Two Years of U.S. COVID-19 Vaccines Have Prevented Millions of Hospitalizations and Deaths
It’s An Unreferenced BLOG Post, Not An Actual Scientific Study! More Biased Data Propaganda
It seems official at this point. I have used the word so many times in the last few posts that I think it’s my new favorite word 😊. Propaganda.
To be clear, propaganda doesn’t have to be messaging from some evil corporation, government, or dictator, it can be messaging from an appearing benign government to ensure their narrative becomes the dominant narrative. This messaging can be in the form of smear/cancel campaigns against dissenting experts and groups, or it can be the vast dissemination of information propped up as objective truth that in reality is exceptionally poor-quality.
And this is where we find ourselves for today’s post. Not that I believe our governments or media are benign at this point, but they have certainly propagated mass amounts of terribly poor-quality data and studies as objective truths in attempt to support their narrative of lockdowns, masking and vaccines during the pandemic.
And it’s no different with the following headline:
“Two Years of U.S. COVID-19 Vaccines Have Prevented Millions of Hospitalizations and Deaths “
This so-called study that isn’t a study (more on this below) is being propped up as definitive proof of vaccine efficacy by those who are promoting the safe and effective COVID-19 vaccine narrative at all costs. Many are calling them the covidians or the vaccinators.
Anthony Fauci referenced it on CNN as a “study”.
Steven Colbert makes disgusting comments about republicans who have died from COVID citing this non-study as part of his covidian gospel doctrine.
But here’s the kicker, this thing isn’t actually a study at all, it’s basically a blog post without any references. It is not peer-reviewed or published on a pre-print server.
It’s literally posted on the Commonwealth Fund’s blog page. https://www.commonwealthfund.org/blog/2022/two-years-covid-vaccines-prevented-millions-deaths-hospitalizations
This was a mathematical modelling article, meaning they did not use real world data and observations, they made estimates for many variables and put them into their equation. As anyone with experience with such models knows….garbage in = garbage out.
The authors state that vaccine efficacy was drawn from published estimates, but there is no reference for where such estimates came from or what they were.
Which is interesting because it’s pretty clear that the vaccines fail and the unvaccinated end up with lower rates of cases, hospitalizations and deaths. But hey, in this mathematical fantasy world, they are assumed to be effective.
The authors also state the following:
Basically, they say that they simulated (mathematically) a scenario where there was no vaccine and then compared that to their simulated scenario where there was a vaccine. And remember this second scenario is supposed to represent what happened in the “real” world even though it’s just as much of a simulation with variable assumptions as the first non-vaccine scenario.
We know that the original estimates that government used for how bad COVID-19 would be if no draconian control measures were implemented came from Neil Ferguson of the Imperial College. One of the assumptions used in their model was an excessively high R0 (basic viral reproductive number), thereby significantly overestimating the impact COVID-19 itself would have. They also assumed that the non-pharmaceutical control measures would have a positive impact on COVID-19 spread even though it was well known before COVID-19 that such measures were not effective against aerosolized viral spread. So, when the model overpredicts the spread of COVID-19 and over predicts the positive impact control measures will have, then your model shows that such interventions will save millions of lives. But, when the assumptions plugged into the model actually match real world data and previously understood facts, the interventions do not come out looking good at all.
The authors of the blog post in question don’t show their calculations or provide references so we don’t know what the assumptions/data were that they entered into the models they compared in their exercise.
Given the amount of propaganda, censorship, poor data collection, and poor quality and fraudulent science that has happened over the last 3 years, we must assume that if an author doesn’t show the details of their work and/or provide high quality references that there is a good reason for them to hide it. Meaning it’s garbage and wouldn’t stand up to true peer-review scrutiny by actual experts.
Here is a short, simple explanation of how such models can be manipulated.
Mainstream Narrative Hypocrisy
And here is one area of the covidian vaccinators hypocrisy
“You may not use non-peer-reviewed evidence/data/articles to try and prove we are wrong, but we can use non-peer-reviewed evidence/data/articles to prove we are right, because our narrative is obviously the only narrative.”
It looks something like this:
And if you call them out on their BS, they gaslight you.
This all leaves me with one final question. When people in positions of authority such as Anthony Fauci cite a blog post such as the one discussed today as proof the vaccines saved millions of lives, is that fraud? Is it at the very least false advertising (which is illegal)?
If a company other than pharmaceuticals makes a claim about their product without sufficient substantiation, they can be sued, it’s fraud. And to be clear, a non-peer-reviewed, unreferenced blog post that doesn’t contain a detailed outline of methods and calculations is not sufficient substantiation evidence.