Here is a little break down on the PCR Test they've been using to "justify" the #SCAMdemic for over two years Keep in mind, the PCR Test has been classified as "unable to distinguish between types of Viruses" THREAD 🧵 1/

Nic shared a German paper he considers a landmark “red-light special 🚨,” claiming that “86% of PCR-positive cases were not real infections.” I encourage everyone to read the paper carefully and evaluate how the authors reach this conclusion — particularly the assumptions underlying their analysis and whether those assumptions are justified. The authors essentially offset an estimate of the baseline IgG seropositivity in week 10 of 2020 and then compute a ratio R = (weekly % IgG seropositivity) / (cumulative sum of weekly PCR positivity rates). They treat the numerator as the true proportion ever infected and the denominator as the proportion inferred from PCR testing. They then assume that any R < 1 must be caused entirely by PCR false positives. Because they find R ≈ 1/7, they infer that 6 / 7 = 86% of positive PCR tests were “not real infections.” This reasoning is deeply flawed. In real-world data, there is no reason to expect R = 1.0 unless all of the following conditions are simultaneously true: 1. IgG testing is completely random. 2. PCR testing is completely random. 3. PCR testing rates are constant over time. 4. No individual is ever tested twice by PCR. 5. No one is ever reinfected. 6. No one is vaccinated. 7. Every infection leads to detectable IgG antibodies. 8. No one loses antibodies over time (seroreversion). 9. IgG tests are 100% sensitive and specific. 10. PCR tests are 100% sensitive and specific. Only if all ten conditions held could we expect R = 1.0. In practice, deviations from any of them will alter R. Yet the paper essentially ignores points 1–9 and attributes the entire deviation solely to PCR false positives—despite laboratory data showing that PCR false-positive rates are extremely low (nowhere near 86%). The modeling approach is naïve and built on implausible assumptions, many demonstrably false. I have no idea how this paper passed peer review; its conclusions are not supported by its analysis, which rests on a fundamentally unsound premise. The resulting claim that 86% of PCR positives were not true infections is scientifically indefensible.