Imperial College London: Pseudo-Science, Cracked Computer Models And Coverup

Imperial College

TN was one of the first to blast Prof. Neil Ferguson and Imperial College London as the sole perpetrators of the Great Panic of 2020 over COVID. Ferguson’s computer modeling software has been thoroughly discredited and yet the lies and coverups continue. ⁃ TN Editor

A fascinating exchange played out in the UK’s House of Lords on June 2, 2020. Neil Ferguson, the physicist at Imperial College London who created the main epidemiology model behind the lockdowns, faced his first serious questioning about the predictive performance of his work.

Ferguson predicted catastrophic death tolls back on March 16, 2020 unless governments around the world adopted his preferred suite of nonpharmaceutical interventions (NPIs) to ward off the pandemic. Most countries followed his advice, particularly after the United Kingdom and United States governments explicitly invoked his report as a justification for lockdowns.

Ferguson’s team at Imperial would soon claim credit for saving millions of lives through these policies – a figure they arrived at through a ludicrously unscientific exercise where they purported to validate their model by using its own hypothetical projections as a counterfactual of what would happen without lockdowns. But the June hearing in Parliament drew attention to another real-world test of the Imperial team’s modeling, this one based on actual evidence.

As Europe descended into the first round of its now year-long experiment with shelter-in-place restrictions, Sweden famously shirked the strategy recommended by Ferguson. In doing so, they also created the conditions of a natural experiment to see how their coronavirus numbers performed against the epidemiology models. Although Ferguson originally limited his scope to the US and UK, a team of researchers at Uppsala University in Sweden borrowed his model and adapted it to their country with similarly catastrophic projections. If Sweden did not lock down by mid-April, the Uppsala team projected, the country would soon experience 96,000 coronavirus deaths.

I was one of the first people to call attention to the Uppsala adaptation of Ferguson’s model back on April 30, 2020. Even at that early date, the model showed clear signs of faltering. Although Sweden was hit hard by the virus, its death toll stood at only a few thousand at a point where the adaptation from Ferguson’s model already expected tens of thousands. At the one year mark, Sweden had a little over 13,000 fatalities from Covid-19 – a serious toll, but smaller on a per-capita basis than many European lockdown states and a far cry from the 96,000 deaths projected by the Uppsala adaptation.

The implication for Ferguson’s work remains clear: the primary model used to justify lockdowns failed its first real-world test.

In the House of Lords hearing from last year, Conservative member Viscount Ridley grilled Ferguson over the Swedish adaptation of his model: “Uppsala University took the Imperial College model – or one of them – and adapted it to Sweden and forecasted deaths in Sweden of over 90,000 by the end of May if there was no lockdown and 40,000 if a full lockdown was inforced.” With such extreme disparities between the projections and reality, how could the Imperial team continue to guide policy through their modeling?

Ferguson snapped back, disavowing any connection to the Swedish results: “First of all, they did not use our model. They developed a model of their own. We had no role in parameterising it. Generally, the key aspect of modelling is how well you parameterise it against the available data. But to be absolutely clear they did not use our model, they didn’t adapt our model.”

The Imperial College modeler offered no evidence that the Uppsala team had erred in their application of his approach. The since-published version from the Uppsala team makes it absolutely clear that they constructed the Swedish adaptation directly from Imperial’s UK model. “We used an individual agent-based model based on the framework published by Ferguson and coworkers that we have reimplemented” for Sweden, the authors explain. They also acknowledged that their modeled projections far exceeded observed outcomes, although they attribute the differences somewhat questionably to voluntary behavioral changes rather than a fault in the model design.

Ferguson’s team has nonetheless aggressively attempted to dissociate itself from the Uppsala adaptation of their work. After the UK Spectator called attention to the Swedish results last spring, Imperial College tweeted out that “Professor Ferguson and the Imperial COVID-19 response team never estimated 40,000 or 100,000 Swedish deaths. Imperial’s work is being conflated with that of an entirely separate group of researchers.” It’s a deflection that Ferguson and his defenders have repeated many times since.

As it turns out though, Ferguson and the Imperial College team were being less than truthful in their attempts to dissociate themselves from Sweden’s observed outcomes. In the weeks following the release of their well-known US and UK projections, Ferguson and his team did in fact produce a trimmed-down version of their own modeling exercise for the rest of the world, including Sweden. They did not widely publicize the country-level projections, but the full list may be found buried in a Microsoft Excel appendix file to Imperial College’s Report #12, released on March 26, 2020.

Imperial’s own projected results for Sweden are nearly identical to the Uppsala adaptation of their UK model. Ferguson’s team forecast up to 90,157 deaths under “unmitigated” spread (compared to Uppsala’s 96,000). Under the “population-level social distancing” scenario meant to approximate NPI mitigation measures such as lockdowns, the Imperial modelers predicted Sweden would incur up to 42,473 deaths (compared to 40,000 from the Uppsala adaptation).

The Imperial team did not specify the exact timing of when they expected Sweden to reach the peak of its outbreak. We may reasonably infer it though from their earlier US and UK model, which anticipated the “peak in mortality (daily deaths) to occur after approximately 3 months” following the initial outbreak. That would place Sweden’s peak daily death toll around mid-June, or almost the exact same time period as the Uppsala team’s adaptation.

imperial model

Figure I: Imperial College Model for Sweden, March 26, 2020

It turns out that Viscount Ridley’s line of questioning was correct all along. The Uppsala adaptation of Ferguson’s model not only projected exaggerated death tolls in Sweden. Ferguson’s own projections for Sweden advanced similar numbers, all wildly off the mark from what happened.

Imperial College’s multi-country model used its earlier and more famous projections for the US and UK to claim validity for its more expansive set of international extrapolations. As Ferguson’s team wrote on March 26, 2020: “Our estimated impact of an unmitigated scenario in the UK and the USA for a reproduction number, R0 , of 2.4 (490,000 deaths and 2,180,000 deaths respectively) closely matches the equivalent scenarios using more sophisticated microsimulations (510,000 and 2,200,000 deaths respectively)” that they released a few weeks prior. If Imperial’s US and UK projections matched, a similar validity could be inferred for the other countries they modeled in the multi-country report.

The Imperial College team fully intended for its multi-country model to guide policy. They called on other countries to adopt lockdowns and related NPIs to reduce the projected death toll from the “unmitigated” scenario to “social distancing.” As Ferguson and his colleagues wrote at the time, “[t]o help inform country strategies in the coming weeks, we provide here summary statistics of the potential impact of mitigation and suppression strategies in all countries across the world. These illustrate the need to act early, and the impact that failure to do so is likely to have on local health systems.”

Failure to act, they continued, would lead to near-certain catastrophe. As Ferguson and his team wrote, “[t]he only approaches that can avert health system failure in the coming months are likely to be the intensive social distancing measures currently being implemented in many of the most affected countries, preferably combined with high levels of testing.” In short, the world needed to go into immediate lockdown in order to avert the catastrophes predicted by their multi-country model.

(Note: Imperial College also included a third possible mitigation scenario for stricter measures on top of general population NPIs, aimed at further isolating elderly and vulnerable people, projecting it could reduce Sweden’s numbers to between 16,192 and 33,878. They further modeled a fourth possible “suppression” scenario consisting of a severe lockdown that would reduce human contacts by 75% for the duration of the pandemic and maintain them for a year or more until population-wide vaccination was achieved. It predicted 14,518 deaths. Sweden clearly did not adopt either of these approaches).

One year later we may now look back to see how Imperial College’s international projections performed, paying closer attention to the small number of countries that bucked his lockdown recommendations. The results are not pretty for Ferguson, and point to a clear pattern of modeling that systematically exaggerated the projected death tolls of Covid-19 in the absence of lockdowns and related NPIs.

