COVID-19 Data & Docs

[alloneword]

On the subject of 'CTCCTCGGCGGGCACGTAG' being a genetic sequence found in SARS-CoV2 and also - entirely coincidentally no doubt - in a patent filed by Moderna in 2015.

A recap on Moderna:

How did Moderna's 2015 patented code end up in SARS-CoV-2 and what were they developing in 2017?

What was Moderna secretly working on in 2017 and how did a patented section of genetic code end up in SARS-Cov-2?

Moderna was founded in 2010 and within two years managed to raise $40 million. In 2013 it signed a $240 million five year option agreement to discover, develop and commercialise mRNA for treatments in the therapeutic areas of various diseases, as well as selected targets for cancer.

The company continued it’s impressive fund raising, valued at around $5 billion in 2016, and culminating in 2018 when it became a public company and raised a further $621 million. From its creation until the end of 2019 it had lost $1.5 billion, losing $514 in 2019 alone.

It was notoriously secretive and was criticised by Nature for not publishing any peer reviewed papers on its novel technology. Another publication in Stat in 2016, which was summarised in Thrillist, continued the criticism and compared the company to the scandalised medical company Theranos. It highlighted reasons Moderna might be the next Theranos, including that the mRNA treatments are potentially very dangerous. It explained “On their own, RNA molecules have a hard time reaching their targets. They work better if they're wrapped up in a delivery mechanism, such as nanoparticles made of lipids. But those nanoparticles can lead to dangerous side effects, especially if a patient has to take repeated doses over months or years.” Luckily nobody has taken repeated doses over a few months yet - oh wait.

https://nakedemperor.substack.com/p/ctcctcggcgggcacgtag





There is a set of very detailed step-by-step instructions on how to go about confirming this yourself here:

https://arkmedic.substack.com/p/how-to- ... -the-truth (also archived: archive.is/18Ho4 ).

I was about to try calculating the probability of this match having occurred by chance, but then noticed the 'Ethical Skeptic' appears to have already done the work:


https://twitter.com/EthicalSkeptic/stat ... 3478322179

[alloneword]

^^ This should probably be viewed in the context of what Dr. David Martin told Dr. Reiner Fuellmich in the interview back in July 2021:

https://bluecat.media/dr-reiner-fuellmi ... 1999-2002/

[Elvis]

Great thread. I barely understand any of it, but that 2-week window of susceptibility is pretty interesting.

there is quite a lot of evidence that these vaccines trigger a ~2 week window of significant immune suppression.

[MacCruiskeen]

h/t "antitermite", commenter at OffGuardian:

a comprehensive summary of all the athletes dropping like flies,
with annotation as to how likely the “vaccines” are complicit:

461 Athlete Cardiac Arrests, Serious Issues, 269 Dead, After COVID Shot [as of: 16 Jan 2022]
https://goodsciencing.com/covid/athlete ... ovid-shot/

[alloneword]

^^^ Sadly, Mac, I think they'll be pretty busy keeping that list up to date.

A good find, thanks Alwyn.

i found it, i found it! the original research paper first published in 2007, and then republished in Dec 2020, authors from the Wuhan Lab in China (with Aussie partners!) talk about the gain of function they did on the Corona Virus, where they mated it with HIV. putting link here. The PDF would NOT download, so go to the site to read it.

https://journals.asm.org/doi/10.1128/jv ... ently=true

(I got it to download OK - I clicked through the 'PDF/EPUB' button, then the blue download button top right, a dropdown offers pdf or epub)

[alloneword]

This guy ('John Dee') is a former head of clinical audit at a busy NHS teaching hospital with specialism in assessment of clinical outcomes, serving on a regional clinical reference committee in addition to supporting directors of his NHS Foundation & Primary Care Trusts. As such, he has access to datasets that are not in the public domain and the skill set to deal with them. He is the sort of person that is worth paying attention to.

Report 19: Vaccination Efficacy & Hospitalisation
Lessons from an undisclosed NHS Trust

John Dee

I have been pedal to the metal these last two weeks working on this report. I am aware that the last decent argument on the table is one of vaccines reducing disease severity and risk of hospitalisation but it's hard to nail severity so what folk are naturally doing is going for hospitalisations - it's easy to count beds than consider FEV1, PF ratio, PEEP, LIS, PaO2 and all the rest.

For those short on time there are six bullet points at the back that summarise key results. For those very short on time (and for those allergic to numbers) then I'll summarise matters by stating that there is no firm evidence in this report to support the notion that mRNA vaccines are reducing the risk of consequent hospitalisation for asymptomatic and symptomatic COVID admissions to A&E.

