On Non-Infectious Covid Positives

Daniel Payne writes at Just the News Growing research indicates many COVID-19 cases might not be infectious at all. Excerpts in italics with my bolds.

Elevated ‘cycle thresholds’ may be detecting virus long after it is past the point of infection.

A growing body of research suggests that a significant number of confirmed COVID-19 infections in the U.S. — perhaps as many as 9 out of every 10 — may not be infectious at all, with much of the country’s testing equipment possibly picking up mere fragments of the disease rather than full-blown infections.

Yet a burgeoning line of scientific inquiry suggests that many confirmed infections of COVID-19 may actually be just residual traces of the virus itself, a contention that — if true — may suggest both that current high levels of positive viruses are clinically insignificant and that the mitigation measures used to suppress them may be excessive.

Background from previous post: New Better and Faster Covid Test

Kevin Pham reports on a breakthrough in coronavirus testing. Excerpts in italics with my bolds.

Another new test for COVID-19 was recently authorized — and this one could be a game-changer.

The Abbot Diagnostics BinaxNOW antigen test is a new point-of-care test that reportedly costs only $5 to administer, delivers results in as little as 15 minutes, and requires no laboratory equipment to perform. That means it can be used in clinics far from commercial labs or without relying on a nearby hospital lab.

That last factor is key. There are other quick COVID-19 tests on the market, but they have all required lab equipment that can be expensive to maintain and operate, and costs can be prohibitive in places that need tests most.

This kind of test is reminiscent of rapid flu tests that are ubiquitous in clinics. They’ll give providers tremendous flexibility in testing for the disease in not just clinics, but with trained and licensed medical professionals, in schools, workplaces, camps, or any other number of places.

So what’s new about this test? Most of the current tests detect viral RNA, the genetic material of SARS-CoV-2. This is a very accurate way of detecting the virus, but it requires lab equipment to break apart the virus and amplify the amount of genetic material to high enough levels for detection.

The BinaxNOW test detects antigens — proteins unique to the virus that are usually detectable whenever there is an active infection.

Abbott says it intends to produce 50 million tests per month starting in October. That’s far more than the number tested in July, when we were breaking new testing records on a daily basis with approximately 23 million tests recorded.

There’s a more important reason to be encouraged by this test coming available.  The viral load is not amplified by the test, so a positive is actually a person needing isolation and treatment.  As explained in a previous post below,  the PCR tests used up to now clutter up the record by showing as positive people with viral loads too low to be sick or to infect others.

Background from Previous Post The Truth About CV Tests

The peoples’ instincts are right, though they have been kept in the dark about this “pandemic” that isn’t.  Responsible citizens are starting to act out their outrage from being victimized by a medical-industrial complex (to update Eisenhower’s warning decades ago).  The truth is, governments are not justified to take away inalienable rights to life, liberty and the pursuit of happiness.  There are several layers of disinformation involved in scaring the public.  This post digs into the CV tests, and why the results don’t mean what the media and officials claim.

For months now, I have been updating the progress in Canada of the CV outbreak.  A previous post later on goes into the details of extracting data on tests, persons testing positive (termed “cases” without regard for illness symptoms) and deaths after testing positive.  Currently, the contagion looks like this.

The graph shows that deaths are less than 5 a day, compared to a daily death rate of 906 in Canada from all causes.  Also significant is the positivity ratio:  the % of persons testing positive out of all persons tested each day.  That % has been fairly steady for months now:  1% positive means 99% of people are not infected. And this is despite more than doubling the rate of testing.

But what does testing positive actually mean?  Herein lies more truth that has been hidden from the public for the sake of an agenda to control free movement and activity.  Background context comes from  Could Rapid Coronavirus Testing Help Life Return To Normal?, an interview at On Point with Dr. Michael Mina.  Excerpts in italics with my bolds. H/T Kip Hansen

A sign displays a new rapid coronavirus test on the new Abbott ID Now machine at a ProHEALTH center in Brooklyn on August 27, 2020 in New York City. (Spencer Platt/Getty Images)

Dr. Michael Mina:

COVID tests can actually be put onto a piece of paper, very much like a pregnancy test. In fact, it’s almost exactly like a pregnancy test. But instead of looking for the hormones that tell if somebody is pregnant, it looks for the virus proteins that are part of SA’s code to virus. And it would be very simple: You’d either swab the front of your nose or you’d take some saliva from under your tongue, for example, and put it onto one of these paper strips, essentially. And if you see a line, it means you’re positive. And if you see no line, it means you are negative, at least for having a high viral load that could be transmissible to other people.

An antigen is one of the proteins in the virus. And so unlike the PCR test, which is what most people who have received a test today have generally received a PCR test. And looking those types of tests look for the genome of the virus to RNA and you could think of RNA the same way that humans have DNA. This virus has RNA. But instead of looking for RNA like the PCR test, these antigen tests look for pieces of the protein. It would be like if I wanted a test to tell me, you know, that somebody was an individual, it would actually look for features like their eyes or their nose. And in this case, it is looking for different parts of the virus. In general, the spike protein or the nuclear capsid, these are two parts of the virus.

The reason that these antigen tests are going to be a little bit less sensitive to detect the virus molecules is because there’s no step that we call an amplification step. One of the things that makes the PCR test that looks for the virus RNA so powerful is that it can take just one molecule, which the sensor on the machine might not be able to detect readily, but then it amplifies that molecule millions and millions of times so that the sensor can see it. These antigen tests, because they’re so simple and so easy to use and just happen on a piece of paper, they don’t have that amplification step right now. And so they require a larger amount of virus in order to be able to detect it. And that’s why I like to think of these types of tests having their primary advantage to detect people with enough virus that they might be transmitting or transmissible to other people.”

The PCR test, provides a simple yes/no answer to the question of whether a patient is infected.
Source: Covid Confusion On PCR Testing: Maybe Most Of Those Positives Are Negatives.

Similar PCR tests for other viruses nearly always offer some measure of the amount of virus. But yes/no isn’t good enough, Mina added. “It’s the amount of virus that should dictate the infected patient’s next steps. “It’s really irresponsible, I think, to [ignore this]” Dr. Mina said, of how contagious an infected patient may be.

We’ve been using one type of data for everything,” Mina said. “for [diagnosing patients], for public health, and for policy decision-making.”

