Understanding the Impact of Bad COVID-19 Case Data

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When #covid case definition & identification is handled so poorly, it throws ALL #covid stats into question👇🏼

🎥 Here’s a 10min explanatory whiteboard video be me (Steve Katasi) on the downstream effects when “cases’ are falsely created.

📸 The whiteboard visual is captured below, as are a few other useful links that support the statements within the video. 👇🏼

If it makes sense and helps bring into focus the data issues we are dealing with, please share widely. 🔄

Here’s what is covered:

  • The absence of true clinical case diagnosis
  • Why most of the “cases” are likely to be false results
  • Some background on the PCR test and it’s limitations
  • The categories of Positive individuals
  • Following three journeys through the system
  • Why we cannot rely on Hospital & Death data at present
  • The web of disparate systems that contribute to COVID reporting
  • Understanding the endemic resurgence of respiratory pathogens at this time
  • The one simple change to COVID reporting that would allow for much more public trust


🤞🏼 Of course, this could have been an hour talk on all the intricacies of the systems and systems failures, but it should act a decent grounding.

❗️As part of an urgent reform, we must also ask for confirmation tests to be performed for positive results, with a uniformly low (~25) Cycle Threshold applied across all labs.

😬 Now, I avoided the topic of dying WITH vs OF #covid, as well as conflating Flu etc as #covid, as I think everyone understands this is very murky water already.

#FactsNotFear #DemandBetter 

Understanding the Bad COVID Case Data




The Original Facebook Post

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