COVID CONSPIRACY Part 2 –

BY AN ANONYMOUS INTERNET MALE –

 

PROVES THEY HAVE CONSPIRED TO PUT THIS ON US – IT’S A FAKE PANDEMIC, THEY ARE USING IT  TO CONTROL US, MAKE US DO WHAT WE DON’T WANT TO DO, BREAK US, PRETEND THEY ARE HELPING US, DESTROY THE ECONOMY, MAKE THE POOR PEOPLE MORE POOR, & THEN WHAT? SOMETHING TO DO WITH VACCINES, GETTING THE ENTIRE WORLD VACCINATED, SO PEOPLE BE CAREFUL. DO NOT GET VACCINATED—THERE IS TERRIBLE DANGER THERE.

 

 

Countries that have not enforced any lockdown: 4 Countries that have employed the strictest lockdown measures: Country Population (millions) Covid-19 Deaths % Deaths per Population Covid-19 Cases % Cases per Population % Deaths per Cases Japan 126.47 143 0.00011% 7645 0.00604% 1.9% South Korea 51.27 222 0.00043% 10564 0.02060% 2.1% Taiwan 23.82 6 0.00003% 393 0.00165% 1.5% Cambodia 16.72 0 0.00000% 122 0.00073% 0.0% Sweden 10.1 1033 0.01023% 11445 0.11332% 9.0% Belarus 9.45 33 0.00035% 3281 0.03472% 1.0% Hungary 9.66 122 0.00126% 1512 0.01565% 8.1% Mexico 128.93 332 0.00026% 5014 0.00389% 6.6% Jamaica 2.96 4 0.00014% 73 0.00247% 5.5% Uruguay 3.47 8 0.00023% 483 0.01392% 1.7% Cameroon 26.55 14 0.00005% 848 0.00319% 1.7% Somalia 15.89 2 0.00001% 60 0.00038% 3.3% Chad 16.43 0 0.00000% 23 0.00014% 0.0% Madagascar 27.69 0 0.00000% 108 0.00039% 0.0% Mozambique 31.26 0 0.00000% 21 0.00007% 0.0% AVERAGE 33.378 128 0.00087% 2773 0.01448% 2.8% 5 Country Population (millions) Covid-19 Deaths % Deaths per Population Covid-19 Cases % Cases per Population % Deaths per Cases Malaysia 32.37 82 0.00025% 4987 0.01541% 1.6% India 1380 358 0.00003% 10541 0.00076% 3.4% Iran 83.99 4683 0.00558% 74877 0.08915% 6.3% Pakistan 220.89 96 0.00004% 5837 0.00264% 1.6% New Zealand 4.82 9 0.00019% 1366 0.02834% 0.7% Bangladesh 164.69 46 0.00003% 1012 0.00061% 4.5% France 65.27 14967 0.02293% 136779 0.20956% 10.9% Germany 83.78 3215 0.00384% 130383 0.15563% 2.5% UK 66.65 11329 0.01700% 88621 0.13296% 12.8% Italy 60.36 20465 0.03390% 159516 0.26427% 12.8% Spain 46.94 18056 0.03847% 172541 0.36758% 10.5% Belgium 11.46 4157 0.03627% 31119 0.27154% 13.4% Austria 9.01 384 0.00426% 14159 0.15715% 2.7% Romania 19.24 346 0.00180% 6879 0.03575% 5.0% Greece 10.42 99 0.00095% 2145 0.02059% 4.6% Netherlands 17.28 2945 0.01704% 27419 0.15867% 10.7% Czech Republic 10.71 147 0.00137% 6059 0.05657% 2.4% Portugal 10.2 567 0.00556% 17448 0.17106% 3.2% Poland 37.85 251 0.00066% 7049 0.01862% 3.6% Ecuador 17.64 355 0.00201% 7529 0.04268% 4.7% Argentina 45.2 101 0.00022% 2277 0.00504% 4.4% Peru 32.97 216 0.00066% 9784 0.02968% 2.2% Colombia 50.88 112 0.00022% 2852 0.00561% 3.9% Honduras 9.9 26 0.00026% 407 0.00411% 6.4% Bolivia 11.67 28 0.00024% 354 0.00303% 7.9% Venezuela 28.44 9 0.00003% 189 0.00066% 4.8% Haiti 11.4 3 0.00003% 40 0.00035% 7.5% South Africa 59.31 27 0.00005% 2415 0.00407% 1.1% Rwanda 12.95 0 0.00000% 127 0.00098% 0.0% Angola 32.87 2 0.00001% 19 0.00006% 10.5% AVERAGE 88.30533333 2769 0.00646% 30824 0.07510% 5.6%

 

6 Countries that don’t appear either had too small a population, employed restrictions that were neither most strict nor most lax, or were missing from the dataset/s (eg. Israel, Turkey, Nepal, etc..). As the lockdown countries have a population more than double the non-lockdown, when comparing averages we can disregard totals, and focus instead on the percentages. Firstly, the highest number of cases per population of any country is just 0.37% (Spain). An absolutely minuscule number that does not signify pandemic.