The PCR test amplifies genetic matter from the virus in cycles; the fewer cycles required, the greater the amount of virus, or viral load, in the sample. The greater the viral load, the more likely the patient is to be contagious.

This number of amplification cycles needed to find the virus, called the cycle threshold, is never included in the results sent to doctors and coronavirus patients, although if it was, it could give them an idea of how infectious the patients are.

One solution would be to adjust the cycle threshold used now to decide that a patient is infected. Most tests set the limit at 40, a few at 37. This means that you are positive for the coronavirus if the test process required up to 40 cycles, or 37, to detect the virus.

Any test with a cycle threshold above 35 is too sensitive, Juliet Morrison, a virologist at the University of California, Riverside told the New York Times. “I’m shocked that people would think that 40 could represent a positive,” she said.

A more reasonable cutoff would be 30 to 35, she added. Dr. Mina said he would set the figure at 30, or even less.

Another solution, researchers agree, is to use even more widespread use of Rapid Diagnostic Tests (RDTs) which are much less sensitive and more likely to identify only patients with high levels of virus who are a transmission risk.

Comment:  In other words, when they analyzed the tests that also reported cycle threshold (CT), they found that 85 to 90 percent were above 30. According to Dr. Mina a CT of 37 is 100 times too sensitive (7 cycles too much, 2^7 = 128) and a CT of 40 is 1,000 times too sensitive (10 cycles too much, 2^10 = 1024). Based on their sample of tests that also reported CT, as few as 10 percent of people with positive PCR tests actually have an active COVID-19 infection. Which is a lot less than reported.

Here is a graph showing how this applies to Canada.

It is evident that increased testing has resulted in more positives, while the positivity rate is unchanged. Doubling the tests has doubled the positives, up from 300 a day to nearly 600 a day presently.  Note these are PCR results. And the discussion above suggests that the number of persons with an active infectious viral load is likely 10% of those reported positive: IOW up from 30 a day to 60 a day.  And in the graph below, the total of actual cases in Canada is likely on the order of 13,000 total from the last 7 months, an average of 62 cases a day.

WuFlu Exposes a Fundamental Flaw in US Health System

Dr. Mina goes on to explain what went wrong in US response to WuFlu:

In the U.S, we have a major focus on clinical medicine, and we have undervalued and underfunded the whole concept of public health for a very long time. We saw an example of this for, for example, when we tried to get the state laboratories across the country to be able to perform the PCR tests back in March, February and March, we very quickly realized that our public health infrastructure in this country just wasn’t up to the task. We had very few labs that were really able to do enough testing to just meet the clinical demands. And so such a reduced focus on public health for so long has led to an ecosystem where our regulatory agencies, this being primarily the FDA, has a mandate to approve clinical medical diagnostic tests. But there’s actually no regulatory pathway that is available or exists — and in many ways, we don’t even have a language for it — for a test whose primary purpose is one of public health and not personal medical health

That’s really caused a problem. And a lot of times, it’s interesting if you think about the United States, every single test that we get, with the exception maybe of a pregnancy test, has to go through a physician. And so that’s a symptom of a country that has focused, and a society really, that has focused so heavily on the medical industrial complex. And I’m part of that as a physician. But I also am part of the public health complex as an epidemiologist. And I see that sometimes these are at odds with each other, medicine and public health. And this is an example where because all of our regulatory infrastructure is so focused on medical devices… If you’re a public health person, you can actually have a huge amount of leeway in how your tests are working and still be able to get epidemics under control. And so there’s a real tension here between the regulations that would be required for these types of tests versus a medical diagnostic test.

Footnote:  I don’t think the Chinese leaders were focusing on the systemic weakness Dr. MIna mentions.  But you do have to bow to the inscrutable cleverness of the Chinese Communists releasing WuFlu as a means to set internal turmoil within democratic capitalist societies.  On one side are profit-seeking Big Pharma, aided and abetted by Big Media using fear to attract audiences for advertising revenues.  The panicked public demands protection which clueless government provides by shutting down the service and manufacturing industries, as well as throwing money around and taking on enormous debt.  The world just became China’s oyster.

Background from Previous Post: Covid Burnout in Canada August 28

The map shows that in Canada 9108 deaths have been attributed to Covid19, meaning people who died having tested positive for SARS CV2 virus.  This number accumulated over a period of 210 days starting January 31. The daily death rate reached a peak of 177 on May 6, 2020, and is down to 6 as of yesterday.  More details on this below, but first the summary picture. (Note: 2019 is the latest demographic report)

  Canada Pop Ann Deaths Daily Deaths Risk per
Person
2019 37589262 330786 906 0.8800%
Covid 2020 37589262 9108 43 0.0242%

Over the epidemic months, the average Covid daily death rate amounted to 5% of the All Causes death rate. During this time a Canadian had an average risk of 1 in 5000 of dying with SARS CV2 versus a 1 in 114 chance of dying regardless of that infection. As shown later below the risk varied greatly with age, much lower for younger, healthier people.

Background Updated from Previous Post

In reporting on Covid19 pandemic, governments have provided information intended to frighten the public into compliance with orders constraining freedom of movement and activity. For example, the above map of the Canadian experience is all cumulative, and the curve will continue upward as long as cases can be found and deaths attributed.  As shown below, we can work around this myopia by calculating the daily differentials, and then averaging newly reported cases and deaths by seven days to smooth out lumps in the data processing by institutions.

A second major deficiency is lack of reporting of recoveries, including people infected and not requiring hospitalization or, in many cases, without professional diagnosis or treatment. The only recoveries presently to be found are limited statistics on patients released from hospital. The only way to get at the scale of recoveries is to subtract deaths from cases, considering survivors to be in recovery or cured. Comparing such numbers involves the delay between infection, symptoms and death. Herein lies another issue of terminology: a positive test for the SARS CV2 virus is reported as a case of the disease COVID19. In fact, an unknown number of people have been infected without symptoms, and many with very mild discomfort.

