After excluding Covid-19 deaths, they found that the standardized mortality rate was:
- Pfizer: 0.35
- Moderna: 0.34
- Unvaccinated: 1.11
How can this be? Excluding Covid19 these number should have been roughly equal!!
The real question is, why do the authors make all these "mental gymnastics" when simply calculating an all-cause standardized mortality rate by vaccinated vs unvaccinated groups would have been sufficient?
If we simply sum up the deaths reported in the study by vaxx status and compare it to unvaxxed, we can see that roughly the same amount of people died in each group. (The small differences are likely due to chance)
Unfortunately many of the unvaccinated were excluded, after they had received their vaccine. Hence this is not a true/valid comparison... But it raises serious questions!
If it is so clear, why does the CDC not simply publish these numbers:
- All cause mortality by age/vaxx ??
Calculated it by age group too...
Disclaimer: This is not representative... But it shows, that there's likely no or possibly very small benefit....
The authors of the study also write that the reason for the much lower all-cause mortality risk in vaccinated population is because they are generally healthier...
While this completely goes against common knowledge - we know that many 100% healthy people do not take the vaxx.
We can also see that in the data by CDC itself!
Vaccinated people were 2x more likely to have an underlying COVID19 health risk, than unvaccinated!
BTW: First tweet should read:
"good thing the authors delivered proof in their study" :)
💥💥💥 An official CDC FOIA response confirms that the validation of the SARS-CoV-2 genome has not been completed to scientific standards! 💥💥💥
CDC Unable to Scientifically Verify Full SARS-CoV-2 Genome, Leaving Potential for Semi-Random Construct.
🧵 A thread...
The response:
CDC has responded to my FOIA request, in which I have asked for records related to these four points:
1. Records on single virion sequencing of SARS-CoV-2 that ensured the virion was physically isolated from any other genetic material before sequencing.
2. Records of a single sequencing (long-)read from the first position [..] to the last position [..] of the genome. [..]
This may as well be part of the script/disinformation campaign after all:
>> This is important to understand <<
Hypothetical Disinformation Campaign Scenario
1. Initial Denial:
• Key Players: Military, secret agencies, health authorities, virologists, philanthropists, etc.
• Action: Strongly deny any allegations of a secret operation involving a lab-manufactured virus leak (commonly referred to as the “Lab Leak Theory”).
• Narrative: Label the lab leak theory as a baseless conspiracy, dismissing it without thorough investigation.
2. Diversion:
• Media Strategy: Shift the focus of the media to alternative explanations, such as the “Zoonosis Theory” (natural transmission from animals to humans).
• Examples: Highlight potential sources such as bats and pangolins to distract and redirect public attention.
• Impact: This redirection aims to convince the majority of the population (~70%) to believe in a "viral spillover", thus novelty of the virus.
3. False Confirmation:
• Controlled Leaks: Release unverifiable “evidence” that appears to confirm the lab leak theory through credible sources.
• Staged Reports: Media outlets present findings like the Furin Cleavage Site or HIV inserts as proof of the lab-manufactured origin.
• Public Reaction: Skeptics (~25%) quickly adopt this narrative, now able to direct their frustration towards those seemingly responsible.
4. Framing:
• Agenda Alignment: Shape the lab leak confirmation to justify specific actions or policies that stakeholders wish to implement.
• Justifications: Use this narrative to defend the necessity of questionable virological surveillance, mass testing, lockdowns, masking, and mass vaccinations as preventive measures.
5. Public Manipulation:
• Perceived Investigation: Convince the public that the origin of the virus has been thoroughly investigated and validated, creating a false sense of certainty.
• Acceptance: The public now either believes in the perpetual risk of natural spillover or lab leak pandemics, leading to widespread acceptance of continuous countermeasures.
• Focus Shift: Rather than calling for the cessation of Gain-of-Function (GoF) research, the narrative shifts to the inevitability of such research due to its international nature, emphasizing the need for ongoing measures like viral surveillance, mass testing and vaccinations.
Summary: Stakeholders deny the “Lab Leak Theory,” redirect media to natural origins, then release false evidence supporting the lab leak to win over skeptics. This frames their original techniques and countermeasures as necessary, manipulating the public into accepting any future measures.
Instead, people like Dr. Binder have pointed out since 2020, that the use of mass PCR testing, is entirely responsible for this phenomenon:
In addition, Dr. Rancourt has shown strong epidemiological evidence, that the mass casualties that were observed in some regions cannot be caused by a novel risk-additive pathogen:
There are several problems with the reference genome (b) published by Wu et al. 2020 (a):
1. The sequenced patient sample contained genetic material from different sources: human, bacterial, viral, etc. Although known sequences were filtered out after sequencing, there is no guarantee that all non-novel-viral sequences were actually removed.
2. The patient's human genome was not sequenced for control.
3. Reassembly of the dataset published by Wu using Megahit does not provide the exact or complete sequence as published.
4. Trinity, the second program used for de novo sequencing, is unable to generate the identical contig.
5. When using untrimmed or protocol-trimmed reads (Takara), no reads are found that perfectly match both ends of the genome. This is unusual because, according to a theoretical simulation, several ends should be found in the sample. (c)
6. It has not yet been proven that the entire sequence (~30 KB) actually occurs in this form in the samples, e.g. by agarose gel electrophoresis or (Sanger/whole genome) sequencing.
7. Wu et al. published three versions of the reference genome, the first of which contained known sequences from the human reference genome. The fact that the first version contained human sequences suggests possible problems with sequencing or analysis.
8. The amplicons, i.e. the sequences of the ends found using RACE, have not been published. The non-publication of the amplicon sequences raises questions about the transparency and reproducibility of the study.
9. The only non-Chinese author of this paper, Eddie Holmes, confirmed to me by email that he had no detailed knowledge of these issues. There was silence from the Chinese side, although questions were asked via Holmes. (d)
These clear scientific problems therefore clearly call into question the validity of the SARS-CoV-2 sequence.
10. Wu et al., only published a single run, which is supposed to prove the sequence.
11. Wu claims they found a complete sequence before they actually knew its true length - they were just missing the ends, which they then added manually via RACE.
That's a fallacy - because how can one determine the length of a new sequence without first finding the ends, and thus the true length?
A list of statistical tricks, that can be used to calculate an illusion of vaccine efficacy with a placebo alone.
For this exercise, I have used a sine wave to simulate weekly deaths:
... and a logistic growth function to simulate placebo vaccination from 0 to 75% of the population.
By the green/red dots, we can see no difference/effect, as no statistical tricks are applied yet.
Trick 1: Unknown Vaccination Status --> Unvaccinated.
If 50% of Unknown vaccination status is treated as unvaccinated, almost 3x higher mortality rates appear for unvaccinated. This is entirely an illusion.