April 1, 2022 JV

Hide and Seek

The youngins may have been a bit naïve in the way they chose to hide, but as they got older, they were definitely harder to find, especially if you pretend that you don’t see the tell-tale lumps and bumps of human bodies.

The game of Hide and Seek brings back so many great memories.  Whether with your own children or as a youngster, with cousins or friends, it was always a lot of fun.

Governments and medical institutions have also been busy playing this game.  Unfortunately, there are just so many bodies that can be swept under the rug without putting the tell-tale lumps and bumps of human bodies clearly on display.

Enter Professor Norman Fenton.  I don’t know why I’ve only just heard of him.

Norman Fenton is a Professor of Risk Information Management at Queen Mary University of London and a Director of Agena, a company that specialises in risk management for critical systems.  Fenton holds a BSc, MSc and PhD in Mathematics, is a Chartered Engineer (IET) is a Fellow of the IMA (Institute of Mathematics and its Applications), BCS (British Computer Society) and the Higher Education Academy and has completed Expert Witness Training with Bond Solon under the auspices of Cardiff University Law Dept.

He has leveraged his background and training to focus on critical decision-making and, in particular, on quantifying uncertainty using causal, probabilistic models that combine data and knowledge (Bayesian networks).  The approach can be summarized as ‘smart data rather than big data’.  Applications include law and forensics (e.g. expert witness in major criminal and civil cases), health, security, software reliability, transport safety and reliability, finance, and football prediction.

Early on in the COVID pandemic, he was a highly revered voice in analysis of pandemic data, publishing many peer-reviewed reports

This was the case until his work began to show that the disease was far less lethal than was being reported. 

Fenton’s group did extensive research into COVID risk factors and symptoms working with many clinical partners to develop a personalised symptom-tracking and risk-assessment app.  I entered my personal details, risk factors and the following risk figures popped out. 


Scary numbers.

I dare not leave the house.

In September of 2020, Fenton began to challenge the accepted narrative, saying that mass testing of asymptomatic people caused major statistical errors.  He dared to divide two numbers:  the total positive case numbers by the total number of tests conducted.  For this sin, he was labelled a subversive spreader of misinformation and was soon afterward cancelled from all publications and throttled / shadow-banned on social media.

Many of the following images are screen grabs from the Fenton video and are therefore, fuzzy. They are placed in this article specifically for those readers who do not watch the 40 minute video.

This video retraces Fenton’s work in COVID data analysis and then goes on to explain, with simple graphics why:

The base definitions used for the COVID pandemic are error-prone (8:55 min mark)

Even if the key metrics, based on the flawed definitions, were real, the lack of highlighted data, renders proper analysis impossible.  (9:25 min mark).

[I raised this very important issue in previous articles.]

The uncertainty and unreliability of the data is even worse because everything is based on “cases” = PCR Positive. (10:00 min mark)

and Fenton is able to simply explain why adopting a mass campaign of asymptomatic testing yields a False Positive rate > 90%.

Watch from the 10:10 min mark.  It’s simply amazing what a bad policy like asymptomatic testing did to the COVID data.

The best empirical evidence for the exaggerated cases numbers is to simply compare the daily COVID cases with total number of COVID triage calls (17:45 min mark)

Professor Fenton no longer trusts any of the COVID data as a result of this combination of definitional and data issues.

He therefore, believes that the ONLY simple objective way to measure the risk / benefit of vaccines is to

compare the all-cause mortality of the unvaccinated against the vaccinated.

The Professor states that

“If COVID is as deadly as claimed and the COVID shots are as safe and effective as claimed, then COVID deaths should be much higher in the Unvaccinated group than the Vaccinated group.”


“there should be only a few more Non-COVID deaths (due to adverse reactions) in the Vaccinated versus the Unvaccinated.”

So, although the data regarding case numbers and hospitalisations cannot be trusted, it is possible to easily compare death rates among the Vaccinated and the Vaccinated by age range.  This should provide a clear and immediate answer to the question of whether the COVID shots are safe and effective.  Only time can answer the question of long-term safety (including the compounding effect of the ongoing booster program).  This will help to make informed decisions about who should be vaccinated.

Fenton’s group was able to secure age-distributed death data from the ONS (Office for National Statistics) UK and used it to develop the following set of All-cause Mortality graphs for Unvaccinated versus Vaccinated by Age range (25 min mark).

The all-cause mortality for the Unvaccinated seemed to track with the vaccine roll-out dates, despite the fact that they played no part in it.  Funny, that!

To show that these strange peaks in All-cause mortality rate by age have nothing to do with COVID, Fenton produced a graph of Non-COVID mortality rate (27:21 min mark).

“Why would NOT taking the vaccine cause people to die of something other than COVID?”

Professor Norman Fenton.

That’s freaky!  Fauci told us the shots were good, but he never promised Thanos-like, Infinity Stone, click-your-fingers-and-other people-die kind of super-powers!

