Now the kids are in bed, I can focus on updating my model to deal with the many events of today. These include 1) Boris’s plan 2) recent case trends 3) news on vaccine effectiveness vs. serious disease, and vs. transmission. Results below: mostly good news. (thread)
1) Boris’s plan is not very different from some other scenarios we have already looked at – and just a few weeks slower to unlock than our base case. Regular readers will be able to predict that it pushes the “4th wave” peak into the autumn – but no real change to outcomes.
Note I’ve assumed that the final unlocking on June 21 goes into a “near normal” state with some continuing social-distancing requirements e.g. masks in crowded spaces, limits on large indoor events, ongoing WFH etc. – which lasts until 31 Dec.
(without this final step we would get a larger peak in hospitalisations, which would challenge NHS capacity – so need to avoid)
2) Case trends in England and Scotland over the last week are rather concerning, with R seeming to increase and even heading >1 in some local areas. I’m not sure why this is, it could just be a temporary effect of the cold weather in 2nd week of Feb.
But let’s assume instead it’s a sign of the public gradually taking more risks, and reducing compliance with lockdown rules. So I’ve added a temporary boost to R over the next couple of months, which tapers out as the government controls relax (catching up with public behaviour)
This roughly counteracts the slower relaxation in the government plan, and pulls the “4th wave” peak back into the summer. (in reality I reckon seasonality would push this into autumn/winter, but it doesn’t really matter – we have to deal with it whenever it happens)
Counterintuitively, the final outcomes (i.e. total deaths and hospitalisations) are marginally improved. I think this is because by taking a bit more pain on the downswing from wave 3, we build up more immunity, so wave 4 when it comes is slower, with less overshoot beyond HIT.
3) So here comes the bit you’re really interested in: what does all the news on vaccine effectiveness today (from PHE, PHS and the SIREN study) all mean? I’ve spent a while staring at the data, and had to upgrade my model to handle age-stratified VE estimates, as follows:
Updated figures are in red: as you can see I’ve tweaked my assumption for first-dose VE vs. hospitalisation down to 80% in the older age groups (JCVI categories 1-4), in response to the PHE and PHS data. But I’ve left the second-dose figure at 93%, consistent with PHE.
In the younger age groups, I’ve left the first-dose protection vs. hospitalisation at 85% (consistent with PHS) but increased the second-dose assumption to 96% (from 93%), more in line with SIREN. And the net effect of these changes is: zilch. nada. rien.
The transmission assumptions are harder to pick: we now have decent data (from SIREN) suggesting Pfizer stops 70% of infections after 1st dose, and 85% after 2 doses. But AZ data is still sketchy, with suggestions of 67% after 1st dose, but some rather odd data for 2 doses.
So I welcome suggestions, but in the mean time I’ve gone for a compromise of 67% after dose 1, and 75% after dose 2, for all age groups other than the over-70s (which I’ve left at 50% and 65% as before). Still, this is enough to flatten the fourth wave:
In fact, on these assumptions we could get rid of the ongoing social-distancing controls in the second half of 2021, and still have an unpleasant but manageable fourth wave:
But we’re not out of the woods yet – that transmission assumption for AZ after 2 doses is still questionable, and we’re also dependent on other assumptions e.g. vaccine take-up in the under 50s. If that dropped to 60% for first dose and 50% for second, we’d get this:
...which is starting to look more challenging for the NHS again. So continued vigilance is required: however good the vaccines are, we still need very high take-up if we’re going to reach, or get close to, herd immunity without requiring a dangerous fourth wave. /end

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More from @JamesWard73

24 Feb
Since I updated my “vaccination & release” model last weekend, many people have asked me why it predicts a possible 4th wave, when so many people have already caught covid, or will have been vaccinated. To answer this it may be helpful to look at this chart: (thread) Image
This shows the proportion of the UK population that is immune either by having had covid (blue area), or by having been vaccinated (grey area, on top). In my model, the blue zone starts around 20% on Jan 1st (note this includes 2% who are infected with covid at that point) 2/n
This then grows to around 25% by the end of February, and stays flat around that level (i.e. not very many infections happening) until we remove the final set of controls in late June – note this is following the plan announced by Boris on Monday. 3/n
Read 25 tweets
23 Feb
Just updating the deviation graphs that I posted a few days ago: no major changes in trend, but good to see the cases in 75-79s continuing to head strongly downward under the influence of the vaccine, following the 80+: (short thread) Image
(to be clear on method, these figures are the cases in each age group expressed as a % of the total, and then indexed to 100% on 25th Jan so that we can see the deviation in each group on the same scale) 2/n
here's the chart for each of the subgroups within the 80+, you can see the 85-89s falling furthest, but the 90+ now working to catch up. 3/n Image
Read 7 tweets
21 Feb
Thank you for all the replies to my thread yesterday on the exit strategy from lockdown – lots of positive and constructive comments, and a few helpful suggestions for additional scenarios or varied assumptions. I’ve done some of the easy ones: (thread)
1)Higher vaccine effectiveness vs. hospitalisation and transmission, responding to the most recent Pfizer data from Israel
2)Lower vaccine take-up in the under-50s / non-vulnerable group
3)Higher starting immunity
4)Stronger seasonality effect
Please note that our starting point for all these sensitivities is my controlled scenario with a moderate “fourth wave” in the summer, based on an opening to near-normality at the end of May. (and yes, I know seasonality might squash that wave – we’ll come to that). Image
Read 20 tweets
21 Feb
OK, I think I’m ready to call this as a new trend in the 75-79s (cases as a % of total, for England). The timing fits for this to be a vaccine effect; we know vaccination of this group started in earnest from 25th Jan (although some may have had it slightly before then) 1/5 Image
and the deviation is from 9th Feb i.e. 15 days later. Data from Phase 3 trials and from Israel was suggesting a delay of 14-19 days, perhaps longer in the older age groups. This is the link showing over-70s started booking from Monday 25th Jan:… 2/5
And this NHS data shows that 83% of 75-79yos were vaccinated by Sunday 31st Jan – so we should be getting the full effect of that over the next few days of data.…
Read 6 tweets
20 Feb
Since the question of how far, and how fast, we can relax our current lockdown restrictions is very much current, I have updated my “vax & release” model with the latest data. And it’s mostly good news: we should be able to unlock faster than I previously thought, 1/8
returning to near-normality in June, without causing the many tens or hundreds of thousands of further deaths that were predicted by the Warwick and Imperial models. (I’m sure their modelling is much better than mine – it's just their assumptions are out-of-date). 2/8
However, there is a sting in the tail: there is still a strong risk of a further (fourth) wave in the summer or autumn, with maybe another ~20-30k deaths. To avoid that wave causing a peak of hospitalisations that would overwhelm the NHS, it may be necessary to continue 3/8
Read 9 tweets
31 Jan
I've had a lot of positive comments on this theory, and some helpful challenges. The most common of which was: surely a single-day effect wouldn't be big enough to cause the 'twist' in the data that we're seeing in those age groups? So I set out to find out if it was (thread)
This is one of those university / job interview 'order of magnitude' estimation problems. So feel free to disagree with any or all steps on my logic chain, and please explain why - it will help improve / refine (or falsify) the analysis.
So let's focus on the primary-school-age kids as that's where the effect is strongest. We have 3.5m 5-9 year-olds in England. I don't know how many were in school on 4th Jan - we know some regions (London / Kent etc.) didn't go back, and a lot of schools had INSET days etc
Read 19 tweets

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