🚨 BREAKING: German govt data shows vaccinated died at 3x the rate of unvaccinated.

Vaccinated: 2.13 per 1,000
Unvaccinated: 0.68 per 1,000

But there's a problem...

Thread 🧵👇 76% of deaths have UNKNOWN vaccination status.

They imposed mandates, lockdowns & fired workers based on data where 3 out of 4 deaths were... unknowns.

💥 Bombshell: German RKI Admits No Negative Controls in Viral Genome Sequencing

The German RKI has confirmed they conducted no negative controls during genome sequencing of SARS-CoV-2 and measles viruses. Negative controls—where identical methodology is applied to samples without the target virus—are essential for validating results. Instead, they relied solely on unvalidated PCR testing to confirm assumed presence prior to sequencing.
Full FOIA:

By 2020, ~90% of U.S. teens (12–17) were vaccinated against HPV — with no meaningful difference by race.

But look at cervical cancer mortality since the vaccine’s rollout:

🔺 Asian women: mortality increased
➖ Native American women: unchanged
🔻 Black women: biggest decline

Same vaccine coverage. Different outcomes.

If HPV vaccination drove the drop in deaths, we’d expect similar trends across groups. This strikes at the core of today’s vaccination debate. Even if we accept—despite questionable evidence, such as the lack of decline among Asian populations—that there has been a reduction, it amounts to just 1.5 fewer cervical cancer deaths per million white females..

🔥 MEGA ANALYSIS on #Measles #Outbreak: The Recent “Measles Outbreak” Scare Appears to Be a Big Pharma Campaign, Unsupported by Data!

Let's look at actual data & studies. Notably, before the introduction of the first measles vaccine ...
Mega Thread 🧵 1/n ... by Enders et al. in 1963, measles deaths had already declined by 97.2%, from 12,992 in 1919 to 364 in 1963—without vaccination. While deaths dropped to typically less than 10/year.

I’ve recently updated German data to use more user-friendly 10-year age groups instead of the previous 5-year intervals. The difference was surprisingly large, so I decided to investigate—digging deep into the code for about three days to verify everything. Here’s what I found:

Overview of Mortality Watch Data Handling

Mortality Watch integrates multiple data sources and can handle different temporal resolutions (yearly, monthly, weekly). A few months ago, I refined the logic to select the best available data for each resolution—e.g., using yearly data for yearly resolutions.

For these recent updates, I also added 5-year resolution yearly data:
• Yearly Deaths: These match exactly with the underlying dataset from Destatis, which provides official yearly figures.
• Yearly Population: This is reported as of December 31st each year. The simplest approach would be to join the data by year, but that would be inaccurate. Since the beginning of Mortality Watch, I’ve always calculated daily population estimates—breaking down deaths and population into daily values and interpolating as needed. This ensures consistency, as summing up daily data correctly matches the totals.

When comparing Mortality Watch population data with the Destatis source, Mortality Watch reports the mean of daily interpolated data. This may seem counterintuitive at first, but it provides a more accurate representation.

Verifying the Accuracy of Mortality Watch

To ensure my calculations were correct, I conducted a deep dive and even discovered a small bug. The issue, which had only a minor impact, affected age-stratified CMR calculations:
github.com/MortalityWatch…

To further validate the methodology, I created an example script demonstrating different approaches to calculating ASMR. The results vary significantly based on the method used:

1️⃣ Simple year joins (joins deaths of 2020 to population of 12/31/2020), using Mortality.org age groups (0–14, 15–64, 65–74, 75–84, 85+)
2️⃣ Simple year joins, using 10-year age groups (0–10, …, 80+) – Currently used on Mortality Watch
3️⃣ Daily year joins (interpolated daily population joined on daily death data (evenly split)), using 10-year age groups (0–10, …, 80+)
4️⃣ Simple year joins, using single-year age groups (0–85)
5️⃣ Daily year joins, using single-year age groups

As you can see, the red values match MortalityWatch figures identically, (besides 2023 🥴).
It’s good news that there aren’t any bugs in the calculations. It’s also important to be aware of the sensitivity of the methodology used, and that we should always use the highest resolution possible. This analysis demonstrates how different age groupings and interpolation methods can have a significant impact on ASMR calculations.

So here are the chart with the highest precision.

🛠️ GUIDE: How to Spot Vaccine Fraud 💉

>> 5 Key Steps to Evaluate Any Trial Study <<

🧵A thread... Evaluating the safety and effectiveness of vaccines requires careful scrutiny of clinical trial methodologies. Here are five essential steps to identify potential flaws or fraud in vaccine studies.