 

Secondly, we clearly see that those non-lockdown countries have just 13% of deaths per population to those in lockdown, 19% of cases per population, and 50% of deaths per cases. This means that by far the safest place to be in the world right now, if you don’t wish to catch Covid-19, is in a country not enforcing lockdown! The worst affected non-lockdown country is Sweden, and its percentages are still better than most of its Western European neighbours. This is the most conclusive data I can offer to prove that lockdowns have not prevented any rise in cases or deaths. There are other things worth noting. There is a huge variety in percentages across both tables. If Covid-19 was equally dangerous to everyone, everywhere, globally, as we have been lead to believe, then you would not expect this – UNLESS some countries took more effective measures to deal with it than others. In which case you would expect countries who took the least measures to be the worst effected – yet we have just proven the opposite is true.

 

Countries who took the least measures are better off. And why is that? We may assume it is because countries taking the most measures and going in to lockdown are also doing the most overcounting of deaths. Some experts have proposed we should end lockdown and just let everyone catch Covid-19 so as to develop herd immunity, and you may think the findings above support this theory, except that the least affected countries also have the least number of cases per population, meaning herd immunity has not been required to keep the numbers low. We are told the virus affects the elderly in greater numbers than the young, but Japan, one of the least affected countries, has the second highest life expectancy and median age of population in the world. So the data discrepancies cannot be attributed to solely to population age. Nor can it be put down to wealth, at least not in the expected manner. You would think wealthy countries, with increased hygiene and better medical facilities, would be less affected than poor countries, yet another paradox, the opposite proves closer to the truth. I present below two maps, the first from Wikipedia, the second from Bloomberg.

 

7 Wikipedia’s map shows cases per million inhabitants, maroon being highest and grey being lowest. There is not a perfect correlation between wealth and area affected, yet there is no doubt the most highly affected areas, such as North America and Western Europe, are wealthy areas, while the least affected areas, such as Africa, Southern Asia and Mongolia, are poorer. That is because there are more spooks in first-world countries, faking statistics. Bloomberg’s map simply shows cases confirmed, with maroon being highest and pale yellow lowest. You would expect nations with larger populations to be more significantly represented here, and that plays out with China and Russia being more prominent than Australia and New Zealand compared to Wikipedia’s map, and Brazil being more prominent than Chile.

 

Southern Asia, particularly India, and parts of Africa, also become more highly represented, yet still mostly remain at the lower end, with Central Africa and Mongolia still lowest. So with many of the worlds least hygienic and most susceptible countries fairing far better through this pandemic than everyone else, how then can you explain the above findings, other than to say some countries simply didn’t agree to go all in on the planned event, while others did? Some have suggested quantity of testing plays a part. Either: a) The faster you find the infected, the faster they can be isolated, thus slowing the spread, or b) Countries that test only the sickest people will find a larger percentage of cases per tests, and/or deaths per cases/tests Worldometer does not provide data for who has only tested the sickest, but it does provide data for total testing, and it seems a safe assumption that countries conducting the least number of tests limit themselves to the most in need – the sickest.

 

So once again, lets analyse the data. We will look at the same countries we looked at above. I have removed the countries they have no testing data for. Non-lockdown: 8 Country Total Tests % T e s t s p e r population % C a s e s p e r Tests % D e a t h s p e r Tests Japan 89551 0.07% 8.54% 0.16% South Korea 534552 1.04% 1.98% 0.04% Taiwan 49748 0.21% 0.79% 0.01% Cambodia 5768 0.03% 2.12% 0.00% Sweden 54700 0.54% 20.92% 1.89% Belarus 71875 0.76% 4.56% 0.05% Hungary 37326 0.39% 4.05% 0.33% Mexico 40091 0.03% 12.51% 0.83% Jamaica 1290 0.04% 5.66% 0.31% Uruguay 9929 0.29% 4.86% 0.08% AVERAGE 89483 0.34% 6.60% 0.37% Strict lockdown: 9 At a glance it certainly doesn’t appear more testing slows the spread, with nonlockdown countries, who average fewer cases and deaths per population, conducting only half the number of tests (per pop). If testing is supposed to help slow the spread, it appears they didn’t get the memo, and are better off for it.