August 7 in the UK it was reported (here) that around 10% of coronavirus deaths recorded in England – almost 4,200 – could be wiped from official records due to an error in counting.  Last month, Health Secretary Matt Hancock ordered a review into the way the daily death count was calculated in England citing a possible ‘statistical flaw’.  Academics found that Public Health England’s statistics included everyone who had died after testing positive – even if the death occurred naturally or in a freak accident, and after the person had recovered from the virus.  Numbers will now be reconfigured, counting deaths if a person died within 28 days of testing positive much like Scotland and Northern Ireland…

Professor Heneghan, director of the Centre for Evidence-Based Medicine at Oxford University, who first noticed the error, told the Sun:

‘It is a sensible decision. There is no point attributing deaths to Covid-19 28 days after infection…

For this discussion let’s assume that anyone reported as dying from COVD19 tested positive for the virus at some point prior. From the reasoning above let us assume that 28 days after testing positive for the virus, survivors can be considered recoveries.

Recoveries are calculated as cases minus deaths with a lag of 28 days. Daily cases and deaths are averages of the seven days ending on the stated date. Recoveries are # of cases from 28 days earlier minus # of daily deaths on the stated date. Since both testing and reports of Covid deaths were sketchy in the beginning, this graph begins with daily deaths as of April 24, 2020 compared to cases reported on March 27, 2020.

The line shows the Positivity metric for Canada starting at nearly 8% for new cases April 24, 2020. That is, for the 7 day period ending April 24, there were a daily average of 21,772 tests and 1715 new cases reported. Since then the rate of new cases has dropped down, now holding steady at ~1% since mid-June. Yesterday, the daily average number of tests was 45,897 with 427 new cases. So despite more than doubling the testing, the positivity rate is not climbing.  Another view of the data is shown below.

The scale of testing has increased and now averages over 45,000 a day, while positive tests (cases) are hovering at 1% positivity.  The shape of the recovery curve resembles the case curve lagged by 28 days, since death rates are a small portion of cases.  The recovery rate has grown from 83% to 99% steady over the last 2 weeks, so that recoveries exceed new positives. This approximation surely understates the number of those infected with SAR CV2 who are healthy afterwards, since antibody studies show infection rates multiples higher than confirmed positive tests (8 times higher in Canada).  In absolute terms, cases are now down to 427 a day and deaths 6 a day, while estimates of recoveries are 437 a day.

The key numbers: 

99% of those tested are not infected with SARS CV2. 

99% of those who are infected recover without dying.

Summary of Canada Covid Epidemic

It took a lot of work, but I was able to produce something akin to the Dutch advice to their citizens.

The media and governmental reports focus on total accumulated numbers which are big enough to scare people to do as they are told.  In the absence of contextual comparisons, citizens have difficulty answering the main (perhaps only) question on their minds:  What are my chances of catching Covid19 and dying from it?

A previous post reported that the Netherlands parliament was provided with the type of guidance everyone wants to see.

For canadians, the most similar analysis is this one from the Daily Epidemiology Update: :

The table presents only those cases with a full clinical documentation, which included some 2194 deaths compared to the 5842 total reported.  The numbers show that under 60 years old, few adults and almost no children have anything to fear.

Update May 20, 2020

It is really quite difficult to find cases and deaths broken down by age groups.  For Canadian national statistics, I resorted to a report from Ontario to get the age distributions, since that province provides 69% of the cases outside of Quebec and 87% of the deaths.  Applying those proportions across Canada results in this table. For Canada as a whole nation:

Age  Risk of Test +  Risk of Death Population
per 1 CV death
<20 0.05% None NA
20-39 0.20% 0.000% 431817
40-59 0.25% 0.002% 42273
60-79 0.20% 0.020% 4984
80+ 0.76% 0.251% 398

In the worst case, if you are a Canadian aged more than 80 years, you have a 1 in 400 chance of dying from Covid19.  If you are 60 to 80 years old, your odds are 1 in 5000.  Younger than that, it’s only slightly higher than winning (or in this case, losing the lottery).

As noted above Quebec provides the bulk of cases and deaths in Canada, and also reports age distribution more precisely,  The numbers in the table below show risks for Quebecers.

Age  Risk of Test +  Risk of Death Population
per 1 CV death
0-9 yrs 0.13% 0 NA
10-19 yrs 0.21% 0 NA
20-29 yrs 0.50% 0.000% 289,647
30-39 0.51% 0.001% 152,009
40-49 years 0.63% 0.001% 73,342
50-59 years 0.53% 0.005% 21,087
60-69 years 0.37% 0.021% 4,778
70-79 years 0.52% 0.094% 1,069
80-89 1.78% 0.469% 213
90  + 5.19% 1.608% 62

While some of the risk factors are higher in the viral hotspot of Quebec, it is still the case that under 80 years of age, your chances of dying from Covid 19 are better than 1 in 1000, and much better the younger you are.

Why Wu Flu Virus Looks Man-made

A virologist who fled China after studying the early outbreak of COVID-19 has published a new report claiming the coronavirus likely came from a lab.  This adds to the analysis done by Dr. Luc Montagnier earlier this year, and summarized in a previous post reprinted later on.  Dr. Yan was interviewed on Fox News, and YouTube has now blocked the video.

If you are wondering why Big Tech is censoring information unflattering to China, see Lee Smith’s Tablet article America’s China Class Launches a New War Against Trump  The corporate, tech, and media elites will not allow the president to come between them and Chinese money

Doctor Li-Meng Yan, a scientist who studied some of the available data on COVID-19 has published her claims on Zenodo, an open access digital platform. She wrote that she believed COVID-19 could have been “conveniently created” within a lab setting over a period of just six months, and “SARS-CoV-2 shows biological characteristics that are inconsistent with a naturally occurring, zoonotic virus”.

The paper by Yan, Li-Meng; Kang, Shu; Guan, Jie; Hu, Shanchang  is Unusual Features of the SARS-CoV-2 Genome Suggesting Sophisticated Laboratory Modification Rather Than Natural Evolution and Delineation of Its Probable Synthetic Route.  Excerpts in italics with my bolds.

Overview

The natural origin theory, although widely accepted, lacks substantial support. The alternative theory that the virus may have come from a research laboratory is, however, strictly censored on peer-reviewed scientific journals. Nonetheless, SARS-CoV-2 shows biological characteristics that are inconsistent with a naturally occurring, zoonotic virus. In this report, we describe the genomic, structural, medical, and literature evidence, which, when considered together, strongly contradicts the natural origin theory.

The evidence shows that SARS-CoV-2 should be a laboratory product created by using bat coronaviruses ZC45 and/or ZXC21 as a template and/or backbone.

