Miles Mathis – Covid-19: What They Don’t Tell You
This is the work of Miles Mathis – http://mileswmathis.com/covid.pdf, reposted here for your consideration….
Covid-19:
What They Don’t Tell You
by a man with an internet connection
April 17, 2020
Is Covid-19 a real pandemic, thrust unexpectedly onto the entire world by a mischievous Chinese bat, somehow far more deadly than the hundreds of other coronaviruses in common existence, from all variety of animals – bats inclusive – or is it in fact a highly coordinated event, planned in advance, and blown out of all proportion by governments, media and corporations? Are the heavy restrictions, that will prevent things like four people walking side-by-side on the sidewalk, but allow them to share a car, consequence of biological disaster, or political?
Many reading this will have formed their opinions after watching an awful amount of mainstream media, saturated with Covid content. However with the media controlled by governments and corporations with vested interests, there are certain things they won’t tell you. If you want to see the bigger picture, and discover the less reported details, read on. As much as possible, this paper will attempt to avoid speculation, looking instead at observable, published data. It is out there, and I have compiled a great deal. Please do not let this be in vain. I hope it may be shared amongst the community, for all with eyes to read.
All sources are linked.
Before December
The first reported case of Covid-19 was in December 2019, in Wuhan, China. At the time it was supposedly an unknown quantity, yet On October 18, 2019, two months earlier, the Johns Hopkins Center for Health Security (who are now providing a huge amount of statistics for this event) along with policymakers, business leaders, and health officials, undertook a carefully designed simulation of a coronavirus epidemic entitled nCoV-2019. (n-CoV-2019 was the initial acronym adopted by WHO (World Health Organisation) before it was changed to Covid-19). How did they know to prepare for the outbreak of a virus that wasn’t known to exist, and simulate its spread and response?
Sponsors of the simulation were the Bill and Melinda Gates Foundations, and WEF (World Economic Forum). The Bill and Melinda Gates Foundation, along with WHO, the European Commission, and the UK government, have funded a group named the Pirbight Institute, which owns the patent for coronavirus and following vaccines. The patent was filed June 19, 2015, and approved November 20, 2018 .
Lets quickly look at the definition of a patent:
“A patent is an exclusive right granted by a country to an inventor, allowing the inventor to exclude others from making, using or selling his or her invention.”
That’s right, major corporations are actually inventing viruses, and selling the resulting vaccines to you for profit, backed by Government.
As further proof they saw this coming, the CDC (Centres for Disease Control and Prevention), another major authority in this event, started hiring for state-wide coordinators of its Quarantine Program back in mid-November. They also own more than 50 patents connected to vaccinations, showing they too have a major financial incentive to promote viruses.
In 2017, World Bank launched $500 million in pandemic bonds… 3 years before the coronavirus outbreak, “to cover developing countries against the risk of pandemic outbreaks over the next five years.” Given how rare pandemics are, and how supposedly unpredictable, that’s an awful lot to devote to pandemic outbreaks. Any chance they saw it coming?
In January, close to Wuhan, a hospital has reportedly been built in just days, specifically to deal with Corona Virus. While built after the outbreak, we know major facilities like that take months or years of planning and funding, meaning development on the facility started well before December.
We might further ask if it was just coincidence that a coronavirus outbreak occurred in the only Chinese city (Wuhan) where a coronavirus study was being conducted? The lab cost $44 million to build, specifically for “studying the pathogen that caused SARS,” which was a type of coronavirus.
Perhaps even more spooky is a novel published in 1981 predicting the outbreak of pneumonia-like illness spreading throughout the globe in 2020, originating from… Wuhan.
Deaths Misrepresented
OK, so this looks like a planned event, supported by Governments and major health organisations worldwide, but the pandemic is still real, right? Massive numbers of people are still dying?