 

You will say the only reason they’ve had fewer cases is because they’ve had fewer tests. In other words, Covid-19 isn’t any less prevalent in those places, it just hasn’t been found yet. On the surface that idea is not without merit. There is a correlation (0.7 coefficient [where 0 is no relation and 1 is max], or 50% coefficient squared, meaning 50% of the variable is related) between percentage of tests (per pop) and percentage of cases (per pop), regardless of lockdown measures in place. To some this will prove they need to do 10 Country Total Tests % Tests per population % Cases per Tests % Deaths per tests Malaysia 84791 0.26% 5.88% 0.10% India 244893 0.02% 4.30% 0.15% Iran 299204 0.36% 25.03% 1.57% Pakistan 73439 0.03% 7.95% 0.13% New Zealand 66499 1.38% 2.05% 0.01% Bangladesh 14868 0.01% 6.81% 0.31% France 333807 0.51% 40.98% 4.48% Germany 1317887 1.57% 9.89% 0.24% UK 382650 0.57% 23.16% 2.96% Italy 1073689 1.78% 14.86% 1.91% Spain 600000 1.28% 28.76% 3.01% Belgium 128132 1.12% 24.29% 3.24% Austria 156801 1.74% 9.03% 0.24% Romania 74827 0.39% 9.19% 0.46% Greece 48798 0.47% 4.40% 0.20% Netherlands 134972 0.78% 20.31% 2.18% Czech Republic 137409 1.28% 4.41% 0.11% Portugal 191680 1.88% 9.10% 0.30% Poland 148321 0.39% 4.75% 0.17% Ecuador 25347 0.14% 29.70% 1.40% Argentina 22805 0.05% 9.98% 0.44% Peru 102216 0.31% 9.57% 0.21% Colombia 45423 0.09% 6.28% 0.25% Honduras 1600 0.02% 25.44% 1.63% Bolivia 2185 0.02% 16.20% 1.28% Venezuela 225009 0.79% 0.08% 0.00% Haiti 365 0.00% 10.96% 0.82% South Africa 87022 0.15% 2.78% 0.03% Rwanda 6237 0.05% 2.04% 0.00% AVERAGE 207961.241 4 0.60% 12.70% 0.96% more tests, but if it were so important, why aren’t countries doing the least testing having the most deaths?

 

Death doesn’t wait for diagnosis, so if a virus was very deadly, you would expect to find people succumbing no matter how many tests were carried out, and that isn’t happening. We have seen in the data above, that even including assumptions and multiple co-morbidities, death percentages are minuscule, and superior in non-lockdown countries. Testing appears to be a marker of statistical prevalence, more than threat. We also find there is no correlation between tests per population and cases per test (0.09 coefficient, 0% coefficient squared), deaths per cases (0.2 coefficient, 0% coefficient squared), or deaths per tests (0.17 coefficient, 0% coefficient squared), meaning more targeted testing cannot predict a change in percentage. This flies in the face of reports suggesting Germany’s far superior death per cases ratio to Italy’s, Spain’s, and the UK’s is due to more testing.

 

In this case we should ask why there is such a rush to increase testing? Authorities are not just waiting for sick people to walk into a clinic, they have programs and targets for mass testing, and are walking door to door to swab residents in multiple countries. The real pandemic here is not of deaths, but of testing. Unreliable Testing OK, so it’s a planned event, everyone’s in on it, deaths are largely misrepresented and lockdown measures are useless, but there are still some genuine deaths, right? There is still a virus?

 

Well, that all depends on whether you trust the testing being conducted. Not being scientists or medical experts, most of us do, but the matter is far from settled. The main test being used – selected by WHO – is a PCR (Polymerise Chain Reaction), which detects RNA – genetic information – of the virus. It was invented by Dr Kary Mullis to detect HIV. He subsequently won a Nobel Prize, yet outlined himself its serious limitations: “Quantitative PCR is an oxymoron. 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.” In a 34-page document, problems specific to Covid-19 testing have been outlined extensively by researcher David Crowe. I suggest you read it in full to come to your own conclusions about his methods and rationale, but his findings are certainly eyeraising. Here is a short list: 1. Diagnosis for Covid-19 requires NO symptoms, and present symptoms are NOT UNIQUE from other varieties of influenza. This means diagnosis relies solely on the PCR test, which: 2. Cannot isolate specific strain of corona virus. 3. Cannot determine viral load. 4. Returns many false positives. 5. Flip flops between negatives and positives based on arbitrary numbers decided for testing. These arbitrary numbers can be raised or lowered pending whether you want to have more or less people diagnosed. It also shows many real world examples of how the virus is not as transmissible as implied.

