We were greeted by good news yesterday. A new UK population study from the University of Oxford, based on the ONS Infection Survey, shows that in fully vaccinated people asymptomatic infections were down 70% and symptomatic infections by 90%. The Telegraph has the story:
In the first large real-world study of the impact of vaccination on the general population, researchers found that the rollout is having a major impact on cutting both symptomatic and asymptomatic cases.
Sarah Walker, Professor of Medical Statistics and Epidemiology at Oxford and Chief Investigator on the Office for National Statistics COVID-19 Infection Survey, said that Britain had “moved from a pandemic to an endemic situation” where the virus is circulating at a low, largely controllable level in the community.
The new research, based on throat swabs from 373,402 people between December 1st last year and April 3rd, found three weeks after one dose of either the Pfizer or AstraZeneca jab, symptomatic infections fell by 74% and infections without symptoms by 57%.
By two doses, asymptomatic infections were down 70% and symptomatic by 90%.
But is it all as it seems? I wrote last week about vaccine studies that have glaring issues that everyone, including the authors, seem content to gloss over. Sadly, the same appears to be true of this study.
Here’s one of the key figures. Look at diagram A in the top left. The dots represent the infection rate in seven different groups of people defined by how long before or after vaccination they are and whether they’ve had Covid before.
It starts at the top with the group of people who are more than 21 days prior to being vaccinated and who haven’t had Covid before (and who may not have a vaccine booked or even be eligible yet for a vaccine). This group is the baseline so is given the value 1, and the number of infections in other groups are compared to this as a proportion. So the next group are those people who are less than 21 days before their first jab and who haven’t had Covid before, and they had 0.28 of the rate of infections that the first group had (once adjusted for various confounding factors such as location, age and sex).
This is the first oddity. Why do those less than three weeks before their first jab have around a quarter of the infections of those more than three weeks away from their jab? What is it about crossing that three-week threshold that has such a massive impact on infection risk, by far the biggest effect in the study?
The authors do offer a brief explanation, putting it down to “changes in behaviour due to either receiving the vaccination invitation letter or knowledge that individuals from their age or risk group are about to get vaccinated in their area”. But they offer no evidence of this mass change in behaviour triggered by the approach of the vaccination, and the vaccine invitation letter includes no advice to make any new effort to avoid people. In any case, it means the headline finding of the study should probably have been that being less than three weeks before your jab cuts infections by 72% – even more than being fully vaccinated!
Whatever the explanation (which I’ll return to below), this effectively gives us a new baseline for what we see next. Which is something that has become very familiar from Covid vaccine studies: the post-vaccine spike in infections. The infection rate rises to 0.38 in the first week after the first jab and then 0.45 in weeks two and three, a 61% jump above the pre-jab ‘baseline’. Yet this worrying phenomenon, which appears consistently in Covid vaccine studies, passes once more without mention. Why are researchers so uninterested in this?
We then see the infection rate drop until it hits 0.3 after the second dose, which would be encouraging were it not higher than the 0.28 pre-jab ‘baseline’.
Another point to note is that during the post-jab spike the proportion of symptomatic infections versus asymptomatic infections increases (look at the blue and yellow dots getting closer together 2-3 weeks post-jab in diagram C in figure 3, above). Since asymptomatic infection is associated with immunity (see the blue and yellow dots further apart for the fully-vaccinated and post-infection categories), this is corroborating evidence that the mechanism causing the spike may be a depression in immunity, possibly caused by the reduction in white blood cells post-jab observed in both the Pfizer and AstraZeneca trials.
Why is the infection rate in the more-than-21-days pre-jab group so much greater (nearly four times higher) than in the less-than-21-days pre-jab group? The authors propose (without evidence) mass behavioural change, but I’d suggest it’s more likely to do with when the tests were done. The period covered by the study is December 1st to April 3rd. In that period vaccinations increased by about the same amount each day and the halfway point was around February 14th. This means the less-than-21-days pre-jab group came on average from much later in the period than the more-than-21-days pre-jab group, as most of the jabs occurred after mid-February. This is significant since according to the ONS (below) infections declined very quickly in the first half of February so that any group weighted towards the latter half of the study period would have a much lower infection rate than one weighted to the earlier part.
Another possibility is that it was to do with age, as the vaccinated, and hence the less-than-21-days pre-jab group, were mostly over-60s, which was a low prevalence age bracket in this period. It may be both.
The authors control for a number of confounding factors, including age, location and high-exposure occupation (e.g. patient-facing health care worker), so in theory they should have eliminated many of these biases. However, presumably not all of them, as something must explain the 21-day threshold drop.
A further confusion is where the claim in the Telegraph article that “by two doses, asymptomatic infections were down 70%” comes from, as the post-second dose asymptomatic dot (the blue one) is 0.51 or 49% down, not 70%. Perhaps the 70% was taken from the drop in overall infections for the fully vaccinated. But if so this is, at best, sloppy reporting.
The 0.51 figure for asymptomatic infections for the fully vaccinated is relatively high, and is identical to the figure for the asymptomatic infection rate among those who have had Covid before. It most likely reflects immunity, as asymptomatic infection is typical of the immune system working (as is infection with low viral load, seen in diagram B in figure 3 to be more common in the fully vaccinated and previously infected). But those who believe in the myth of asymptomatic transmission will likely worry about this.
