False Data and False Positive Paradox - DSA ADS Course - 2022
DSA ADS Course - 2022
Discuss quality of data; quality and strength of evidence; false data, decision making; and the false positive paradox.
Widespread screening of asymptomatic people leads to high numbers of false positives when background prevalence is low, even with accurate tests. During the Covid-19 pandemic, not only has the background prevalence been low (vaccine clinical trial baseline testing finds 0.5-0.6% even during periods of higher prevalence), but the various COVID-19 tests are not very accurate. When inaccurate tests are combined with a low background prevalence, this results in a massive and unacknowledged problem of far more false positive test results than true positive test results, leading also to inaccurate characterization of COVID-19 hospitalizations and deaths.
See also: The Illusion of Evidence Based Medicine
See also: DSA ADS Course - 2022
COVID19, Public Policy, Health Policy, SARS-CoV-2, RT-PCR Test, Infectious Potential, Laboratory Quality Assurance, Dr. Kary Mullis, Cycle Threshold Values
RT-PCR test was never fit to act as diagnostic tool as stated by inventor of PCR test, Dr. Kary Mullis. Considering massive false positives by detecting minute amounts of virus particles, the PCR test created an illusion of reality. Mislabeling positive tests as "cases" - and "cases" as purported "infections" - is gross negligence and violates basic tenets of data science practice. Data science requires precise uniform definitions to find truth and objective reality to enable optimal decision-making and appropriate policy.
Intentional decision to increase cycle threshold (Ct) values of PCR test created a "casedemic" to justify certain policy decisions like lockdowns, business closures and school closures - and justify emergency use authorization for an experimental vaccine strategy instead of therapeutic drug treatment strategy (more accurately a combined short-term therapeutic treatment strategy of early treatment with repurposed therapeutics and nutraceuticals - and longer-term vaccine strategy with long-term data/evidence of vaccine efficacy and safety).
Yet intentional decision to lower cycle threshold values for vaccines manipulates "case" numbers and games the system to provide a pre-determined result.
Some assert the increase of PCR cycle threshold values was intended to juice "case" numbers - creating an illusion of reality - with intent to create fear in the public to increase policy compliance.
This is a textbook example of data science malpractice and may result in civil and criminal liablility.