💥💥💥 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.

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:

https://x.com/Thomas_Binder/status/1795691467760968078

🧵Thread of posts showing no COVID-19 vaccine efficacy!

https://twitter.com/USMortality/status/1483995426839986183

https://twitter.com/USMortality/status/1499417726342868994

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.

(a)
(b)
(c)
(d) ncbi.nlm.nih.gov/pmc/articles/P…
ncbi.nlm.nih.gov/labs/virus/vss…
usmortality.substack.com/p/why-the-ends…
usmortality.substack.com/p/why-do-wu-et… 10. Wu et al., only published a single run, which is supposed to prove the sequence.

There's a large pharma funded Measles Scare Campaign ongoing.
The actual data doesn't support this. ecdc.europa.eu/sites/default/…

‼️ Phantom Vaccine Efficacy!

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.

🔥 All-cause mortality by vaccination status from the Netherlands shows likely no vaccine efficacy, possible harm!
Deaths per 100k population by vaccination status shows an initial spike for the vaccinated during the vaccination rollout, and consistently higher mortality levels. The initial peak may be related to confounding as more elderly/frail were prioritized, to reporting artifact (Fenton et al.), or vaccine harm.
Only focusing on the mid 2021 data, where the lines move in tandem, we still see a diverging of rates after the late 2021 winter peak.

💥💥💥 The latest official New Zealand FOIA data of All-Cause Mortality by COVID-19 vaccination status & age, shows that the vaccinated are the driver of all-cause excess mortality!
Clearly, unvaccinated deaths did not account for any major spikes in excess mortality! I have analyzed the official NZ data which was published due to a FOIA, and initially analyzed by @sco0psmcgoo.
Here split by age group & vaccination status!
0-20 and 100+ may be incomplete, but those are also rather small numbers.

They give it all away in the package insert. Flu vaccines are a scam. By definition, they cannot work! Moreover, they did not even bother to test for potential of these vaccines to cause cancer or mutations.

Yet, they use the say 14 day trick to claim efficacy!

💥 Age-Adjusted Excess Mortality now available for Germany 2023!
> Reversal to trend in 2023, to only +0.8% excess.
> Confirms high excess mortality during vaccination campaigns.
> Both 2020 and 2023 completely normal.
#Covid #Covid19 #mrna #Vaccine Same chart with 95% prediction intervals.

There's just no denying with this. The increase of disabilities in the US since Covid-19 vaccine rollout is statistical significant.

https://twitter.com/USMortality/status/1733421623196500058

This is not based on any y-axis trickery either.

🔥 Switzerland's birth rates are now at a record low! Excess Mortality:

There's definitely a signal in the NZ death/CMR data: However, that's completely de-confounded or masked when using ASMR - which would suggest, that it's mostly elderly that are dying?

🔥 Median all-cause excess mortality in the 20 most vaccinated highly developed countries increased by +149% after vaccination rollout!

#COVID #COVID19 #Vaccine #MRNA #Excess #ExcessDeaths

Results

In 2020 median excess mortality in the top 20 highly developed countries was +4.5%, with the mass vaccine rollout in 2021, it increases to +9.9% and even further deteriorated in 2022 to +11.2%.

❓ How many countries saw an improvement of relative excess mortality with mass vaccination?
📉 3 📈 17
➡️ Only three countries saw an improvement in excess mortality, 17 had their excess mortality increase.

❓ How many countries achieved normal excess mortality levels (<1%) with vaccination?
📉 0 📈 20
➡️ None of the countries saw a return to negative or close to zero excess mortality levels.

Here are the individual excess mortality charts for all 20 countries:

Methodology

The 20 most vaccinated countries of the @OurWorldInData COVID-19 dataset were selected (min. 1 dose) and filtered by a very high Human Development Index (HDI) >=0.8 value. (1)

Excess Mortality was assessed by age-standardized mortality rates, where available, otherwise crude mortality rate (CMR) was used. A conservative pre-pandemic three-year average 2017-2019 (as used by Levitt et al. (9)) of the mortality rate was used as baseline, except for the United Arab Emirates, where the average of 2018-2019 was used, due to limited data availability.

Relative excess mortality was calculated by the @MortalityWatch tool, all links to charts can be found in the provided spreadsheet. (2)

Discussion

In contrast to general expectations, excess mortality continued and almost tripled with global mass vaccine rollouts.

