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Coronavirus in the UK - news, lockdown and discussion

If you get it wrong one way you end up with a load of people whinging about how the scientists got it wrong. Get it wrong the other way and you end up with a lot of dead people.
If you get it completely wrong when there are others telling you you're completely wrong and telling you why you're completely wrong, which is what has happened with Ferguson and other modellers over omicron, at what point should people just stop listening to you? Various other epidemiologists at the time pointed out flaws in the modelling presented by Sage last month, including completely ignoring T-cell priming from vaccination/previous infections and assuming that omicron is just as deadly, which is why it was so at odds with the emerging picture from South Africa. They cherry-picked from the South Africa data, taking on board the bits about increased infectiousness but ignoring the bits about less severe disease.

The London School of Hygiene and Tropical Medicine predicted 25,000 more deaths by spring as its optimistic scenario unless we went beyond Plan B. Its pessimistic scenario was 75,000. So even at its most optimistic, it was predicting that omicron would kill more people in the UK than the delta variant has killed since May (around 20,000). Its most pessimistic scenario saw the worst wave of the pandemic yet. This stuff is used to justify the imposition of restrictions. It matters when it is badly wrong.
 
If you get it completely wrong when there are others telling you you're completely wrong and telling you why you're completely wrong, which is what has happened with Ferguson and other modellers over omicron, at what point should people just stop listening to you? Various other epidemiologists at the time pointed out flaws in the modelling presented by Sage last month, including completely ignoring T-cell priming from vaccination/previous infections and assuming that omicron is just as deadly, which is why it was so at odds with the emerging picture from South Africa. They cherry-picked from the South Africa data, taking on board the bits about increased infectiousness but ignoring the bits about less severe disease.

The London School of Hygiene and Tropical Medicine predicted 25,000 more deaths by spring as its optimistic scenario unless we went beyond Plan B. Its pessimistic scenario was 75,000. So even at its most optimistic, it was predicting that omicron would kill more people in the UK than the delta variant has killed since May (around 20,000). Its most pessimistic scenario saw the worst wave of the pandemic yet. This stuff is used to justify the imposition of restrictions. It matters when it is badly wrong.
Have Ferguson's models ever been published? I'd like to look at one.
 
I was out with two people on NYE and NYD that tested positive. I'm still negative.
Everyone I know in London has got sick. We are very lucky its not more closely related to MERS which is also a corona virus.

If you get it completely wrong when there are others telling you you're completely wrong and telling you why you're completely wrong, which is what has happened with Ferguson and other modellers over omicron, at what point should people just stop listening to you? Various other epidemiologists at the time pointed out flaws in the modelling presented by Sage last month, including completely ignoring T-cell priming from vaccination/previous infections and assuming that omicron is just as deadly, which is why it was so at odds with the emerging picture from South Africa. They cherry-picked from the South Africa data, taking on board the bits about increased infectiousness but ignoring the bits about less severe disease.

The London School of Hygiene and Tropical Medicine predicted 25,000 more deaths by spring as its optimistic scenario unless we went beyond Plan B. Its pessimistic scenario was 75,000. So even at its most optimistic, it was predicting that omicron would kill more people in the UK than the delta variant has killed since May (around 20,000). Its most pessimistic scenario saw the worst wave of the pandemic yet. This stuff is used to justify the imposition of restrictions. It matters when it is badly wrong.
Its just a computer model based upon the severity of Delta. I don't think a modeller can decide that a virus is more or less severe in advance of knowing the reality? It'd give a huge number of outcomes most of which would be wrong.

There has been a study in South Africa that has suggested Omicron is giving some protection against delta. This is a stroke of luck as it protects the unvaxiccnated against delta and omicron. Might be able to refuse a vaccine but I'm wondering how your virus refusal is going?

 
Forget the stats, look beyond them, everything is fine...


Triggle continues to provide a guide as to what the propaganda would have continued to look like in the first wave if the government had not been forced to abandon its original herd immunity plan.