Fenton is able to simulate (30:35 min mark) the above mortality graph shapes simply by mis-classifying people who die within one week of getting vaccinated as an “Unvaccinated Death.” 

An astute regular reader of my blogs will recall the following from my Oct 8 2021 article Pawn

“..almost 50,000 people died within 14 days of receiving XGT but were not being classified as a vaccine-related death.”

It would appear that the governments of many nations are using the same data-manipulation play-book.  Hmmm.

Other strange data anomalies are discussed by Fenton (31:20 min mark) in this graph of Non-COVID mortality for the vaccinated, split into three categories:

  1. Within 21 days of receiving the first dose (blue line);
  2. More than 21 days after receiving the first, but not the second dose (orange line); and
  3. After receiving the second dose.

All three curves should converge on the Lifetime value (thick grey line).  Instead, the data appears to indicate that the first dose is deadly. 

It may be possible to play games with case numbers and hospitalisations, but it’s difficult to play Hide and Seek with death numbers.  The bodies will inevitably show up somewhere.

In stark contrast, if someone receives the second dose and survives, the COVID shot seems to act like Captain America’s mighty shield, protecting them against FOUR OUT OF FIVE MAJOR CAUSES OF DEATH.  The data indicates they are 80 % less likely to DIE from ANY DISEASE!  It’s a miracle!

Sorry to burst the bubble, folks, but the COVID shot is not the elixir of life.

The Professor (33:00 min mark) provides an explanation for these ridiculous contrivances:

That’s UK data, but let’s see whether Professor Fenton’s findings can be validated more broadly.  Yes, world-wide.  If the vaccines were safe and effective, you’d expect to see (36 min mark) something like this? Right? 

Modern countries with high vaccination rates leading to reduced death rates.

Instead, what we see is almost the opposite – high vaccination rates leading to increased death rates among many modern countries!!!

Once again, I’d like to remind my regular readers of a hope and a prediction made over two years ago from my article A New Hope (22 Mar 2020):

“Africa is one of the last continents on earth to contract COVID-19.  With relatively poor hygiene and a weak medical system, it could be hit very hard by the new virus.  Africa was hit hard by Ebola.  It has also carried a disproportionately high share of the global malaria burden for many decades…. Perhaps the already high use of HCQ throughout Africa may hold them in good stead, as both a preventive and treatment option.  Time will tell.  Africa could do with a break.”

Fenton concludes his presentation with the following statement:

There is no evidence that the risks of the shots outweigh the benefits.

Professor Norman Fenton

Therefore, all-cause mortality destroys any argument for vaccine mandate.

Norman Fenton, Professor of Risk Information Management, is a risk expert with a unique and highly developed set of skills.  Leaders in business and government are strongly encouraged to review his work prior to making any decisions regarding COVID-19, especially vaccine mandates.  His work can be found at:  https://www.normanfenton.com/ and https://probabilityandlaw.blogspot.com/

Fenton’s work does not require advanced mathematics.  It’s based on addition, division and a bit of probability theory.  Why then did he get cancelled?  Do world leaders not understand basic maths?  I doubt that very much.  

There is likely a bigger agenda, hiding in plain sight. 

Are you willing to seek it out or would you rather just keep playing the game?


I need to provide a brief update to a DoD COVID adverse effects data manipulation story I wrote I wrote about in my early 2022 article Collapse.

If you recall, three DoD whistle-blowers reported that:

“DMED showed a massive increase in numerous diagnosis codes ranging from cancers, blood disorders, and heart ailments to strokes, nervous system disorders, and reproductive issues. They attested in sworn statements that the increase in the data reflected their clinical experience in the military over the past year and is, in their professional opinion, the result primarily of mass vaccine injury from the COVID shots.” 

Daniel Horowitz from The Blaze continues with:

“In a bizarre twist, the military went on to change the data in the ensuing days without ever conducting a formal investigation into what went wrong or releasing a statement to the public. Rather, a week later, in a terse statement to PolitiFact, of all places, officials claimed the high numbers for 2021 were indeed correct, but that there was a glitch in the data for 2016-2020 used by the whistle-blowers to establish a baseline, rendering those years way too low.”

Here is the whistle-blower data pulled before the military “fixed” the “glitch.”

And here is the data after “the fix” was in.

Horowitz writes:

“…this is completely implausible because A) the baseline of pericarditis from 2016-2020 is simply too high, and B) it would mean that there was absolutely zero increase in pericarditis, either from COVID or from the vaccines. That has already been proven.”

“However, there is something officials are forgetting. They are alleging that the glitch was only for 2016-2020 simply because those are the arbitrary years chosen by the military doctors to establish a baseline average. However, one of the whistle-blowers who signed an affidavit for Sen. Johnson pulled the data prior to 2016, and guess what? That data matches the baseline found by the whistle-blowers and makes the new “updated” data out of sync with historical context.”

I graphed these 2001 – 2015 figures for historical context.


It would appear that the COVID data of medical establishments across the world cannot be trusted.  What are they trying to hide?

To be Continued….

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