As before, I’m not trying to suggest that the vaccines don’t work. We know they are effective at increasing antibody prevalence and this must presumably have a significant impact on a person’s level of immunity. But this study is not a good example of how to show that they work. It has the oddity of the 21-day threshold drop in infections, and appears to show the vaccines being less effective than being in the pre-jab group. This is likely because it doesn’t differentiate the vaccine effect from the drop in infections that occurred anyway in January and February. It also has, once again, the worrying post-jab infection spike, that we are still waiting for anyone in a position of authority or influence to acknowledge, let alone investigate.
In the meantime, we’re left wondering. How much of the pattern of Covid surges coinciding with vaccine rollouts in various countries is occurring despite the vaccination programme, and how much because of it? Until the question is properly investigated – which will mean governments releasing data on the vaccination status of all who have died of all causes – the troubling questions will remain.
To join in with the discussion please make a donation to The Daily Sceptic.
Profanity and abuse will be removed and may lead to a permanent ban.
I suppose they didn’t want to discuss the discrepancy between the two pre-vaccinated groups because it highlights the large drop in infections that occurred without any intervention by vaccines.
>the discrepancy between the two pre-vaccinated groups
>because it highlights the large drop in infections that
>occurred without any intervention by vaccines.
No! Use your head! No discrepancy exists, merely a difference, since they are different types of people.
The first group are mostly not eligible for the vaccine at all, hence are far younger, and mingling or working.
The second group are eligible for vaccines in the near future, hence they must be elderly,retired, shielding, less risk of catching covid, as they are not not mingling.
How much are you getting paid?
£693, universities superannuation fund.
That’s almost as ridiculous as Fauci’s recent explanation for the declining cases in non-lockdown states while case sin lockdown states were increasing.
Keep it up and you’ll soon make Chief Medical Officer. Whitty can’t have many more years left before retirement.
Yes – Whitty retired his brain some while back
Correction: Whitty retired his integrity some while back.
And South Africa has herd immunity without any vaccinations.
fon doesn’t do science and evidence – just religion.
sell your balls and buy some brains.
> Why do those less than three weeks before their first jab
> have around a quarter of the infections of those more than three weeks away from their jab?
It’s obvious. those less than three weeks before their first jab are elderly retired people who are shielding, hence lower chance. While those more than three weeks away from their jab are a random selection, younger mostly workers, mingling , may never have a jab, hence have a much larger chance of getting covid19.
In figure 5 in the study, they do separate out the 75+ age group and it shows the same pattern for them, with a small spike following vaccination and no difference between the 3-weeks-before group and the post-second-dose group. The main confounder in the study, as pointed out in the article above, is likely to be the massive variation in the background infection rate during the study period – which in principle might even be responsible for the “small spike” effect, although that effect has been noted in other studies, and in this study that same pattern was seen in the older and younger groups, which would have been treated at different average times during the study period, suggesting that it’s a real effect. Beyond that, because of the huge variation in background infection rates during the period, I find it difficult to know what to make of the numbers.
I would agree. If the variation is so huge that a study of this magnitude produces inexplicable and contradictory results, then it’s hard to draw any clear conclusions from it.
Essentially there’s not enough virus circulating to make sense. Nor enough virus circulating to necessitate a mass vaccination campaign.
Looking again at the study, the second half of page 7 says they did adjust for calendar time and age, although if anything that also strengthens Will’s points about the small spike and the lack of difference between the 3-weeks-before and the post-second-dose groups.
It’s slightly interesting. The first group, miles away from vaccine day, are hence automatically more likely to be younger, working and mingling more.
The second group, near to vaccine day, are hence automatically more likely to be older, retired, not mingling much. That accounts for the difference, they are different types of people. your analysis was simplistic/superficial.
Over the population, older people have a greater chance of getting infected than younger people. Absent a time-based reduction in infections, the top data point would be to the left of the one below, not far to the right.
My theory for what it is worth relates to socialising. The older population particularly 70s and over do not mix as much as the younger generation thereby reducing the effectiveness of their immune systems. Lockdowns only added to the problem and mask wearing too. Everything this government has done has damaged our immunity to disease. The long term effects will not be known for a while but truth will be revealed over time by which time this globalist puppet and his henchmen will be living a very comfortable life someplace else after wrecking ours.
> Over the population, older people have a greater chance of getting infected
> than younger people.
Without lockdown that would be true. Point of restrictions is to keep older people from mingling.
But the top line group were by and large younger, since they did not get vaccinated, their chance of getting covid19 was deemed to be normal/baseline, i.e 1.
while those in the second group were in due course vaccinated, and hence were on average older, not working or mingling. They were locked down, not going to work or not going out much, reducing the chance of illness. That was the point of lockdown, to protect the vulnerable.
> Absent a time-based reduction in infections, the top data point woudl be to the left of the one below, not far to the right.
That was the mystery, I’ve explained it all.
But it is not to the left, hence the older lockdown (not working or mingling) group had lower chance of illness due to lockdown.Yet more evidence on how effective lockdown was at that time.
I thought the point of restrictions was to keep EVERYONE from mingling?
If you are correct – and you may well be – then surely it’s a gross misrepresentation of the data to say that the vaccines reduced infections by 70-90%?
Or put another way, yet again they are lying to us.
Excellent point.
Not so much ‘lying’ as obfuscating by using relative rather than absolute risk reduction figures.
It’s the oldest PR trick in the statistical book – but even most medical professionals don’t get it.
It’s worth taking a bit of time out to get your head around this scientific three card trick.
I know what you mean – but I’m online only briefly, and am thus tempted towards shorter succinct words …
yes, I am correct .Thanks. In the first group, it is a random selection not destined to get vaccinated, so they are doing the background rate of mingling, hence they have the baseline chance of getting the virus, designated here as 1.