Figure II compares the Imperial College model’s projections for its “social distancing” scenario and “unmitigated” scenario against the actual outcomes at the one-year mark after its release. These projections reflect an assumed replication rate (R0) of 2.4 – the most conservative scenario they considered, meaning Imperial’s upper range of projections anticipated substantially higher death tolls. The countries examined here – Sweden, Taiwan, Japan, and South Korea – are distinctive for either eschewing lockdowns and similar aggressive NPI restrictions entirely or for relying on them in a much more limited scope than Imperial College advised. The United States, where 43 of 50 states adopted lockdowns of some form, is also included for comparison.

Figure II: Performance of Imperial College Modeling in 4 Non-Lockdown Countries & the United States

Country (assumed R0 = 2.4) Imperial Model projected deaths – social distancing (lockdowns) Imperial Model projected deaths -unmitigated spread 1 year actual deaths (3/26/21) Overestimate, Lockdown scenario Overestimate, Unmitigated scenario Overestimate Percentage – Lockdowns Overestimate Percentage – Unmitigated
Sweden 30,434 66,393 13,496 16,938 52,897 126% 392%
Taiwan 93,712 179,828 10 93,702 179,818 937020% 1798180%
South Korea 141,198 301,352 1,716 139,482 299,636 8128% 17461%
Japan 469,064 1,055,426 8,967 460,097 1,046,459 5131% 11670%
United States 1,099,095 2,186,315 563,285 535,810 1,623,030 95% 288%

As can be seen, Imperial College wildly overstated the projected deaths in each country under both its “unmitigated” scenario and its NPI-reliant “social distancing” scenario – including by orders of magnitude in several cases.

Similar exaggerations may be found in almost every other country where Imperial released projections, even as most of them opted to lock down. The Imperial team’s most conservative model predicted 332,000 deaths in France under lockdown-based “social distancing” and 621,000 with “unmitigated” spread. At the one year mark, France had incurred 94,000 deaths. Belgium was expected to incur a minimum of 46,000 fatalities under NPI mitigation, and 91,000 with uncontrolled spread. At the one year anniversary of the model, it reached 23,000 deaths – among the highest tolls in the world on a per capita basis and an example of extreme political mismanagement of the pandemic under heavy lockdown to be sure, but still only half of Imperial College’s most conservative projection for NPI mitigation.

Just over one year ago, the epidemiology modeling of Neil Ferguson and Imperial College played a preeminent role in shutting down most of the world. The exaggerated forecasts of this modeling team are now impossible to downplay or deny, and extend to almost every country on earth. Indeed, they may well constitute one of the greatest scientific failures in modern human history.

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Scientific Ethics Crater As Vaccine Makers Ditch Control Groups

Because their gene therapy technology cannot be seen to be wrong, Technocrat-minded vaccine makers are ditching the proven methodology of using double-blind trial studies to prove efficacy and risk of their treatments. Without a control group, trials are essentially worthless and misleading. ⁃ TN Editor

While reports of side effects from COVID-19 gene therapies, including life-threatening effects and deaths, continue to climb at breakneck speed,1 a one-sided narrative of safety and effectiveness permeates mainstream media and medical news.

These “vaccines” are so safe and so effective, according to this narrative, that keeping control groups intact for long-term study and comparison of outcomes is now being derided as “unethical,” despite the fact that there is absolutely no non-fraudulent data to support their perverse assertions. Truly, what we’re watching is the active destruction of basic medical science in a surreal dystopian nightmare.

Vaccine Makers to Ditch Control Groups

Consider this report in JAMA by Rita Rubin, senior writer for JAMA medical news and perspectives, for example.2 According to Rubin, the launch of “two highly efficacious” COVID-19 vaccines has “spurred debate about the ethics, let alone the feasibility, of continuing or launching blinded, placebo-controlled trials …”

Rubin recounts how Moderna representatives told a Food and Drug Administration advisory panel that rather than letting thousands of vaccine doses to go to waste, they planned to offer them to trial participants who had received placebo.

Pfizer representatives made a similar announcement to the advisory panel. According to a news analysis published in The BMJ,3 the FDA and U.S. Centers for Disease Control and Prevention are both onboard with this plan, as is the World Health Organization.4

In the JAMA report by Rubin, Moncref Slaoui, Ph.D., chief scientific adviser for Operation Warp Speed, is quoted saying he thinks “it’s very important that we unblind the trial at once and offer the placebo group vaccines” because trial participants “should be rewarded” for their participation.

All of these statements violate the very basics of what a safety trial needs, which is a control group against which you can compare the effects of the drug or vaccine in question over the long term. I find it inconceivable that unblinding is even a consideration at this point, seeing how the core studies have not even concluded yet. The only purpose of this unblinding is to conceal the fraud that these vaccines are safe.

None of the COVID-19 vaccines currently on the market are actually licensed. They only have emergency use authorization — which, incidentally, also forbids them from being mandated, although this is being widely and conveniently ignored — as trials are still ongoing.

At the earliest, they may be licensed two years from now, at the completion of the follow-up studies.5 This is why those in the military are allowed to refuse it, and refuse they have. Among Marines, the refusal rate is nearly 40%.6

So, before the initial studies are even completed, vaccine makers and regulatory agencies are now deciding to forgo long-term safety evaluations altogether by giving placebo recipients the real McCoy, and so-called bioethicists are actually supporting this madness. As reported in The BMJ:7

“Although the FDA has granted the vaccines emergency use authorization, to get full license approval two years of follow-up data are needed. The data are now likely to be scanty and less reliable given that the trials are effectively being unblinded.”

Hypocrisy Abounds

It’s ironic in the extreme, because vaccine mandates are being justified on the premise that the benefit to the community supersedes the risk of individual harm. In other words, it’s OK if some people are harmed by the vaccine because the overall benefit to society is more important.

Yet here they’re saying that participants in the control groups are being harmed by not getting the vaccine, so therefore vaccine makers have an obligation to give it to them before the long-term studies are completed. This is the complete opposite argument used for mandatory vaccination.

If we are to accept the “greater good” justification for vaccination, then people who agree to participate in a study, and end up getting a placebo, need to roll the dice and potentially sacrifice their health “for the greater good.” Here, the greater good is the study itself, the results of which are of crucial importance for public health decisions.

Without this data, we will never know whether the vaccines work in the long term and/or what their side effects are. If an individual in the control group gets COVID-19, then that’s the price of scientific participation for the greater good of society, just as when a vaccinated person gets harmed, that’s considered an acceptable price for creating vaccine-induced herd immunity.

Put another way, when it comes to mandating vaccines, harm to the individual is acceptable, but when it comes to doing proper safety studies, all of a sudden, harm to the individual is not acceptable, and protecting the controls is more important than protecting the integrity of the research. The fact that they’re this inconsistent in their “ethics” could be viewed as proof positive that public health isn’t even a remote concern.

Scientific Ethics Are Eroding

Apparently, concern about risk to the individual only matters when vaccine makers have everything to gain. By eliminating control groups, we’ll have no way of really proving the harm that these “vaccines” might impart over time, as all participants will be in the same proverbial boat.

I remain confident that we’ll continue to see many more health problems and deaths develop in time, but without control groups, these trends can more easily be written off as “normal” and/or blamed on something else. As noted by Dr. Steven Goodman, associate dean of clinical and translational research at Stanford University, who is quoted in Rubin’s JAMA article:8

“By unblinding trial participants, ‘you lose a valid comparison group,’ Goodman said. ‘There will be this sense, and it will be sort of true, that the study is over.’ Unlike, say, a highly effective cancer drug, ‘the vaccine is not literally a life-and-death issue today and tomorrow’ for most trial participants, Goodman said.