Well, not so fast. According to official CDC data , from February 1 through April 7, within the 50 states, and district of Columbia, there were 2,214 total deaths from Covid-19, and 476,602 deaths from all causes. That’s just 0.5% of deaths from Covid19! Updated data as of April 16 shows 11,356 deaths from Covid-19, and 569,403 total deaths. Just 2% of all deaths. Some pandemic! The updated data looks pushed, since it is impossible that the percentage jumped from .5 to 2 in one week.
For a country in lockdown those numbers are shockingly low, but even more so when you consider this:
NVSS (The National Vital Statistics System) is a US Government supported system of sharing data, which coordinates different state health departments and the National Center for Health Statistics, which is a division of the CDC. On March 24 they released a statement announcing a new ICD code for Covid-19 deaths. I quote:
“Should “COVID-19” be reported on the death certificate only with a confirmed test?
COVID-19 should be reported on the death certificate for all decedents where the disease caused or is assumed to have caused or contributed to death.
Believe it or not, that is their use of bold, not mine. This means no confirmation of Covid-19 is required in order for it to be listed as cause of death. Just 2% of total deaths are listed as Covid-19, and they may be based on nothing more than an assumption! If a patient has five serious illnesses, but blows their nose ten seconds before dying, the doctor may whimsically put it in the Covid-19 column.
The waters are muddied in Germany by other means. The Robert Koch Institute, which is the public health institute in Germany, is now advising against autopsies of test-positive deceased persons, because risk of droplet infection by aerosols is allegedly too high. Without autopsies, real cause of death cannot be confirmed. This despite two facts, 1) With necessary precautions, for all manner of conditions, including contagious diseases, autopsies have been carried out safely for eons, and 2) At the end of March, WHO had no evidence for aerosol dispersal of the virus, instead considering it a “possibility” based on rumoured, unpublished studies, and an experimental study which does not reflect normal human conditions. Are we really to believe THIS is scary enough to abandon proper procedure? If WHO is one of the leading authorities on this matter, and they cannot produce evidence the virus remains airborne under normal human conditions, why do we see no ease in restrictive measures like self isolation and social distancing, which have lead to disastrous social and economic consequences, undertaken for fear of transmitting the virus through air? And are we really to believe Government health institutes can’t afford proper protective equipment for autopsies against this virus, yet will happily advise us to wear simple face masks as protection? Or is it more likely they are looking for ways to cover up the real cause of death, which was not Covid-19?
The answer is given to us by the President of the Robert Koch Institute, who on March 20, confirmed , same as America, that test-positive deceased are counted as “corona deaths” regardless of the real cause of death. English translation:
“We consider a corona death to be someone who has been diagnosed with a coronavirus infection.”
Died with Coronavirus, not of or from. Two terms with very different meanings, now being used as the same, thus generating completely unreliable and unethical data.
The median age of the deceased is over 80 years, usually with the existence of one or more serious illnesses. German virologist Hendrik Streeck gave the example of a 78year-old man who died of heart failure, but was included in the statistics of Covid-19 deaths after testing positive. Head of Forensic Medicine in Hamburg, professor Klaus Püschel, is quoted (via translation), “not a single person who was not previously ill died of the virus. All those we have examined so far had cancer, a chronic lung disease, were heavy smokers or severely obese, suffered from diabetes or had a cardiovascular disease.”
It is not just America and Germany that treats deaths in this manner, but Italy too. Here is a quote from the Scientific Advisor to Italy’s Minister of health:
“The way in which we code deaths in our country is very generous in the sense that all the people who die in hospitals with the coronavirus are deemed to be dying of the coronavirus.”
Meanwhile, the ISS (Istituto Superiore di Sanità), the leading technical-scientific body of the Italian National Health Service, conducted a study, published March 17, showing 99.2% of Italy’s coronavirus fatalities were people who suffered from previous medical conditions. Hypertension exists in 76.1%, Diabetes in 35.5%, Heart Disease in 33%, Atrial Fibrillation in 24%. 25.6% had two or more pathologies, while 48.5% had three or more. The average age of those who died is 79.5.