The second group,destined to soon get vaccinated, are mingling less (older on average, more retired on average) Not mingling, more careful, that all reduces your chance of getting the virus, hence ~0.25 of the chance. But the vaccine reduces the chance of getting the virus for everybody who gets it.
They are not lying to say vaccine reduces chance, the design of the trial was random. To eliminate bias, everybody had the vaccine, and they were only counted in the results after they caught covid19. They waited for that event, before stopping the trial. Hence no bias.
results here show not mingling (i.e lockdowns) and vaccines work to reduce chance.
There is of course another group not shown on the chart, sceptics, who are in general too thick and dumb to understand reasoning.
the vaccine reduces the chance of getting the virus for everybody who gets it
How would you explain figure 5 in the study, which appears to show that for people aged 75+, the risk increases in the short term after vaccination and later returns to the level of risk just before vaccination?
behaviour
I’m not saying you are correct – just that you may be, and that what you’ve put forward concerning the make up of the groups stands as a relevant point until shown otherwise.
However, I remain unconvinced that the differences in infection between the first and second groups is entirely explained by differences in behaviour – part may be, but not entirely. Empirical evidence has shown, beyond reasonable doubt surely , that lockdowns don’t work very well in preventing infection. And if they don’t prevent infection, then your explanation based on relative mingling can’t be completely correct – part maybe, but not completely.
And then why don’t the vaccinated group show a relative decrease in infection rate, rather than the consistent 0.26 to 0.4 (as an outlier) shown in the graph? An answer might be that they suddenly start mingling extensively again. But by that point we are far into the realms of selective speculation, which goes against much anecdotal evidence of many vaccinated people not feeling safe to mix yet.
If they are mixing up control groups and then selectively presenting the data, to show what they want, then they are being misleading – deliberately misleading. A shorter word for which is lying.
Your final comment: There is of course another group not shown on the chart, sceptics, who are in general too thick and dumb to understand reasoning.
That is uncalled for. I’ve tried to have a reasonable conversation concerning this data, and conceded that you may have a point, or part point, while explaining that I don’t follow your whole argument.
If someone can present me with data showing that the vaccines are effective, then that’s fine. But in his article Will asks some very pertinent questions, which are deserving of civilised debate and, if possible, answers.
It’s all obvious, if you use your head.There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) were not scheduled for vaccination, hence were not vulnerable to die if they got covid.
The rest were scheduled (or had had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, i.e. the people scheduled for but not yet vaccinated. I break it all down at the end, see below, full explanation for every data point.
He just knocked down your theory, Fon, It was an interesting guess before that.
It’s all obvious, if you use your head.There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) were not scheduled for vaccination, hence were not vulnerable to die if they got covid.
The rest were scheduled (or had had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, i.e. the people scheduled for but not yet vaccinated.
there must be some difference between the first cohort and the second cohort.
There are only two forms of protection, lockdown and vaccine.
The difference was not vaccine. take a wild guess what is left?
“The second group, near to vaccine day, are hence automatically more likely to be older, retired, not mingling much. That accounts for the difference, “
Not true. At least half the people in the first 5 priority groups are working age (care home workers and health and social care workers).
NO!
the headline is wrong and highly misleading. Shame on you.
The Study does not Confirm a Spike in Infections Following Vaccination.
The infection rates in all the plots following vaccination is somewhat lower than the plot for No Vaccination. It just takes time after vaccine to become far lower.
it’s more of a pimple than a spike.
I am normally pretty good at quickly deducing the implications of statistics and charts and making a critical assessment of how they have been interpreted.
The government has very kindly given us all plenty of opportunity to hone that critical assessment skill over the last year!
I was busy yesterday so only had time to have a brief look at the report (in a link to a Daily Mail piece?) and found it quite hard to understand what was being presented.
Therefore my critical assessment was here is some complex information being presented in an obscure way – leading to the conclusion that the official interpretation would be subjective and used to provide a misleading headline message which most people will take on board without questioning.
That approach has worked on some of the very simple reports, so will work in spades on something more complex.
The good news is that it does provide some statistics that can now be examined objectively by those with an open mind.
The whole study is totally meaningless and flawed.
Asymptomatic ‘infections’, really? Bogus positive results mean nothing.
Especially with the reduction in the PCR cycles. (Didn’t the WHO conveniently make some sort of announcement that the PCR test cycles should be capped at 25 or something), wouldn’t that alone make any stats a bit suspect.
Yes – any PCR result obtained by cycles >25 are garbage in determining real infection rates.
… and if an experimenter ignores this crucial variable … you can draw your own conclusion re. the validity of the research.
The whole Covid1984 agenda is built on scientific fraud. Intro to Dr Stefan Lanka:
“In November 2011, virologist Stefan Lanka guaranteed a prize of €100,000 for proof of the existence of the measles virus, specifically the determination of its diameter.[3] Lanka claims the measles are basically a skin irritation caused by a mixture of psychosomatic triggers and poisoning.[4] Bardens contacted Lanka on 30 January 2012 for confirmation of the contest and eventually provided six publications as an answer to the questions.