So, he noted, those running COVID-19 vaccine trials shouldn’t feel obligated to unblind participants and vaccinate placebo recipients right away. Doing so implies ‘you can just blow up the trial’ on the basis of promising preliminary results, establishing ‘an ethical model for future trials that we maybe don’t want to set,’ Goodman said.”

Indeed, this strategy will set a dangerous precedent that will probably lead to vaccine and drug studies being conducted without control groups in the future, which could spell the end of medical science as we know it. At bare minimum, future variations of the current COVID-19 vaccine trials are likely to be conducted without control groups.

Trial Participants Told Not to Unblind Themselves

Goodman is also quoted in another article,9 this one in MedPage Today, discussing the problems with trial participants unblinding themselves by taking an antibody test:

“‘There is no good scientific reason for someone to do this,’ he told MedPage Today. ‘I can understand why they want that information, but it can only serve to diminish the value of the trial. Getting tested is not right unless there is a pressing need for unblinding for health reasons.'”

Here, yet another hypocritical irony arises, as the reason they don’t want trial participants to unblind themselves is because if they know they got the vaccine, they’re statistically more likely to take more risks that might expose them to the virus.

This, then, will skew the results and “could make the vaccine look less effective than it is,” Dr. Elizabeth McNally of Northwestern University explained to MedPage Today.10 So, whether vaccine scientists agree with unblinding or not, unblinding really only has to do with whether it will skew results in their favor.

Trial participants unblinding themselves might make the vaccine appear less effective if they alter their behavior as a consequence, whereas vaccine makers unblinding the entire control group will allow them to hide side effects, even if participants alter their behavior.

Justification for Elimination of Controls Is Flimsy at Best

While pro-vaccine advocates insist the elimination of control groups is justified on the “moral grounds” that it’s unethical to not provide volunteers with something of value, this argument completely ignores the undeniable fact that no vaccine is 100% safe.

Getting the active vaccine comes with risk, not merely benefit. This is particularly true for the novel mRNA technology used in COVID-19 vaccines. Historical data are troubling to say the least, and the U.S. Vaccine Adverse Event Reporting System (VAERS) is rapidly filling up with COVID-19 vaccine-related injury reports and deaths.

Reports of Side Effects and Deaths Are Piling Up

As reported by The Defender,11 as of April 1, 2021, VAERS had received 56,869 adverse events following COVID-19 vaccination, including 7,971 serious injuries and 2,342 deaths. Of those deaths, 28% occurred within 48 hours of vaccination! The youngest person to die was 18 years old. There were also 110 reports of miscarriage or premature birth among pregnant women.

As reported in “COVID-19 Vaccine To Be Tested on 6-Year-Olds,” between January 2020 and January 2021, COVID-19 vaccines accounted for 70% of the annual vaccine deaths, even though these vaccines had only been available for less than two months!

In my view, it’s unconscionable and morally reprehensible to not take these data into account. Clearly, these “vaccines” have risks. Pretending like they don’t, and that all placebo recipients in vaccine trials are at a distinct disadvantage simply isn’t true.

Keep in mind that we still do not know the percentage of adverse effects being reported. Is it between 1%12 and 10%13 as past inquiries into VAERS reporting have shown, or is it higher?

If only 10% are reported, we may be looking at 23,420 deaths, but if it is as low as 1%, it jumps to more than 230,000 deaths. We will never know because there are major attempts to suppress this information, as we have already witnessed with the deaths of sport celebrities Hank Aaron and Marvin Hagler, both of whom died shortly after COVID vaccinations.

Regardless, it’s hard to justify even a single death of an otherwise healthy individual, seeing how the survival rate for COVID-19 across all age groups is 99.74%. If you’re younger than 40, your survival rate is 99.99%.14

There’s every reason to suspect that these reports account for just a small percentage of actual side effects. Just think of all those who get the vaccine at grocery stores or temporary vaccination sites, for example. First of all, are all Americans even aware that VAERS exists and that they need to file a report if they suffer an adverse reaction post-COVID vaccination?

Who is going to file the adverse report if you get vaccinated in a grocery or convenience store? Will they return to the pharmacist and report their side effects? Will the pharmacist file the report? Who’s responsible for filing the report if you go to a temporary vaccination site?

CDC Stays Mum on How It’s Ensuring Reporting Compliance

According to the CDC, deaths from COVID-19 vaccines are required to be reported to VAERS.15 It’s not supposed to be voluntary, as with other vaccines. However, it is not being transparent about how it is ensuring this “requirement” is being followed, so it’s impossible to confirm that all related deaths are in fact being reported. As reported by The Defender:16

“We … inquired about whether healthcare providers are reporting all injuries and deaths that might be connected to the COVID vaccine, and what education initiatives are in place to encourage and facilitate proper and accurate reporting.

Twenty-two days later a representative from the CDC’s Vaccine Task Force responded by saying the agency had never received our questions — even though the employees we talked to several times said their press officers were working through the questions we sent. We provided the questions again and requested a response by April 7. To date, the CDC has not responded despite our repeated follow-up attempts.”

Absolute Versus Relative Risk Reduction

Vaccine makers are also very careful about only referencing relative risk, not absolute risk. By doing so, the vaccines appear far more protective than they actually are. It’s a commonly used statistical trick that I encourage you to familiarize yourself with.

For example, in his November 26, 2020, BMJ article,17 Peter Doshi, associate editor of The BMJ, pointed out that while Pfizer claims its vaccine is 95% effective, this is the relative risk reduction. The absolute risk reduction — which is far more relevant for public health measures — is actually less than 1%!

I recommend listening to the interview with Dr. Ron Brown above, in which he explains the ins and outs of relative and absolute risks, and the differences between them. He’s also written two papers detailing the problems with this kind of reporting bias: “Outcome Reporting Bias in COVID-19 mRNA Vaccine Clinical Trials”18 and “Public Health Lessons Learned From Biases in Coronavirus Mortality Overestimation.”19

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Urgent: Peer-Reviewed Study Exposes Massive Corruption At CDC

CDC

The CDC controls COVID-19 policies in the United States, and yet has been busted for massive data fraud designed to overstate the death count from COVID during the election cycle leading up to the 2020 elections. Every state should launch an investigation and rip this fraud wide open. ⁃ TN Editor

100 Percent Fed Up reports – Throughout the election, Donald Trump was battered by CCP Virus statistics in order to hurt the American economy and his political campaign. We know that it was shamelessly wielded as a political weapon to prevent President Trump and his supporters from rallying as Antifa and Black Lives Matter burned progressive poor and minority neighborhoods to the ground throughout the entire year. Now that Biden has been installed into the office of president, he promises to increase Covid lockdown measures and extend them further into your ability to travel and force unscientific mask-wearing for at least 100 days.

But, a new peer reviewed study has been released that finds the CDC numbers to be so wildly unsupported as to be pure propaganda that is based on wholly unscientific practices that were needlessly created on-the-spot.

THE CENTERS FOR DISEASE CONTROL AND PREVENTION (CDC) STANDS ACCUSED OF VIOLATING FEDERAL LAW BY INFLATING CORONAVIRUS FATALITY NUMBERS, ACCORDING TO STUNNING INFORMATION OBTAINED BY NATIONAL FILE.

CDC illegally inflated the COVID fatality number by at least 1,600 percent as the 2020 presidential election played out, according to a study published by the Public Health Initiative of the Institute for Pure and Applied Knowledge. The study, “COVID-19 Data Collection, Comorbidity & Federal Law: A Historical Retrospective,” was authored by Henry Ealy, Michael McEvoy, Daniel Chong, John Nowicki , Monica Sava, Sandeep Gupta, David White, James Jordan , Daniel Simon, and Paul Anderson.”

The study is 25 pages long with over 100 citations.  However, the main main points can be summarized.

