"Modelers" and their fake simulations need to be reined in, NOW!

Public Health’s journey into counterfactual modeling and propaganda

There are those of us who live in reality and there are those who are playing along with a simulated narrative fabricated to shape the “new normal.” Those who are both ethical and rational are awake in this dystopian nightmare.

Had the global response to Covid-19 been grounded in reality, the last 2.5yrs would have proceeded much the same as it had the decade prior. The Covid-19 episode would’ve been but a blip — a mostly unimpactful scare that had little effect on our children and the economy.

The data was clear from the get-go: the risk to children was close to zero and the risk to most of the labour force was small and manageable. The elderly and those with comorbid conditions were at significant risk; resources should have been directed towards improving hospitals, long-term care and early intervention.

What actually played out was surreal.

A flawed mathematical model developed by a highly connected professor, Neil Ferguson, drastically overstated covid fatality thus triggering worldwide lockdowns. The professor’s ties to the WHO and the Imperial College London no doubt played a large role in global leaders’ willingness to accept the model’s dire predictions, despite its obvious detachment from reality. Whether the model simulation caused the lockdowns or was simply a tool to legitimize them is unclear. Either way, it began a disturbing trend of contriving unrealistic models to justify egregious human rights violations in the name of “science” and “safety.”

In Ontario and throughout Canada, we witnessed the progressive manipulation of real-world data during the pandemic and increasing reliance on model simulations to guide the public through waves of fear and propaganda. But once the Omicron variant hit, no amount of data manipulation could hide the colossal failure of our government’s response to the pandemic, so their roster of “experts” abandoned real data altogether. Absurd models such as that developed by Fisman et al.¹ were contrived in an attempt to rewrite history — an unprecedented misuse of mathematical modeling. While real-world observations showed that the vaccinated and boosted had higher covid incident rates during the Omicron wave, Fisman et al. concocted a model under the opposite assumption, simulating outcomes that flipped reality. The purely simulated findings magically aligned with the government’s malignant vaccination policies and PM Trudeau’s vilification of the “unvaccinated.”

This plunge into the world of dark mathematical fantasy has continued, showing no semblance of reality whatsoever or signs of letting up anytime soon. For example, a recent paper co-authored by Dr. Theresa Tam, the chief public health officer of Canada, asserts that up to 30.7 MILLION Covid-19 cases were prevented in our country (of population 38 million) due to vaccines and public health measures.² This delusional thinking follows RECORD SURGES in Canadian cases that occurred AFTER the vaccine rollout and during yet another semi-lockdown with travel restrictions in place for the unvaccinated, vaccine passports in use, another round of Ontario school closures as well as select business closures. Perhaps even more incriminating, incident rates were found to be disproportionately greater amongst the vaccinated, especially boosted.

These same disturbing trends — surges in cases after vaccine rollout and higher incident rates amongst the vaccinated — were found globally. To counter these real-world observations, another magic model from the Imperial College London has been published that comes to the aid of global leaders. The Lancet preprint³ asserts that COVID-19 vaccinations prevented 20 million deaths in their first year alone (Dec. 8 2020 - Dec. 8 2021). This despite the fact that clinical trials did not establish a reduction in all-cause mortality (in fact, mRNA vaccines were associated with an increased risk of serious adverse events and death⁴) and despite many countries reporting an “unexplained” increase in excess deaths following their vaccine rollout.

Welcome to the new world of counterfactual modeling and what-ifs, it’s such a dreamy place.

They've gone from manipulating real data to relying fully on contrived mathematical models to fabricate faux benefits in support of unconstitutional policies.

How can we take such nonsense seriously?

And how can we continue to call these compromised researchers “experts” after they publish such tripe?

Hierarchy of Evidence: Sifting through the nonsense

As I have stated in earlier posts, the use of models to simulate hypothetical outcomes and explore real-world phenomenon can, when properly constructed, provide great insight into real-world issues. However, such simulations are not evidence and when models fail to adhere to proper scientific methodology they can be used to mislead.

When evaluating the safety and efficacy of any pharmaceutical there must be due regard for the hierarchy of evidence and sufficient knowledge of the nuances involved when drawing inferences.

The scientific reality is that officials cannot claim that the Covid-19 vaccines and public health measures saved more lives than they cost. The evidentiary failure of the vaccines to show a reduction in serious illness and all-cause mortality cannot be overcome by any amount of weak observational studies and wishful simulations conjured up or concocted by government officials and their roster of “experts”.

