UK: Four in Five Covid Deaths Triple Vaxxed By Richard Miller (London)

Is it any wonder that the British government is ending the Covid mandates, since they do not work? At present four in five covid deaths come from the double/triple vaccinated population. How can this be if the vaccines are said to “work”? What we are seeing, as reported the previous paper published in the blog here, is that the vaccines seem to make it easier to get Omicron, as well as other disease by weakening the immune system. In other words, it is counter-productive.

https://dailyexpose.uk/2022/01/08/bbc-forgot-to-tell-you-4-in-5-covid-deaths-triple-vaccinated/

“The latest figures published by the UK Health Security Agency show that despite the elderly and vulnerable receiving a booster shot in September and October, and the NHS turning into the National Booster Service ever since, the triple/double vaccinated population still accounted for 4 in every 5 Covid-19 deaths throughout December 2021.

The ‘Covid-19 Vaccine Surveillance Report – 2022 – Week 1’ was published by the UK Health Security Agency (formerly Public Health England) on Thursday 6th January 2022, and it shows that the vast majority of Covid-19 cases between 6th Dec 21 and 2nd Jan 22 were among the fully vaccinated population.

https://principia-scientific.com/lancet-89-of-new-uk-covid-cases-among-fully-vaxxed/

“High COVID-19 vaccination rates were expected to reduce transmission of SARS-CoV-2 in populations by reducing the number of possible sources for transmission and thereby to reduce the burden of COVID-19 disease.

Recent data, however, indicate that the epidemiological relevance of COVID-19 vaccinated individuals is increasing. In the UK it was described that secondary attack rates among household contacts exposed to fully vaccinated index cases was similar to household contacts exposed to unvaccinated index cases (25 percent for vaccinated vs 23 percent for unvaccinated).

12 of 31 infections in fully vaccinated household contacts (39 percent) arose from fully vaccinated epidemiologically linked index cases. Peak viral load did not differ by vaccination status or variant type [[1]]. In Germany, the rate of symptomatic COVID-19 cases among the fully vaccinated (“breakthrough infections”) is reported weekly since 21. July 2021 and was 16.9 percent at that time among patients of 60 years and older [[2]].

This proportion is increasing week by week and was 58.9 percent on 27. October 2021 (Figure 1) providing clear evidence of the increasing relevance of the fully vaccinated as a possible source of transmission. A similar situation was described for the UK. Between week 39 and 42, a total of 100,160 COVID-19 cases were reported among citizens of 60 years or older. 89,821 occurred among the fully vaccinated (89.7 percent), 3395 among the unvaccinated (3.4 percent) [[3]].

One week before, the COVID-19 case rate per 100.000 was higher among the subgroup of the vaccinated compared to the subgroup of the unvaccinated in all age groups of 30 years or more. In Israel a nosocomial outbreak was reported involving 16 healthcare workers, 23 exposed patients and two family members. The source was a fully vaccinated COVID-19 patient. The vaccination rate was 96.2 percent among all exposed individuals (151 healthcare workers and 97 patients).

Fourteen fully vaccinated patients became severely ill or died, the two unvaccinated patients developed mild disease [[4]]. The US Centres for Disease Control and Prevention (CDC) identifies four of the top five counties with the highest percentage of fully vaccinated population (99.9–84.3 percent) as “high” transmission counties [[5]].

Many decision makers assume that the vaccinated can be excluded as a source of transmission. It appears to be grossly negligent to ignore the vaccinated population as a possible 

https://stevekirsch.substack.com/p/new-big-data-study-of-145-countries

“The next time you see you county health officer, President Biden, or Boris Johnson why not ask them if they can find a mistake in this study by Kyle A. Beattie entitled Worldwide Bayesian Causal Impact Analysis of Vaccine Administration on Deaths and Cases Associated with COVID-19: A BigData Analysis of 145 Countries (the PDF version is here).

The study found that the COVID vaccines cause more COVID cases per million (+38% in US) and more deaths per million associated with COVID (+31% in US).

The abstract says:

The statistically significant and overwhelmingly positive causal impact after vaccine deployment on the dependent variables total deaths and total cases per million should be highly worrisome for policy makers. They indicate a marked increase in both COVID-19 related cases and death due directly to a vaccine deployment that was originally sold to the public as the “key to gain back our freedoms.” The effect of vaccines on total cases per million and its low positive association with total vaccinations per hundred signifies a limited impact of vaccines on lowering COVID-19 associated cases.

These results should encourage local policy makers to make policy decisions based on data, not narrative, and based on local conditions, not global or national mandates. These results should also encourage policy makers to begin looking for other avenues out of the pandemic aside from mass vaccination campaigns.