But unlike that occasion, Triggle is able to use a lot of stuff that is arguably true in his articles this time around. He is relying on only a few dodgy aspects in order to push a particular agenda this time. And I wont bother to discuss those properly right now, rather I will return to them later when it becomes much clearer whether any of them hold up.
 
I wonder why that might be?
I will dwell on this point by the end of the week at the latest, once I've seen what happens next with Londons hospital data.

One thing that distorts almost all of the narratives about hospital data is that there is very little acknowledgement about the substantial role that hospital infections play in these figures. Admissions figures are not pure admissions figures, they are admissions/diagnoses, so they include people that came in for other reasons and happened to hae covid, but also people that catch it while in hospital. And I'm tracking that stuff as best I can, because some data on this is actually available, although its far from complete.

And Omicron has a lot of hospital transmission potential. But since we have vaccines that still hold up quite well, hopefully the worst implications will be much less substantial than they were in the first two waves.
 
A couple of interesting quotes here, with all the usual caveats, firstly from Hopson of NHS Providers saying that care home omicron outbreaks are not translating into hospital admissions, secondly from Neil Ferguson saying he's “cautiously optimistic” that cases are starting to plateau in London.


Ferguson is correct to highlight the ages up to 50 as being a key driver of the overall number of Covid cases. And I'd agree that a notable downward trend for these age groups has been seen in London for a while now. I posted a graph about this the other day. The older age groups show a different pattern of continuing rises so far, and they are a key group too, but hopefully vaccines will continue to have a big impact on the implications of that.

As for Hopsons remarks, NHS England does publish figures on Covid admissions/diagnoses from care homes. Its probably not capturing the whole picture, and it will invariably include people who were admitted from care homes for other reasons and then caught it in hospital. But here is the graph anyway, which does not cover the first wave since this data only became available from August 2020 onwards:

Screenshot 2022-01-04 at 12.41.jpg
Data comes from Statistics » COVID-19 Hospital Activity
 
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Yes omnicron has a lot of in-hospital transmission potential. But there is much more testing going on and better ways of separating people with COVID than in the horrendous early days. Plus improved treatments if people get it.

Plus IF it is intrinsically milder impact will be less.

Plus if there was a big problem with hospital squired omnicron wouldn’t that be showing up by now in London? In terms of use of ventilation etc
 
This is the most striking difference, I think. The number on ventilation beds has barely changed. It's up slightly in London (approx 240 from 200), but only slightly, and not at all elsewhere.

If even Neil Ferguson is saying this is different, we must be onto something. ;) Wonder what he's saying now about the apocalyptic forecasts he was giving two weeks ago? Why did he get it so wrong? (Again)
Do you have any links to examples of what he was saying some weeks ago?

He is Imperial College if I remember correctly, and unlike earlier waves I dont think I saw any Imperial modelling for the Omicron wave, at least not as part of the SAGE documents released so far. The modelling this time seemed to come from Warwick and the London School of Tropical Hygiene and Medicine. And as usual those modelling exercises contained a range of scenarios, which I will compare to what actually happened when the time comes.

But of important note is that these modelling exercises dont include behavioural changes that happen as a result of gloomy mood music and appeals from the government. They tend to model only the expected impact of formal measures introduced, so of course the reality tends to end up not being as bad, because people are not stupid and a lot of behavioural changes happen, some of which are the result of the gloomy modelling you seem to despise!

I've said it before and I'll say it again, I love self-defeating prophecies, they make a big difference to how bad the pandemic waves end up. They inevitably lead to bogus criticisms but plenty of people see through those false criticisms.
 
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Yes omnicron has a lot of in-hospital transmission potential. But there is much more testing going on and better ways of separating people with COVID than in the horrendous early days. Plus improved treatments if people get it.

Plus IF it is intrinsically milder impact will be less.

Plus if there was a big problem with hospital squired omnicron wouldn’t that be showing up by now in London? In terms of use of ventilation etc
Ventilation beds data shows hugely impressive impact of vaccines (and Omicron reduced severity) so far in this wave, its my main source of optimism so far, and I hope it continues to hold true.