 

11 It is no surprise then that the BMJ (British Medical Journal) reports 78% of new testpositive cases in China show no symptoms, and over 90% of test-positive persons develop at most mild or moderate symptoms. This virus doesn’t sound too threatening. Could they simply be suffering from misdiagnosed cold or flu? An extensive survey in Iceland tells a similar story, where, “about half of those who tested positive are non-symptomatic [..] The other half displays very moderate coldlike symptoms.” As of April 3, UK studies show total number of deaths for the year is down 6% compared to 2018, when there was no pandemic, and the sum of deaths from both Covid-19 and respiratory diseases is also less than the same time in 2018, despite some of the 2020 statistics being double counted.

 

WHO estimates an annual one billion cases of influenza globally. Worldometer states there are currently 2 million global Covid-19 cases. The year is little more than a quarter old. If we divide 1 billion by a quarter we get 250 million. That means Covid19 is not even 1% as prevalent as influenza. So why haven’t we closed the borders for influenza before? You might argue its because Covid-19 is more deadly. Well according to the same sources, there are 290,000 to 650,000 influenza related deaths annually. If we divide that by a quarter we get 72,500 to 162,500 deaths. Compared to 128,041 reported Covid-19 deaths.

 

That number is near the middle of influenza mortality range, so the question remains. A French study done in March show a mortality rate similar to other corona viruses. “Covid-19′s mortality is not significantly different from ordinary coronaviruses (common cold viruses) tested in a hospital in France.” A German study examining 73 hospitals shows that Covid-19 as a percentage of all respiratory diseases corresponds to the typical prevalence of other coronaviruses, which is between 5 and 15%. In a coronavirus pandemic, you would expect to see those numbers skyrocket, but it is not the case. This graph confirms that in both Germany and Switzerland, test-positive cases have remained in the normal 5-15% range for coronaviruses since March.

 

12 If the media are reporting a rapid rise in the number of Covid-19 cases, it is only because the number of tests are increasing, not the test-positive ratio. Where else is this occurring? Norway, fluctuating in normal range between 2-10%, and USA, fluctuating between 10-20%. Given all we’ve discovered, it would not be surprising to find it happening the world over. [Miles: the CDC has now admitted that their corona tests were tainted from the beginning with. . . corona virus. Yes, you read that right. You really have to laugh, it is all so comical.

 

The tests were not only absolutely useless, they were guaranteed to find a positive result, since the corona virus came packaged with the test. The CDC tries to pretend that tainting was accidental, but given what we now know, that wouldn’t be my assumption. My assumption is the tests were tainted with corona virus on purpose. This whole thing has been about promoting and selling vaccines, so of course the people behind this fraud tainted the corona tests on purpose, to guarantee the maximum number of positive tests.]

 

False Reporting Of course we should now be asking why the mainstream media, who are running worldwide 24/7 coverage on Covid-1, haven’t picked up on any of this, and seem to be propagating falsehoods. The Sun reported people dropping dead on the street in Wuhan, despite that lung collapse caused by Covid-19 allegedly occurs gradually over days. US Television CBS station has admitted to using footage from an Italian hospital in a report on New York. Cases were “confirmed” in Santa ClaraCounty despite having no testing facility. A UK Coroner reported the death of a 21 year old as Covid-19, despite medics not reporting it as a coronavirus incident. In China, the number of cumulative deaths reported has a 99.99% variance by an equation used predict future outcomes. Quoting from the article: 13 “Put in an investing context, that variance, or so-called r-squared value, would mean that an investor could predict tomorrow’s stock price with almost perfect accuracy.

 

In this case, the high r-squared means there is essentially zero unexpected variability in reported cases day after day.” “Real human data are never perfectly predictive when it comes to something like an epidemic, Goodman says, since there are countless ways that a person could come into contact with the virus. “ “For context, Goodman says a “really good” r-squared, in terms of public health data, would be a 0.7. “Anything like 0.99,” she said, “would make me think that someone is simulating data. It would mean you already know what is going to happen.”