What I did find is evidence that mRNA vaccines are reducing risk of hospitalisation for everything else including knee injuries, knife wounds and intoxication with illegal substances. Make of that what you will; I shall be filing it under utter tripe and nonsense!

On a more serious note this wacko finding means vaccine benefit is an illusion, likely brought about by various clinical and patient management biases that we haven't bothered to even think about let alone tackle.

Now for a cuppa...

(My bold)

Report (pdf)

[alloneword]

COVID-19 Cases and Hospitalizations by COVID-19 Vaccination Status and Previous COVID-19 Diagnosis — California and New York, May–November 2021
Early Release / January 19, 2022 / 71

Tomás M. León, PhD1; Vajeera Dorabawila, PhD2; Lauren Nelson, MPH1; Emily Lutterloh, MD2,3; Ursula E. Bauer, PhD2; Bryon Backenson, MPH2,3; Mary T. Bassett, MD2; Hannah Henry, MPH1; Brooke Bregman, MPH1; Claire M. Midgley, PhD4; Jennifer F. Myers, MPH1; Ian D. Plumb, MBBS4; Heather E. Reese, PhD4; Rui Zhao, MPH1; Melissa Briggs-Hagen, MD4; Dina Hoefer, PhD2; James P. Watt, MD1; Benjamin J. Silk, PhD4; Seema Jain, MD1; Eli S. Rosenberg, PhD2,3 (View author affiliations)

View suggested citation
Summary
What is already known about this topic?

Data are limited regarding the risks for SARS-CoV-2 infection and hospitalization after COVID-19 vaccination and previous infection.

What is added by this report?

During May–November 2021, case and hospitalization rates were highest among persons who were unvaccinated without a previous diagnosis. Before Delta became the predominant variant in June, case rates were higher among persons who survived a previous infection than persons who were vaccinated alone. By early October, persons who survived a previous infection had lower case rates than persons who were vaccinated alone.

What are the implications for public health practice?

Although the epidemiology of COVID-19 might change as new variants emerge, vaccination remains the safest strategy for averting future SARS-CoV-2 infections, hospitalizations, long-term sequelae, and death. Primary vaccination, additional doses, and booster doses are recommended for all eligible persons. Additional future recommendations for vaccine doses might be warranted as the virus and immunity levels change.

https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e1.htm

(Yeah, that CDC).

John Campbell discusses:


https://www.youtube.com/watch?v=25-iJKPA1CA

[Grizzly]

^^^


John Dee, eh?


Large, Peer-Reviewed Research Study Proves Ivermectin Works Against COVID-19
https://www.zerohedge.com/covid-19/large-peer-reviewed-research-study-proves-ivermectin-works

[alloneword]

Fenton on 'Life expectancy: a counterfactual example'.


https://www.youtube.com/watch?v=DSqjZ71GWCY

More on this: https://probabilityandlaw.blogspot.com/ ... e-why.html

[alloneword]

BMJ:

Covid-19 vaccines and treatments: we must have raw data, now

Data must be available when trial results are announced, published, or used to justify regulatory decisions. There is no place for wholesale exemptions from good practice during a pandemic. The public has paid for covid-19 vaccines through vast public funding of research, and it is the public that takes on the balance of benefits and harms that accompany vaccination. The public, therefore, has a right and entitlement to those data, as well as to the interrogation of those data by experts.

[alloneword]

So... what have we here?

https://www.ons.gov.uk/peoplepopulation ... tusengland

Deaths by vaccination status, England

Deaths occurring between 1 January and 31 October 2021 edition of this dataset:

[Edited to remove chunk of erroneously pasted text that belonged to a subsequent post! I'd wondered where that had gone.]

https://www.ons.gov.uk/file?uri=%2fpeop ... ble31.xlsx

(Release date: 20 December 2021 Next release: To be announced)

9 tables.

The first 8 list the stats in various forms, all helpfully offering 'Age-standardised mortality rate per 100,000 person-years', so you can compare those filthy 'unvaxxed' to the various flavours of wholesome 'vaxxed' goodness.

Note how table 2 (non-Covid deaths) places the ASMR(100k/py) figures for the 'unvaxxed' and those for 'ever vaccinated' in a convenient position to aid comparison (this is sarcasm). Note that the death rate for the unvaxxed is on average TWICE that for the 'ever vaccinated'. For non-Covid deaths. Marvel once again at the awesome healing powers of the 'vaccine' in preventing non-Covid deaths. Right-o.