The authorities understand the Omicron implications for hospital spread and they know it will be a big issue, despite testing etc. Largely due to the huge numbers infected int he community and bringing it in, and how much more transmissive Omicron seems to be.

There are many factors that make these things worse in some places than others. London seems to have had some advantages on this front in previous waves, and some places up north seem to have had especially bad and persistent issues with this.

Thanks to vaccines I expect a lower death burden as a result this time. Many of the implications are those that impact on broader hospital care issues, this stuff erodes capacity and affects staffing levels. Fingers crossed the wave passes through quite quickly and we dont get stuck with a high level of ongoing community infections once the peaks are well past, since what we really dont want to see is that situation dragging on for a long time.
 
If you get it completely wrong when there are others telling you you're completely wrong and telling you why you're completely wrong, which is what has happened with Ferguson and other modellers over omicron, at what point should people just stop listening to you? Various other epidemiologists at the time pointed out flaws in the modelling presented by Sage last month, including completely ignoring T-cell priming from vaccination/previous infections and assuming that omicron is just as deadly, which is why it was so at odds with the emerging picture from South Africa. They cherry-picked from the South Africa data, taking on board the bits about increased infectiousness but ignoring the bits about less severe disease.

The London School of Hygiene and Tropical Medicine predicted 25,000 more deaths by spring as its optimistic scenario unless we went beyond Plan B. Its pessimistic scenario was 75,000. So even at its most optimistic, it was predicting that omicron would kill more people in the UK than the delta variant has killed since May (around 20,000). Its most pessimistic scenario saw the worst wave of the pandemic yet. This stuff is used to justify the imposition of restrictions. It matters when it is badly wrong.

I understand the need for caution of course but this annoyed me. The relative young age of the South African populas was also sighted as meaning hospitlisation there couldn't be used to indicate what might happen here. Which is fair enough. But rarely if ever mentioned in the same discussion by those were the greater numbers of people with HIV in SA, with the implication for immune systems and the lower vaccination rates.
 
Modelling covers a range of scenarios and of course there is more opportunity to criticise the modelling when the media and people with an axe to grind insist on simplifying the modelling down to one central scenario.

I've found it extremely hard to discuss all the modelling properly without doing that, since my posts would end up nearly as long as the modelling documents themselves if I included all the detail. So I end up somewhere in the middle when it comes to getting into the detail.

But here is one example from the Warwick modelling in December. They considered a range of scenarios in terms of disease severity and escape from vaccines. I'm putting these charts in a spoilers tag because they will probably end up as a rather large image.

Screenshot 2022-01-04 at 13.04.43.png
From https://assets.publishing.service.g...99_S1441_Warwick_Omicron_for_release_v2.0.pdf

Its not their fault if people only focus on the worst-case modelling output! The worst case needs mentioning and taking into account by those that have to plan for every eventuality, but its hardly the only thing modelling brings to the table.
 
Have Ferguson's models ever been published? I'd like to look at one.
Imperial College modelling, of which plenty is available for past waves but not this current one (or at least I havent found any for current wave yet). I'll be very happy to get into loads of detail about this, but now is not a good time beyond what I've already said on this thread today. Could you remind me again in a few weeks? Maybe we need another dedicated thread for this topic. There has been at least one review of how well the initial modelling did, a necessary exercise because those with agendas have spewed vast amounts of misleading shit about this ever since.
 
There has been plenty of focus on London so far this time because they were first to experience the Omicron wave in full.

People have drawn attention to the relatively low vaccine uptake rates in London. Some portion of that is real, some portion of it is down to less accurate population figures for London, including people who left London during the pandemic or left at other times but remained registered there.

But there is apparently another factor which impacts both on those figures but also on how well London is coping in this wave. I have heard it said that London has a population which is younger than the average for the country as a whole. So I'm asking if anybody has a nice source of data on that, perhaps a nice visualisation of it?
 
