In “COVID-19—THE DATA or “the data”?” I described a way in which the Johns Hopkins “COVID-19 Map” is misleading concerning the likelihood that people will become seriously ill or die from COVID-19. The post was about how the map’s “Confirmed Cases” understates the number of people who have been infected, and why the understatement is important in understanding how dangerous COVID-19 is. Studies confirming that the COVID-19 Map’s “Confirmed Cases” are highly misleading about the likelihood of a person becoming seriously ill or dying have now been done.
On March 11, Dr. Fauci said the following in testimony to Congress, “The flu has a mortality rate of 0.1%. [COVID-19] has a mortality rate of ten times that.”
In THIS VIDEO, Dr. Bhattacharya describes the results of the first few studies based on tests for the presence of antibodies in randomly selected people. The research found that the mortality rate of COVID-19 is about the same as the flu. Instead of one in every hundred infected persons dying from COVID-19 (as Dr. Fauci’s testimony asserted), only about one in 1000 infected people die, i.e., the likelihood of death from a COVID-19 infection is about the same as the flu.
Given, however, that (1) the “number of deaths” data upon which these studies are based come from CDC records, even these new studies overstate the COVID-19 death rates. The CDC counts as a “COVID-19 Death” the death of any person who happens to also be infected with COVID-19.[i] Consequently, many of the counted deaths are predominately attributable to causes other than COVID-19. For example, the CDC would classify the death of a person killed in an auto accident who tests positive for COVID to be a “COVID-19 Death.” Such accounting would be malpractice in any other setting.