I am in the process of writing a quick but useful overview of the type of evidence used throughout the pandemic and why government and public health officials have no scientific case for their vaccine mandates and discriminatory restrictions. When completed I will update with a link [HERE] as well as at the end of this post. In that post, I’ll address the top manipulative tactics that have been used to push the pandemic narratives — there are too many to address them all.

For the time being, I’d like to draw attention to two particularly irritating examples of deception that show such stupefying statistical incompetence they deserve special mention.

Example 1: Health Canada’s comparison of vaccinated to unvaccinated

This first example is in regards to how Health Canada has been comparing covid cases, deaths and hospitalizations between the vaccinated and the unvaccinated groups by cumulating numbers since the start of the vaccine rollout in December 2020.

Recall that, for the general population, the first dose didn’t start ramping up until mid-March and the second dose in June. So, the timeframe being used for this comparison includes a good chunk of the second and third waves when almost none of the population was vaccinated. Most cases, hospitalizations and deaths from those waves were obviously “unvaccinated.” Accumulating case counts and deaths over this period is a shamefully obvious means of biasing reports in favour of vaccines.

That’s the deception part. Let’s move on to the stupefying part.

During this time period, December 2020 to present day, the majority of the population has transitioned between (1) unvaccinated to (2) partially vaccinated or (2) not-fully vaccinated to (3) fully-vaccinated back to (2) not-fully vaccinated to (4) boosted. It gets so confusing to keep up; the classifications and their definitions are constantly shifting.

Anyway, depending on when an individual got covid and how many times, they can be counted in up to 4 groups. So, who’s in each group? The groups are clearly not separable since any one person can belong to two or more groups during this time interval. What’s the population size of each group? How would you even compute it? What meaning can even be given to a comparison of vaccinated to unvaccinated numbers aggregated this way? It’s completely nonsensical.

Now add to the mess the changing methods and definitions used to establish case counts, hospitalizations, and deaths over this period … which seems to differ by province… and by hospital…

This is just one example of many in the Government of Canada's⁵ grand repertoire of how not to do statistics.

Keeping track of all the manipulation and nonsense is overwhelming, so I stopped trying to do so a long time ago. I simply file such analysis under “GARBAGE”. Once in a while I check on things to see what new tricks they’ve got going, but it gets dizzying pretty darn quick.

Example 2: Correlation doesn’t equal causation… but let’s cherry-pick a relationship anyway

“Correlation doesn't equal causation” is perhaps the catchiest statistical phrase in history. It’s so trendy, everyone’s saying it.

But WHY has it become such a fashionable phrase?

Indeed, one of the limitations of observational analysis is that while they can be used to show relationships, they don’t prove causation and if there’s no causation then changing one factor won’t change the other. With observational studies, the myriad of possible confounding variables are left uncontrolled and the ability to account for them in the statistical analysis is limited. The more confounding variables there are, the weaker the evidence for causation. That's why double-blind randomized clinical trials are so important in assessing treatment effects. When done properly, and with adequate sample size, they are able to eliminate bias and control for confounding factors thus providing much stronger evidence of causation.

Except the mRNA clinical trials didn't establish a reduction in hospitalization, serious adverse events nor death.

And they didn't assess transmission nor infection.

So, researchers are now looking to “real-world” observational data for evidence of effectiveness wrt these key measures.

Unfortunately, real-world data has proven that these vaccines do not stop transmission and during the Omicron wave greater infection rates were observed in those vaccinated, especially boosted. While “correlation doesn’t equal causation” the fact that observed data shows an OPPOSITE trend to what is expected is extremely problematic to those advocating vaccine effectiveness.

So, vaccine enthusiasts turn to even weaker data with an even greater number of confounding variables.

Instead of examining trends within a given region over time, there have been numerous reports where researchers cherry-pick countries with different overall vaccination rates at a singular point in time (argh!) and say: “Hey look, countries with lower vaccination rates have higher covid deaths.” That is precisely what was presented in Table 2 of the Government of Canada’s CCDR report co-authored by Theresa Tam, ref (2), “Tam’s (counterfactual) paper”. Don’t worry, the authors provide a brief discussion of why their cherry-picking is okay — except it's not.

So much for “correlation doesn’t equal causation.”

Public Health bureaucrats like Tam are playing a dangerous game with people’s health. They know better. One could just as easily select countries that show the opposite trend — countries with higher vaccination rates that have higher covid deaths. There’s certainly no shortage of such examples posted on social media by those countering the government’s pro-vax narrative.

