You have to wonder what they were doing for 7 months while waiting for the 2021 census data? Obviously not working on the integrity of their analysis. Perhaps, they were busy finding other ways to hide the signals that the jab is really, really bad?
Indeed, Sarah Caul now clarified that 'The main focus on the release is looking at the COVID-19 mortality rates, as the vaccine was brought in to help prevent deaths involving this condition.'
I asked the ONS simply to publish the underlying data. They claimed privacy reasons. I said, "here's how to get around that." They stopped responding to me at that point. I guess that is how science works.
Would ONS provide counts of census records that were not matched to an NHS number, broken out by reason?
1) not found in patient demographic service
2) multiple NHS nbrs in patient demographic service
3) other (characterize)
Item 2 is of particular interest. Here they found more than one match in the personal demographics service (not uncommon in identity mapping - could be caused by clerical errors, moves, marriages, bugs in the matching software, etc).
In this case they throw out all the matches, and the record will default to unvaccinated.
- Is this a lot of records or just a handful?
- Since these records seem to have an NHS presence, albeit ambiguous, is it proper to default them to unvaccinated? Shouldn't the same vaxxed/unvaxxed be used that was applied to the whole population?
There is a simple explanation for all of this: 1. Ghosts simply live longer than people do. 2. Even ghosts live longer if they are unvaccinated, just like people.
I first learned about ghosts in the NHS system in the mid-80's. They inflate GP incomes, which are capitation-based. So GPs have no reason or incentive to exorcise them.
Most dead people are not ghosts: most ghosts are lively and healthy people who insist on popping up here and there like jack-in-the-boxes: a student in Nottingham one year, and backpacking around Asia the next. They rarely bother to re-register with a new GP when they leave or come home, because they have zero incentive to do so.
Old people are mostly registered with a GP, because they are more likely to be sick.
Many asylum seekers, overstayers and illegals very sensibly do not register with a GP either: I've been an undocumented worker overseas for four years, and you keep your head down.
Excellent analysis to explain the anomalies. Sadly, the only thing that a large majority of the population will ever see is the Daaily Mail headlines presenting "proof" that the "vaccines" are safe and effective using the faulty ONS data.
And given that ONS never made any attempt to correct the issues highlighted by Norman Fenton, although they had 7 months to do so, the chances of ONS admitting errors and re-issuing the data is approximately zero.
Perhaps it was NOT an honest mistake but a dishonest mistake built on the premise of "plausible deniability" - Governments specialize in those!!
The finding that the vaxed mortality is higher in the vaxed ghost population is important
However it is less clear we can draw any conclusions about the unvaxed ghost population mortality.
It is an assumption to assume that NIMS doesn’t overcount the population of England which is what is effectively happening in calculating the unvaxed ghost mortality here. I'm not challenging that assumption, I'm just saying as you will see the assumption is not required.
If you make the less restrictive and I suspect close to correct assumption (in my view at least) that the % unvaccinated in each age group is near to correct in NIMS, but that there is also some level of proportionate overcount of people both vaxed and unvaxed in NIMS you reach similar conclusions.
Let’s work on that basis and assume, right or wrong, that the population of England is correctly counted by the 2021 census total at March 2021 and that the NIMS percentages unvaccinated are correct.
Pretty much making these less restrictive assumptions gives us this linked chart for example for the 70-79 age group
This clearly shows how deaths and populations are likely to be biased in the ONS dataset.
Firstly note in this age group the much lower % of all unvaxed deaths captured in the dataset than the % of vaxed deaths captured in the ONS dataset (the solid green line is below the solid red line). That is there regardless of any assumption about NIMS unvaccinated percentages being right. It’s a big warning flag that there are strong biases at play and this discrepancy in other age groups is huge; in the 40-49 age group 57% of unvaxed deaths are included contrasting to 85% of vaxed deaths being included for example. ONS hide this huge anomaly by just stating, that very broadly to round their figures, about 90% of all deaths and 90% of the population over the whole period is included in the dataset totalled across all age and vax statuses.
Secondly in the 70-79 age group a higher percentage of the vaccinated population are included in the ONS dataset than the percentage of vaccinated deaths included (the dashed red line is above the solid red line). To state the obvious the ONS vaxed mortality is hence understated by the ONS. The vaxed ghosts have higher mortality than the vaxed in the dataset.