Contents

Consistent with this notion, genomic, structural, and literature evidence also suggest a non-natural origin of SARS-CoV-2. In addition, abundant literature indicates that gain-of-function research has long advanced to the stage where viral genomes can be precisely engineered and manipulated to enable the creation of novel coronaviruses possessing unique properties. In this report, we present such evidence and the associated analyses.

Part 1 of the report describes the genomic and structural features of SARS-CoV-2, the presence of which could be consistent with the theory that the virus is a product of laboratory modification beyond what could be afforded by simple serial viral passage. Part 2 of the report describes a highly probable pathway for the laboratory creation of SARS-CoV-2, key steps of which are supported by evidence present in the viral genome. Importantly, part 2 should be viewed as a demonstration of how SARS-CoV-2 could be conveniently created in a laboratory in a short period of time using available materials and well-documented techniques. This report is produced by a team of experienced scientists using our combined expertise in virology, molecular biology, structural biology, computational biology, vaccine development, and medicine.

We present three lines of evidence to support our contention that laboratory manipulation is part of the history of SARS-CoV-2:

i. The genomic sequence of SARS-CoV-2 is suspiciously similar to that of a bat coronavirus discovered by military laboratories in the Third Military Medical University (Chongqing, China) and the Research Institute for Medicine of Nanjing Command (Nanjing, China).

ii. The receptor-binding motif (RBM) within the Spike protein of SARS-CoV-2, which determines the host specificity of the virus, resembles that of SARS-CoV from the 2003 epidemic in a suspicious manner. Genomic evidence suggests that the RBM has been genetically manipulated.

iii. SARS-CoV-2 contains a unique furin-cleavage site in its Spike protein, which is known to greatly enhance viral infectivity and cell tropism. Yet, this cleavage site is completely absent in this particular class of coronaviruses found in nature. In addition, rare codons associated with this additional sequence suggest the strong possibility that this furin-cleavage site is not the product of natural evolution and could have been inserted into the SARS-CoV-2 genome artificially by techniques other than simple serial passage or multi-strain recombination events inside co-infected tissue cultures or animals.

Background from Previous post June 30, 2020:  Pandemic Update: Virus Weaker, HCQ Stronger

In past weeks there have been anecdotal reports from frontline doctors that patients who would have been flattened fighting off SARS CV2 in April are now sitting up and recovering in a few days. We have also the statistical evidence in the US and Sweden, as two examples, that case numbers are rising while Covid deaths continue declining. One explanation is that the new cases are younger people who have been released from lockdown (in US) with stronger immune systems. But it may also be that the virus itself is losing potency.

In the past I have noticed theories about the origin of the virus, and what makes it “novel.” But when the scientist who identified HIV weighs in, I pay particular attention. The Coronavirus Is Man Made According to Luc Montagnier the Man Who Discovered HIV. Excerpts in italics with my bolds.

Contrary to the narrative that is being pushed by the mainstream that the COVID 19 virus was the result of a natural mutation and that it was transmitted to humans from bats via pangolins, Dr Luc Montagnier the man who discovered the HIV virus back in 1983 disagrees and is saying that the virus was man made.

Professor Luc Montagnier, 2008 Nobel Prize winner for Medicine, claims that SARS-CoV-2 is a manipulated virus that was accidentally released from a laboratory in Wuhan, China. Chinese researchers are said to have used coronaviruses in their work to develop an AIDS vaccine. HIV RNA fragments are believed to have been found in the SARS-CoV-2 genome.

“With my colleague, bio-mathematician Jean-Claude Perez, we carefully analyzed the description of the genome of this RNA virus,” explains Luc Montagnier, interviewed by Dr Jean-François Lemoine for the daily podcast at Pourquoi Docteur, adding that others have already explored this avenue: Indian researchers have already tried to publish the results of the analyses that showed that this coronavirus genome contained sequences of another virus, … the HIV virus (AIDS virus), but they were forced to withdraw their findings as the pressure from the mainstream was too great.

To insert an HIV sequence into this genome requires molecular tools

In a challenging question Dr Jean-François Lemoine inferred that the coronavirus under investigation may have come from a patient who is otherwise infected with HIV. No, “says Luc Montagnier,” in order to insert an HIV sequence into this genome, molecular tools are needed, and that can only be done in a laboratory.

According to the 2008 Nobel Prize for Medicine, a plausible explanation would be an accident in the Wuhan laboratory. He also added that the purpose of this work was the search for an AIDS vaccine.

In any case, this thesis, defended by Professor Luc Montagnier, has a positive turn.

According to him, the altered elements of this virus are eliminated as it spreads: “Nature does not accept any molecular tinkering, it will eliminate these unnatural changes and even if nothing is done, things will get better, but unfortunately after many deaths.”

This is enough to feed some heated debates! So much so that Professor Montagnier’s statements could also place him in the category of “conspiracy theorists”: “Conspirators are the opposite camp, hiding the truth,” he replies, without wanting to accuse anyone, but hoping that the Chinese will admit to what he believes happened in their laboratory.

To entice a confession from the Chinese he used the example of Iran which after taking full responsibility for accidentally hitting a Ukrainian plane was able to earn the respect of the global community. Hopefully the Chinese will do the right thing he adds. “In any case, the truth always comes out, it is up to the Chinese government to take responsibility.”

Implications: Leaving aside the geopolitics, this theory also explains why the virus weakens when mutations lose the unnatural pieces added in the lab. Since this is an RNA (not DNA) sequence mutations are slower, but inevitable. If correct, this theory works against fears of a second wave of infections. It also gives an unintended benefit from past lockdowns and shutdowns, slowing the rate of infections while the virus degrades itself.

Trump Did Listen to Pandemic Experts. They just failed him.

President Trump, accompanied by, from left, Anthony S. Fauci, Vice President Pence and Robert Redfield, reacts to a question during a news conference on the coronavirus in the press briefing room at the White House in Washington on Feb. 29. (Andrew Harnik/AP)

Marc Thiessen writes at Washington Post Trump did listen to experts on the pandemic. They just failed him. Excerpts in italics with my bolds.

A narrative has taken hold since the release of Bob Woodward’s latest book that President Trump was told in late January that the coronavirus was spreading across America at pandemic rates but ignored the dire warnings of government experts.

