Supremes Hear Biden Case For Vaccine Mandates

This one will be close.

Justices are hearing arguments on vaccine mandates. It could go either way, with smart money against Biden. But watch Roberts, he’s gone south on important health issues before.

FNC: “The U.S. Supreme Court heard arguments Friday in a high-stakes public session to decide whether the U.S. government can begin enforcing sweeping COVID 19 vaccine requirements affecting nearly 100 million workers.

For three hours and 40 minutes, the justices heard oral arguments over federal vaccine and testing rules for businesses with 100 employees or more, and on vaccine mandates for health care workers at facilities receiving Medicaid and Medicare funding.  (See link for article)



Unfortunately, the author of the article didn’t utilize facts & data that are now available:

  • Nobody has a clue what’s in these injections and this should alarm everyone.
  • Independent researchers from across the globe are finding frightening contaminants.
  • Independent researchers are finding frightening objects in the blood of the “vaxxed”.
  • These injections have caused more adverse reactions & death than any other vaccine in VAERS history.
  • An American Heart Association study shows a link between the shots and heart attacks and the data has been confirmed by others.
  • Even the CDC admits the shots are causing heart disease.
  • A large Israeli study shows the Pfizer shot increases myocarditis three-fold.
  • None of these injections stop transmission or infection so the argument that lives are being lost daily due to dropping the “vaccine” mandate is completely false.
  • Although there are obvious holes in ‘breakthrough’ case reporting, data on the “vaccinated” shows many things:

The following data completely dismantles the narrative that these fast-tracked, experimental, unproven injections which aren’t vaccines, do not stop infection or transmission, have a boat-load of contaminants, and have caused more sickness and death than any vaccine in the history of VAERS, reduce hospitalization & death.  That is a lie.

The following independent analysis has been ignored by the CDC, the FDA and the NIH since October of 2021.

Worldwide Bayesian Causal Impact Analysis of Vaccine Administration on Deaths and Cases Associated with COVID-19: A BigData Analysis of 145 Countries



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. 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 (y1) and total cases per million (y2). After eliminating all results from countries with p > 0.05, there were 128 countries for y1 and 103 countries for y2 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 y1 or y2 over and above what would have been expected with no treatment. y1 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 (vaccine) initiation.

y2 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 (vaccine) initiation.

Causal impacts of the treatment on y1 ranges from -19% to +19015% with an average causal impact of +463.13%. Causal impacts of the treatment on y2 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.E and y2.E respectively, with the independent numeric variables of: days elapsed since vaccine rollout began (n1), total vaccination doses per hundred (n2), total vaccine brands/types in use (n3) and the independent categorical variables continent (c1), country (c2), vaccine variety (c3). All categorical variables showed statistically significant (avg. p: < 0.001) postive Wilcoxon signed rank values (y1.E V:[c1 3.04; c2: 8.35; c3: 7.22] and y2.E V:[c1 3.04; c2: 8.33; c3: 7.19]). This demonstrates that the distribution of y1.E and y2.E was non-uniform among categories. The Spearman correlation between n2 and y2.E was the only numerical variable that showed statistically significant results (y2.E ~ n2: ρ: 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.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 c1, c2, c3 and n2 and cannot be rejected for n1, and n3.

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.

Untruth naturally afflicts historical information. There are various reasons that make this unavoidable. One of them is partisanship for opinions and schools. If the soul is impartial in receiving information, it devotes to that information the share of critical investigation the information deserves, and its truth or untruth thus becomes clear. However, if the soul is infected with partisanship for a particular opinion or sect, it accepts without a moment’s hesitation the information that is agreeable to it. Prejudice and partisanship obscure the critical faculty and preclude critical investigation. The result is that falsehoods are accepted and transmitted” — Ibn Khaldun, 1379 A.D.

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