1.We showed ONS England data from November did NOT support vaccine efficacy claims. The ONS’s December report claims anomalies we identified are caused by healthy vaccinee effect.
2. The healthy vaccinee effect occurs when people closer to death are too ill to be vaccinated and so become concentrated in a shrinking unvaccinated population, thus increasing the group’s mortality rate.
3. NHS recommended that the most critically ill people be prioritised for vaccination in each age group.
However, the ONS not only contradict NHS guidance but also contradict themselves in their own report:
4. If the ONS are right then we would see:
a) The percentage of the unvaccinated in poor health rise as vaccine rollout progresses
b) A steady non-Covid mortality rate among the unhealthy (because they are dying at same rate as they always have done).
5. To support their claim the ONS released the percentage of 70-79 age group with "very poor" health in each vaccination category.
Oddly, the vaccinated population contains a higher percentage of those in very poor health and this increases over time.
6. Surprisingly the unvaccinated population has the LOWEST concentration of the unhealthy and the percentage declines over time.
7. In this unhealthy sub-population we found the non-Covid mortality rate for the unvaccinated is HIGHER than for the vaccinated. Both rates should be equivalent
Again, we see unnatural spikes in non-Covid mortality just after vaccine roll out as seen before in whole population
8. Therefore, those in poorest health were NOT more likely to remain unvaccinated.
Also, there is a rise in non-Covid mortality, coincidental with vaccine rollout that is not only seen in the population as a whole but is also seen in those with the poorest health.
9. We conclude that healthy vaccine effect cannot explain the anomalies we discovered in the ONS data and believe it is up to advocates for this hypothesis to now prove their case using the released data.
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1/ A friend asked me what I thought about the new AI system called Perplexity. I was eager to test it out
- Would it tell the truth about the Covid-19 event?
- Would it use credible sources?
- Would it stick to the narrative or cite other causal explanations?
.@perplexity_ai
2/ So, I thought I should try it out, just to see how misinformed I could become using this new AI. I started off with some simple, yet probing, questions about the covid-19 event.
3/ It's answers were incredibly accurate, and more importantly it is ‘aware’ of what actually happened. It also isn’t shy about finding and using sources that support honest explanations.
A thorough review of the available evidence suggests that the emergence of a novel engineered virus is the least likely explanation for the event known as the ‘covid pandemic’.
1 / The discovery of ‘novel’ viruses is a function of how determined we are to find them. The attribution of novelty to a virus is as much the result of a politicised process rather than something based on an objective analysis of its properties.
2 / The features of SARS-CoV-2 do not appear to be as ‘special’ or ‘unique’ as claimed.
There is no good evidence that the many and complex hurdles in front of deliberately engineering viruses to become more pathogenic or transmissible in humans have been overcome.
1. Without PCR testing data mortality and morbidity would not be attributable to the novel virus and if this attribution is false there must therefore be other explanations for the ‘pandemic’.
.@Jikkyleaks .@Kevin_McKernan
2. Whilst scandals about PCR testing are well known a materially important aspect of PCR testing has been given scant attention and that is cross reactivity (or crosstalk). This is where other viruses, such as common colds or flus etc., trigger a false positive PCR result.
3. We analysed the very few studies that used blind samples of colds or flu viruses to undertake ‘mystery shopper’ testing of laboratories using PCR.
In these studies, we found strong evidence for up to 25% cross reactivity between other competing viruses and the PCR tests.
1. How did antibiotics use change during the Covid-19 'pandemic'?
And what happened to the stockpiles of antibiotics Fauci said were needed?
In our latest article we pulled together material on policy changes on antibiotic use during the 'pandemic'.
2. Antibiotics are essential in a pandemic. This view is motivated by the fact that in his 2008 article in the Journal of Infectious Diseases (which reported on autopsies of well-preserved victims of the Spanish Flu pandemic), Anthony Fauci concluded that:
3. Despite this UK NICE guidance NG165, on managing suspected or confirmed pneumonia, explicitly forbade the use of antibiotics in Covid-19 cases:
1. Did flu vanish or was there a failure to search for it?
There has been a collective and systemic failure in flu surveillance and flu death reporting systems in the UK during 2020 and into 2021.
2. The UK FluDetector system, which uses machine learning to track flu using Google Trends data, reported that flu disappeared in 2020/21. This is contradicted by Google’s own data and UKHSA reports, both of which report a clear signal for flu in the UK in 2020/21.
3. The UK FluSurvey system tracked flu incidence only until week 20 of 2020 and never updated this data. They then abandoned flu tracking until January 2021 when they announced that the flu season had now restarted, after supposedly witnessing the near eradication of flu.
Quarantines were sold as measures to reduce spread of SARS-CoV-2 and are claimed to have prevented the spread of flu.
But what if there is another explanation.......
1. Flu tests are recommended to be administered within 4 days of symptom onset. If they are administered after 4 days, they would likely produce a false negative result for someone with flu (flu tests are rarely administered routinely anyway).
2. Mandatory Covid-19 tests, run at high cycle thresholds and suffering from cross-reactivity with other pathogens (amongst other operational issues), may well have resulted in false positives for Covid-19, when in fact the pathogen causing symptoms may have been flu.