I've said it before and I'll say it again, I love self-defeating prophecies, they make a big difference to how bad the pandemic waves end up. They inevitably lead to bogus criticisms but plenty of people see through those false criticisms.
I think the exemplar for this was the Y2K bug, where there were doom and gloom predictions of a lot of stuff failing, some over blown but a lot not. In the end nothing happened. So the fuckwits kick off in the papers saying what a load of scaremongering it all was; when in fact the reason nothing happened was that a lot of people spent a lot of time ensuring nothing happened and fixing code to correct the issue.
 
Hopefully someone will be able to come up with how many lives were saved through restrictions and vaccination...
There wont be a straightforward figure because of interplay between factors - for example if we hadnt had vaccines, we'd have ended up with far more restrictions in the UK in the Delta and Omicron waves.

Plus there are all the informal behavioural changes. eg Big impacts on the virus were seen for a period before formal lockdown in the first two UK waves, because people took matters into their own hands.

Studies have still been attempted that look at very specific waves and scenarios. For example there were estimates for how many less deaths there would have been if the first UK lockdown had been done a bit earlier, The differences were large. I dont have such studies to hand right now.

But one that I did notice recently involved comparing the approaches of Sweden, the UK and Denmark in the first wave, which also mentions timing:

We use two approaches to evaluate counterfactuals which transpose the transmission profile from one country onto another, in each country’s first wave from 13th March (when stringent interventions began) until 1st July 2020. UK mortality would have approximately doubled had Swedish policy been adopted, while Swedish mortality would have more than halved had Sweden adopted UK or Danish strategies. Danish policies were most effective, although differences between the UK and Denmark were significant for one counterfactual approach only. Our analysis shows that small changes in the timing or effectiveness of interventions have disproportionately large effects on total mortality within a rapidly growing epidemic.


For many months of the initial vaccination campaign, estimates were done by PHE involving how many deaths vaccines had already prevented, but again these are an oversimplification. The results were still a useful guide though, vaccines have made a huge difference both to illness and death but also to the alternative measures we'd have needed to impose had vaccines still not been available.
 
Hopefully someone will be able to come up with how many lives were saved through restrictions and vaccination...

Restrictions is a bit harder to ascertain , there was no control group. But I recall hearing a figure like 30,000 as an estimate for lives saved by vaccination in the UK. This was back towards the end of last summer. Apologies for lack of citation.
 
Restrictions is a bit harder to ascertain , there was no control group. But I recall hearing a figure like 30,000 as an estimate for lives saved by vaccination in the UK. This was back towards the end of last summer. Apologies for lack of citation.
I lost track of the official estimates once PHE turned into UKHSA, but I have now found some PHE figures from September:


The latest estimates indicate that the vaccination programme has directly averted over 230,800 hospitalisations. Analysis on the direct and indirect impact of the vaccination programme on infections and mortality, suggests the vaccination programme has prevented between 23.7 and 24.1 million infections and between 119,500 and 126,800 deaths.

But as I mentioned in my previous post, what would actually have happened in a no-vaccine scenario over that time period is that we'd have ended up with further restrictions to put a dent in those figures, since the system would not have coped with over 230,000 extra hospital admissions in that period, and peoples attitudes and behaviours would also have been different to what we actually managed.
 
Ah I found out what happened to such estimates later on. They stopped doing them for reasons I already mentioned:

UKHSA previously reported on the number of hospitalisations directly averted by vaccination. In total, around 261,500 hospitalisations have been prevented in those aged 45 years and over up to 19 September 2021.

UKHSA and University of Cambridge MRC Biostatistics Unit previously reported on the direct and indirect impact of the vaccination programme on infections and mortality. Estimates suggest that 127,500 deaths and 24,144,000 infections have been prevented as a result of the COVID- 19 vaccination programme, up to 24 September.

Neither of these models will be updated going forward. This is due to these models being unable to account for the interventions that would have been implemented in the absence of vaccination. Consequently, over time the state of the actual pandemic and the no-vaccination pandemic scenario have become increasingly less comparable. For further context surrounding this figure and for previous estimates, please see previous vaccine surveillance reports.

 
It regularly crosses my mind about what might have been if no vaccines had been forthcoming... very, very grim.
 
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