A major point is that testing inaccuracies and unreliability combined with unscientific procedures and methods resulted in demonstrably massive false-positive spikes:

“The CDC is now legally requiring red-blooded Americans to wear face masks on all public transportation as globalists try to push the concept of “double-masking” on the populace. Since the election, the World Health Organization admits that PCR tests are not totally reliable on the first try and a second test might be needed. This corresponds with CDC’s quiet admission that it blended viral and antibody test results for its case numbers and that people can test positive on an antibody test if they have antibodies from a family of viruses that cause the common cold. Hospitals in Florida had so many accuracy complications that Orlando Health had to admit that its 9.4 percent positivity rate got recorded at 98 percent. (READ: The TRUTH About Fauci and Gates And NIH Owning A Stake in the Vaccine).”

There are also profound legal implications raised by the study that need to be dealt with in courts around the country if America is to ever be free again.  The data you have been spoonfed by the CDC for a year raises serious legal issues.  Much like the 2020 election, massive changes in rules for reporting and collecting data were instituted exclusively for Covid which resulted in a 1600% inflated number of fatalities:

“The groundbreaking peer-reviewed research…asserts that the CDC willfully violated multiple federal laws including the Information Quality Act, Paperwork Reduction Act, and Administrative Procedures Act at minimum. (Publishing Journal – Institute for Pure and Applied Knowledge / Public Health Policy Initiative) Most notably, the CDC illegally enacted new rules for data collection and reporting exclusively for COVID-19 that resulted in a 1,600% inflation of current COVID-19 fatality totals,” the watchdog group All Concerned Citizens declared in a statement provided to NATIONAL FILE, referring to the Institute for Pure and Applied Knowledge study.

“The research demonstrates that the CDC failed to apply for mandatory federal oversight and failed to open a mandatory period for public scientific comment in both instances as is required by federal law before enacting new rules for data collection and reporting. The CDC is required to be in full compliance with all federal laws even during emergency situations. The research asserts that CDC willfully compromised the accuracy and integrity of all COVID-19 case and fatality data from the onset of this crisis in order to fraudulently inflate case and fatality data,” stated All Concerned Citizens.”

But that is not all.  Other major Covid collection and reporting standards created on March 24th of 2020 that inexplicably and intentionally changed decades old practices in order to hide comorbidities and preexisting health conditions on death reports.  These underlying health conditions may likely have been the actual or most important cause of death:

“On March 24th the CDC published the NVSS COVID-19 Alert No. 2 document instructing medical examiners, coroners, and physicians to deemphasize underlying causes of death, also referred to as pre-existing conditions or comorbidities, by recording them in Part II rather than Part I of death certificates as “…the underlying cause of death are expected to result in COVID-19 being the underlying cause of death more often than not.” This was a major rule change for death certificate reporting from the CDC’s 2003 Coroners’ Handbook on Death Registration and Fetal Death Reporting and Physicians’ Handbook on Medical Certification of Death, which have instructed death reporting professionals nationwide to report underlying conditions in Part I for the previous 17 years. This single change resulted in a significant inflation of COVID-19 fatalities by instructing that COVID-19 be listed in Part I of death certificates as a definitive cause of death regardless of confirmatory evidence, rather than listed in Part II as a contributor to death in the presence of pre-existing conditions, as would have been done using the 2003 guidelines. The research draws attention to this key distinction as it has led to a significant inflation in COVID fatality totals. By the researcher’s estimates, COVID-19 recorded fatalities are inflated nationwide by as much as 1600% above what they would be had the CDC used the 2003 handbooks,” stated All Concerned Citizens.

And, still, there is MORE!  Last spring the CDC created covid-19-exclusive rules that violated federal law by outsourcing data collection rule development and wrote new rules to count probable cases without any definitive proof of infection.  The new rules also allowed tracers to practice medicine without a license while disallowing any measures to be put in place to prevent a patient from being counted multiple times:

“Then on April 14th, the CDC adopted additional rules exclusive for COVID-19 in violation of federal law by outsourcing data collection rule development to the Council of State and Territorial Epidemiologists (CSTE), a non-profit entity, again without applying for oversight and opening opportunity for public scientific review. On April 5th the CSTE published a position paper Standardized surveillance case definition and national notification for 2019 novel coronavirus disease (COVID-19) listing 5 CDC employees as subject matter experts. This key document created new rules for counting probable cases as actual cases without definitive proof of infection (section VII.A1 – pages 4 & 5), new rules for contact tracing allowing contact tracers to practice medicine without a license (section VII.A3 – page 5), and yet refused to define new rules for ensuring that the same person could not be counted multiple times as a new case (section VII.B – page 7),” stated All Concerned Citizens.

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The Mysterious Case Of Disappearing Influenza

Ample historical data is available for cycles of influenza and its impact on overall death rates. Technocrats have successfully buried influenza data, and dishonestly rolled it into COVID-19 deaths. To say this is corrupt is an understatement because tens of thousands have died because of it.

Of course, regular influenza has not vanished. There are tests to determine whether a sick patient has a strain of influenza or COVID-19, but those tests are not being used or reported. Thus, every case is being wrongly reported as COVID-19. ⁃ TN Editor

At the end of 2020 many statisticians, doctors and independent scientists noticed something amiss about this extraordinary year. The Office of National Statistics, Public Health England shows that the numbers for death from influenza and those from Covid-19 are askew.

Despite the media and government pandemic presentations, we need to step back and consider the larger picture.

Sometimes it is difficult to see the forest for the trees, but perhaps we have succumbed to seeing a single tree and ignoring the rest of the forest.

Is the fact that one virus has suddenly been given a name, Covid-19, (with wildly hyped media coverage) taken our focus off the overall reality of the annual flu season group of viruses? Has one name and media hype highjacked our lives?

With the 2019–2020 flu season, there have been a number of reports of Covid-19 illnesses in the UK and USA well before the end of 2020. Just today there was a report of Covid-19 illnesses in China as early as August, 2019. [1A]

Until the introduction of the PCR test for Covid-19 in late February, Covid-19 cases and deaths did not exist. This gives the impression that the virus appeared just then, while it was undoubtedly present much earlier as part of the flu season, from numerous anecdotal reports. Various reports indicate symptoms typical of Covid-19 in the U.S. as early as November–December, 2019 and likely even earlier.

With growing attention given to the virus and the increasing availability of PCR testing, we started receiving regular accounts of the number “cases” of the virus. Stepping back a bit and looking at general numbers and ignoring the contentious PCR accuracy regarding positive and negative cases, we see an overall pattern that is very similar to past flu seasons. Cases of flu-like illness generally start in October/November and last until March or April in the UK [1].

The observation can be made that this fairly well describes the 2019–2020 flu season, including Covid-19. The 2019–2020 Covid-19 death numbers appear as a spike because there was no PCR test until about the middle of the flu season, giving the impression that Covid-19 physically appeared late in the season. No, the test appeared late in the season. Despite the testing results, the UK government actually declared the pandemic over in March, but then, oddly, imposed a lockdown a week or two later.

The government declaration of the pandemic’s end can be considered innocently valid and devoid of politics. The advent of lockdowns and more could then be considered political. [So often, when an event occurs, the first observations prove to be the most honest, while the spin and changes come later.]

Much confusion has been generated by different accounting systems regarding illnesses and deaths. There are disparities in the cause of death, whether with the virus or without, and with an over reliance on the PCR test. In addition, many Covid-19 cases were diagnosed solely from symptoms, ignoring the fact that such symptoms are often seen during the flu season.

The observation that some people loose their sense of smell and taste with Covid-19 clearly ignores that these effects occur in every flu season, but now people are told that this is diagnostic for Covid-19. [Dogs are animals and can have spots, but all spotted animals are not dogs.]