Legal procedures began on 29 September 2013, when Lanka demonstrated that the papers were not suitable as proof. A first court session took place in April 2014 and ended with the court’s decision to halt procedures, to support its judgment with the scientific expertise of Andreas Podbielsky, virologist at the University of Rostock. The case was continued in March 2015 and ended with the decision in support of Bardens’ claims.[5]
The court’s ruling received global press attention in light of the present, heavy campaigning of anti-vaccination protesters on web forums and in books. Bardens told the press weeks after the court’s ruling that he could no longer appear as a public speaker without the protection of bodyguards.[6]
Lanka eventually challenged the judgment.[7] The case was re-evaluated at the Higher Regional Court (Oberlandesgericht) Stuttgart on 16 February 2016, where the original judgment was reversed. Six publications were submitted purporting to show the existence of measles, however they failed to meet the contest requirements as set by Lanka.[1] Bardens commented on the case in an extensive interview on 5 May 2016 and announced his decision to appeal to Germany’s Federal Court BGH. In December 2016, Bardens tried to get this ruling revised, but the court saw no reason to do so.”
So Dr Stefan Lanka is a REAL SCIENTIST.
Here is what he has to say about the scientific house of cards that the Coronavirus Fraud is built on:
Project Immanuel – Announcement
https://lbry.tv/@Projekt-Immanuel:3/Announcement_Eng:6
Compare UK to South Africa , who also had a huge Dec/Jan spike. The infection curve looks exactly the same with a 95% (UK ) and 94% (SA) drop-off in positive tests, hospitalization and deaths in February. This is with the scary SA variant!. SA’s vaccination program started on 17 Feb and so far, a few thousand healthcare workers have been vaccinated.
The drop on infections in the UK probably has nothing to do with the vaccine program
Infections peaked here in the UK around 3rd January and the vaccination programme didn’t seem to get going on any scale until mid February. Is the decline not just the natural wave of a winter virus?
As many have been saying for months.
https://twitter.com/Humble_Analysis/status/1385352168228589570
Cases for Israel, the UK, Portugal, and South Africa all peaked in January and declined 90%+ since; at peak Israel had vaccinated 24%, UK 3%, Portugal 2%, South Africa 0%. Current rates are 62%, 48%, 20%, and 0%, respectively. Can you guess which curve belongs to which nation?
Answer Green = Israel Blue = Portugal Red = South Africa Pink = UK
Well done! Yes , being nearer, I have concentrated on Portugal, and I think there is a very good case to be made that vaccines have had minimal effect on the drop in ‘cases’. Two effects are far more significant. Drop in PCR CT values used in tests , and natural virus evolution/seasonality through populations ( human and other mammals).
It’s not the first time that this sort of study has tried this sort of trickery and relied on mainstream media to parrot their ‘conclusions’.
I remember this earlier study that came out in the beginning of March looking at the effectiveness of English experimental vaccination (Pfizer and AZ)
https://www.medrxiv.org/content/10.1101/2021.03.01.21252652v1
From my first glance at that study they ignored the 48% higher covid positive rate in the vaccinated over 80s group in the 9 days following vaccination vs the unvaccinated group. They made the sweeping claim that this first 9 day effect was entirely due to the vaccinated group being at greater risk of testing positive than the unvaccinated group. And so they assumed that from 9 days onwards the vaccinated group would be expected to produce 48% more positives from day 9 than the unvaccinated group if the vaccine was completely ineffective.
A more plausible starting assumption would be that the vaccinated group and unvaccinated group were similar in terms of risk of testing positive. Certainly experience across a number of countries since then of infection spikes after first vaccination, point strongly to the 48% including a significant if not predominant real affect of vaccination not a difference between the vaccinated and unvaccinated populations.
Firstly this would mean in this old study that they have ignored the detrimental effect of the vaccine in the first 9 days in the over 80s causing 48% more positives than the unvaccinated.
And secondly in that old study they seriously overstated the effectiveness after 9 days by claiming a non existent 48% benefit derived from their dodgy assumption that the vaccinated group was more vulnerable to covid before starting the comparison. So it would only be any effect above 48% (and there was a bit of a seemingly positive effect in the long term vs the negative effect in the short term) that would be a real effect, but the first 48% of this long term effect was entirely manufactured out of thin air.
I never got round to looking at that old study in detail, and I never saw anyone do a proper analysis. So I don’t know if overall there was any possible benefit of the vaccines in terms at looking at the total area under the death curve from date of first vaccination. And just based on short term adverse reactions we know there are significant harms from the vaccines, and the long term harms are of course currently unknowable.
But just that glance at the old study started the alarm bells ringing. And it’s why we shouldn’t take these studies at face value but should objectively analyse them.
A big thanks to Will for looking at this latest study in detail. It takes a lot of effort to do this sort of analysis. This is not easy to do journalism, but it is proper investigative journalism that Lockdown Sceptics can be proud of. So while I’ll keep an open mind until I’ve read the latest study, from my previous look at these sort of studies I would expect what Will is saying to be substantively correct.
Yes, very well done to Will – great effort, excellent measured journalism. It’s amazing how he finds the time and mental energy to keep producing articles such as this.
To quote the villains from Scooby Do, I can imagine the authors of the paper saying
“We would have gotten away with it, if it weren’t for that meddling Will Jones”
Looks like they are keeping the ‘experimental stage’ , pure, ie what happens, happens, we’ll collate the data in 2023
Yes. Thanks, Will. As a non mathematician, I had no idea till this began that it is as easy to lie with statistics as with words, And more effectively, since most assume that since 2+2=4, presentations based on maths must be truth. This explains why so many are still living in terror.
Got the hang of it now, though.
Page 11 of the study addresses the <21 days group and gives a possible explanation for the lower figure. Since the crux of your argument is that this lower figure should be the true baseline and that the authors didn’t address it, perhaps you should delete this whole article?
Could this simply be a case of vulnerable people coming out of isolation at the peak of an infection and catching the virus along the way?