And thirdly a lower percentage of the unvaccinated population are included in the ONS dataset than the percentage of unvaccinated deaths included (the dashed green line is below the solid green line). The ONS unvaxed mortality is therefore overstated by the ONS. Or to put it another way the ghost unvaccinated population have lower mortality than the unvaccinated actually in the ONS dataset.
When you adjust mortality rates for this, the inexplicable ONS dataset implied lower recent non-covid mortality in the vaxed disappears in this 70-79 age group.
These conclusions replicate in some other age groups the 50-59 and 60-69 age groups for example, and the 40-49 age group although there are a few population anomalies there.
Of course the assumption that the percentage of unvaxed is right in NIMS can’t easily be proved although there are many reasons that have been set out before by HART and others to believe it is a broadly correct assumption. Importantly the ONS dataset doesn’t contradict this assumption that the NIMS unvaxed percentage is correct, on the information given to us by ONS. The assumption just determines what proportions of the unvaxed and vaxed English population are included in the ONS dataset, and hence the extent of the biases present.
Add in other likely miscategorisations of the unvaxed and unvaxed still there in the data, then it is highly likely the vaxed are experiencing perhaps 10% higher mortality than the unvaxed (of similar health) but that’s impossible to prove at the moment and is nothing more than a wild informed guess.
A little bit of information I found that might be relevant, although I have no experience in this area:
There should be a Personal Demographics Service record for most of the deaths.
NHS digital receives a copy of the ONS death data from ONS at weekly intervals. The PDS upload their digital record and return to the ONS a weekly matched death response notification including NHS number. See the mortality data review download at
I was starting to get confused with all the different groups who were either in or not in the census, who were linked to the PDS or not, were in NIMS or not and so on and how that tied in with the table 2 (also tables 3 and 4) main ONS dataset and the wider table 5 ONS dataset of all deaths in the census not just those linked to NHS number by the Personal Demographics Service.
So to help my clarity of thought in relation to the deaths rather than the population side, I drew a diagram (not to scale) of all cause deaths to help me think it through. Here it is.
I’ve ignored the under 18 deaths for the purpose of that diagram for which there is only limited data in table 1. As an aside the ONS don’t even tell us what age group table 1 covers; another sloppy error on their part if that’s right. It looks like it might be 10+.
The ONS started with the census population and then after linking to the PDS allocated deaths but I’ve put all the deaths in the column on the far left as it's easier to visualise it that way.
The accuracy of the diagram is subject to challenge but is my best understanding of what the categories are.
Shown on there are the categories that make up table 2, the main ONS dataset (and tables 3 and 4) of people linked to the 2021 census who don’t have erroneous or ambiguous vaccination status. So it excludes "199,772 people who have multiple entries for the same dose or who have a recorded first and third dose or booster but not a second dose”. These excluded are shown on the diagram as ‘unclear vax status’. Note it includes an unknown number who are linked to the census but are not in NIMS; these have unknown vax status but are problematically assumed by ONS to be unvaxed. That issue is talked about in Clare’s article.
And table 5 is all the deaths except the two groups excluded because of erroneous or ambiguous vax status. Note table 5 does include those not in NIMS not linked to NHS number by the census and although they have unknown vax status they are are problematically assumed by ONS to be unvaxed. Again a problem but only in relation to table 5 analyses.
Note that table 5 deaths are very close to all the all cause deaths (numerically 883,784 of about 889,000 total deaths). So roughly speaking table 5 covers most deaths.
What the ONS must have done to get the table 5 figures is work back from the deaths and in relation to those not linked to the census through the PDS look them up in NIMS. They had to have done that to get the extra 62,603 vaxed deaths (= 823,502 – 760,899) in table 5 but not in table 2.
What we need is to know from the ONS the numbers in each category in that chart.
The mismatches of percentages of deaths and population by status included in the ONS dataset are significant, the selection biases potentially very large, and that's on top of the misallocated unvaxed.
Could we ask the ONS to do a simple straightforward sesnsitivity test and change the assumption "then they were assumed to be unvaccinated" to "then they were assumed to be vaccinated" just to see what difference this makes? Presumably, somewhere in between would be closer to the truth?
Unfortunately, unvaccinated people are just people with no vaccine record. Therefore a mismatched vaccinated person looks unvaccinated and we don't know how many are mismatches and how many are genuine unvaccinated people.