Considering, that:

1) The COVID-19 vaccines are said to offer a protection against death of up to 94%, according to the CDC, this should have led to a massive reduction in overall excess mortality, not an increase. (3)

2) 2020 IFR estimates by Ioannidis et al., already demonstrated a moderate IFR for all age-groups of 0.23%, and 0.05% for <70-year-olds. (6)

3) Subsequent virus variants decreased in CFR/IFR, a drop of 79% in IFR from previous variants was reported for the Omicron variant in 2022. (8)

4) Early variants of 2020 lead to many deaths in the most vulnerable, hence a temporary mortality deficit should be expected going forward. (“Pull forward effect”)

5) The early diamond princess outbreak of 2020, demonstrated that only about 20% of people tested positive. Since measures and awareness were non-existent at that point in time on the ship, it is clear, that a significant amount of the population must have pre-existing immunity. (7)

6) Use of a conservative three-year average baseline method. In many countries, a declining mortality trend can be observed pre-pandemic. In this case, the average method, could even lead to underestimation of excess mortality.

Given these reasons, it appears mathematically impossible, for excess mortality to rise in subsequent years after a novel virus outbreak, and with a “highly effective vaccine” available.

HDI was used as an indicator, to quickly control for several confounders, that typically impact mortality levels in lower developed nations. E.g. general health status, income levels, poverty levels, that typically impact health outcomes. A common example is when mistakenly comparing lower HDI countries such as eastern European nations, e.g. Bulgaria, with higher HDI countries.

Often, restrictions & NPI's are brought forward as the reason why 2020 saw relatively fewer excess deaths. However, it is already established by now again - as it was pre-2020 for seasonal influenza - that none of the NPI's have significantly reduced the spread of COVID-19. (Meta studies by Cochrane and Johns Hopkins (4) (5)). Germany's R value already decreased below 0, before the first lockdown in March 2020, indicating the natural limitation of outbreaks.

Sources & Data

1)
2)
3)
4)
5)
6)
7)
8)
9)






Again noteworthy, due to the average baseline method, the increase is not due to a modeling artifact!

Has the Measles (MMR) vaccine scientifically been shown to reduce measles cases or deaths?

A deep dive into the scientific literature!
⬇️⬇️⬇️

A Vaccine For Measles Prevention
CDC currently states (1), that they recommend the MMR vaccine to protect against measles.
Two products are available: M-M-R II and PRIORIX.

What is Measles?
According to Mayo Clinic (2) a 'red, blotchy rash' usually appears on the face.
Also, the following clinical symptoms are listed:

Now that we know what clinical measles looks like, let's take a look at the package inserts of the two products!

M-M-R II (rHA)
Merck's package insert (3) lists the clinical trials on page 20.
Here it says, that only the antibody response was compared against the previous M-M-R II HSA vaccine.
--> No clinical indications as endpoints, no real placebo used!

PRIORIX
The second current vaccine, was also simply compared to the antibody response of M-M-R II.
--> Also, here: No clinical indications as endpoints, no real placebo used!

1/2 (See next Tweet for continuation)

Sources:
(1)
(2)
(3)
(4)






M-M-R II (HSA)
Licensed since 1978 - the package insert (5) of Merck's original MMR vaccine, lists the following clinical trials
Of which, only reference 29 and 32 mention measles.

Reference 32 is not a clinical trial, only 29 is, in which I will discuss the details now:

Development and Evaluation of the Moraten Measles Virus Vaccine
According to the study published in 1968 in JAMA (6), three vaccines were compared for their clinical reaction and their antibody response.
I do not have access to the full text, but according to the abstract, the endpoints did not include the case rate of measles cases or deaths.

Also, no placebo was used!

Finally, let's look at the first measles vaccine created by Enders:

Edmonston B and a further attenuated measles vaccine--a placebo controlled double blind comparison.

In this original study from 1967 (7), the authors describe, how they performed a clinical trial of 300 children, that did not have measles before. Split into three groups, they used two measles vaccines, and one placebo, then monitored them for only three weeks (!!).

Moreover, there's no mention in the study of any surveillance of clinical signs of measles illness. They simply measured the fever of the children, and implied that this infers protection. No long term follow up or monitoring was performed. They did simply rely on antibody serum tests, again, to establish efficacy.

As you can see, we've now reached the end of the trail. It goes back to an experiment in 1967 in Honduras where 300 children were monitored for 3 weeks. No efficacy for measles cases or deaths was established. All subsequent studies rely on this original study.

Sources:
(5)
(6)
(7)