We have always taken these symptoms in stride and happily waited until our senses returned. Suddenly, these symptoms are unique and diagnostic of Covid-19. It simply defies reality. If they suddenly reported that you could get a flesh-eating disease from a hang-nail, we would suddenly start considering every incipient hang-nail as a life-threatening event, when, in fact, they are not.

No careful lines have been defined to tell whether deaths have been due to a single virus, multiple viruses, comorbidities (conditions already burdening an individual’s health), or a virus with complications, such as pneumonia. Bacterial pneumonia often has a chance to take hold when one’s lungs are compromised by a flu-type illness. [Note that subsequent pneumonia is not a comorbidity.] Curiously deaths from influenza in the US have recently dropped to about zero; more on this below. [2]

Making our understanding of illness and death in the UK and other regions more difficult are the inclusion of diagnoses determined solely by the PCR test and others solely by symptoms. It is very clear that the traditional symptoms of cold and influenza broadly overlap those of Covid-19, thus making definitive diagnoses very difficult. Add to this the purported rate of false positives from the PCR test (now +97% according to the WHO) [3] and accounting of nonlethal “cases” becomes what they call “problematic.”

To really eliminate the many possible confusions and conditions that can be placed on death rates and possible death causes, it is useful to step back and look at the overall death rate, from all causes, for a country or state. The focus here is on the UK, but the US also provides some guidance. [4]

First, the concept of a pandemic needs to be addressed. A pandemic is the  movement of a disease, bacterial or viral, that moves around the world and has a higher than normal damaging effect. Until recently this was described as a higher than normal mortality. The definition has been changed at WHO’s website such that the flu season is now a pandemic despite death rates being within a normal range. [5] (It is also curious that the definition of herd immunity originally included the benefits of natural and vaccinated immunity, but the definition now only includes vaccinated immunity. Very curious.)

Flu season viruses move around the world every year, largely deriving from farms in Southeast Asia where flu-type viruses are exchanged and hybridized between fish, pigs, and chickens and eventually transmitted to farmers, thus starting the next round of viruses for the annual newly-defined “pandemic.” From teaching Environmental Science, I learned that there has been an effort to break this chain of virus evolution by encouraging farmers to specialize in only one major livestock, thus decreasing viral exchanges between these species. This virus hybridization (mixing) is the source of the H#N# marker recombinations that vaccine labs try to detect early for each new flu season and then attempt to offer appropriate vaccines.

The flu season in the tropics is actually all year round and, because of the humidity, virus transmission is low but constant. However, in the more temperate regions, transmission blossoms when Fall arrives and people start spending more time indoors, in a relatively closed environment, and closer to each other.

It is a bit counter intuitive that humidity (which goes with warm temperatures) decreases transmission rates. It is a good deal in the tropics, sunlight on clear days kills viruses and humidity is always on the job. Small water droplets containing virus, from speaking, coughing, sneezing, and even breathing, tend to gain weight under humid conditions and fall to the ground more quickly than under dry conditions.

Flu season in the Southern hemisphere appears to mirror the Northern hemisphere, but flu viruses are likely introduced to the south by air travel during their summer and, thus, possibly starts and dilutes their six-month later flu season over a longer period.

For all of this, it is very difficult to see the forest for the tree (Covid-19, highlighted by the PCR test), but one statistic that sums up and ignores all the various causes of death and various biases in categories is the overall death rate of a country or state [4], such as the UK, which is a well-defined population with good reporting capabilities. [6]

There are some interesting aspects to death rates. Again, from Env. Sci. teaching, when a heat wave hits a city, as happened in Paris a number of years ago, the death rate rises as people succumb to the physiological burden of heat. However, after the heat wave is gone, the death rate tends to dip below normal for a time. This indicates that the heat wave took people who were already very frail and likely to die in the near future, in a couple of weeks or months, the old “one foot in the grave,” which is not an inaccurate description in many cases..

With cold snaps, there is also a spike in the death rate, but after it is over, there is no dip in the death rate, as it goes back to normal. This is because cold does not discriminate and kills all ages. Heat tends to impose a physiological burden on those already heavily burdened, but cold is a much simpler core temperature problem that is a critical problem for all ages.

That said, is there anything we can learn by comparing the death rates from the last year of “the Covid” and previous years? Focusing mainly on the UK as a single, well-defined population and putting aside all reporting bias and possible cause of death confusions, what do the overall death rates tell us?

It has been speculated, not unreasonable, that many more people died from Covid-19 at home, fearful, unwilling, unable to go to hospital, and thus not counted in the Covid death total.  However, overall deaths in the UK in the last year would also include those who died at home. Overall deaths effectively eliminates all biased death factors and includes deaths not immediately reported.

The excess total deaths for the UK show a well-defined peak in the 2nd quarter of 2020, from mid-March to mid-May. Looking at the age break-down, it is clear that those over 45 and particularly over 65 were most susceptible to whatever virus or viruses of the flu season were making people ill. The rest of the year showed a low (normal) death rate that was low until Fall, when the new flu season arrived, which showed then a broader peak more similar to a flu season. [1]

It is a realism that every year more people have aged or developed infirmities that make them susceptible to a flu-like illness and/or complications. The fact that there is an annual peak does not indicate unusual illness or mortality; it’s the flu season that we have had for many years.

We need to resist the temptation to think that we are seeing something new in our world. By the same token, with a focus on flu-type infections and the elderly, it is easy to conclude just from the effective hyping of such deaths that many people are dying.

Elderly with complications die from complications all year round, just more in the flu season and this is very usual. It is curious that suddenly the public has been sensitized to the elderly death rate, as if it was a new thing. Suddenly, a virus is singling out the elderly, while, in fact, the elderly are always at risk, while the risk to other age groups varies from season to season.

It is also clear that the overall death rate in 2020 was exceeded by the five years of 1999-2003. [2] I need to define the death rate here, as it is based on the deaths per thousand people, which eliminates the fact that populations were lower in earlier years. It’s a given that larger population might have a higher death total from a given disease, but not a higher death rate. Diseases work on the susceptible individuals of a population and, thus, it is a proportion of the population that becomes ill or dies. [6]

That said, how does the death rate in the UK for 2020 compare to previous years? It is clear that the death rate in the UK for 2020 was not exceptional compared to previous years [4]. How can that be? If you have Covid-19 as well as influenza killing people, what is going on?  An observation has been made that, for some mysterious reason, influenza, as of April in the US, dropped to zero and continues at zero in the latest flu season. [6]

In light of the apparent missing influenza, claims have been made that masking, distancing, and lockdowns were completely effective against influenza, but then there is no talk about its failure in stopping Covid-19, which is a virus of the same size and transmission mode.

Then, we are told that Covid is still around because people are not masking and such properly, which means influenza should also still be around in the US. Since these are infectious viruses, how can these restrictions be effective against one virus and not the other? It does not make sense.

It is also easy to find that US states with strict mandates have the same rates of PCR-positive cases as those who do not. The conjecture can be made that influenza cases are largely reported as Covid-19, based either on a positive PCR test result or on symptoms alone.

In the US, it is clear that there has been a monetary incentive for diagnosing the disease and encouraging hospitalizations. The cessation of other medical procedures and tests during this period clearly is going to lead to increased overall deaths. The fact that there appears to be no excess deaths despite this, indicates that the C-19 virus itself was not as lethal as they claim.

Overall, the death rate in the UK is not out of line with the normal death rates from other years and clearly not close to the highest in the last 22 years. [1] It is difficult to consider influenza deaths when there appears to be a bias toward categorizing influenza and other causes as Covid-19 deaths.

Every year and, for that matter, all year long, there is a population of health-critical individuals who may be overwhelmed by a flu-like illness and open to pneumonia complications. The questionable Covid-19 PCR test appears to be keeping the presence of Covid-19 alive, possibly detecting viruses of the current flu season.