There seems to be significant healthy vaccinee bias in the ONS data. I wonder whether the mortality profile is different for a person depending on whether they were unable to get vaccinated because of covid or because of a non-covid health condition. Perhaps if someone missed their jab because of covid they either died quickly or got better whereas if someone missed their jab for another health reason they remained unhealthy for longer and either died or got better later. Your data starts on April 21 which is after the first dose rollout (particularly for the elderly) where only healthy people are allowed to move from the unvaccinated to the first dose cohort. If the difference is caused by healthy vaccinee effect before 1 April you might get different results running the data from Jan 21 to include most of the 1st dose rollout. The healthy vaccinee bias is significant in the ONS data - for example, in July 21 a 65 year old who has had a single dose of the vaccine is 10 times more likely to die a non-covid death than a 65 year old who has had a second dose. https://dontboremewithdetail.substack.com/i/106190743/are-non-covid-mortality-rates-correlated-with-vaccine-rollout
I have a certain amount of scepticism about the "healthy vaccinee" effect. It does make sense of this data but, in the real world, the sickest were prioritised for vaccination - even in patients were put to the top of the list.
The new analysis starts in April 2021 because it uses census data from 29th March 2021. If you're in the census then you didn't die before then!
I have now read the paper you co-authored which attributes these anomalies to misclassification. I now believe that the illusion of vaccine effectiveness during rollout is caused by selection of the incorrect population denominator. The ONS calculate mortality by dividing the number of deaths by the number of people in the cohort in the same month. However, covid-19 deaths are caused by an infection 3 weeks prior when, during rollout, the at risk population was much higher. Thus if you divide covid-19 deaths by size of the cohort in the same month, the illusion of vaccine effectiveness is created. https://dontboremewithdetail.substack.com/p/the-denominator-of-death
v. A few really sick people prioritised for vaccination but roll out is so fast, any changes in mortality dwarfed by the mass vaccination of only healthy people. Within the really sick cohort, people with coughs etc (who are most likely to die) not allowed to be vaccinated.
Rob Kay yes so do I, only it would have gone against the narrative that is unfolding through the WhatsApp messages at the minute. Valance, Witty and Van Tam are far from idiots, they were happy to intentionally mislead the public with their rendition of misinformation, disinformation the government wished to project. Of course they had vested interest to keep the shenanigans going to fleece the taxpayers.
I pointed out at the start of the daily updates, if it was so deadly and contagious, then why were they happy to mix and appear on tv along with politicians everyday. They knew the risks were low otherwise they wouldn’t have gone to all that time and effort drilling the nation to be scared. They spent a year gaslighting and mocking people with their nightly routine.
I was called a covid denier and antivaxxer for pointing the absurdity of them and their mantra out.
Well said. They have given the world a Great Experiment to wake us up from our reverie that vaccines, all vaccines ever, are a Very Bad Idea. If they hadn't done what they did the world would probably be still stuck thinking that vaccines might be of some use, although I never willing took any, let alone big pharma drugs in general.
If I haven't said before when I was asked (not unkindly) if I was an anti-vaxxer I said No, I am anti-stupid, I don't like sticking poisons into myself.
Some of the earlier messages show that Valance and Whitty were trying to steer the more conventional narrative of pandemic preparedness: keeping to the script and avoiding exceptionalism. I haven't studied the scripts well enough to see where the pivot points were, yet, but at some point the whole thing just went belly-up: groupthink of some kind: there is massive scope there for analysis, but it needs a bit of time, not just quick soundbites.
You have to wonder what they were doing for 7 months while waiting for the 2021 census data? Obviously not working on the integrity of their analysis. Perhaps, they were busy finding other ways to hide the signals that the jab is really, really bad?
Indeed, Sarah Caul now clarified that 'The main focus on the release is looking at the COVID-19 mortality rates, as the vaccine was brought in to help prevent deaths involving this condition.'
I asked the ONS simply to publish the underlying data. They claimed privacy reasons. I said, "here's how to get around that." They stopped responding to me at that point. I guess that is how science works.
Would ONS provide counts of census records that were not matched to an NHS number, broken out by reason?
1) not found in patient demographic service
2) multiple NHS nbrs in patient demographic service
3) other (characterize)
Item 2 is of particular interest. Here they found more than one match in the personal demographics service (not uncommon in identity mapping - could be caused by clerical errors, moves, marriages, bugs in the matching software, etc).
In this case they throw out all the matches, and the record will default to unvaccinated.
- Is this a lot of records or just a handful?
- Since these records seem to have an NHS presence, albeit ambiguous, is it proper to default them to unvaccinated? Shouldn't the same vaxxed/unvaxxed be used that was applied to the whole population?
Very good points - definitely worth including.