Nursing home deaths in Italy have been attributed to Covid-19, without any tests even carried out for it.
The same is occurring in Austria. Bernhard Benka, member of the Corona Task Force in the Ministry of Health, confirmed that patients dying both with and from coronavirus were counted as a coronavirus death.
We have an example in Spain of a 21-year-old male being marked in the Corona column despite suffering from Leukemia.
If these five countries are treating deaths in this manner, how many others are?
Another way of determining the impact of Covid-19 is to observe statistical data on mortality rates in 24 European Countries , dating back to 2016. In a pandemic we would expect greatly increased numbers, yet at time of writing (April 14) only Italy, Spain, UK, Netherlands, and Belgium have peaks marginally higher than those that came before, with trendlines already starting to come back down. That is 5 out of 24 countries listed, or just 21%. The majority are continuing in line with the average, give or take a little. A report dated March 30 – around or even after the time many countries had decided to take major precautionary measures like lockdowns, closed borders, self isolation and social distancing – presented data from the same site, showing only Italy in a state other than ‘no excess deaths’, meaning 96% of countries listed were at either average or below average death levels. A separate report on mortality rate in Switzerland supports this. Are those the kind of scary numbers that would cause Governments to implement such severe restrictions?
You will say the subsequent rise post March 30 in a few countries proves the Government made the correct decision, so lets compare data from countries that are not following lockdown measures to those that are.
As reported by the BBC on April 7, just one week ago, I will compare countries who have not put any lockdown measures in place to those who put the strictest lockdown measures in place. So as to reduce research time, I will limit it to countries that have a population greater than 9 million, or one per region.
The data I have used to fill these tables is sourced from Worldometer , who are counting Coronavirus cases and deaths by Country.
As time of writing (April 14/15) is a week later than the BBC article, this will create a discrepancy for any countries that have altered lockdown rules in the meantime, but it was the most extensive, up-to date list I could find, and will be close enough together to show the trends.
Countries that have not enforced any lockdown:
| 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% |
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 |
| 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% |
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.
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:
- The faster you find the infected, the faster they can be isolated, thus slowing the spread, or
- 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:
| Country | Total Tests | % T e s t s
population |
p e r | % Cases per
Tests |
% D eaths per
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:
| 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% |
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 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:
- 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:
- Cannot isolate specific strain of corona virus.
- Cannot determine viral load.
- Returns many false positives.
- 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.
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.
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 Clara County 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:
“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.”
Quiet Hospitals
I encourage you to view some of the following links, where you will witness quiet hospitals, parked ambulances, empty corona virus tents and empty car parks – situations completely unrepresentative of a pandemic, despite all media reports to the contrary.
https://www.youtube.com/watch?v=xPM8n-wBWh4&feature=youtu.be https://www.youtube.com/watch?v=kamZlRikarU https://www.youtube.com/watch?v=5pIMD1enwd4
For more, search #filmyourhospital on youtube. Many of these are recorded on camera phones by regular people.
Reports from Switzerland and Germany both show “less activity than normal times”, that staff “are still waiting for patients,” and have had “no increase in patient numbers.”
Health Consequences
OK, so it’s a planned event, everyone’s in on it, deaths are largely misrepresented, lockdown measures are useless, testing is unreliable, reports are false, and there is no dramatic emergency situation, but at least we’re not putting anyone at risk, right? Wrong.
While hospitals are preparing for large numbers of Covid-19 cases, other services are cut back – such as operations.
In Romania a Hospital has closed and medical staff placed into quarantine, thus depriving critically ill people of the care they need.
The number of heart attack and stroke patients who receive emergency medical care worldwide is declining, for fear of leaving the house or nursing home due to Covid-19 threat.
