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July 16, 2020
Why COVID-19 Statistics Aren't Credible
by Anon
The entire lockdown/mask narrative hinges on a daily rising case count—a hammer to bludgeon the entire population. If we understand how this statistic is produced, we can decide for ourselves if the numbers are believable. I'll call the novel Chinese coronavirus discovered in 2019 “COVID-19”. [9]
Garbage in—Garbage Out
The CDC's April 5, 2020 Interim Case Definition combines confirmed and probable cases. [3] [4] A case is “probable” if it meets a range of subjective criteria without confirmatory lab testing. E.g.:
—You had a cough and had contact with someone else who had a cough.
—You had a headache and felt feverish without taking your temperature, in a community with “sustained ongoing community transmission.”
—You might be counted twice if you later got a positive test result.
—You got a positive antibody test in a community deemed to be infected (more on test accuracy coming up).
—A doctor wrote COVID-19 on your death certificate but you were not tested.
This is poor science. The CDC should break out two categories—confirmed and probable—and state the criteria for each. If someone is counted as probable and later receives a positive test, they should not be counted twice. Pennsylvania, Texas, Georgia, and Vermont blend their data the same way. Virginia and Maine were too, but began separating their data. Combining PCR and antibody tests into a single heap vastly inflates the number of cases.[2] The old computing axiom—garbage in, garbage out—applies here. Statistics based on bad data are inherently faulty.
[Image: Discrepancies_In_Data_Reporting]
False positives
Pathologists haven’t identified any antibodies specific to the hypothesized pathogen, SARS-CoV-2. Without monoclonal antibodies, no one really knows what caused the illnesses attributed to it, or whether a pathogen was even present. No one has proven there is an infectious agent “SARS-CoV-2” that causes the same discrete disease in all the victims. Nor has a virus been isolated, reproduced and then shown to cause this discrete illness. [1]
Thus, the tests themselves are suspect: what exactly are they testing? The PCR tests do amplify a specific RNA fragment, but no one has shown whether that RNA fragment causes illness or is present in healthy people.
Anecdotes of test kit anomalies and false positives abound. A testing lab employee, baffled by an uptick in positives, bought 200 test kits. He made the kits appear tested without actually testing anyone, and submitted them to a competing lab. Over 50% of these unused kits turned up positive. [6] President John Magufuli of Tanzania, desiring to sanity-check the accuracy of tests, had a pawpaw fruit, a goat, and a quail tested; all tested positive. [7]
Financial motivations
Doctors and hospitals stand to gain by diagnosing inpatients with COVID-19. Medicare pays for inpatient hospital care using a diagnosis-related group (DRG) system. Hospitals classify patients based on the main diagnosis and treatment given. Medicare reimburses a flat amount per DRG code. For comparable respiratory conditions, Kaiser estimated that Medicare payments average $13,297 for a less severe hospitalization, versus $40,218 for a hospitalization involving ventilator treatment for 96+ hours. Moreover, the March 27, 2020 CARES stimulus package adds 20% to the Medicare reimbursement for COVID-19. [8]