Now lets go on. The age banding (tables 5, 6 & 7) gives us 18 - 39yrs, then on up in 10 yr increments. Not massively useful if we're looking to avoid our friend Simpson and his paradox. But there again is that ASMR figure. We're doing well.

Table 8 reiterates the miracle, with those nice ASMR figures that we're getting used to... but then we get to table 9. No more ASMR figures for you, bucko!

But we do have much better age stratification! 5 year increments, from 10 yrs old! w00t. But no ASMR to compare our new age groups. We also have death counts and 'Person-years' for each row - so we can calculate the ASMRs ourselves.

Let's take the first line: 10-14 yr olds who 'Received only the first dose, at least 21 days ago', of which there were 6,648 'person years', tragically with 3 (non-Covid) deaths. So we can divide the 6,648 by 100,000 to get 0.06648 then divide the 3 deaths by the 0.06648 to get a figure for 'Deaths per 100,000 person-years' of '45.13'. Then so on for the rest of the table...

So lets plot the youngest age groups...



Woah.

Well, OK, we could say that it's a small number of deaths (single and double digits) so you can't read too much into it - but with well over 2.5 million (32k of them vaxxed) in the 10-14 age group alone, the statistical power compares favourably with other datasets, like, for example... oh, I dunno... the clinical trials upon which the 'Emergency Use Authorisation' was based.

To see the above in context, here's the rates for NON-Covid deaths split according to vax status (no vax at all vs. at least 1 dose, ever):



..and the same with a LOG scale on the y-axis, so we can see what's going on in the lower age groups:



From this we see an apparent increase in the risk of (non-Covid) death below ~30 years old, with a decrease in risk of (non-Covid) death once you get over 40 associated with the vaccines. Miraculous. This fits well with the discoveries of 'John Dee' regarding the vaccines apparent curative properties in preventing hospitalisation from stuff like accidental injuries.

It's a question that Profs Fenton, Neil et al have been looking at for a while, but haven't come up with anything solid. Occam's Razor might suggest it's because the stats are simply all a total confection. I'd venture that it's possibly the result of a nexus of weakly interacting systemic biases - so Occam can stick his razor (although I'd advise him to get a Covid vaccine first, to minimise the risk of serious injury).

Certainly worth keeping an eye on, should they ever decide to publish an updated dataset.

[Harvey]

Data crunching is far outside my area of interest or competence... but, without reading the links (yet) and coming to these graphs completely cold, does it not seem that graph 2 demonstrates a high probability that the source data is systematically corrupted and even the exact percentage of corruption? And if so, how might that figure be used with other data sets? In that sense, it might be the Rosetta Stone for understanding every single data based claim about Covid and the alleged effectiveness of vaccines...



If graph 3 shows anything - like graph 2 it might show that vaxxed are being counted as non-vaxxed in the source statistics and that in younger age groups where the trend seems reversed, it probably isn't! The data could indicate either a much higher percentage of lethality by vaccines in the young, AND OR more reliable recording of vaccine status in those age groups for some reason, therefore, are these graphs not showing an extremely enhanced danger from Covid vaccination for their age groups?

Is the method of data collection - labelling vaxxed as unvaxxed for two to three weeks after vaccination - not only the method of concealing vaccine related deaths, but the major driver of all deaths attributed to Covid? Could this data be used to prove that in the post vaccine era - Covid deaths are actually a combination of flu, Remdesivir/Midazolam posioning and vaccination injury?

[alloneword]

..it might be the Rosetta Stone for understanding every single data based claim about Covid and the alleged effectiveness of vaccines...

I'd caution against applying such a ratio to other data without understanding what's causing it in the first place - if only because for all we know, whatever that cause is might be masking an opposite trend to the one apparent in the raw data. For instance, just say you wished and/or accidentally managed to devise a way of biasing the collection of data to mask an increase in non-covid deaths amongst the vaccinated, then it transpired that this increase was not as severe as you'd anticipated... What would the data now look like?

So by applying a ratio 'correction' to offset the data back to where we suppose it should be, we risk missing something important. That said, here's a plot:



..or flipped:

If graph 3 shows anything - like graph 2 it might show that vaxxed are being counted as non-vaxxed in the source statistics and that in younger age groups where the trend seems reversed, it probably isn't! The data could indicate either a much higher percentage of lethality by vaccines in the young, AND OR more reliable recording of vaccine status in those age groups for some reason, therefore, are these graphs not showing an extremely enhanced danger from Covid vaccination for their age groups?

