IFR (Infection Fatality Rate) for COVID: how did experts get is so wrong (10-fold) & Why Stanford's Ioannidis provides most robust data & Carl Heneghan, Jason Oke & Tom Jefferson sets IFR straight!

by Paul Alexander

The Infection Fatality Ratio: the Errors in the Early Estimates. Recent data shows that early estimates of fatality in the covid pandemic over-predicted deaths by as much as tenfold in younger people

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This is the BLUE-PRINT for a Republican congress and senate to hold the proper investigations to get to the bottom of the manufacture (Gain-of-Function) and release of COVID-19, as well as investigations of all of the COVID lockdown polices, mandates, and the decisions and actions taken in developing and bringing the COVID gene injection (vaccine). This is imperative for any administration to get accountability for all of the policies and decisions made with regards to COVID-19:

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10-fold mistake? Is this ineptness or deliberate? I think Heneghan is being too kind.

A recent publication by Stanford researchers based on seroprevalence studies in the covid pre-vaccination era provides a more robust estimate of the IFR.

Across 32 studies, the median IFR of COVID-19 was estimated to be 0.035% for people aged 0-59 years and 0.095% for those aged 0-69.

We compared the two IFR estimates, which shows the Imperial College estimates are much higher than Stanfords across the age groups.’  

Trust the Evidence
The Infection Fatality Ratio: the Errors in the Early Estimates
The Infection Fatality Ratio (IFR) estimates the percentage deaths in all those with an infection: the detected (cases) and those with undetected disease (asymptomatic and the not-tested group). The IFR is used to model the estimated number of deaths in the population at large. If it's a large number approaching one percent, then the modelled outputs can…
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