We need to practice the following:
1. How to produce patronuses
2. How to apparate - I think it will cost more than 7 galleons to lean now.
We needed to be able to do Riddikulous spells. I try my best.
https://baldmichael.substack.com/p/nhs-covid-19-vaccine-advice
https://alphaandomegacloud.wordpress.com/rule-of-six-or-now-we-are-six/
https://alphaandomegacloud.wordpress.com/3-tier-system/
https://alphaandomegacloud.wordpress.com/g-is-for-guidance/
There is a simple explanation for all of this: 1. Ghosts simply live longer than people do. 2. Even ghosts live longer if they are unvaccinated, just like people.
Does that solve it?
I first learned about ghosts in the NHS system in the mid-80's. They inflate GP incomes, which are capitation-based. So GPs have no reason or incentive to exorcise them.
Most dead people are not ghosts: most ghosts are lively and healthy people who insist on popping up here and there like jack-in-the-boxes: a student in Nottingham one year, and backpacking around Asia the next. They rarely bother to re-register with a new GP when they leave or come home, because they have zero incentive to do so.
Old people are mostly registered with a GP, because they are more likely to be sick.
Many asylum seekers, overstayers and illegals very sensibly do not register with a GP either: I've been an undocumented worker overseas for four years, and you keep your head down.
I think the ONS would go with that :)
Keep chewing that bone!
Excellent analysis to explain the anomalies. Sadly, the only thing that a large majority of the population will ever see is the Daaily Mail headlines presenting "proof" that the "vaccines" are safe and effective using the faulty ONS data.
And given that ONS never made any attempt to correct the issues highlighted by Norman Fenton, although they had 7 months to do so, the chances of ONS admitting errors and re-issuing the data is approximately zero.
Perhaps it was NOT an honest mistake but a dishonest mistake built on the premise of "plausible deniability" - Governments specialize in those!!
Thanks for all your hard work Dr.Clare !
The finding that the vaxed mortality is higher in the vaxed ghost population is important
However it is less clear we can draw any conclusions about the unvaxed ghost population mortality.
It is an assumption to assume that NIMS doesn’t overcount the population of England which is what is effectively happening in calculating the unvaxed ghost mortality here. I'm not challenging that assumption, I'm just saying as you will see the assumption is not required.
If you make the less restrictive and I suspect close to correct assumption (in my view at least) that the % unvaccinated in each age group is near to correct in NIMS, but that there is also some level of proportionate overcount of people both vaxed and unvaxed in NIMS you reach similar conclusions.
Let’s work on that basis and assume, right or wrong, that the population of England is correctly counted by the 2021 census total at March 2021 and that the NIMS percentages unvaccinated are correct.
Pretty much making these less restrictive assumptions gives us this linked chart for example for the 70-79 age group
https://ibb.co/PNWkvr7
This clearly shows how deaths and populations are likely to be biased in the ONS dataset.
Firstly note in this age group the much lower % of all unvaxed deaths captured in the dataset than the % of vaxed deaths captured in the ONS dataset (the solid green line is below the solid red line). That is there regardless of any assumption about NIMS unvaccinated percentages being right. It’s a big warning flag that there are strong biases at play and this discrepancy in other age groups is huge; in the 40-49 age group 57% of unvaxed deaths are included contrasting to 85% of vaxed deaths being included for example. ONS hide this huge anomaly by just stating, that very broadly to round their figures, about 90% of all deaths and 90% of the population over the whole period is included in the dataset totalled across all age and vax statuses.
Secondly in the 70-79 age group a higher percentage of the vaccinated population are included in the ONS dataset than the percentage of vaccinated deaths included (the dashed red line is above the solid red line). To state the obvious the ONS vaxed mortality is hence understated by the ONS. The vaxed ghosts have higher mortality than the vaxed in the dataset.
And thirdly a lower percentage of the unvaccinated population are included in the ONS dataset than the percentage of unvaccinated deaths included (the dashed green line is below the solid green line). The ONS unvaxed mortality is therefore overstated by the ONS. Or to put it another way the ghost unvaccinated population have lower mortality than the unvaccinated actually in the ONS dataset.
When you adjust mortality rates for this, the inexplicable ONS dataset implied lower recent non-covid mortality in the vaxed disappears in this 70-79 age group.
These conclusions replicate in some other age groups the 50-59 and 60-69 age groups for example, and the 40-49 age group although there are a few population anomalies there.