As I noted, the actual numbers in the lowest age group are pretty low (the big red spike in the 10 - 14 age group for '2 doses over 21 days ago' represents only 4 actual deaths occurring in 1,678 person-years in that group. So a 'low statistical power'. For all we know, those 4 unfortunate individuals might have been in the same car crash. As we move up the age group, that 'statistical power' rapidly increases, though.

It's also worth noting that (AFAIAA) the vax has only been rolled out to 12 and 13 year olds in the UK (so far), so likely around half of the 10 - 14 yrs age group would never enter the equation, as the probability of them being vaxxed is practically zero. I'm not completely clear on what effect (if any) this factor would have.

But yes, a much higher risk of death of non-covid causes for the vaccinated younger age groups looks likely.

The data itself is compiled by the ONS and drawn from death certificates [ICD10 codes U07.1 (COVID-19, virus identified) or U07.2 (COVID-19, virus not identified)]. Their population estimates (used to calculate the 'unvaccinated' cohort person-years etc) appears to be drawn from the 2011 census data. I'd assume the vaccination data is from NIMS.

Is the method of data collection - labelling vaxxed as unvaxxed for two to three weeks after vaccination - not only the method of concealing vaccine related deaths, but the major driver of all deaths attributed to Covid? Could this data be used to prove that in the post vaccine era - Covid deaths are actually a combination of flu, Remdesivir/Midazolam posioning and vaccination injury?

The ONS are actually helpful in this regard, in that the break out separate figures:

Vaccination status is defined on each day for each person and is one of:
unvaccinated 
vaccinated with 1 dose only, less than 21 days after vaccination 
vaccinated with 1 dose only, at least 21 days after vaccination 
vaccinated with 2 doses, less than 21 days after second vaccination 
vaccinated with 2 doses, at least 21 days after second vaccination 

Where I've plotted 'vaxxed', I've combined the figures for anyone who has ever had a vaccine, regardless of how long ago (to keep the graph simple).

I think the only thing that these stats can truly be said to show is that 'there's something funky going on with the stats', in that they appear to show the covid vaccine being effective against non-covid causes of death. Once we get to the bottom of why that is, we stand a chance of gaining meaningful information from them.

I'm currently looking at the baseline population denominator, to see if undercounting the 'unvaxxed' would produce the observed divergence... stay tuned!

[alloneword]

One thought that occurred regarding the source of the problem lies in the baseline stats used to calculate the number of 'unvaccinated'.

A curious thing in this data... if you total up all of the person-years across all ages and all vax status, adjust for it only being 10 months of data (so x ~1.2 to make it a year), you get a figure of 39,217,662 persons (or person-years over 1 year, to be exact).

The population of England (2020 mid year estimate) was 56,550,000... so where are the ~17 million missing people? They don't all appear to have died!

The ONS tell us that in 2020, 6,778,905 people were under 10 years old, so that leaves us with 49,771,233 over 10 yrs. That's still 10,553,571 people missing from our data.

The UKGov 'covid dashboard' informs us that (on 31/10/21 - when our dataset runs up to) 86.6% of the population of England were vaxxed with at least 1 dose.

If we work out the percentage (based on person years) of vaxxed in our data, we get a mere 59.61%. Odd.

It doesn't seem too unreasonable to assume that of these missing 10 million+ 'missing' people, most would be 'unvaxxed', since if they did take the shot, they'd be in the data, whereas if they didn't, they might well not be in the NIMS database at all.

So what happens if we simply throw these 10m into the 'unvaxxed' catagory?

We get a vax uptake figure of 47% for all those 'eligible' (over 10), which drops to 41% population wide.

OK, so what happens if we take those 10m people, then distribute them proportionally across the 'unvaxxed' age groups in our data?

This:



..and the ratio:



Which, to my eye, looks like the amazing magic healing properties of the vax on non-covid deaths have all but disappeared - only to be replaced by a much greater risk profile for younger people.

Now, here's my theory:

In an effort to inflate the vax uptake figures, they've been systematically suppressing the population denominator of the unvaxxed. This has had unintended consequences, as discussed, but nobody cares enough to worry about/notice it. That is all.

[Harvey]

Which, to my eye, looks like the amazing magic healing properties of the vax on non-covid deaths have all but disappeared - only to be replaced by a much greater risk profile for younger people.

Not only has Magic Vax healing disappeared, the adjusted data appears to show that 'vaccination' with mRNA comes with a significant cost in deaths across most age ranges, and most particularly among the young.

The one thing this definitively shows is that the figures have been cooked. The implications are huge.