1) Es ist kein statistisch relevantes Ergebnis. Geringe Korrelation; p>0.05
2) Zu kleiner Zeitraum
Wenn wir den Zeitraum auf die letzten 8 Wochen erweitern, ergibt sich ein ähnliche Bild. (auch keine stat. relevanz)
Aber, selbst dieser Zeitraum ist immer noch zu klein...
Hier, das ganze nochmal als Liste.
Sachsen und Thüringen wurden von den Autoren als Beispiele für niedrige Übersterblichkeit bei gleichzeitiger niedriger Impfquote angeführt.
Das ist zwar faktisch korrekt, lässt aber aussen vor, dass beide Länder schon enorme Übersterblichkeit hinter sich hatten, und warscheinlich jetzt über viele Genesene und Immune Personen verfügen.
Hier das ganze im Quartalsvergleich:
Und hier als Ranking für die letzten 2 Jahre:
Insgesamt würde ich sagen, dass die Analyse der Authoren korrekt erscheint, aber die Schlussfolgerung ist falsch.
Wir können bisher weder sagen, dass die Impfung zu mehr Übersterblichkeit führt NOCH dass die Impfung Übersterblichkeit verhindert.
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:
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.
🔥 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.
Here adjusted for the levels during extremely low COVID-19 prevalence in Summer of 2021, we can possibly see, no efficacy and a drop for unvaccinated and slight increase for vaccinated, possibly even indicating negative efficacy?
💥💥💥 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.
Plotted against the official total monthly all-cause deaths from , shows a very close match of this dataset. stats.govt.nz