The Blog

2020-

2020-12-18 d
THE COVID-CON III
"This inflated death toll has then been, and continues to be, used by fascist-style bureaucracies, in conjunction with scientific priesthoods, to terrify the general public into obedience."
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"it is acceptable to “report COVID–19 on a death certificate without” the need for the patient to test positive for Covid-19"
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"the way many countries have and continue to categorize Covid-19 deaths produces an inflated death count, giving a distorted impression of the scale of Covid-19"

The Covid-19 Data are a ‘Travesty’

How the UK and US Covid Death Data are Inflated

Although people have tragically died from Covid-19, the way the Covid-19 death data is recorded in many countries around the world has produced, and continues to produce, an inflated death toll. This inflated death toll has then been, and continues to be, used by fascist-style bureaucracies, in conjunction with scientific priesthoods, to terrify the general public into obedience.

Correlation Does Not Equal Causation

One of the most basic laws of statistics is that correlation does not equal causation. Although this may sound complicated, it’s not. It simply means that just because there is a correlation between two variables, or to put this another way, a close relationship between two things in the world, this does not mean that one thing is causing the other thing to happen.

A third factor may be causing the correlation that is observed for instance. As an example, there is usually a correlation in many countries between cold weather and people buying more goods in shops, or online, but this increase in buying is not caused by cold weather. Instead, it is caused by the Christmas period, when people spend more money, and it just happens to be the case that the weather is usually cold in December in many parts of the world that celebrate Christmas. So, even though there is a correlation between cold weather and increased buying patterns, cold weather does not cause increased buying patterns, but the Christmas period causes people to buy more goods.

Furthermore, the correlation that is observed between two things in the world may just be a product of random chance. This has led people to point to some funny correlations, such as the fact that there was a correlation between margarine consumption and divorce rates in the Maine between 2000 and 2009. There was also a correlation between per capita cheese consumption and the number of people who died by becoming tangled in their bedsheets, or the number of people who drowned by falling into a pool and films Nicholas Cage appeared in.

Once again, correlation does not equal causation.

Inflated Death Data

If we turn our attention back to the Covid death data, just because someone has tested positive for Covid-19 and died sometime after (even if we put aside for a second that some tests are known to give false positives), that does not mean that Covid-19 caused that person to die. Yet, the main figure certain countries around the world are using to express Covid-19 deaths is simply recorded, or coded, as essentially any death involving a positive Covid-19 test within 28 days of death.

Because correlation does not equal causation, simply recording Covid-19 deaths as any deaths involving a positive Covid-19 test within a given period of time is an extremely poor way to measure how many people have died.

... From my perspective, the main figure countries should use to categorize Covid-19 deaths has to include (1) the need for the patient to test positive for Covid; and (2) the need for a medical professional to examine the patient and conclude that Covid-19 was the primary, or underlying, cause of death. This should be the main figure that officials and the media then quote, because the average person who hears what the latest death count is on a 2-minute news segment presumes that this figure actually expresses how many people have died of Covid-19 – not with Covid-19, not with suspected Covid-19, but actually of Covid-19.

,,, Imprecise Tests

Today, history looks to be repeating itself once again. Governments around the world are selectively using statistics in a way that inflates the scale of the Covid pandemic. For instance, over the past month or two, there has been a clear shift in the emphasis that the government and the media are placing on the number of positive Covid-19 cases. Yet with this shift in emphasise, both parties have largely failed to contextualise why this was always going to be the case once mass testing began.

It doesn’t take a rocket scientist to work out that even if the Covid-19 tests being used are 100% accurate, the more tests you conduct, the more positive cases you are going to find. If we take the UK for example, the number of virus tests being conducted has been
increasing month-by-month since May of this year.

... Furthermore, what percentage of tests are producing false positives? How sensitive are these tests? What is the margin of error in these cases, as some tests are reportedly picking up fragments of dead viruses from infections months ago that are no longer a potential issue? There are questions over the validity of the Polymerase Chain Reaction (PCR) test for instance, a popular test used.

Kary Mullis, the inventor of the PCR test has said that “quantitative, PCR is an oxymoron.” As John Lauritsen, who quoted Mullis in a 1996 article on the use of PCR tests for HIV patients, wrote:

“PCR is intended to identify substances qualitatively, but by its very nature is unsuited for estimating numbers. Although there is a common misimpression that the viral load tests actually count the number of viruses in the blood, these tests cannot detect free, infectious viruses at all; they can only detect proteins that are believed, in some cases wrongly, to be unique to HIV. The tests can detect genetic sequences of viruses, but not viruses themselves."

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