Watch the video above for fresh insight into this pandemic.
Initial modeling predicted COVID-19 would have a fatality rate of 2% to 3%. In reality, it’s nowhere near that, except for the elderly. One research team puts the overall infection fatality rate for all age groups at 0.32%
Modelers were also incorrect when they predicted that 70% to 80% would get infected before herd immunity would naturally allow the spread of infection to taper off. More than a dozen scientists now claim the herd immunity threshold is below 50%, perhaps even as low as 10%
Since lockdowns are a public health intervention aimed at saving lives, both the benefits and the costs of this strategy must be calculated and taken into account
The cost for lockdowns in Canada, in terms of Quality-Adjusted Life Years and Wellbeing Years, is at least 10 times greater than the benefit. In Australia, the minimum cost is 6.6 times higher, and in the U.S., the cost is estimated to be at least 5.2 times higher than the benefit of lockdowns
Researchers have estimated that in order to “break-even and make a radical containment and eradication policy worthwhile,” the infection fatality rate of SARS-CoV-2 would need to be 7.8%
As Ivor Cummins demonstrates in the video above, available data reveal lockdowns have been completely ineffective at lowering positive test rates, while extracting a huge cost in terms of human suffering and societal health. All of the reports and studies reviewed in his video are also available on his website, TheFatEmperor.com.1 To that long list of evidence, we can add yet another report from Canadian pediatric infectious disease specialist Dr. Ari Joffe, which shows lockdown harms are about 10 times greater than the benefits.2 In his 51-page paper,3 “COVID-19: Rethinking the Lockdown Groupthink,” Joffe reviews how and why initial modeling predictions failed to match reality, what the collateral damage of lockdown policies have been, and what cost-benefit analyses tell us about the efficacy of the lockdown strategy
Herd Immunity Threshold Vastly Overestimated
Modelers were also incorrect when they predicted that 70% to 80% would get infected before herd immunity would naturally allow the spread of infection to taper off.
In reality, the herd immunity threshold has turned out to be far lower, which removes the justification for social distancing and lockdowns. More than a dozen scientists now claim the herd immunity threshold is likely below 50%,6 perhaps even as low as 10%.7,8 Data from Stockholm County, Sweden, show a herd immunity threshold of 17%.9 In an essay, Brown University professor Dr. Andrew Bostom noted:10
“Lead investigator Dr. Gomes, from the Liverpool School of Tropical Medicine, and her colleagues concluded: ‘naturally acquired immunity to SARS-CoV-2 may place populations over the herd immunity threshold once as few as 10-20% of its individuals are immune.’11
Separate HIT [herd immunity threshold] calculations of 9%,12 10-20%,13 17%,14 and 43%15,16 — each substantially below the dogmatically asserted value of ~70%17 — have been reported by investigators from Tel-Aviv University, Oxford University, University College of London, and Stockholm University, respectively.”
How could they get this so wrong? Herd immunity is calculated using the reproductive number, or R-naught (R0), which is the estimated number of new infections that may occur from one infected person.18 R0 of below 1 (with R1 meaning that one person who’s infected is expected to infect one other person) indicates that cases are declining while R0 above 1 suggests cases are on the rise.
It’s far from an exact science, however, as a person’s susceptibility to infection varies depending on many factors, including their health, age, and contacts within a community. The initial R0 calculations for COVID-19’s herd immunity threshold were based on assumptions that everyone has the same susceptibility and would be mixing randomly with others in the community.
That doesn’t happen in real life though. According to professor Karl Friston, a statistician, “effective susceptible population,” meaning those not already immune to COVID-19 and therefore at risk of infection, was never 100%. At most, it was 50% and most likely only around 20%.19
Despite the mounting of such data and the clear knowledge that lockdowns were causing unimaginable harm to mental health, physical health, education, and local economies, lockdowns were repeatedly implemented in various parts of the world.
The initial modeling report from the Imperial College COVID-19 Response Team actually admitted it did “not consider the ethical or economic implications” of the pandemic measures proposed, noting only that “The social and economic effects of the measures which are needed to achieve this policy goal will be profound.” Today, we have a much better grasp on just how profound the social and economic effects have in fact been, and they’re devastating.