to stay up-to-date with new data, let’s re-run that to be sure. First, here’s the results of increasing R0 for the new variant, with no change to current plans (and no baseline controls after the end of July). 8/ Image
So that’s a big wave in the autumn, nearly as bad as December/January, and with around 34,000 additional deaths. But with baseline controls retained indefinitely (or until we can achieve higher levels of immunity via booster vaccinations), we get this: 9/ Image
That looks a bit less worrisome, although there’s still around 16,000 deaths in the wave. If we decide it’s OK to vaccinate teens (or can find another 3 million adults to vaccinate in July), then we can go further and squash the wave nearly completely: 10/ Image
(It’s worth noting that vaccinating teens in July may not be possible if we have run out of our initial order of Pfizer, and won’t get more until Sept – as @PaulMainwood has flagged. But there are ways this could happen – see his thread for detail) 11/
OK but given the new data on vaccine escape, we don’t actually think the whole transmission gain for B.1.617.2 is likely to be just an uplift in R0. So, what happens if it’s actually all down to immunity escape? To analyse this, I can force my model to assume an escape.. 12/
… which creates a 55% transmission gain in the short term. This requires very pessimistic assumptions re. the properties of the vaccines with the new variant, and re. protection from prior immunity– so much that I find them hard to believe. But let’s go with this for now. 13/
With these assumptions, we again get a large wave – in fact larger than the pure R0 gain scenario, with peak hospitalisations c. 50% higher than in January, and around 42,000 deaths. 14/ Image
Again, we can try retaining baseline controls after June, which definitely helps: 15/ Image
But even if we are able to vaccinate teenagers in July, we’re still left with a moderately large wave, within NHS capacity but entailing c. 22,000 deaths: 16/ Image
The one thing that would kill this wave off is deferring the Step 4 changes on 21st June, until such time as we’re able to rebuild our immunity levels (e.g. via booster vaccines, maybe ‘tuned’ to the new variant). But clearly that’s not the outcome we really wanted: 17/ Image
Before we start debating policy implications, let’s look at the middle scenario, which is the one I consider most likely. Here the 55% gain in transmission is a combination of vaccine escape driving a 25% increase, and R0 gain of a further 25% (note 1.25*1.25 = 1.55 ish) 18/
Again with no policy response we get a large wave, similar in peak hospitalisations to January, and with around 38,000 deaths: 19/ Image
With baseline controls extended indefinitely, it gets better: 20/ Image
And with teenagers (or another 3 million adults) added, better again. But still with a non-trivial 13,000 or so deaths in that exit wave. 21/ Image
This time, if we want to kill off the wave, we don’t need to defer Stage 4 until we’ve completed booster vaccinations in late autumn– we just need to defer it until the end of August / early September, by which time we should have given nearly all adults their second dose. 22/ Image
So, what does this all mean for policy, and particularly for the roadmap?
Well, first note that in none of my scenarios was there any need to reverse Step 3, nor was there a dangerous explosion before 21st June (in some cases, R went up to ~1.3, and then came down again). 23/
In the more pessimistic scenario (where the gain in transmission is mostly/all down to immunity escape) there was a potential case to defer Stage 4 until the late autumn, once a booster vaccination campaign could be completed. 24/
In the more likely scenario (with a mix of R0 gain and immunity escape), deferring Stage 4 to early September, and then reverting to baseline controls, was sufficient to suppress the exit wave. 25/
Of course, we don’t have to do that: we could decide to open up on schedule, and accept the likely numbers of deaths and hospitalisations that would result. I expect different people will have different reactions to that trade-off, and I’m not going to give a view now. 26/
Staying in Step 3 for a few more months might seem OK to some: after all, if you don’t spend much time in bars and nightclubs, and aren’t that fussed about foreign travel or large stadium events, life probably feels relatively normal. 27/
But if your livelihood depends on indoor hospitality, travel or entertainment, and you note that a good proportion of the deaths that would result from Step 4 are amongst those who have chosen not to take the vaccine, you might see the trade-off a little differently. 28/
So, I expect this debate is going to get intense over the next few weeks, in the run-up to decisions in early/mid June. Of course, we may get more data before then that pushes us more clearly in one direction or the other. 29/
(please read the following very quickly, in the manner of the “small print” at the end of a TV/radio advert: It may turn out that my assumed 55% transmission gain is too high or low, or that immunity escape is larger or smaller than we currently think. My model doesn’t… 30/
…account for local hotspots, nor for waning immunity, which may become significant in the longer-term predictions. And other models (e.g. Warwick, Imperial) will give you different results – most likely more pessimistic ones, if previous experience is anything to go by. /end