That narrative is wrong and unfair.

The truth is that during the crucial early weeks of the pandemic, the government’s public health leaders assured Trump that the virus was not spreading in communities in the United States. They gave him bad intelligence because of two catastrophic failures: First, they relied on the flu surveillance system that failed to detect the rapid spread of covid-19; and second, they bungled the development of a diagnostic test for covid-19 that would have shown they were wrong, barred commercial labs from developing tests, and limited tests to people who had traveled to foreign hot spots or had contact with someone with a confirmed case.

As a result, according to former Food and Drug Administration chief Scott Gottlieb, they were “situationally blind” to the spread of the virus.

In an interview Sunday on CBS News’s “Face the Nation,” Gottlieb said officials at the Department of Health and Human Services “over-relied on a surveillance system that was built for flu and not for coronavirus without recognizing that it wasn’t going to be as sensitive at detecting coronavirus spread as it was for flu because the two viruses spread very differently.” Officials were looking for a spike in patients presenting with flu-like respiratory symptoms at hospitals. But there was a lag of a week or more in reporting data, and because many of those infected with the novel coronavirus didn’t develop symptoms, or did not present with respiratory illness, they were not picked up by this monitoring. As a result, officials concluded “therefore, coronavirus must not be spreading.”

They also failed to detect the spread, Gottlieb said, because for six weeks, they “had no diagnostic tests in the field to screen people.” That is because the FDA and HHS refused to allow private and academic labs to get into the testing game with covid-19 tests of their own. The FDA issued only a single emergency authorization to the Centers for Disease Control and Prevention — and then scientists at the CDC contaminated the only approved test kits with sloppy lab practices, rendering them ineffective. The results were disastrous.

How badly did the system fail? Researchers at the University of Notre Dame found that only 1,514 cases and 39 deaths had been officially reported by early March, when in truth more than 100,000 people were already infected. Because of this failure, Gottlieb said that as covid-19 was spreading, CDC officials were “telling the coronavirus task force … that there was no spread of coronavirus in the United States,” adding “They were adamant.”

It is often noted that on Feb. 25, Nancy Messonnier, director of the CDC’s National Center for Immunization and Respiratory Diseases, pointed to the spread of the virus abroad and said, “It’s not a question of if this will happen but when this will happen and how many people in this country will have severe illnesses” — and Trump reportedly nearly fired her. But Messonnier also said in that same interview, “To date, our containment strategies have been largely successful. As a result, we have very few cases in the United States and no spread in the community.” She added that the administration’s “proactive approach of containment and mitigation will delay the emergence of community spread in the United States while simultaneously reducing its ultimate impact” when it arrives. She had no idea it already had.

On Feb. 20, Gottlieb co-authored a Wall Street Journal op-ed raising concerns that infections were more widespread than CDC numbers showed. The next day, on Feb. 21, Anthony S. Fauci said in a CNBC interview that he was confident this was not the case. “Certainly, it’s a possibility,” Fauci said, “but it is extraordinarily unlikely.” He explained that if there were infected people in the United States who were not identified, isolated and traced, “you would have almost an exponential spread of an infection of which we are all looking out for. We have not seen that, so it is extremely unlikely that it is happening.” Fauci said the “pattern of what we’re seeing argues against infections that we’re missing.”

It was not until Feb. 26 that the first possible case of suspected community spread was reported. Even then, senior health officials played down the danger. On Feb. 29, CDC director Robert Redfield said at a White House press briefing: “The American public needs to go on with their normal lives. Okay? We’re continuing to aggressively investigate these new community links. … But at this stage, again, the risk is low.” It was not until early March that the experts realized just how disastrously wrong they had been.

So, when Trump told the American people on Feb. 25 that “the coronavirus … is very well under control in our country. We have very few people with it,” he was not lying or playing down more dire information he was being told privately. He was repeating exactly what experts such as Fauci, Redfield and Messonnier were telling him.

Trump did make serious errors of his own during this early period. On deputy national security adviser Matthew Pottinger’s advice, he barred travel by non-U.S. citizens from China on Jan. 31. But he did not also shut down travel from much of Europe, as Pottinger recommended, until March 11 — almost six weeks later — because of objections from his economic advisers. The outbreak in New York, the worst of the pandemic, was seeded by travelers from Italy.

But the main reason we were not able to contain the virus is that for six critical weeks, the health experts told the president covid-19 was not spreading in U.S. communities when it was, in fact, spreading like wildfire. They were wrong. The experts failed the president — and the country.

Footnote:  For What President Trump has done to fight the Chinese virus, see:

Trump DPA Initiatives Against China Virus

 

Attenborough’s Pandemic Porn

Ross Clark writes at The Spectator What David Attenborough’s ‘Extinction: The Facts’ didn’t tell you.  Excerpts in italics with my bolds.

It was only a matter of time before Covid-19 got swept up into the wider narrative of humans facing impending doom thanks to our abuse of the planet. But one might have expected better of Sir David Attenborough. His latest BBC documentary, Extinction: The Facts, broadcast on Sunday night might as well have been produced by Extinction Rebellion, so determined was it to present an hysterical picture of apocalypse caused by consumerism and capitalism. Just to ram home the point, one contributor, naturalist Robert Watson, spoke of ‘many in the private sector making a huge profit at the expense of the natural world’, seemingly oblivious to the far greater rape of the environment committed by the former Soviet Union and other socialist countries.

But it was the section on Covid-19 which really made the jaw drop. ‘Scientists have even linked the destructive relationship with nature to the emergence of Covid-19,’ we were told. ‘If we carry on like this we will see more epidemics.’ It went on: ‘We’ve seen an increasing rate of pandemic emergencies. We’ve had swine flu, SARS, ebola. We’ve found that we’re behind every single pandemic. One of the most obvious ways we’re making it more likely that a virus would jump [from animals to humans] is that we’re having lots of contacts with animals – wildlife trade is at unprecedented levels.’

It then tried to present two examples of food production – intensive cattle ranching and wildlife markets in China – as part of the same problem.

It is perfectly true that Chinese ‘wet markets’, where many different species are sold and killed alongside each other, have been implicated in SARS and Covid-19, the former involving civets and the latter most likely bats. Breeding poultry and pigs in close proximity has also been suggested as a breeding ground for flu viruses which can then jump to humans.