Plausible thought, but It happens in nursing homes where they are very controlled too.
Have spoken to a few older people who have no underlying health conditions and they have been laid low by the second jab and confined to bed for 3-4 days after
Lets hope they aren’t laid to rest if they encounter the wild virus when it re-emerges in the autumn.
The ‘minor’ (and probably unreported) side effects like this seem to be massively higher than for most medicines.
https://www.ukcolumn.org/article/covid-injections-tip-spear-global-cities-militant-pursuit-equity, a long read but interesting to see what Boris Johnson, who appears to still have his liberal leanings is up against and why if he doesnt comply they will find things to make him comply.
Bozo and liberal leanings: Hahaha!
Yes, not seen much sign of those recently. If he has any, which I strongly doubt, they are far outweighed by his cowardice.
All this study shows is that if you have the vaccine, you are more likely to test positive for covid after you have the vaccine than you were in the 3 weeks prior to vaccination. Given that most vaccines have been delivered after the so called cases peaked, and hence you would expect prevaccination case rates to be higher for most people tested, this is a very concerning finding, suggesting that the vaccines are actually worse than useless, especially when you take all the adverse vaccine events into consideration.
there is some difference between the top cohort and the second cohort, with lower risk.
There are only two forms of protection, lockdown and vaccine.The difference was not vaccine (none had it in the top two cohorts). Take a wild guess what is left that might have reduced risk in the second cohort? Might it be lockdown?
To be fair UK Column were reporting this back in February. They also did an interview with an NHS insider who confirmed that the vaccine suppresses the immune system for approx 9 days after so the people who have received are even more susceptible to catching it if they are in the target demographic.
As many of them were already in the care system or in hospital they were particularly vulnerable as it was prevalent in those places especially hospital.
They reported that this was the reason for the spike in infections as the vaccine rollout began and was correlated by similar observations in Italy and S. Africa among others.
It was also noted that places in receipt of the AZ jab these were places linked to the new variant strains. Something I don’t think as pointed out.
I don’t have a link for the show perhaps check their website?
The people who take part in the ONS study are in the general community not care homes or hospitals.
Surely the mystery is explained as a selection effect.
Enough people who become ill in the 3 week run up to their injection do not then go on to have the injection and so they drop out of the second group, which is then left only with the non-sufferers by definition.
Looked at another way, people who are currently ill with CoVid don’t go and get the injection, at least until they are feeling better.
And when they get better they don’t need the injection (not vaccinated, previously positive) in the headline graph.
No need to resort to complex answers. IIt’s all obvious, if you use your head.There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) were not scheduled for vaccination, hence were not vulnerable to die if they got covid.
The rest were scheduled (or had had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, i.e. the people scheduled for but not yet vaccinated.
The study covers only the general public and as such excludes hospital and care home residents. We know, however that these two account for the vast majority of all cases.
We also know that the testing is via PCR and the study states that “we do not know the true sensitivity and specificity of the test”; the outcome of the PCR has been questioned more as prevalence of the disease diminishes (it was designed to test symptomatic patients, not random asymptomatic members of the public) and the prevalence is considerably lower when you exclude nosocomial infections.
I am also suspicious that they headline an “odds ratio” which is a metric I have never seen used before in this context.
As a consequence I would be careful drawing any conclusion from the study.
“As a consequence I would be careful drawing any conclusion from the study.”
Indeed. The data collection, measures, methods generally used during this madness have been so sloppy, inconsistent or deliberately obfuscated as to be close to useless in enabling any useful conclusions to be drawn that might inform public health and spending decisions. A massive failure of honesty and execution on the part of the UK govt and other bodies here and elsewhere.
I know someone who is taking part in this (it is a nice little earner for him, since they are paid to take part).They swab themselves and return the sample to someone who turns up at the door with the kit and waits for the test to be completed (no quality control as it is done whilst the collector waits outside on the doorstep).
He ended up with a positive result and subsequently had an antibody test which eventually (after some weeks and 10 days isolation) showed he wasn’t really positive.
I have both had Covid and had both shots of AZ >21 days ago. I recently did an antibody test for Biobank and had no antibodies. So I am not sure you can draw the conclusion that the test was wrong.
there is some difference between the top cohort and the second (lower risk)cohort. There are only two forms of protection, lockdown and vaccine.
The difference was not vaccine (neither cohort had any). take a wild guess what is left, might it be lockdown?
The ONS have started to use this type of analysis all over their reporting. Allied to the fact that many of their ‘data’ points are now modelled because they were unable to keep enough participants, and I think they can prove whatever the customer wants. Isn’t that what their new leader has said what people want? Things that are inclusive and mean something to the customer. Facts? Pah who wants them any more?
Shame, the ONS was such a good source of neutral information during lockdown 1. and lockdown lite (last summer). I wonder when they were got at ?
Page 11 of the report offers an explanation for the <21 days pre- group. So your assertion that the authors ignored it is false. Would you like to withdraw the article?
“The reduced risk observed in the 21 days prior and 0-7 days after vaccination is likely due to this reverse causality, specifically changes in behaviour due to either receiving the vaccination invitation letter or knowledge that individuals from their age or risk group are about to get vaccinated in their area, rather than a biological effect.”
A piece of speculation with no data to back it up; not an explanation.
The assertion in the article is that:
“You would have thought that the authors would be keen to explain or at least discuss this curious data point, particularly as it gives the pre-vaccinated group the lowest infection rate of all the groups. But not a word.”
Which is demonstrably false. Since the article’s focus is to rubbish the report for ignoring this data point and drawing false conclusions, I would expect the author to have read the report in full and honestly reported what it contained.