The WHO is now admitting that that this test can be 97% false positives or more, with higher processing cycle numbers. [3] The argument could be made that we have an epidemic of testing.

A little exploration of the Office of National Statistics, Public Health England shows that the numbers for death from influenza and those from Covid-19 are askew. [7] They show 4649 cases mentioning influenza and only 380 with influenza only. This means 92% of these cases had other complicating conditions. However, the same week they report 6057 cases mentioning Covid-19 and 5387 mentioned only Covid-19, with 89% being Covid-19 only.

This defies logic. What happened to pneumonia? It is well-known that flu-like illnesses open one up to pneumonia but, according to the above numbers, 89% of deaths from this virus were ONLY from this virus. That does not correlate with the many reports of illnesses with complications and does not at all correlate with the US CDC’s report that only 6% of their Covid-19 related deaths were from Covid-19 only, which means 94% had comorbidities or complications, such as pneumonia.

This is pretty much the exact opposite of UK statistics. [8] However, the CDC is not that far off from the UK’s own death numbers, showing a small fraction of defined Covid-19 deaths, showing 13,844 deaths from Covid and 50,000 with Covid.  [9]

One could ask what happened to influenza. There appears to be a strong tendency to list illnesses as Covid-19 to make the situation appear more dire and possibly more profitable. In the US, there is a financial incentive to diagnose Covid-19 and encourage hospitalizations.

A sad fact is that unethical medical personnel can talk people into feeling sicker than they really are, particularly when they are primed by fears of a deadly virus.  From multiple points of view, looking at the lack of a proper virus isolation and description, the highly variable Covid-19 symptoms, and the fact that a variety of viruses comprise the flu season, I believe that this undescribed virus is most likely not present anymore, but there is no way to show that it is or not because the only “evidence” is the poorly designed PCR test. It is very hard to prove a negative.

[1A] “More evidence of ‘suspicious activity’ at the Wuhan Institute of Virology emerges”

[https://www.skynews.com.au/details/_6225724386001]

[1] Euromomo, Graphs and Maps

[https://www.euromomo.eu/graphs-and-maps/]

[2] “REPORT: Surge in COVID Coincides w/ Suspiciously Mild Flu Season”

[https://headlineusa.com/surge-covid-suspicious-flu-season/]

[3] “COVID-19: A Very Different Truth“

[https://thenaturaldoctor.org/article/covid-19-a-very-different-truth/

[4] “Beware Those Excess COVID-19 Death Analyses”

<https://principia-scientific.com/beware-those-excess-covid-19-death-analyses/>

[5] “WHO exposed: How health body changed pandemic criteria to push agenda”

[https://www.express.co.uk/news/world/1281081/who-world-health-organisation-coronavirus-latest-swine-flu-covid-19-europe-politics-spt]

[6] “Neither US Nor UK Have ANY Excess Deaths From COVID19” [

[https://principia-scientific.com/?s=neither]

[7] Weekly deaths for January 1–8, 2021

[https://www.ons.gov.uk]

[8] “How Many Americans Has Covid-19 Really Killed?”

[https://principia-scientific.com/?s=How+Many+Americans+Has+Covid-19+Really+Killed%3F]

[9] “Breaking: UK Govt’s OWN NUMBERS Expose Their COVID19 Fraud!”

[https://principia-scientific.com/breaking-uk-govts-own-numbers-exposes-their-covid19-fraud/]

About the author: Banson Wilcot PhD holds degrees in Marine Biology and Biochemistry, with a focus on dermatology and lipid biochemistry, and taught university courses for 12 yearsDr. Wilcot has been professionally editing and critiquing foreign-source research papers for publication and grant applications for 16 years (1000+ items). Being a generalist, he has edited papers ranging from coal-fire dynamics, nanotechnology, material science, electrochemistry, all areas of biochemistry and molecular biology, and organic applications as well as oceanography/marine biology and many marine research topics.

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The Flu Has ‘Mysteriously’ Vanished Into Thin Air

Technocrats have cleverly swept virtually all seasonal flu data under the carpet, categorizing most as COVID and simply not reporting the rest. Technocrats have no ethical boundaries in corrupting data for their own ends. This could be the biggest data fraud in history.

On November 3, I reported that the CDC Will Map COVID-19 But Suspends Tracking Of Influenza . This was a global signal to showcase massive numbers of COVID ‘cases’ while disappearing seasonal influenza cases. ⁃ TN Editor

One of the most bizarre features of the alleged COVID-19 ‘global pandemic’ has been the mysterious disappearance of the seasonal flu in medical and public health record keeping. It’s as if the Flu just vanished into thin air after being the most common perennial seasonal respiratory virus.

As it turns out, recorded seasonal influenza cases have literally nosedived by 98% across the globe.

This improbable phenomenon has led a number of experts to ask, “Has Covid killed off the flu?”

“The disappearing act began as Covid-19 rolled in towards the end of our flu season in March. And just how swiftly rates have plummeted can be observed in ‘surveillance’ data collected by the World Health Organisation (WHO),” reported the UK’s Daily Mail.

WHO spokesperson, Dr Sylvie Briand, recently claimed during a press briefing that “literally there was nearly no flu in the Southern hemisphere” of the planet Earth in 2020, but gave no real explanation as to why. She then went on to extend this magical thinking saying that, “We hope that the situation will be the same in the Northern Hemisphere.”

Truly extraordinary science by the health experts at the WHO.

Earlier in December, Southern California news outlet KUSI raised the alarm which prompted an audit of COVID statistics in their region…

SAN DIEGO (KUSI) – COVID-19 cases continue to increase across California, and here in San Diego County, but flu cases remain extremely low in comparison to this time in previous years.

We are well into flu season, but San Diego County’s data for flu infections only shows 36 reported cases so far this year. Carl DeMaio tweeted out this shocking revelation, comparing it to this time in other years saying, “In a typical year we get over 17,073 on average!”

 

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Study: Mask Mandates Seen To Make COVID-19 Rates Climb

Technocrat scientists undoubtedly grind their teeth when confronted with actual science that refutes their pseudo-science bullying in public policy. Masks, social distancing and lockdowns have not and cannot curtail the spread of a virus. ⁃ TN Editor

Protective-mask mandates aimed at combating the spread of the CCP virus that causes the disease COVID-19 appear to promote its spread, according to a report from RationalGround.com, a clearinghouse of COVID-19 data trends that’s run by a grassroots group of data analysts, computer scientists, and actuaries.

Researchers examined cases covering a 229-day period running from May 1 through Dec. 15 and compared the days in which state governments had imposed mask mandates and the days when they hadn’t.

In states with a mandate in effect, there were 9,605,256 confirmed COVID-19 cases, which works out to an average of 27 cases per 100,000 people per day. When states didn’t have a statewide order—including states that never had mandates, coupled with the period of time masking states didn’t have the mandate in place—there were 5,781,716 cases, averaging 17 cases per 100,000 people per day.

In other words, protective-mask mandates have a poor track record so far in fighting the coronavirus. States with mandates in place produced an average of 10 more reported infections per day than states without mandates.

“The reverse correlation between periods of masking and non-masking is remarkable,” RationalGround.com co-founder Justin Hart tweeted on Dec. 20.

The 15 states that went without a statewide mask mandate for the duration of the analysis were Alaska, Arizona, Florida, Georgia, Idaho, Iowa, Missouri, North Dakota, Nebraska, New Hampshire, Oklahoma, South Carolina, South Dakota, Tennessee, and Wyoming, Daniel Horowitz notes in an explainer at Conservative Review.

The analysts allowed the mandate states a 14-day grace period from the time of implementation in order to begin counting cases against mask efficacy in order to arrive at accurate results.