“The number of Americans filing claims for unemployment benefits shot to a record high of more than 6 million last week as more jurisdictions enforced stay-at-home measures to curb the coronavirus pandemic.” One in five households in the US have had someone laid off, while those earning less than $50,000 had one in four. There is a large body of literature establishing a link between unemployment and suicide rates. More than 10,000 suicides were tied to the financial crisis of 2008, a dramatic increase on years prior. Researchers at University of Otago found unemployment was associated with a two to threefold increased risk of suicide. A study from Taiwan found, “Unstable employment had a significant impact on suicide among people aged 25–34,” and, “Economic factors, especially decrease in GDP per capita, also turned out to be a good predictor of increased suicide rates.” Another found suicide rates decreased during economic boom, and increased in the elderly during a recession.
A study from the US has found, “the quality and quantity of individuals’ social relationships has been linked not only to mental health but also to both morbidity and mortality.”
“Indiana’s 211 hotline went from receiving roughly 1,000 calls a day regarding mental health – including suicidal ideation – to 25,000 calls a day. Calls to Indiana’s addiction hotlines went from an average of 20 a week to 20 a day.”
Nursing homes have been particularly affected. In some instances nursing staff are no longer able to visit. In Italy, some nurses have left the country in a hurry due to fearmongering, curfews and border closures, leaving the elderly, disabled, and those in need of care, helpless. Elderly have been found dead from abandonment in Spain.
“In a German retirement and nursing home for people with advanced dementia, 15 test-positive people have died, without showing symptoms of corona. A German medical specialist informs us, “From my medical point of view, there is some evidence that some of these people may have died as a result of the measures taken. People with dementia get into high stress when major changes are made to their everyday lives: isolation, no physical contact, possibly hooded staff.“ Nevertheless, they are counted as “corona deaths“ in German and international statistics.
We are told the elderly are most at risk from Covid-19. Perhaps now we know why. The very measures designed to keep them safe may in fact only make things worse.
Conclusion
It is clear an awful lot of misrepresentation is occurring in both reporting and governance of the situation, which needs to be condemned and acted against in the swiftest manner. If this paper has resonated with you, please give it the opportunity to resonate with others. Lest the whole world be locked inside indefinitely, our families existing but on a screen, lifestyles reduced to the home, and business in tatters, now is not the time for quiet. You can’t solve a problem until you know it exists, and the greater society currently has no idea.
The first step of action is to spread the truth. That is action, and this is a great opportunity. Do not miss it. Do not be deterred. Freedom is a fundamental human right. Let’s help each other get it back. Like the greatest contagion, sharing this paper with your contacts will spread it faster than any virus.
You may link to this address, or download the PDF and distribute freely. For those with social media, use the following hashtags:
#Covid19 #Coronavirus #Inittogether #Plandemic #Lockdown #Socialdistancing #Publichealth #Mentalhealth #Filmyourhospital
It’s probable you will be shot down in the comments by people trying to prevent the truth getting out, or those unable to see past the television reports. Ignore them. Their voice may be louder, but our numbers are greater, and if we speak up, it is our choir that will sing loudest.
Miles: good data analysis there, proving this coronahoax is manufactured from the ground up. What I would like to see next from a guest writer is an analysis of the financial side of this, looking closely at the Gates Foundation, Blackrock, the fake stimulus package, and huge loans being forced on all of us right now, via the Fed raising the debt ceiling by an awesome amount. I think this is the biggest treasury theft in the history of the world, though that side of it is getting little attention. They are sending you a check for $1200 while picking your pocket for 50 times that. Or is it a hundred? Or is it five hundred? Only time will tell.
About every ten years now they come back with an even more colossal scam. It started with 911, the greatest theft and scam of all time up to that point. Then we had the bailouts, which were an even bigger theft from the treasury. Since the American people did absolutely nothing about either theft, the thieves returned for a third time this year, and they will keep coming back until they are stopped. I guarantee you the next theft will be even larger. This is how it works. They will keep robbing you until there is literally nothing left to take. You won’t be left alone until you are living in a cave sucking on cold potatoes.