Below, I’ve reproduced Table 2 from Tam’s paper, modified to include a few extra countries not provided in her counterfactual paper. A graphical representation is also provided, with and without the additional countries. Notice how the correlation almost disappears with the additional countries (and without the Zero-COVID countries it would essentially be zero).

There’s something very perverse about the chief public health officer of Canada using such deceptive tactics. Table 2 in Tam’s counterfactual paper is a perfect example of “correlation doesn’t equal causation.” Tam and her colleagues have managed to maximize (or close to it) the number of confounding variables, thus maximizing the potential for misinterpretation and deception. They have used an analytical technique that accomplishes the very opposite of what randomized clinical trials are designed to do; they are obfuscating the true relationship.

Another disconcerting feature of Table 2 is that it purports to be giving us the cumulative number of deaths DUE to coronavirus. That is, our chief public health officer seems to have confused “died WITH covid” and “died FROM covid” — an all-too-common mistake amongst laypeople. By labelling the counts as “Covid Deaths” causality is often assumed, though Dr. Tam really should know better. This, of course, is a big problem since the number of incidental deaths is quite large (the majority of deaths are individuals over 80yrs and over 90% of “Covid Deaths” occur in people with one or more underlying conditions).

There is more to say about Table 2 in Tam’s ridiculous paper, including how absolutely meaningless it is to compare numbers at a singular point in time across countries. There’s so much wrong with this, too much to unpack in this one post.

I've included a few papers in the refence section regarding the Bradford Hill criteria for determining causality⁶ ⁷ along with a paper⁸ discussing some of the nuances of using the criteria. It's a good place to start for those interested in looking into the topic of causality further.

Phantom benefits

After cherry-picking some countries and choosing a single point in time that “illustrates the relative effectiveness of the Canadian response,” Tam’s paper walks the reader through a graphical fantasy showing how each covid wave apparently was controlled by restrictions or a combination of restrictions + vaccination and how each increase apparently was due to an easing of restrictions.

But I thought “correlation doesn’t equal causation”?

Again, it doesn’t. Maybe that’s why the the authors felt justified in not mentioning how the third wave coincides with the uptake in first dose vaccine in the general population. Just because an increase in cases as the first dose was rolled out was shown in many other countries, it doesn’t mean that the vaccination campaign caused the increase. But the fact that there is an abundance of evidence showing individuals are more susceptible to covid within the first 14 days post-first dose is problematic, so they just ignored that. Incidentally, these individuals are generally misclassified as “unvaccinated.”

What about the HUGE surge in cases in December 2021, AFTER the vaccine rollout?

Well, that’s the exciting part of the story: apparently, it’s because “easing restrictions allowed for transmission of the immune escape Omicron variant of concern.”

But wait… weren’t the unvaccinated BANNED from public spaces, BANNED from universities and BANNED from travel during that time? Wasn’t the Omicron brought here via the vaccine passport??? Isn’t this AFTER unvaccinated federal employees and many healthcare workers got turfed or put on leave without pay?

Yes.

But the VACCINATED were permitted to travel internationally and socialize, so Tam et al. must be referring to the VACCINATED passing the virus around. So why not mention it?

Well, that’s awkward. Are they suggesting that the government restricted the wrong group? Umm… no. Obviously they need to get everyone vaccinated and then lock us all down, so the story goes.

Here’s Tam’s view of what transpired (Figure 1 from her counterfactual report). Take note of the accounts of control as though the system were a child responding to parental whims.

But alternative views are left completely unexplored. Here’s one of them: Hypothetically, covid waves follow a natural cycle driven by seasonal variations but with additional drivers such as vaccine rollout. The government responds to these variations believing they are controlling them.

It’s fun creating your own narrative. Give it a try. Everyone’s doing it!

While narrative 1B is a legit account of events, Tam et al. prefer the version that attempts to justify locking everyone down and forcing injections into every man, woman and child in Canada. So, on that note, the authors proceed with their “study” based on the fairy-tale assumptions that:

1. Public Health restrictions are all beneficial — they reduce covid hospitalizations and death. Any adverse health outcomes or unanticipated mortalities caused by the restrictions are ignored, and

2. Vaccines reduce infection, hospitalization and death — despite the fact that these conjectures weren’t established in the RCTs. No matter. The model assumes that 96% of deaths are averted after a two-dose series, no waning. The assumption of protection against hospitalization is 96% pre-Omicron, 86% for Omicron. They further assume waning in hospitalized protection begins after about 3 months for both the vaccines and natural infection, eventually dipping to zero in 15 to 17.5 years).