Supporters of the protective-mask mandates might say that the mandates were often imposed once cases already spread quickly, so there’s a negative bias of increased cases in those areas (or times) that had mandates in place, but there was “no evidence of any reduction in cases or even better outcomes many weeks later,” Horowitz writes.

RationalGround.com researcher Ian Miller discovered that three counties in Florida—Manatee, Martin, and Nassau—that let their mandates expire, had fewer cases per capita than those counties that kept the mandate.

Miller tweeted sarcastically on Dec. 20 that it was “extremely confusing how this could happen, considering” the pro-mandate side’s claim that protective masks “are the single most important public health tool we have” and that masks “provide protection for the wearer, too.”

“The mask religion will have a number of inaccurate excuses ready to go, but of course, they’re obscuring and ignoring that this should not be possible, no matter what the mitigating circumstances, if masks were as effective or important as we were told,” Miller wrote.

Nor, according to Miller, has the protective-mask mandate worked in states such as California, where it was imposed long before the surge in cases began.

“The simple reality is that there is no legitimate data showing the mandates worked,” Horowitz concludes.

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AZ Lawmaker: State COVID-19 Hospitalization Counts Are Inflated

When the official COVID narrative is challenged, as in Arizona by a duly elected State Representative, the investigators are vilified, ridiculed and marginalized without any serious debate or consideration. ⁃ TN Editor

A Southern Arizona lawmaker is claiming that a new study he commissioned shows that there is no correlation between the rising number of people being infected with COVID-19 and the number who wind up in the hospital.

And Rep. Mark Finchem, R-Oro Valley, is accusing the state health department of withholding the raw numbers that will let he and other lawmakers decide whether the trends being cited by Gov. Doug Ducey to justify his actions are merited.

On Tuesday, Finchem said it is wrong to focus on the rising number of positive test results as a reason to impose restrictions on individual and business activity. He said that fewer than 10 percent of those people who test positive wind up in the hospital.

The study done for Finchem by the Tennessee firm of Anchor-Helm also claims that “daily hospitalizations peaked June 16 and (have) fallen dramatically since.” And what that means, the report says, is there is “no reason to expect a dramatic increase in cases will be associated with a dramatic increase in hospitalizations.”

But former state Health Director Will Humble said the most obvious flaw in the report is that the person who did the analysis — Finchem’s brother, Kirk — did not recognize that there is a delay in the data. What that means, Humble said, is that the claimed downward trend in hospitalizations just is not occurring.

Rep. Finchem said he’s not buying it. In fact, he claims that the data being produced by the health department — what winds up on its daily public “dashboard” that’s also used by Gov. Doug Ducey to support his actions — may be skewed.

“The agency is refusing to release the data to me as a legislator so we can attempt to replicate their work,” he told Capitol Media Services. Instead, Finchem said, what’s produced for public consumption is “the preferred narrative from the agency.”

No one from either the governor’s office or the state health department would comment.

But Humble dismissed the contention that the real numbers are being hidden.

“I find no reason to believe that,” he said, saying there are people at the agency now who were there when he was director, prior to 2015, whose judgment he trusts.

And Humble has not been a defender of the governor, saying that Ducey moved too quickly to lift restrictions in May.

It’s not just Humble who is noticing the delays in reporting and how that affects the numbers — and the conclusions that can be drawn. Joe Gerald, an associate professor at the Zuckerman College of Health at the University of Arizona, concluded in a recent report that it can take time to actually record deaths.

And there’s something else.

Gerald said that the number of people hospitalized with COVID-19 went from a plateau of 1,093 on May 22 — a week after the governor lifted his stay-at-home order — to 4,834.

And Gerald, in his report, said that as of July 10, 3,485 of Arizona’s 7,971 general ward beds were occupied by patients with suspected or confirmed COVID-19 infection, a 16 percent increase from the week before. So there is a trend — toward increased hospitalizations.

Kirk Finchem concedes there are weaknesses in his report.

He said any relationship between tests and hospitalization is masked by the time lag between the two as well as the average duration of hospitalization, all numbers he said he has been unable to get.

Rep. Finchem has accused Gov. Doug Ducey of continuing to overreact to the virus even after the main danger had passed.

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Nursing Homes Shocked At ‘Insanely Wrong’ COVID-19 Data

There are only 13,600 nursing homes in America but COVID-19 data is wildly inaccurate. Yet, the narrative surrounding this faulty data has fomented a national meltdown. Corrupted COVID-19 data is fully comparable to corrupted climate change data. ⁃ TN Editor

When the administrator of the Saugus Rehab and Nursing Center in Saugus, Massachusetts, heard that a new Medicare website reported her facility had 794 confirmed cases of COVID-19 — the second highest in the country — and 281 cases among staff, she gasped.

“Oh my God. Where are they getting those numbers from?” said Josephine Ajayi. “That doesn’t make any sense.”

Those weren’t the numbers that her facility reported to the CDC’s National Healthcare Safety Network, under new rules from the Centers for Medicare & Medicaid Services (CMS), she said.

Ajayi said her 80-bed facility actually reported 45 residents have tested positive and five residents died, although the CMS website showed no Saugus deaths. About 19 staff members tested positive for the virus, and most have returned to work, she said.

Officials at skilled nursing facilities around the country said Monday they were shocked to see their data reported inaccurately — wildly so in some cases, as at the Saugus home — on the new CMS public website launched Thursday. The numbers are scaring families, harming their reputations, and in some cases are physically impossible, given the number of beds or staff in their facilities, they said.

CMS approved an interim final rule May 1 requiring more than 15,000 nursing homes receiving Medicare or Medicaid reimbursement to report COVID data by May 31, and weekly going forward.

The data fill 56 columns detailing COVID-19 infected residents, staff, testing, and equipment, going back to at least May 1. As of Thursday, CMS said 88% of the nursing homes in the country had reported. Going forward after a grace period ended June 7, they risk fines of $1,000 and up for every week they fail to update their data.

But in many cases, nursing home officials said their data were somehow scrambled, either because nursing home personnel reported in the wrong columns, or the numbers were loaded incorrectly somewhere between the CDC and CMS.

For example, Southern Pointe Living Center in Colbert, Oklahoma, with 95 beds, was reported to have had 339 residents die of COVID-19, yet no confirmed or suspected cases.

“We have not lost anyone nor have we had a [COVID-19] case in the building,” said a woman identifying herself as an assistant at Southern Pointe but who declined to give her full name. The day after CMS released the data, on Friday, she said someone from the CDC called the facility to ask if their numbers were correct as reported, “and we told them no.”

She added, “I don’t know how that happened but that is an error on their end.” As of Tuesday morning, the posted data had not been corrected.

“Insanely wrong”

MedPage Today first learned of the inaccuracies shortly after publishing an article Friday on the new public database. In that article was a list (since removed) of “outliers” — those with the highest numbers of cases and deaths among residents and staff — that included Dellridge Health and Rehabilitation Center in Paramus, New Jersey. The CMS data indicated it had the most COVID-19 deaths of any nursing home in the country at 753.

That number is “insanely wrong,” Jonathan Mechaly, Dellridge’s marketing director, wrote in a frantic email. “We are a 90-bed center and have had less than 20 deaths!! How do you report such inaccurate numbers?”

After a download of the data, a quick sort of the columns easily reveals extreme totals in various categories. But no one called those nursing homes before the data were released to doublecheck, for example, when 100-bed Smith Village in Chicago was shown to have 1,105 confirmed COVID-19 cases among residents and 955 confirmed COVID-19 cases among staff, the most in the country.