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More from @JamesWard73

Dec 29, 2022
When I wrote my thread before Christmas (linked below) I promised you a postscript on the interaction between vaccines and NPIs, so here it is. And it’s good news – the combined effect is more powerful than I expected. 🧵 1/25
Let’s start with what I expected. In an endemic model, with no vaccines, there is essentially a “required” rate of infection which is needed to keep immunity levels topped up at their equilibrium (herd immunity threshold) level. 2/25
NPIs can reduce this equilibrium level, and hence also the required rate of infection – although as we’ve seen in other threads, the % reduction in the infection rate is usually less than the % impact of the NPIs on transmission. 3/25
Read 26 tweets
Dec 19, 2022
So Covid Twitter (and Twitter in general) seems to be dying… with just a few limbs still twitching. To be fair I haven’t been helping much this year, having held true to my New Year’s Resolution to spend time talking to my family rather than on Excel and Twitter. 🧵
So many thanks to those (including but not limited to @BristOliver @PaulMainwood @kallmemeg @john_actuary @chrischirp) who have kept things going this far. But I haven’t completely given up the modelling thing, and there’s a couple of new things I’d like to show you.
So for old times’ sake, and as an early Christmas present, here goes with probably my last ever Twitter thread (and yes, I know it would probably work better on Substack, and maybe I should replicate it on Mastodon &/or Post, but just for today, let’s remember Twitter as it was).
Read 46 tweets
Oct 11, 2022
@TAH_Sci @i_petersen @karamballes @chrischirp @CathNoakes @MichaelSFuhrer @MichaelPlankNZ I agree our biggest concern right now should be booster take-up in 65+, but I’d be a bit more open to the case for “clean air” interventions. It’s possible that the business case for doing this in some settings actually does stack up, and imo we should be investigating this, …
@TAH_Sci @i_petersen @karamballes @chrischirp @CathNoakes @MichaelSFuhrer @MichaelPlankNZ … while being very realistic about the costs and benefits. If we’re happy to start with a basic model (and then refine it) then there’s really only three things we need to work out:
1) what % of transmission do we expect to be interrupted in the specific settings it is used?
@TAH_Sci @i_petersen @karamballes @chrischirp @CathNoakes @MichaelSFuhrer @MichaelPlankNZ 2) what % of overall transmission is accounted for by those specific settings?

Multiplying 1) and 2) gives us the overall % reduction in transmission. And then we need to convert that into a reduction in medium-term prevalence. @MichaelPlankNZ gives us the formula here:
Read 9 tweets
Jul 26, 2022
really nice analysis from @Jean__Fisch here using the SIREN data to imply changing patterns in the amount of protection that previous infection is giving in each wave. A few points to note:
- infection seems to give strong protection through the autumn 2020 and the Alpha wave
- this reduces (through the combined effects of waning, immune escape and impact of vaccinations) for the Delta wave
- and for the first Omicron (BA.1) wave, there appears to be no benefit from prior infection. I don’t believe this is literally true: more likely there was a…
…small benefit but it may be offset by demographic confounders eg those more likely to be exposed in one wave are also more likely to be exposed in the next
- the good news is that the protection recovers in the later Omicron waves, suggesting that BA.1/BA.2 infection does…
Read 5 tweets
Jun 30, 2022
I’ve been looking at this question the other way around, but it’s still very gently encouraging I think. The case curve is continuing with exponential growth (straight line on the log plot) for much longer than we would have liked. We might have expected it to curve over by now
…as the effect of growing immunity to the latest variant starts to bring the R number down. We’ve had a couple of false dawns already (what do we call these, they can’t be “dead cat bounces” because we’re still going up… so maybe “live cat slumps”?), but still it keeps rising.
I *think* what’s going on here is not that immunity has suddenly stopped working, but rather that the effect of growing immunity is being offset by something else. The obvious candidate (as explored in Oliver’s thread) is the upwards pressure from continued variant mix changes.
Read 8 tweets
Jun 14, 2022
This is important from @AdamJKucharski – and consistent with the maths I outlined in threads about endemicity late last year: (which will have been very familiar territory to Adam and other experts in this field). To draw out a couple of key implications:
1. From a mathematical perspective, the arrival of new variants is a bit like waning host immunity, and has much the same effect (i.e. immunity gets lower). It arrives in a slightly different way (at the same time for everyone, rather than gradually across the population)
...which will affect the short-term dynamics, and mean that we’re more likely to get new peaks and troughs, rather than settling into a more stable equilibrium. But viewed over periods of several months or years, the impact on total infection rates will be similar.
Read 9 tweets

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