But these are hardly examples of the mass, intensive agriculture which feeds an increasing proportion of the global population. On the contrary, it is the exact opposite.

It is all those old-fashioned farmyards depicted in children’s books which mixed species and brought humans into close contact with animals. Modern livestock farming, by contrast, involves huge monocultures, bred in environments where infectious disease is very tightly-controlled. An outbreak, say, of swine flu is not going to be tolerated for long in a pig farm in a developed country – though it might well be allowed to spread in a developing country where large numbers of people keep pigs in their back yards. The only way in which most of us come into contact with a farm animal now is when a slab of it is presented to us on a plate.

The idea that we face a terrifying future of infectious disease flies in the face of reality. In developed countries infectious disease has gone from being the main cause of death – especially in children – to being a rarity. Globally, the chances of dying from an infectious disease have plummeted in recent decades. According to the Institute for Health Metrics and Evaluation, the proportion of global deaths caused by communicable disease, maternal and neonatal conditions fell from 46 per cent in 1990 to 28 per cent in 2017.

Covid-19 will in no way reverse this: so far, it has caused fewer than two per cent of the 56 million deaths which would have been expected this year anyway.

Pandemics, of course, have always been a regular feature of human life. But are novel diseases becoming more commonplace? Well, yes in the sense that we have become better at identifying them – the first virus, after all, was not discovered until 1900, and we have become ever better at isolating and identifying them.

Little over a century ago, we would have had no idea what Covid-19 was – it might possibly have acquired a name, maybe ‘coughing disease’, but we would have had no real idea whether it was novel or not. A study by Brown university in 2014, published in the Journal of the Royal Society, found that there has been a rise in the number of outbreaks of novel infectious diseases since 1980, but also that there has been a decline in the numbers of people being affected by them. We have become much better at identifying diseases, and much better at controlling them. Covid might have inspired an unprecedented global response, but in historical terms it is a pretty gentle pandemic – even now it has a lower death toll than Hong Kong flu, which hardly affected our lives at all.

It is shocking that the BBC can have allowed such one-sided green propaganda onto our screens without putting issues of human development and the natural world into proper context. But then David Attenborough has become a Greta of the Third Age – no-one dares question what he does because he is a ‘national treasure’. Someone at the BBC needs to pluck up the courage.

 

 

 

TOPICS IN THIS ARTICLE

New Better and Faster CV Tests

Kevin Pham reports on a breakthrough in coronavirus testing. Excerpts in italics with my bolds.

Another new test for COVID-19 was recently authorized — and this one could be a game-changer.

The Abbot Diagnostics BinaxNOW antigen test is a new point-of-care test that reportedly costs only $5 to administer, delivers results in as little as 15 minutes, and requires no laboratory equipment to perform. That means it can be used in clinics far from commercial labs or without relying on a nearby hospital lab.

That last factor is key. There are other quick COVID-19 tests on the market, but they have all required lab equipment that can be expensive to maintain and operate, and costs can be prohibitive in places that need tests most.

This kind of test is reminiscent of rapid flu tests that are ubiquitous in clinics. They’ll give providers tremendous flexibility in testing for the disease in not just clinics, but with trained and licensed medical professionals, in schools, workplaces, camps, or any other number of places.

So what’s new about this test? Most of the current tests detect viral RNA, the genetic material of SARS-CoV-2. This is a very accurate way of detecting the virus, but it requires lab equipment to break apart the virus and amplify the amount of genetic material to high enough levels for detection.

The BinaxNOW test detects antigens — proteins unique to the virus that are usually detectable whenever there is an active infection.

Abbott says it intends to produce 50 million tests per month starting in October. That’s far more than the number tested in July, when we were breaking new testing records on a daily basis with approximately 23 million tests recorded.

There’s a more important reason to be encouraged by this test coming available.  The viral load is not amplified by the test, so a positive is actually a person needing isolation and treatment.  As explained in a previous post below,  the PCR tests used up to now clutter up the record by showing as positive people with viral loads too low to be sick or to infect others.

Background from Previous Post The Truth About CV Tests

The peoples’ instincts are right, though they have been kept in the dark about this “pandemic” that isn’t.  Responsible citizens are starting to act out their outrage from being victimized by a medical-industrial complex (to update Eisenhower’s warning decades ago).  The truth is, governments are not justified to take away inalienable rights to life, liberty and the pursuit of happiness.  There are several layers of disinformation involved in scaring the public.  This post digs into the CV tests, and why the results don’t mean what the media and officials claim.

For months now, I have been updating the progress in Canada of the CV outbreak.  A previous post later on goes into the details of extracting data on tests, persons testing positive (termed “cases” without regard for illness symptoms) and deaths after testing positive.  Currently, the contagion looks like this.

The graph shows that deaths are less than 5 a day, compared to a daily death rate of 906 in Canada from all causes.  Also significant is the positivity ratio:  the % of persons testing positive out of all persons tested each day.  That % has been fairly steady for months now:  1% positive means 99% of people are not infected. And this is despite more than doubling the rate of testing.

But what does testing positive actually mean?  Herein lies more truth that has been hidden from the public for the sake of an agenda to control free movement and activity.  Background context comes from  Could Rapid Coronavirus Testing Help Life Return To Normal?, an interview at On Point with Dr. Michael Mina.  Excerpts in italics with my bolds. H/T Kip Hansen

A sign displays a new rapid coronavirus test on the new Abbott ID Now machine at a ProHEALTH center in Brooklyn on August 27, 2020 in New York City. (Spencer Platt/Getty Images)

Dr. Michael Mina:

COVID tests can actually be put onto a piece of paper, very much like a pregnancy test. In fact, it’s almost exactly like a pregnancy test. But instead of looking for the hormones that tell if somebody is pregnant, it looks for the virus proteins that are part of SA’s code to virus. And it would be very simple: You’d either swab the front of your nose or you’d take some saliva from under your tongue, for example, and put it onto one of these paper strips, essentially. And if you see a line, it means you’re positive. And if you see no line, it means you are negative, at least for having a high viral load that could be transmissible to other people.