“We apparently misread the instructions, which were not very clear,” Yahaira Ramirez, Smith Village’s director of clinical operations told MedPage Today. The facility has had only 38 positive cases among residents and 14 deaths, and among staff, 37 positive or suspected cases but no deaths, she said. But instead of showing up as a total, those numbers somehow appeared as if there were additional cases every day in May. No one caught the error.

It would have been helpful if someone from either agency had at least checked on the highest outliers before publishing, Ramirez said. “We’ve been trying to abide by a lot of the guidelines (from) CMS and CDC, but it’s been challenging. You talk to different people and you get a different answer. Unfortunately, I’m not surprised that they haven’t reached out.”

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CDC Confesses To Lying About COVID-19 Death Counts

Draconian political mandates have been made based on faulty data across the whole nation. Technocrats have knowingly promoted this corrupted data to stoke public fear and political action to promote oppressive shutdown policies. ⁃ TN Editor

Can any government statistics on COVID-19 deaths be trusted?

It is an open question now that we are learning that the highly respected, world-class Centers for Disease Control and Prevention (CDC) has been lying to us.

This revelation comes a few days after I wrote here at American Thinker that New York City was lying about COVID-19 deaths.  The normal rules about reporting deaths have been violated by that city in the rush to inflate the body count, presumably to steer more taxpayer money to the Big Apple.

That the CDC isn’t telling the truth to Americans is no conspiracy theory:  it’s right out there in the open for everyone to see.  The CDC openly admits that it is fudging the COVID-19 death figures.

We know this because, among other truth-tellers, a plainspoken small-town physician from Kalispell, Montana, has pulled back the curtain.

Dr. Annie Bukacek, MD, explained in a presentation how death certificates are made.  (See “Montana physician Dr. Annie Bukacek discusses how COVID-19 death certificates are being manipulated,” YouTube, April 6)

Why should anyone care how a certificate of death is made?

Everyone should care “today when governments are making massive changes that affect our constitutional rights and those changes are based on inaccurate statistics,” Bukacek says.

The system is deeply flawed, she argues.

Few people know how much individual power and leeway is given to the physician, coroner, or medical examiner, signing the death certificate.  How do I know this?  I’ve been filling out death certificates for over 30 years.

More often than we want to admit, we don’t know with certainty the cause of death when we fill out death certificates.  That is just life.  We are doctors, not God.  Autopsies are rarely performed and even when an autopsy is done the actual cause of death is not always clear.  Physicians make their best guesstimate and fill out the form.  Then that listed cause of death … is entered into a vital records data bank to use for statistical analysis, which then gives out inaccurate numbers, as you can imagine.  Those inaccurate numbers then become accepted as factual information even though much of it is false.

This has been the way it has been done for some time, Bukacek says.

So even before we heard of COVID-19, death certificates were based on assumptions and educated guesses that go unquestioned.  When it comes to COVID-19 there is the additional data skewer, that is –get this— there is no universal definition of COVID-19 death.  The Centers for Disease Control, updated from yesterday, April 4th, still states that mortality, quote unquote, data includes both confirmed and presumptive positive cases of COVID-19.  That’s from their website.

Translation?  The CDC counts both true COVID-19 cases and speculative guesses of COVID-19 the same.  They call it death by COVID-19.  They automatically overestimate the real death numbers, by their own admission.  Prior to COVID-19, people were more likely to get an accurate cause of death written on their death certificate if they died in the hospital.  Why more accurate when a patient dies in the hospital?  Because hospital staff has physical examination findings labs, radiologic studies, et cetera, to make a good educated guess.  It is estimated that 60 percent of people die in the hospital.  But even [with] those in-hospital deaths, the cause of death is not always clear, especially in someone with multiple health conditions, each of which could cause the death.

Bukacek refers to a March 24 CDC memo from Steven Schwartz, director of the Division of Vital Statistics for the National Center for Health Statistics, titled “COVID-19 Alert No. 2.”

“The assumption of COVID-19 death,” she says, “can be made even without testing.  Based on assumption alone the death can be reported to the public as another COVID-19 casualty.”

There is a question-and-answer section on the memo.

One question is, “Will COVID-19 be the underlying cause?”

The answer is:  “The underlying cause depends upon what and where conditions are reported on the death certificate.  However, the rules for coding and selection of the underlying cause of death are expected to result in COVID-19 being the underlying cause more often than not.”

Another question is, “Should ‘COVID-19’ be reported on the death certificate only with a confirmed test?”

The answer is:

“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.”  [Boldfacing in original]

“You could see how these statistics have been made to look really scary when it is so easy to add false numbers to the official database,” Bukacek says.  “Those false numbers are sanctioned by the CDC.”

“The real number of COVID-19 deaths are not what most people are told and what they then think,” she says.

“How many people have actually died from COVID-19 is anyone’s guess … but based on how death certificates are being filled out, you can be certain the number is substantially lower than what we are being told.  Based on inaccurate, incomplete data people are being terrorized by fearmongers into relinquishing cherished freedoms.”

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NASA Persists In False Claim That 97% Of Climate Scientists Agree On Global Warming

NASA

Scientific corruption at NASA is blatant, producing propaganda designed to further its own Technocrat existence. NASA has persistently refused to remove false assertions about climate studies.  ⁃ TN Editor

On Tuesday, the Competitive Enterprise Institute (CEI) sent NASA a formal complaint, asking the agency to withdraw the false claim that 97 percent of climate scientists agree that humans are the primary cause of global warming and climate change. The 2013 study purporting to demonstrate that number was fatally flawed and proved no such thing.

“The claim that 97% of climate scientists believe humans are the primary cause of global warming is simply false,” CEI attorney Devin Watkins said in a statement. “That figure was created only by ignoring many climate scientists’ views, including those of undecided scientists. It is time that NASA correct the record and present unbiased figures to the public.”

According to the CEI complaint, NASA’s decision to repeat the false claim violated the Information Quality Act (IQA). Specifically, NASA claimed that “[n]inety-seven percent of climate scientists agree that climate-warming trends over the past century are extremely likely due to human activities.” The claim appears on the NASA website on the page “Climate Change: How Do We Know?”

The claim traces back to a study led by John Cook entitled “Quantifying the consensus on anthropogenic global warming in the scientific literature” and published in the journal Environmental Research Letters in 2013.

The study is fundamentally dishonest, as the CEI complaint explains. The study analyzed all published peer-reviewed academic research papers from 1991 to 2011 that use the terms “global warming” or “global climate change.” The study placed the papers into seven categories: explicit endorsement with quantification, saying humans are responsible for 50+ percent of climate change; explicit endorsement without quantification; implicit endorsement; no position or uncertain; implicit rejection; explicit rejection with qualification; and explicit rejection without qualification.

The study found: 64 papers had explicitly endorsed anthropogenic global warming (AGW) with quantification (attributing at least half of climate change to humans); 922 papers had explicitly endorsed AGW without quantifying how much humans contribute; 2,910 papers had implicitly endorsed AGW; 7,930 papers did not state a position and 40 papers were uncertain; 54 papers implicitly rejected AGW by affirming the possibility that natural causes explain climate change; 15 papers explicitly rejected AGW without qualification; and 9 papers explicitly rejected AGW with quantification, saying human contributions to global warming are negligible.

So how did Cook and his team come up with the 97 percent number? They added up the first three categories (3,896 papers), compared them to the last three categories (78 papers) and the papers expressing uncertainty (40 papers), and completely ignored the nearly 8,000 papers that did not state a position.

Of the papers Cook’s team characterized as stating a position, 97 percent (3,896 of the 4,014 papers) favored the idea of man-made global warming.

See the problem? The study completely discounted the majority of the papers it analyzed (66.4 percent — 7,930 of the 11,944 papers analyzed). With those papers included, only 32.6 percent of the papers explicitly or implicitly endorsed AGW (3,896 of 11,944 papers).

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