It’s not a discussion. It’s a piece of speculation. A discussion would bring up numerous possible explanations.
Do you accept that “not a word” is a false representation of the report?
No need I explain it all. There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) were not scheduled for vaccination, hence were not vulnerable to die if they got covid. The rest were scheduled (or have had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, i.e.the people scheduled for but not yet vaccinated. See below. I break it all down fully. Use your head.
The comment you mentionin the report does not explain or discuss it just speculates and dismisses. This is not science.
Do you accept that “not a word” is a false representation of the report?
I think I accept you are scambling around in the dirt trying deperately to justify your comments.
Ah. the ad hominem attack. Didn’t take long.
‘scramling around in the dirt’ is a description of what you are doing, not you as a person ( well at least it wasn’t meant to be, unless you do regularly scramble around in the dirt, literally?) . So the use of the term ‘ad hominem’ is incorrect. Just like you original comment.
Impugning the motives of the writer instead of addressing the question asked is an ad hominem attack.
I have correctly pointed out that the writer of this piece has based his entire argument on an assertion which is incorrect (that the authors did not comment on the <21 pre- data point). He has gone on to use that assertion to cast doubt on the findings and indeed to reinterpret the findings as a post-vaccination spike compared to the <21 day pre- baseline instead of as clear proof of the efficacy of vaccines.
The baseline group used in the paper (>21 days pre-vaccination) is six times larger than the <21 days pre- group. If both these were combined, then the vaccine effect size would be less than shown but would still be there. By isolating the <21 pre- group the authors have sought to remove a group who, due to timings of the testing, possible changes in behaviour or selection bias, could skew the results. A suitable scientific response to that would be to ask the authors for further commentary during peer review. Not to misrepresent the paper as a “gotcha” piece of journalism.
There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) wwere not scheduled for vaccination, hence were not vulnerable to die if they got covid. The rest wwere scheduled (or have had vaccine) hence they were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, the people scheduled for but not yet vaccinated. See below. I break it all down fully.
It’s all obvious, if you use your head.There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) were not scheduled for vaccination, hence were not vulnerable to die if they got covid.
The rest were scheduled (or had had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, i.e. the people scheduled for but not yet vaccinated.
So the actual vaccination had no effect?
Apparently, the notification to receive the vaccine explains all of the risk reduction.
What utter crap.
Thanks ste eB – good spot. You’re right – they do offer a brief explanation that I’d managed to miss when reading it last night. I have amended the article to reflect this.
Well done Will. Thank you.
Well – that spike is quite likely to be connected with the ‘vaccine’ – which is one of the reasons why I’ve always rubbished the assertion that the vulnerable should engage in this ill-formed experiment.That said, it is necessary to counteract the blithe confidence that this research is good enough to be definitive – and that goes for confirmation bias both ways.
To counteract the obvious statistical legerdemain and just wool-over-the-eyes response that depends upon popular statistical illiteracy :
In terms of proper science – using Popper’s accepted process of hypothesis falsification, there are far too many holes to accept the hypothesis that the ‘vaccines’ provide significant protection. The research is definitely – consciously or unconsciously – propagandised by confirmation bias.
Remember – ‘Science’ means that hypotheses concerning effects have to be clearly established in terms of probabilities – a sadly lacking characteristic in the Covid debacle. Thus :
The null hypothesis stands – that the vaccines do not provide significant overall protection in the real world.
Excellently put!
The spike/pimple is due to people relaxing, once vaccinated. There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) were not scheduled for vaccination, hence were not vulnerable to die if they get covid. The rest were scheduled (or had had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, i.e.the people scheduled for but not yet vaccinated. See below. I break it all down fully, but you are too thick to understand.
there is some difference between the top cohort and the second (lower risk)cohort. There are only two forms of protection, lockdown and vaccine.
The difference is not vaccine (neither cohort had any). take a wild guess what is left, might it be lockdown?
The American Thinker has a pretty big national audience. I appreciate that this news site just published my column wherein I argue that statistics are probably all that is required to make a decision on whether one should or should not get a vaccine. Thanks for clicking on this article – which might help more “contrarian” COVID pieces get published in the future.
https://www.americanthinker.com/articles/2021/04/how_to_make_a_covid_vaccination_decision.html
P8 of study: “Results will be communicated to relevant communities through news media”. So, this is how they communicate their dodgy results, through the even more dodgy media manipulators and propagandists who they work hand in glove with. Disgusting. It would be a lot more informative to just follow the money.
there is some difference between the top cohort and the second (lower risk)cohort. There are only two forms of protection, lockdown and vaccine.
The difference was not vaccine (neither cohort had any). take a wild guess what is left, might it be lockdown?
I wonder if Peter Hitchens has his second ‘vaccine’ appointment booked yet? Surely that will allow him to travel again?
</sarc>
The study is fundamentally flawed because it provides no a priori explanation for excluding the test results of people in the 21 days before first vaccination. In excluding these results from the unvaccinated group against which all other groups are compared, any differences between vaccinated and unvaccinated will be biased towards larger effect sizes. The explanation for excluding the results of participants tested within 21 days before being vaccinated is only provided in the discussion, and as noted in comments below, is based on an unevidenced assumption that people changed their behaviour before being vaccinated to reduce their risk of infection. It’s clear they’ve removed this inconvenient set of data from the main comparison group as it doesn’t fit the “vaccines are super duper effective” narrative. This is incredibly poor science, but I’m afraid to say that this is the standard we’re getting now on a regular basis. Garbage in, garbage out.