Of course the assumption that the percentage of unvaxed is right in NIMS can’t easily be proved although there are many reasons that have been set out before by HART and others to believe it is a broadly correct assumption. Importantly the ONS dataset doesn’t contradict this assumption that the NIMS unvaxed percentage is correct, on the information given to us by ONS. The assumption just determines what proportions of the unvaxed and vaxed English population are included in the ONS dataset, and hence the extent of the biases present.
Add in other likely miscategorisations of the unvaxed and unvaxed still there in the data, then it is highly likely the vaxed are experiencing perhaps 10% higher mortality than the unvaxed (of similar health) but that’s impossible to prove at the moment and is nothing more than a wild informed guess.
A little bit of information I found that might be relevant, although I have no experience in this area:
There should be a Personal Demographics Service record for most of the deaths.
NHS digital receives a copy of the ONS death data from ONS at weekly intervals. The PDS upload their digital record and return to the ONS a weekly matched death response notification including NHS number. See the mortality data review download at
https://digital.nhs.uk/coronavirus/coronavirus-data-services-updates/mortality-data-review
Less than 0.5% of the total deaths registered in England, Wales and the Isle of Man are not matched to NHS Digital records. Source
https://digital.nhs.uk/services/adoption-registration-service/death-registration-enquiries
I was starting to get confused with all the different groups who were either in or not in the census, who were linked to the PDS or not, were in NIMS or not and so on and how that tied in with the table 2 (also tables 3 and 4) main ONS dataset and the wider table 5 ONS dataset of all deaths in the census not just those linked to NHS number by the Personal Demographics Service.
So to help my clarity of thought in relation to the deaths rather than the population side, I drew a diagram (not to scale) of all cause deaths to help me think it through. Here it is.
https://ibb.co/n19YtjJ
I’ve ignored the under 18 deaths for the purpose of that diagram for which there is only limited data in table 1. As an aside the ONS don’t even tell us what age group table 1 covers; another sloppy error on their part if that’s right. It looks like it might be 10+.
The ONS started with the census population and then after linking to the PDS allocated deaths but I’ve put all the deaths in the column on the far left as it's easier to visualise it that way.
The accuracy of the diagram is subject to challenge but is my best understanding of what the categories are.
Shown on there are the categories that make up table 2, the main ONS dataset (and tables 3 and 4) of people linked to the 2021 census who don’t have erroneous or ambiguous vaccination status. So it excludes "199,772 people who have multiple entries for the same dose or who have a recorded first and third dose or booster but not a second dose”. These excluded are shown on the diagram as ‘unclear vax status’. Note it includes an unknown number who are linked to the census but are not in NIMS; these have unknown vax status but are problematically assumed by ONS to be unvaxed. That issue is talked about in Clare’s article.
And table 5 is all the deaths except the two groups excluded because of erroneous or ambiguous vax status. Note table 5 does include those not in NIMS not linked to NHS number by the census and although they have unknown vax status they are are problematically assumed by ONS to be unvaxed. Again a problem but only in relation to table 5 analyses.
Note that table 5 deaths are very close to all the all cause deaths (numerically 883,784 of about 889,000 total deaths). So roughly speaking table 5 covers most deaths.
What the ONS must have done to get the table 5 figures is work back from the deaths and in relation to those not linked to the census through the PDS look them up in NIMS. They had to have done that to get the extra 62,603 vaxed deaths (= 823,502 – 760,899) in table 5 but not in table 2.
What we need is to know from the ONS the numbers in each category in that chart.
The mismatches of percentages of deaths and population by status included in the ONS dataset are significant, the selection biases potentially very large, and that's on top of the misallocated unvaxed.
Could we ask the ONS to do a simple straightforward sesnsitivity test and change the assumption "then they were assumed to be unvaccinated" to "then they were assumed to be vaccinated" just to see what difference this makes? Presumably, somewhere in between would be closer to the truth?
I wish it was that simple!
Unfortunately, unvaccinated people are just people with no vaccine record. Therefore a mismatched vaccinated person looks unvaccinated and we don't know how many are mismatches and how many are genuine unvaccinated people.