In other words, we were lied to.

The vaccines are making this worse, not better. This is why we are not getting ourselves out of the hole.

https://vector-news.github.io/editorials/CausalAnalysisReport_html.html

Abstract

“Policy makers and mainstream news anchors have promised the public that the COVID-19 vaccine rollout worldwide would reduce symptoms, and thereby cases and deaths associated with COVID-19. While this vaccine rollout is still in progress, there is a large amount of public data available that permits an analysis of the effect of the vaccine rollout on COVID-19 related cases and deaths. Has this public policy treatment produced the desired effect?

One manner to respond to this question can begin by implementing a Bayesian causal analysis comparing both pre- and post-treatment periods. This study analyzed publicly available COVID-19 data from OWID (Hannah Ritchie and Roser 2020Hannah Ritchie, Lucas Rodés-Guirao, Edouard Mathieu, and Max Roser. 2020. “Coronavirus Pandemic (COVID-19).” Our World in Data.) utlizing the R package CausalImpact (Brodersen et al. 2015Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9: 247–74. https://doi.org/10.1214/14-aoas788.) to determine the causal effect of the administration of vaccines on two dependent variables that have been measured cumulatively throughout the pandemic: total deaths per million (y1y1) and total cases per million (y2y2). After eliminating all results from countries with p > 0.05, there were 128 countries for y1y1 and 103 countries for y2y2 to analyze in this fashion, comprising 145 unique countries in total (avg. p < 0.004).

Results indicate that the treatment (vaccine administration) has a strong and statistically significant propensity to causally increase the values in either y1y1 or y2y2 over and above what would have been expected with no treatment. y1y1 showed an increase/decrease ratio of (+115/-13), which means 89.84% of statistically significant countries showed an increase in total deaths per million associated with COVID-19 due directly to the causal impact of treatment initiation. y2y2 showed an increase/decrease ratio of (+105/-16) which means 86.78% of statistically significant countries showed an increase in total cases per million of COVID-19 due directly to the causal impact of treatment initiation. Causal impacts of the treatment on y1y1 ranges from -19% to +19015% with an average causal impact of +463.13%. Causal impacts of the treatment on y2y2 ranges from -46% to +12240% with an average causal impact of +260.88%. Hypothesis 1 Null can be rejected for a large majority of countries.

This study subsequently performed correlational analyses on the causal impact results, whose effect variables can be represented as y1.Ey1.E and y2.Ey2.E respectively, with the independent numeric variables of: days elapsed since vaccine rollout began (n1n1), total vaccination doses per hundred (n2n2), total vaccine brands/types in use (n3n3) and the independent categorical variables continent (c1c1), country (c2c2), vaccine variety (c3c3). All categorical variables showed statistically significant (avg. p: < 0.001) postive Wilcoxon signed rank values (y1.Ey1.E VV:[c1c1 3.04; c2c2: 8.35; c3c3: 7.22] and y2.Ey2.E VV:[c1c1 3.04; c2c2: 8.33; c3c3: 7.19]). This demonstrates that the distribution of y1.Ey1.E and y2.Ey2.E was non-uniform among categories. The Spearman correlation between n2n2 and y2.Ey2.E was the only numerical variable that showed statistically significant results (y2.Ey2.E ~ n2n2: ρρ: 0.34 CI95%[0.14, 0.51], p: 4.91e-04). This low positive correlation signifies that countries with higher vaccination rates do not have lower values for y2.Ey2.E, slightly the opposite in fact. Still, the specifics of the reasons behind these differences between countries, continents, and vaccine types is inconclusive and should be studied further as more data become available. Hypothesis 2 Null can be rejected for c1c1, c2c2, c3c3 and n2n2 and cannot be rejected for n1n1, and n3n3.

The statistically significant and overwhelmingly positive causal impact after vaccine deployment on the dependent variables total deaths and total cases per million should be highly worrisome for policy makers. They indicate a marked increase in both COVID-19 related cases and death due directly to a vaccine deployment that was originally sold to the public as the “key to gain back our freedoms.” The effect of vaccines on total cases per million and its low positive association with total vaccinations per hundred signifies a limited impact of vaccines on lowering COVID-19 associated cases. These results should encourage local policy makers to make policy decisions based on data, not narrative, and based on local conditions, not global or national mandates. These results should also encourage policy makers to begin looking for other avenues out of the pandemic aside from mass vaccination campaigns.

Some variables that could be included in future analyses might include vaccine lot by country, the degree of prevalence of previous antibodies against SARS-CoV or SARS-CoV-2 in the population before vaccine administration begins, and the Causal Impact of ivermectin on the same variables used in this study.”