COVID Infection Fatality Rates Used to Justify Lockdowns Were Grossly Inflated, Study Reveals
In early 2020, public health officials made alarming predictions that COVID-19 would be a global disaster with unprecedented case fatality and infection rates. The only virus capable of matching this novel coronavirus’ lethal capabilities was the Spanish Flu—and the only supposed solution was for entire nations to impose lockdowns.
Fear was required to convince people to agree to things they would never, under any other circumstance, agree to. The fear of dying of COVID was essential to convincing people to wear masks, stay in their homes, distance themselves at least 6 ft from other humans, inject themselves with experimental vaccines, and allow themselves to participate in a psychological experiment of epic proportions.
A new pivotal paper challenges the pre-vaccination case fatality rate used to justify global COVID-19 lockdowns. The study, published January 2023 in Environmental Research, sought to accurately estimate the infection fatality rate (IFR) among the non-elderly population without vaccination or prior infection.
The elderly population carries the highest burden of COVID-19, but 95% of the global population is younger than 70 years old, and 86% are under the age of 60. Across 31 systematically identified national seroprevalence studies in the pre-vaccination era, researchers found the median IFRs of COVID-19 were much lower for the non-elderly population than previous calculations and models suggest.
The median IFR was estimated to be .034% for people aged 0–59 years and 0.095% for those aged 0–rs.
Here are the estimated median IFR during the pre-vaccination era for each age group:
- 0.0003% at 0–19 years
- 0.002% at 20–29 years
- 0.011% at 30–39 years
- 0.035% at 40–49 years
- 0.123% at 50–59 years
- 0.506% at 60–69 years
- 0.034% for people aged 0–59 years people
- .095% for those aged 0–69 years.
So, where did our inflated COVID-19 death and infection statistics come from? Dr. Neil Furguson and his epidemiology team at the Imperial College-London (ICL), through an agent-based simulation model, predicted millions of deaths in the UK alone if stringent lockdowns were not imposed.
According to Dr. Robert Malone, it was this unscientific modeling that caused governments across the globe to panic and switch to a lockdown strategy. Once implemented, Furguson and the ICL took credit for the “success” of lockdowns. They asserted lockdowns and accompanying school closures saved an estimated 3.1 million lives in Europe using their own hypothetical projections as a counterfactual of what would have happened without lockdowns.
As described in a paper published on June 8, 2020, in Nature, Furguson and the ICL compared deaths predicted under a model with no interventions to the deaths expected in their intervention model to arrive at the number of deaths prevented by lockdowns.
In other words, they took their own forecasted death rate as a given and calculated the number of lives saved by subtracting from their unproven model. By doing so, they produced an unrealistic and grossly overestimated fatality prediction.
The American Institute for Economic Research explains the problem with this approach:
The problem with this approach is that it attempts to imply causality by attributing the observed death tolls to the effectiveness of the lockdowns, which they then claim to demonstrate through nothing more than a self-referential appeal to their own simulation model for a “no intervention” counterfactual.
These numbers show Ferguson’s infectious disease model used to justify lockdowns—and adopted by world governments and the National Institutes of Health—failed its real-world test. Lockdowns were a complete failure, and the fatality statistics used to justify lockdowns, face masks, COVID-19 vaccines for the non-elderly population, and vaccine mandates were blatantly false. The corporate media and U.S. regulatory agencies have yet to acknowledge this study.