There seems to be significant healthy vaccinee bias in the ONS data. I wonder whether the mortality profile is different for a person depending on whether they were unable to get vaccinated because of covid or because of a non-covid health condition. Perhaps if someone missed their jab because of covid they either died quickly or got better whereas if someone missed their jab for another health reason they remained unhealthy for longer and either died or got better later. Your data starts on April 21 which is after the first dose rollout (particularly for the elderly) where only healthy people are allowed to move from the unvaccinated to the first dose cohort. If the difference is caused by healthy vaccinee effect before 1 April you might get different results running the data from Jan 21 to include most of the 1st dose rollout. The healthy vaccinee bias is significant in the ONS data - for example, in July 21 a 65 year old who has had a single dose of the vaccine is 10 times more likely to die a non-covid death than a 65 year old who has had a second dose. https://dontboremewithdetail.substack.com/i/106190743/are-non-covid-mortality-rates-correlated-with-vaccine-rollout
I can see what you're getting at.
I have a certain amount of scepticism about the "healthy vaccinee" effect. It does make sense of this data but, in the real world, the sickest were prioritised for vaccination - even in patients were put to the top of the list.
The new analysis starts in April 2021 because it uses census data from 29th March 2021. If you're in the census then you didn't die before then!
I have now read the paper you co-authored which attributes these anomalies to misclassification. I now believe that the illusion of vaccine effectiveness during rollout is caused by selection of the incorrect population denominator. The ONS calculate mortality by dividing the number of deaths by the number of people in the cohort in the same month. However, covid-19 deaths are caused by an infection 3 weeks prior when, during rollout, the at risk population was much higher. Thus if you divide covid-19 deaths by size of the cohort in the same month, the illusion of vaccine effectiveness is created. https://dontboremewithdetail.substack.com/p/the-denominator-of-death
I was sceptical too until I looked at the ONS data. Consider this:
i. This study found that the relative risk of death in vaccinated vs unvaccinated was 0.41 before the flu season. https://academic.oup.com/ije/article/35/2/337/694702
ii. The non-covid death rates, which should be unaffected by vaccination status, are all over the place. https://substackcdn.com/image/fetch/w_1456,c_limit,f_webp,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa1f167eb-2f38-48ac-b38b-6428ceadee06_742x450.png
iii. Each successive vaccination rollout selectively removes healthy people from the preceding cohort. The higher concentration of unhealthy people left in the cohort is initially associated with a steep rise in mortality followed by a gradual decline as those ‘too ill to vaccinate’ get better or die. This is true in all age groups. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F410d3562-0134-49a6-a932-f7c58a93ad2c_945x517.png
iv. Healthy people were not allowed to get vaccinated. see page 10. https://www.ulh.nhs.uk/content/uploads/2020/12/PHE-vaccine-leaflet.pdf
v. A few really sick people prioritised for vaccination but roll out is so fast, any changes in mortality dwarfed by the mass vaccination of only healthy people. Within the really sick cohort, people with coughs etc (who are most likely to die) not allowed to be vaccinated.
vi. (I think) Hazard ratio between expect (where there should be no difference in MR between cohorts) and actual Non-Covid mortality can be used to remove (some) healthy vaccine bias from the All cause mortality. https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F60413669-e4e2-4680-b612-259653404d43_782x558.png
She really is, isn't she?
I only wish that she had been the Governments adviser in 2020, instead of the Tweedledee and Tweedledum idiots of Valance and Whitty.
Rob Kay yes so do I, only it would have gone against the narrative that is unfolding through the WhatsApp messages at the minute. Valance, Witty and Van Tam are far from idiots, they were happy to intentionally mislead the public with their rendition of misinformation, disinformation the government wished to project. Of course they had vested interest to keep the shenanigans going to fleece the taxpayers.
I pointed out at the start of the daily updates, if it was so deadly and contagious, then why were they happy to mix and appear on tv along with politicians everyday. They knew the risks were low otherwise they wouldn’t have gone to all that time and effort drilling the nation to be scared. They spent a year gaslighting and mocking people with their nightly routine.
I was called a covid denier and antivaxxer for pointing the absurdity of them and their mantra out.
Well said. They have given the world a Great Experiment to wake us up from our reverie that vaccines, all vaccines ever, are a Very Bad Idea. If they hadn't done what they did the world would probably be still stuck thinking that vaccines might be of some use, although I never willing took any, let alone big pharma drugs in general.
If I haven't said before when I was asked (not unkindly) if I was an anti-vaxxer I said No, I am anti-stupid, I don't like sticking poisons into myself.
Some of the earlier messages show that Valance and Whitty were trying to steer the more conventional narrative of pandemic preparedness: keeping to the script and avoiding exceptionalism. I haven't studied the scripts well enough to see where the pivot points were, yet, but at some point the whole thing just went belly-up: groupthink of some kind: there is massive scope there for analysis, but it needs a bit of time, not just quick soundbites.