The >21 pre- group is six times the size of the <21 pre- group so including them all in the baseline would, presumably, move the baseline “left” a bit and reduce the effect size shown but would not support the post-vaccine spike that the author proposes,. Would it?
There ARE 2 separate groups in Plot A, the normal risk people (top blob in plot A) are not scheduled for vaccination, hence were not vulnerable to die if they get covid. The rest are scheduled (or have had vaccine) hence were vulnerable or they would not have had vaccine, Plot A proves without doubt lockdown worked to protect vulnerable, the people scheduled for but not yet vaccinated. See below. I break it all down fully.
You have not addressed the fundamental flaw with the paper, and hence the invalidity of it’s results and conclusions, that I pointed out above: a failure to justify in the introduction or method section why tests conducted within 21 days before participants were vaccinated were to be removed from the control data for the unvaccinated group.
There is a very encouraging presentation from the US on Pfizer and Moderna:
https://www.youtube.com/watch?v=sIe6wvVoJvg
What isn’t clear (to me) from the video is how representative the unvaccinated and vaccinated groups are of each other, and whether or not this means results might be skewed by a seasonal decline in infections being attributed to the vaccines.
Of course there ARE 2 separate groups , the normal risk people (top blob in plot A) are not scheduled for vaccination, hence were not vulnerable to die if they get covid. The rest are scheduled (or have had vaccine) hence are vulnerable or they would not have had vaccine, It proves without doubt lockdown worked to protect vilnerable, the people scheduled for but not yet vaccinated. See below. I break it all down fully.
For the sake of discussion, if you vaccinate 10 million people at around the seasonal peak in infections, then that’s about 10 million possible transmission events that would not otherwise have occurred. With the best will and PPE in the world, I can’t see how the vaccination programme itself can’t have caused a rise in infections. I would be suspicious if it wasn’t there….
someone may have already answered this – but at a glance…
all the values for vaccinated, about to be vaccinated etc are about the same
the outstanding value is people who havent been vaccinated and arent going to be soon
to me this looks like 2 separate groups – young people and old people
young people are doing a lot of mixing and have higher prevalence
old people do less mixing and have had or are soon going to have a vaccine – and the vaccine hasn’t any effect
I agree – I think it looks like 2 different “populations” but in the study paper it differentiates between over 75s & under 75s and the dramatic reduction is still apparent.
Also, I don’t think the ONS survey, at any point, shows such a big difference in incidence across the ages.
>I think it looks like 2 different “populations”
It is 2 poulation, since those not scheduked to be vaccinaqted (the top blob) are, by defintion not vulnerable, hence different age group, see below for full details, on this, I’m very clearely absolutely correct, and I prove it.
Of course there ARE 2 separate groups , the normal risk people (top blob in plot A)are not scheduled for vaccination, hence not vulnerable to die if they get covid. The rest are scheduled (or have had vaccine) hence are vulnerable or they would not have had vaccine, It proves without doubt lockdown works to protect vilnerable, the people scheduled for but not yet vaccinated. See beloww. I break it all down fully.
Using Plot (A), I’ll demonstrate how sceptics have been comprehensively blown up by thier own petard! Gird your loins, you are not going to enjoy this!
Let me make it crystal clear what plot(A) i means. It’s a complicated plot, and it has a lot of missing data that must be inferred, hence I’ll skip some detail, so that the intelligent amongst you can fill it out on autopilot as it were.
Anyway, about the plot: the x axis, going from left to right,indicates increasing risk.The blobs, represent different groups of people and the risk a person it that group has is indicated by the blob’s position along the x axis.
Hence taking the first blob in the top right corner. This group has a normal risk of having covid19, hence it is given the value 1 (normal). This group consists of people who were never due to be vaccinated and never had the illness and hence are normal everyday people not particularly old, or vulnerable with normal risk.
All the other blobs, nearer the left, are having the vaccine in the near future (or already have) and by their position leftwards on the x axis, they’ve a lower than average chance of having covid19. For those that have had the jab, it may be assumed the vaccine lowered their risk and (for the bottom blob) their risk is very low since they have already had covid19.
But the second blob down is particularly interesting, since those people are soon to be vaccinated, yet their risk of having covid19 is already lower than the ‘normal’ group, even though they have not yet had a vaccine. It is interesting since it’s hard to account for the lower risk. Clearly, there is something different about the people in the second blob down compared to the top blob.
The only difference we can tell from the plot is that the second blob down is scheduled for vaccine, and is hence and, we assume, more likely to be older, retired, or otherwise vulnerable, if not, they would not be scheduled to get the vaccine. So the second blob down are vulnerable people.
So the plot shows clearly that people vulnerable to die of covid19 (if thet get it) are less likely to get covid19 than normal people. Which is of course exactly why the nation has been in lockdown for a year. The whole point of lockdown was to ensure that vulnerable people are less likely to get covid19 than normal people.
Ironic as it seems, Plot A clearly proves that lockdown works to protect the vulnerable.
Blobs below the second blob down, just show that people, once vaccinated, but before full protection comes on, initially have greater chance of getting covid19. I assume they are less careful once vaccinated.
In your reply to an earlier post of mine you’ve written:
The second group,destined to soon get vaccinated, are mingling less (older on average, more retired on average) Not mingling, more careful, that all reduces your chance of getting the virus, hence ~0.25 of the chance.
And now, in this later post, you have written:
Ironic as it seems, Plot A clearly proves that lockdown works to protect the vulnerable.
