The Worry Window is the theory that injection with the experimental Covid vaccines has a magical property of increasing infections immediately after the first dose, and that this magical property is either magically self-hiding in most of the data on post-injection-infection-rates or has been systematically hidden by guilty researchers who know that the Worry Window is real.
Because the Worry Window is real (per the theory), all reported values for infection rates in the Covid vaccinated are not valid. Real Covid vaccine infection efficacy is probably always negative.
If you want to read up on it some more see Brian’s excellent post - “The Worry Window Is Not Real Post, Pt. 1”
And “The Worry Window Is Not Real Post, Pt. 2”
I’ll update this post with - “The Worry Window Is Not Real Post, Pt. 3” once Brian has finished it.
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“The Worry Window Is Not Real Post, Pt. 3” - COMING SOON ****I’d never heard of the “worry window” before reading Brian’s article. Maybe I had, maybe I’d just dismissed it without much thought, because like Brian I didn’t think it had much substance (except for distorting the calculation of effectiveness).
After reading the
and post below:
and reading the phrase “All models are wrong, but some models are useful.” How could I, the author of a substack calling itself “KRAP”, resist the temptation to create a KRAP model that is probably both wrong and useless.
Vaccination Roll-out Model
Figure 1 above closely models the vaccine roll-out in Kakistan, any resemblance to an actual roll-out in any other another country is purely co-incidental.
Worry Window (WW) Model
Now lets calculate the percentage of people in the worry window and make up some squiggly lines to represent deaths (per month) from COVID-19, cases of COVID-19 and a surrogate for the prevalence of COVID-19 (% of positive tests) and plot them on the same chart (Figure 2, below). Again, any resemblance to any real word data is purely coincidental.
Coincidence
As mentioned earlier “All models are wrong, but some models are useful”, this one is surely wrong, but is it useful?
What do we see here?
Delta peak (about 1/10/21) was deadly (low case prevalence high death rate) and a group of people just died from COVID-19 coincidentally at about (slightly after) the same time as there was a peak in those that had freshly received their 2nd jab.
Omicron 1st peak (about 15/1/22) killed more people and another group of people just died from COVID-19 coincidentally at the same time there was a peak in those that had freshly received their 3rd jab.
Omicron 2nd peak (about 15/4/22) killed less people than Omicron peak 1 but more than the delta peak. People kept dying from COVID-19 but at a lower rate that Omicron peak 1 even though the prevalence was higher.
Omicron 3rd peak (about 15/7/22), deaths peak again at a lower rate than Omicron peak 1, and yet another group of people just died from COVID-19 coincidentally at the same time there was a peak in those that had freshly received their 4th jab.
Jabs start rolling off about 1/8/22 and deaths start declining while prevalence remains fairly high.
Keeping Up-to-date with the Vaccine Schedule
As can be seen in Figure 3 (above), it’s important that you get your booster when it’s due. If only everyone had taken their booster as recommended and kept the population at above 75% we could have kept death form COVID-19 below 500 per month like March 2022 when we reached ‘peak-vax’.
Conclusion
All models are wrong, especially this one. All this can be explained simply as a co-incidence. The vaccine roll-out are just made hypothetical numbers that in no way resemble a real vaccine roll-out and the deaths and cases are just squiggly lines that look something like COVID-19 waves in the southern hemisphere. Luckily we have nothing to worry about because this in no way resembles any real life scenario.
If it was real, we could just blame the high salt and fat content of the COVID Meal Deal(TM) for any excess cardiac related deaths and the high sugar content for any excess deaths due to diabetes. Everyone knows it couldn’t possibly be the ‘special sauce’.
Kudos to
for the making me think about this. I actually agree with him that the major reason for the above phenomena is not solely due to the vaccination 'worry window', he believes it doesn't exist, I'm not so sure. I look forward to reading his Part 3.I've also left out some other 'coincidences' and speculation on why the charts look like they do, some reasons are described by Brian in his articles. Some others could really just be pure co-incidence like old people getting vaxxed as cases were increasing, it would have been so much worse if they weren't vaxxed, ending lock-downs and the opening of domestic and international borders.
I also suspect the PCR tests were 'rigged' with the so called S-gene dropout inflating cases and creating a ‘game changer’. Without the ‘game changer’ to explain the absolutely obvious lack of effectiveness, governments and their health minions around the world would have been hard pressed to explain this absolute shitshow. Again another ‘game changing’ coincidence, coinciding with Kakistan ending its lock-downs and opening of borders. I'd love to hear from someone that knows about PCR testing to explain how dropping the S gene as a criteria in the PCR test effects the false positive rate, my gut feel is it could increase it 1-2 orders of magnitude thus creating 'Omicron'. Lots of extra cases due to a change in the definition of a ‘positive test’ and also rolling out free rapid antigen tests to the good people of Kakistan at the same time. This resulted in a much lower case fatality rate (CFR) and a convenient excuse for the loss of vaccine effectiveness. However, more people died. First it was ‘waning’ then ‘Omicron’. ‘Omicron’ is like pulling a rabbit out of a hat and ‘waning’ is just brilliant, it’s the gift that keeps on giving.
makes another important point regarding the so called 'worry window' it distorts the vaccine effectiveness calculation. His article below also implies that 'worry window' may be real.
Apology’s to Brian, I just noticed spell check or my dyslexia kept referring to him as Brain. This was not intentional, I’ve now fixed the spelling mistakes (hopefully).
Maybe, Brain is appropriate, his articles certainly make me think and question my bias.
After reading this for about half an hour:
SARS-CoV-2 Viral Mutations: Impact on COVID-19 Tests
https://www.fda.gov/medical-devices/coronavirus-covid-19-and-medical-devices/sars-cov-2-viral-mutations-impact-covid-19-tests
I'm still none the wiser as to how the test performance is impacted by viral mutations. Maybe I missed something in this labyrinth of links and technical jargon.
Is it that Omicron was so prolific that even a reduction in test specificity (more false negatives) and failure of certain test to pick up Omicron at all, still gave rise to a massive increase in the number of cases?
Wow! That's one impressive mutation. 🤔