An antigen is one of the proteins in the virus. And so unlike the PCR test, which is what most people who have received a test today have generally received a PCR test. And looking those types of tests look for the genome of the virus to RNA and you could think of RNA the same way that humans have DNA. This virus has RNA. But instead of looking for RNA like the PCR test, these antigen tests look for pieces of the protein. It would be like if I wanted a test to tell me, you know, that somebody was an individual, it would actually look for features like their eyes or their nose. And in this case, it is looking for different parts of the virus. In general, the spike protein or the nuclear capsid, these are two parts of the virus.

The reason that these antigen tests are going to be a little bit less sensitive to detect the virus molecules is because there’s no step that we call an amplification step. One of the things that makes the PCR test that looks for the virus RNA so powerful is that it can take just one molecule, which the sensor on the machine might not be able to detect readily, but then it amplifies that molecule millions and millions of times so that the sensor can see it. These antigen tests, because they’re so simple and so easy to use and just happen on a piece of paper, they don’t have that amplification step right now. And so they require a larger amount of virus in order to be able to detect it. And that’s why I like to think of these types of tests having their primary advantage to detect people with enough virus that they might be transmitting or transmissible to other people.”

The PCR test, provides a simple yes/no answer to the question of whether a patient is infected.
Source: Covid Confusion On PCR Testing: Maybe Most Of Those Positives Are Negatives.

Similar PCR tests for other viruses nearly always offer some measure of the amount of virus. But yes/no isn’t good enough, Mina added. “It’s the amount of virus that should dictate the infected patient’s next steps. “It’s really irresponsible, I think, to [ignore this]” Dr. Mina said, of how contagious an infected patient may be.

We’ve been using one type of data for everything,” Mina said. “for [diagnosing patients], for public health, and for policy decision-making.”

The PCR test amplifies genetic matter from the virus in cycles; the fewer cycles required, the greater the amount of virus, or viral load, in the sample. The greater the viral load, the more likely the patient is to be contagious.

This number of amplification cycles needed to find the virus, called the cycle threshold, is never included in the results sent to doctors and coronavirus patients, although if it was, it could give them an idea of how infectious the patients are.

One solution would be to adjust the cycle threshold used now to decide that a patient is infected. Most tests set the limit at 40, a few at 37. This means that you are positive for the coronavirus if the test process required up to 40 cycles, or 37, to detect the virus.

Any test with a cycle threshold above 35 is too sensitive, Juliet Morrison, a virologist at the University of California, Riverside told the New York Times. “I’m shocked that people would think that 40 could represent a positive,” she said.

A more reasonable cutoff would be 30 to 35, she added. Dr. Mina said he would set the figure at 30, or even less.

Another solution, researchers agree, is to use even more widespread use of Rapid Diagnostic Tests (RDTs) which are much less sensitive and more likely to identify only patients with high levels of virus who are a transmission risk.

Comment:  In other words, when they analyzed the tests that also reported cycle threshold (CT), they found that 85 to 90 percent were above 30. According to Dr. Mina a CT of 37 is 100 times too sensitive (7 cycles too much, 2^7 = 128) and a CT of 40 is 1,000 times too sensitive (10 cycles too much, 2^10 = 1024). Based on their sample of tests that also reported CT, as few as 10 percent of people with positive PCR tests actually have an active COVID-19 infection. Which is a lot less than reported.

Here is a graph showing how this applies to Canada.

It is evident that increased testing has resulted in more positives, while the positivity rate is unchanged. Doubling the tests has doubled the positives, up from 300 a day to nearly 600 a day presently.  Note these are PCR results. And the discussion above suggests that the number of persons with an active infectious viral load is likely 10% of those reported positive: IOW up from 30 a day to 60 a day.  And in the graph below, the total of actual cases in Canada is likely on the order of 13,000 total from the last 7 months, an average of 62 cases a day.

WuFlu Exposes a Fundamental Flaw in US Health System

Dr. Mina goes on to explain what went wrong in US response to WuFlu:

In the U.S, we have a major focus on clinical medicine, and we have undervalued and underfunded the whole concept of public health for a very long time. We saw an example of this for, for example, when we tried to get the state laboratories across the country to be able to perform the PCR tests back in March, February and March, we very quickly realized that our public health infrastructure in this country just wasn’t up to the task. We had very few labs that were really able to do enough testing to just meet the clinical demands. And so such a reduced focus on public health for so long has led to an ecosystem where our regulatory agencies, this being primarily the FDA, has a mandate to approve clinical medical diagnostic tests. But there’s actually no regulatory pathway that is available or exists — and in many ways, we don’t even have a language for it — for a test whose primary purpose is one of public health and not personal medical health

That’s really caused a problem. And a lot of times, it’s interesting if you think about the United States, every single test that we get, with the exception maybe of a pregnancy test, has to go through a physician. And so that’s a symptom of a country that has focused, and a society really, that has focused so heavily on the medical industrial complex. And I’m part of that as a physician. But I also am part of the public health complex as an epidemiologist. And I see that sometimes these are at odds with each other, medicine and public health. And this is an example where because all of our regulatory infrastructure is so focused on medical devices… If you’re a public health person, you can actually have a huge amount of leeway in how your tests are working and still be able to get epidemics under control. And so there’s a real tension here between the regulations that would be required for these types of tests versus a medical diagnostic test.

Footnote:  I don’t think the Chinese leaders were focusing on the systemic weakness Dr. MIna mentions.  But you do have to bow to the inscrutable cleverness of the Chinese Communists releasing WuFlu as a means to set internal turmoil within democratic capitalist societies.  On one side are profit-seeking Big Pharma, aided and abetted by Big Media using fear to attract audiences for advertising revenues.  The panicked public demands protection which clueless government provides by shutting down the service and manufacturing industries, as well as throwing money around and taking on enormous debt.  The world just became China’s oyster.

Background from Previous Post: Covid Burnout in Canada August 28

The map shows that in Canada 9108 deaths have been attributed to Covid19, meaning people who died having tested positive for SARS CV2 virus.  This number accumulated over a period of 210 days starting January 31. The daily death rate reached a peak of 177 on May 6, 2020, and is down to 6 as of yesterday.  More details on this below, but first the summary picture. (Note: 2019 is the latest demographic report)

  Canada Pop Ann Deaths Daily Deaths Risk per
Person
2019 37589262 330786 906 0.8800%
Covid 2020 37589262 9108 43 0.0242%

Over the epidemic months, the average Covid daily death rate amounted to 5% of the All Causes death rate. During this time a Canadian had an average risk of 1 in 5000 of dying with SARS CV2 versus a 1 in 114 chance of dying regardless of that infection. As shown later below the risk varied greatly with age, much lower for younger, healthier people.

