10/18/2019
The #Gates Foundation's Event 201 pandemic simulation game simulates a coronavirus outbreak in Brazil and recommends, among other things, consistent media control, algorithm manipulation and fact checking. youtube.com/playlist?list=…
11/2019 (undated)
The National Center for Medical Intelligence (NCMI) reports exceptional cases of illness in #Wuhan. The Pentagon denies it. abcnews.go.com/Politics/intel…
11/14/2019 #Biontech would like to work on mRNA vaccines. #Sahin hopes for the first approval in 5-6 years.
12/19 (undated)
The first clips - later to be found to be staged - from #Wuhan are appearing on social media.
1/10/2020
Professor Edward Holmes posts a message by Prof. Yong-Zhen Zhang about the alleged novel viral genome.
To this date, the exact 'genome contigs' remain unreproducible and no assembler has ever found the entire genome from start to end. usmortality.substack.com/p/sars-cov-2-g…
1/22/2020
The Johns Hopkins University starts its Covid Dashboard, just as practiced in even 201.
1/23/2020
Prof. Dr. Drosten publishes the first PCR test protocol for the 'novel coronavirus' without having access to a real virus sample.
The publication was peer-reviewed in less than 24h, which is unheard of. eurosurveillance.org/content/10.280…
1/21/2020
The first US case, a man who went to an urgent care clinic "after seeing reports about the outbreak." history.com/this-day-in-hi…
Lockdowns begin.
As economies come to a stop, and most people stay at home, mortality rises in certain places.
NYC experiences a rise of 5x normal mortality, while other cities see significantly less deaths.
Dr. Rancourt concludes, that the initial peaks seen synchronously around the world are "from an epidemiological standpoint strictly impossible, because the time from seeding of the infection to the measurable rise in mortality is completely uncertain."
During the covid pandemic, flu completely disappears.
To this date, the exact mechanism remains unclear.
7/30/2021
Dr. Fauci, stating on national TV, that vaccinated people are “capable of transmitting the infection” and stating that the level of virus are “essentially equivalent” between vaccinated and unvaccinated infected people.
8/6/2021
In a COVID-19 "outbreak" in Barnstable County, MA - CDC confirms that "Cycle threshold values were similar among specimens from patients who were fully vaccinated and those who were not." therefore not offering any protection against spread. cdc.gov/mmwr/volumes/7…
6/2023
Yet, 3 year later:
"The WHO will take up the EU system of digital COVID-19 certification to establish a global system that will help protect citizens across the world from on-going and future health threats, including pandemics." commission.europa.eu/strategy-and-p…
🔥 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.
Proving causality of efficacy would require long-term placebo-controlled RCT's. The pre-vaccination trend from '49-'62 shows that both cases & deaths follow the expected trajectory -> the decline might have continued without vaccination.
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.
Since 2000. 3y reference and confidence interval with variance 1970-2019.
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.
1. Double-Blinded, Placebo-Controlled Randomized Trials
These trials are the gold standard for eliminating bias and ensuring reliable results. Participants and researchers don’t know who receives the vaccine or placebo, reducing the influence of expectations on outcomes.
💥💥💥 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: