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Back in August 2020, Dr. Ronald B. Brown, PhD disrupted the academic world’s doomsday predictions about the COVID-19 pandemic when the journal Disaster Medicine and Public Health Preparedness published his first paper on the SARS-CoV-2 virus. As he told me in an interview:
The manuscript cites the smoking-gun, documented evidence showing that the public’s overreaction to the coronavirus pandemic was based on the worst miscalculation in the history of humanity, in my opinion.
On February 26, 2021, the peer-reviewed journal Medicina published another paper by Brown as part of a special issue, “Pandemic Outbreak of Coronavirus.” Brown’s paper, titled “Outcome reporting bias in COVID-19 vaccine clinical trials” is also listed in the U.S. National Library of Medicine of the National Institutes of Health.
In Brown’s first coronavirus paper, he showed how mistaking infection fatality rates for case fatality rates exaggerated the predicted lethality of the SAR-CoV-2 virus. In this second paper, he shows how relative risk reduction measures are being used to exaggerate the efficacy of the COVID-19 vaccines.
I’ve read the latest paper two-and-half times (but only claim to understand 90% of it). The overall conclusion, however, seems clear to me: The COVID-19 vaccine trials, in fact, only showed a negligible reduction in risk of acquiring a symptomatic SARS-CoV-2 infection; not the near perfect immunization the media is portraying.
As Dr. Brown writes in the paper’s conclusion:
Such examples of outcome reporting bias mislead and distort the public’s interpretation of COVID-19 mRNA vaccine efficacy and violate the ethical and legal obligations of informed consent.
The following is an informal interview I conducted with Dr. Brown, from his office in Kitchener-Waterloo, Ontario. It offers a layman’s interpretation of his findings and conclusions.
MANLEY: I’ve run into many people who refuse to even look at the vaccine trial data. They say they leave interpretation of the data to the “experts.” So, I’m glad we now have an expert like yourself to offer another interpretation of the data.