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Ben Profile picture
@USMortality
Sep 22, 2021 • 7 tweets • 4 min read • Read on X
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1/ USA All-cause Excess Mortality. (Up to week 36/2021).

TL;DR: Similar, possibly higher, as last summer.

By age:
- <25: Fairly normal.
- 25-74: More than last year.
- 75+ About the same as last year. (from previous low before outbreak)

#Covid #covid19 #Corona #coronavirus Image
All six age groups: ImageImageImage
Cumulative Excess Deaths up to week 36:
- 2020: 181,323
- 2021: 208,566 (+15% vs same week last year)

Overall:
- 2020: 380,307 deaths (+12.4 %)
- 2021 (until week 38): 208,566 deaths (+9.9%) Image
Excess deaths by z-score map
Left week 34/2020; Right week 34/2021 ImageImage
Excess deaths by week and z-score, all ages: Image
... and by all six age groups. ImageImageImage
Datasource: CDC via USMortality.com

Find all data & sources here: usmortality.com

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More from @USMortality

Ben Profile picture
Mar 4
🔥 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/nImage
... 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. Image
Read 21 tweets
Ben Profile picture
Feb 27
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.Image
Image
Image
So here are the chart with the highest precision. Image
Since 2000. 3y reference and confidence interval with variance 1970-2019. Image
Read 4 tweets
Ben Profile picture
Nov 27, 2024
🛠️ GUIDE: How to Spot Vaccine Fraud 💉

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

🧵A thread...Image
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.
Read 17 tweets
Ben Profile picture
Aug 20, 2024
💥💥💥 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...Image
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. [..]
Read 17 tweets
Ben Profile picture
Jun 5, 2024
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.Image
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:
Read 4 tweets
Ben Profile picture
May 7, 2024
🧵Thread of posts showing no COVID-19 vaccine efficacy!

Read 17 tweets

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