Authored by Kit Knightly via Off-Guardian.org,
New policies will artificially deflate “breakthrough infections” in the vaccinated, while the old rules continue to inflate case numbers in the unvaccinated.
The US Center for Disease Control (CDC) is altering its practices of data logging and testing for “Covid19” in order to make it seem the experimental gene-therapy “vaccines” are effective at preventing the alleged disease.
They made no secret of this, announcing the policy changes on their website in late April/early May, (though naturally without admitting the fairly obvious motivation behind the change).
The trick is in their reporting of what they call “breakthrough infections” – that is people who are fully “vaccinated” against Sars-Cov-2 infection, but get infected anyway.
Essentially, Covid19 has long been shown – to those willing to pay attention – to be an entirely created pandemic narrative built on two key factors:
- False-positive tests. The unreliable PCR test can be manipulated into reporting a high number of false-positives by altering the cycle threshold (CT value)
- Inflated Case-count. The incredibly broad definition of “Covid case”, used all over the world, lists anyone who receives a positive test as a “Covid19 case”, even if they never experienced any symptoms.
Without these two policies, there would never have been an appreciable pandemic at all, and now the CDC has enacted two policy changes which means they no longer apply to vaccinated people.
Firstly, they are lowering their CT value when testing samples from suspected “breakthrough infections”.
From the CDC’s instructions for state health authorities on handling “possible breakthrough infections” (uploaded to their website in late April):
For cases with a known RT-PCR cycle threshold (Ct) value, submit only specimens with Ct value ≤28 to CDC for sequencing. (Sequencing is not feasible with higher Ct values.)
Throughout the pandemic, CT values in excess of 35 have been the norm, with labs around the world going into the 40s.
Essentially labs were running as many cycles as necessary to achieve a positive result, despite experts warning that this was pointless (even Fauci himself said anything over 35 cycles is meaningless).
But NOW, and only for fully vaccinated people, the CDC will only accept samples achieved from 28 cycles or fewer. That can only be a deliberate decision in order to decrease the number of “breakthrough infections” being officially recorded.
Secondly, asymptomatic or mild infections will no longer be recorded as “covid cases”.
That’s right. Even if a sample collected at the low CT value of 28 can be sequenced into the virus alleged to cause Covid19, the CDC will no longer be keeping records of breakthrough infections that don’t result in hospitalisation or death.
From their website:
As of May 1, 2021, CDC transitioned from monitoring all reported vaccine breakthrough cases to focus on identifying and investigating only hospitalized or fatal cases due to any cause. This shift will help maximize the quality of the data collected on cases of greatest clinical and public health importance. Previous case counts, which were last updated on April 26, 2021, are available for reference only and will not be updated moving forward.
Just like that, being asymptomatic – or having only minor symptoms – will no longer count as a “Covid case” but only if you’ve been vaccinated.
The CDC has put new policies in place which effectively created a tiered system of diagnosis. Meaning, from now on, unvaccinated people will find it much easier to be diagnosed with Covid19 than vaccinated people.
Consider…
Person A has not been vaccinated. They test positive for Covid using a PCR test at 40 cycles and, despite having no symptoms, they are officially a “covid case”.
Person B has been vaccinated. They test positive at 28 cycles, and spend six weeks bedridden with a high fever. Because they never went into a hospital and didn’t die they are NOT a Covid case.
Person C, who was also vaccinated, did die. After weeks in hospital with a high fever and respiratory problems. Only their positive PCR test was 29 cycles, so they’re not officially a Covid case either.
The CDC is demonstrating the beauty of having a “disease” that can appear or disappear depending on how you measure it.
To be clear: If these new policies had been the global approach to “Covid” since December 2019, there would never have been a pandemic at all.
If you apply them only to the vaccinated, but keep the old rules for the unvaccinated, the only possible result can be that the official records show “Covid” is much more prevalent among the latter than the former.
This is a policy designed to continuously inflate one number, and systematically minimise the other.
What is that if not an obvious and deliberate act of deception?
https://www.mlo-online.com/home/article/13008268/interpretation-of-qpcr-curve-shapes
“Finally, what about the curve shape as shown in Figure 3? While it may at first appear to be bizarre, this not infrequently encountered curve shape actually is highly distinctive of a very specific reaction situation: when particular software algorithms are being applied. While this curve ends up below the CT threshold and one might be tempted to call the reaction negative, in fact the exact opposite is true: this curve is arising due to an extremely high starting target template concentration.
Recall from above that some software force the phase 1 region to appear flat, by adding or subtracting any curve trends detected in early cycles on the assumption these arise from background changes to fluorophores and that all early cycle signal is noise. If instead, the reaction starts with such a high template load as to already have detectable real signal in these early cycles (that is, the assay has effectively passed a real CT in a very early cycle), then these background correction algorithms now are removing real, target-derived signal.
This subtraction of a real signal has the graphical result of effectively rotating the amplification curve about the axis origin. Phase 1, which should slope upward, now looks flat or even slightly decreasing. Phase 2 appears, but since its actual rate of growth is underreported, it looks “weak” and may in some cases fail to cross the CT line. Finally, phase 3, where an actual plateau occurs, now looks like a negative slope region, since no growth minus a growing “background” yields a negative value.
While this amplification curve shape alone is nearly definitive, if the instrument software being used sets reaction-specific CT values, a second hint that this is occurring is when the CT threshold is very high compared to controls or other well-behaved samples; that is, the ‘background’ signals are much higher than normal. Fortunately, if this readily distinguishable curve shape is observed, proving the root cause is readily done by repeating the test on a greatly diluted sample (1/10,000 would be a reasonable choice). Alternatively, some software packages allow the user to go in and manually switch off background correction; doing so in this case immediately provides a “true” amplification curve (represented as Figure 3, uncorrected line). Note that valid CT values for quantitation are, however, achievable only through dilution and retesting; but for a purely qualitative result, disabling background correction may be sufficient for appropriate result calling”
https://www.preprints.org/manuscript/202104.0688/v1
“Direct Quantitation of SARS-CoV-2 Using Droplet Digital PCR in Suspected Samples With Very Low Viral Load However, evidences reported that real time RT-PCR has a lower sensitivity compared with the droplet digital PCR (ddPCR) leading to possible false negative in low viral load cases. Conclusion: ddPCR, in particular the direct quantitation on swabs, shows a sensitivity advantage for the SARS-CoV-2 identification and may be useful to reduce the false negative diagnosis, especially for low viral load suspected samples.”