Have you heard of the correct meaning of the term ‘begging the question’? It’s a classic logical fallacy – the use of a presumption to construct an argument which leads to the conclusion that the presumption is correct.
It appears to me that you’ve presented a good example.
Here is the reason, but you have to use you head: the blob in the top right of lot A represents people who were never vaccinated.We regard these people as not vulnerable to die if they caught covid19, since they were never in the vaccination plan. The next blob down in Plot A represents people in the vaccination plan who are not yet vaccinated, and we regard them as vulnerable, since they are in the plan. Yet the position of this blob on the x axis shows they have a lower risk of having covid19. So we have to ask why are the unvaccinated vulnerable people at lower risk of catching covid19 than the normal people in the top blob?
The answer is obvious, since we only have two forms of protecting them, lockdown and vaccination, and they have not had vaccination, hence by elimination it is lockdown making a difference.The unvaccinated people vulnerable people are at lower risk of catching covid19 than the normal people due to lockdown. No other game in town. What is begging the question about that? It’s a complete logical explanation
there is some difference between the top cohort and the second (lower risk)cohort. There are only two forms of protection, lockdown and vaccine.
The difference was not vaccine (neither cohort had any). take a wild guess what is left, might it be lockdown?
Your conclusion that the second group had lower infection owing to lockdown is nothing more than wild speculation.
I’ll give you another potential explanation: the figures are incorrect – conjured up out of nowhere by government statisticians bent on vaccine propaganda. I’m not saying that’s what it is – just a possibility. We simply don’t have an explanation for these figures.
Even if you are correct, the conclusion that the vaccine gives 70-90% protection is, on the face of it, grossly misleading, arising from (the deliberate?) mixing up of control groups.
Again, even if you are correct, the best that can be said from this data is that the vaccine offers the same protection as focussed protection. And that’s another debate altogether.
No. As I said below, at least half the people in to top priority groups for covid vaccination are in health and social care, of working age. And let’s not forget that this study does not include people in nursing homes, the most vulnerable. A second point,there is another difference between tests done more than 21 days before vaccination and tests done within 21 days before vaccination: time. The latter will on average have been conducted later in the calendar year than the former, and therefore more likely to have been conducted after so called cases peaked at the end of December/early jan.
So half the people in to top priority groups for covid vaccination are not in health and social care, etc. So your point is at best half relevant.
re: health and social care, nursing homes, the most vulnerable etc.
there is some difference between the top cohort and the second (lower risk)cohort. There are only two forms of protection, lockdown and vaccine.
The difference was not vaccine (neither cohort had any). take a wild guess what is left, might it be lockdown?
Didn’t Gibraltar lose a whole gang of elderly post vaccination. Same in the Uk where many residents in care homes got COVID and died post vaccination and same in the USA. For example, a cloistered group of nuns, in Kentucky, all went down with COVID post vaccination, three died, so far. Cloistered means they did not come into contact with anyone post vaccination. I guess ignoring this reality is what we have come to expect.
To me this shows very clearly that the infection rate was coming down naturally in any event (with the pre-vaccination control groups reducing their risk of infection over time) – as they did after the 1st wave and always do – and the vaccinations have done less to reduce infection rates than the effluxion of time
I’ve spent some time looking at this study and I feel like the questions posed from the author within the study should be taken seriously.
However, the data presented is not cumulative in relation to determining exactly how and why infection rates spiked from the its baseline of 0.25. You could speculate that seeing as this vaccine uses a live virus as opposed to a dead virus, could mean recombinant activation from the immune system as a result of pathogenic travel via reverse transcriptase from cell to cell, therefore increasing the level of infection post vaccination.
It is very malignant and boisterous to presume solely that the differentiation in activity and lifestyle between the age groups is what contributed directly to the infection rates before and after vaccination in the less than and more than 3 week categories. The timing of the tests in the study cannot be ignored. The lateral half of February consisted of the most vaccinations with least infections as opposed to the initial half of the study where infections spiked after the first dose. Meaning that there’s no way age is the main contributing factor to the differentiation in rates of infection.
One of the major key pieces of data to take away from this is the number of infections with vaccination matching the number of infections without vaccination and having had covid already. This (as stated above) is reflective of immunity.
All in all, upon viewing this study there are no doubt holes and discrepancies in the data and a revised sample reflecting this study would be nice to see. Although he we are again with just another prime example of convergent opportunism from authoritarian governments.
With all due respect, I think the entire study is not worthy of notice. In the report they expressly say about the data that:
“The presence of three SARS-CoV-2 genes (ORF1ab, protein (N), and spike protein (S)) was identified using … RT-PCR…. Samples are called positive if either N or ORF1ab, or both, are detected. The S gene alone is not considered a reliable positive, but could accompany other genes (ie, one, two, or three gene positives)…. Positive episodes were defined using study PCR results.”
Normally at least 2 matches are required, and even that is challenged. See here:
https://cormandrostenreview.com/report/
The above report goes on to say that:
“There is a perfect match of one of the N primers to a clinical pathogen (Pantoea)….”
For the record, “Pantoea species have been isolated from feculent material, in soil, water, plant (as epiphytes or endophytes), seeds, fruits (e.g., pineapple, mandarin oranges), and the human and animal gastrointestinal tracts, in dairy products, in blood and in urine.”
https://www.sciencedirect.com/topics/medicine-and-dentistry/pantoea
Therefore, since the data used by this research is useless, the conclusions are meaningless.
The reason for the drop in infections three weeks before the jab is stunningly simple and banal. When you are vaccinated your are asked if you have recently tested positive or shown symptoms – if you have, then you are refused vaccination.