Tam et al. ran an agent-based model with oodles of parameters that took these lofty assumptions and spit out some pretty impressive counterfactual outcomes: Apparently, if our government hadn’t made life a living hell for the past 2.5yrs we could have expected up to 30.7 million more cases, 2 million hospitalizations and 800,000 deaths. It seems pretty hard to imagine, but somehow Tam et al. found a way.

Does their fantasy world account for any uncertainty in their convictions regarding vaccine effectiveness or reduced transmission due to public health measures?

NOPE. None whatsoever.

Simply put, Tam’s counterfactuals do not capture nor reflect the current state of knowledge or uncertainty. The modeling by Tam et al. appears to be a futile attempt to justify government actions when what they really need to do is conduct a post-mortem.

The harsh public health policies and aggressive vaccination campaign failed to stop viral outbreaks. Taking the two-dose vaccine series did not prevent individuals from covid infection, it did not lead to herd immunity and society did not get the freedom back that was promised. What has been the true cost?

Running “what-if” scenarios could have been useful, if the right questions had been asked. What if the vaccines cause an increase in series illnesses and all-cause mortality, like the Fraiman et. al study shows? What if we are damaging the cardiovascular health of our youth by mandating the vaccines, like all the passive databases indicate? Certainly, the latest prospective study by Mansanguan et al.⁹ is deeply concerning for parents and young adults.

It is important to understand the extent of damage that may have been caused by overly-restrictive public health measures and the massive uptake of new covid vaccines. If these measures do provide a net negative effect, what will be the long-term damage if we continue on this destructive path?

Where do we go from here?

When even the weakest evidence and most manipulated statistics don’t produce wanted results, one can either admit failure and learn from the mistakes OR one can double-down and fabricate the results wanted, leading to even greater harm.

Looks like Canada’s trusted experts are choosing the latter. They’ve created a mathematical fantasyland: the world of bogus model simulations and counterfactual what-ifs.

And why not? There seems to be no shortage of politically-charged researchers, like David Fisman and Neil Ferguson, to generate junk-science fantasy narratives.

It’s EASY to manipulate the masses, especially when you control the narrative and don’t allow your message to be challenged.

The only way our government conceivably could’ve gotten away with such obvious anti-science, harmful tactics is through extreme censorship, propaganda and a willingness by legacy media and the medical establishment to go along with it. It MUST stop.

If we don’t rein in enablers of this scientific fraud now, the situation will only get worse.

No model or analysis should be free of open, honest debate and scrutiny from the wider scientific community.

It is clear that proper oversight and stringent guidelines are needed when it comes to how and when mathematical modeling and simulation can be used to influence policy. There must be due regard for the strength of evidence, objectivity and a deep understanding of the nuances of scientific research and statistical analysis. We need to establish this now, the sooner the better.

Ironically, the dire mass suffering predictions of early 2020 have been largely self-fulfilling. What could have passed as a minor blip has become a major disruption, but instead of being caused by the virus, the greater harm has been caused by government intervention and overreach: unnecessary lockdowns, unjustified mandates and stifling restrictions all in the name of “science.” Like everything else during the pandemic, the term “science” has been flipped on its head. Instead, we have anti-science masquerading as fact, recklessness being touted as “safety” and abusive human rights violations being marketed as the “democratic way.” This dystopic nightmare has caused immense harm and immeasurable suffering, the very things we sought out to avoid.

Not only must our politicians be held to account for defrauding Canadians, those researchers and institutions that have enabled such abuse must face consequences.

1. Impact of population mixing between vaccinated and unvaccinated subpopulations on infectious disease dynamics: implications for SARS-CoV-2 transmission (cmaj.ca)

2. Counterfactuals of effects of vaccination and public health measures on COVID-19 cases in Canada: What could have happened? - Canada.ca

3. Global impact of the first year of COVID-19 vaccination: a mathematical modelling study - The Lancet Infectious Diseases

4. Serious Adverse Events of Special Interest Following mRNA Vaccination in Randomized Trials by Joseph Fraiman, Juan Erviti, Mark Jones, Sander Greenland, Patrick Whelan, Robert M. Kaplan, Peter Doshi :: SSRN

5. COVID-19 epidemiology update - Canada.ca

6. procrsmed00196-0010.pdf (nih.gov)

7. ACSH Explains 'Hill's Criteria': Determining Causality from Correlation | American Council on Science and Health

8. b2a03072.dvi (rtihs.org)

9. Cardiovascular Effects of the BNT162b2 mRNA COVID-19 Vaccine in Adolescents