Introduction

Data and official narratives were manipulated, biased and corrupted. This curation includes systemic censorship and suppression, while censorship and suppression of individuals is shown separately here.

The Word “Novel” Implies No Natural Immune Recognition; In Fact, There was Substantial Prior Immunity

It is simplistic to use the word “novel” to refer to “genetically novel” since that would mean every flu was novel. It would also mean that every time there was any mutation of a virus (something which happens with great regularity, even within a single infected person), the result would be a “novel virus”. The use of the word “novel” by the government officials and agencies which promoted the pandemic narrative conveyed the misleading notion that there was no adequate immune recognition, and that the virus was capable of causing a novel disease with unique characteristics or signatures. However, whatever SARS-CoV-2 was, there was in fact substantial and effective prior immunity, correlated to the extent of recent exposure to similar coronaviruses. Nearly every healthy person mounted an immune response which was sufficient to prevent serious illness. This is incompatible with any reasonable or useful understanding of the meaning of “novelty”. – Mar 1, 2024

Professional Who Provided Public Health Projections Later Admitted, “Modeling (forecasting) failed during the pandemic. This was because the models were based on flawed assumptions and non-transparent methods.”

The Imperial College London’s Covid-19 Response Team modeling projected 7 billion infections and 40 million deaths in the first year of the epidemic if lockdowns were not imposed. In an interview with The Epoch Times in November [2023], Dr. Ari Joffe, a clinical professor of pediatrics at the University of Alberta, said that… as the pandemic unfolded, he began to reconsider his position. “In the first few months of lockdown, I realized that my (and similarly trained medical colleagues’) expertise was poorly suited to give advice during a pandemic,” he said. The professor admitted not noticing that the high-risk groups in the Imperial College’s modeling were people aged 70 years and above and those aged 60–69 years who had severe comorbidities. “The modeling was flawed, and in general, modeling (forecasting) failed during the pandemic. This was because the models were based on flawed assumptions and non-transparent methods,”Dr. Joffe said. – The Epoch Times

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