Background Updated from Previous Post

In reporting on Covid19 pandemic, governments have provided information intended to frighten the public into compliance with orders constraining freedom of movement and activity. For example, the above map of the Canadian experience is all cumulative, and the curve will continue upward as long as cases can be found and deaths attributed.  As shown below, we can work around this myopia by calculating the daily differentials, and then averaging newly reported cases and deaths by seven days to smooth out lumps in the data processing by institutions.

A second major deficiency is lack of reporting of recoveries, including people infected and not requiring hospitalization or, in many cases, without professional diagnosis or treatment. The only recoveries presently to be found are limited statistics on patients released from hospital. The only way to get at the scale of recoveries is to subtract deaths from cases, considering survivors to be in recovery or cured. Comparing such numbers involves the delay between infection, symptoms and death. Herein lies another issue of terminology: a positive test for the SARS CV2 virus is reported as a case of the disease COVID19. In fact, an unknown number of people have been infected without symptoms, and many with very mild discomfort.

August 7 in the UK it was reported (here) that around 10% of coronavirus deaths recorded in England – almost 4,200 – could be wiped from official records due to an error in counting.  Last month, Health Secretary Matt Hancock ordered a review into the way the daily death count was calculated in England citing a possible ‘statistical flaw’.  Academics found that Public Health England’s statistics included everyone who had died after testing positive – even if the death occurred naturally or in a freak accident, and after the person had recovered from the virus.  Numbers will now be reconfigured, counting deaths if a person died within 28 days of testing positive much like Scotland and Northern Ireland…

Professor Heneghan, director of the Centre for Evidence-Based Medicine at Oxford University, who first noticed the error, told the Sun:

‘It is a sensible decision. There is no point attributing deaths to Covid-19 28 days after infection…

For this discussion let’s assume that anyone reported as dying from COVD19 tested positive for the virus at some point prior. From the reasoning above let us assume that 28 days after testing positive for the virus, survivors can be considered recoveries.

Recoveries are calculated as cases minus deaths with a lag of 28 days. Daily cases and deaths are averages of the seven days ending on the stated date. Recoveries are # of cases from 28 days earlier minus # of daily deaths on the stated date. Since both testing and reports of Covid deaths were sketchy in the beginning, this graph begins with daily deaths as of April 24, 2020 compared to cases reported on March 27, 2020.

The line shows the Positivity metric for Canada starting at nearly 8% for new cases April 24, 2020. That is, for the 7 day period ending April 24, there were a daily average of 21,772 tests and 1715 new cases reported. Since then the rate of new cases has dropped down, now holding steady at ~1% since mid-June. Yesterday, the daily average number of tests was 45,897 with 427 new cases. So despite more than doubling the testing, the positivity rate is not climbing.  Another view of the data is shown below.

The scale of testing has increased and now averages over 45,000 a day, while positive tests (cases) are hovering at 1% positivity.  The shape of the recovery curve resembles the case curve lagged by 28 days, since death rates are a small portion of cases.  The recovery rate has grown from 83% to 99% steady over the last 2 weeks, so that recoveries exceed new positives. This approximation surely understates the number of those infected with SAR CV2 who are healthy afterwards, since antibody studies show infection rates multiples higher than confirmed positive tests (8 times higher in Canada).  In absolute terms, cases are now down to 427 a day and deaths 6 a day, while estimates of recoveries are 437 a day.

The key numbers: 

99% of those tested are not infected with SARS CV2. 

99% of those who are infected recover without dying.

Summary of Canada Covid Epidemic

It took a lot of work, but I was able to produce something akin to the Dutch advice to their citizens.

The media and governmental reports focus on total accumulated numbers which are big enough to scare people to do as they are told.  In the absence of contextual comparisons, citizens have difficulty answering the main (perhaps only) question on their minds:  What are my chances of catching Covid19 and dying from it?

A previous post reported that the Netherlands parliament was provided with the type of guidance everyone wants to see.

For canadians, the most similar analysis is this one from the Daily Epidemiology Update: :

The table presents only those cases with a full clinical documentation, which included some 2194 deaths compared to the 5842 total reported.  The numbers show that under 60 years old, few adults and almost no children have anything to fear.

Update May 20, 2020

It is really quite difficult to find cases and deaths broken down by age groups.  For Canadian national statistics, I resorted to a report from Ontario to get the age distributions, since that province provides 69% of the cases outside of Quebec and 87% of the deaths.  Applying those proportions across Canada results in this table. For Canada as a whole nation:

Age  Risk of Test +  Risk of Death Population
per 1 CV death
<20 0.05% None NA
20-39 0.20% 0.000% 431817
40-59 0.25% 0.002% 42273
60-79 0.20% 0.020% 4984
80+ 0.76% 0.251% 398

In the worst case, if you are a Canadian aged more than 80 years, you have a 1 in 400 chance of dying from Covid19.  If you are 60 to 80 years old, your odds are 1 in 5000.  Younger than that, it’s only slightly higher than winning (or in this case, losing the lottery).

As noted above Quebec provides the bulk of cases and deaths in Canada, and also reports age distribution more precisely,  The numbers in the table below show risks for Quebecers.

Age  Risk of Test +  Risk of Death Population
per 1 CV death
0-9 yrs 0.13% 0 NA
10-19 yrs 0.21% 0 NA
20-29 yrs 0.50% 0.000% 289,647
30-39 0.51% 0.001% 152,009
40-49 years 0.63% 0.001% 73,342
50-59 years 0.53% 0.005% 21,087
60-69 years 0.37% 0.021% 4,778
70-79 years 0.52% 0.094% 1,069
80-89 1.78% 0.469% 213
90  + 5.19% 1.608% 62

While some of the risk factors are higher in the viral hotspot of Quebec, it is still the case that under 80 years of age, your chances of dying from Covid 19 are better than 1 in 1000, and much better the younger you are.

Sturgis Bikers Not Superspreaders

Jennifer Beam Dowd writes at Slate The Sturgis Biker Rally Did Not Cause 266,796 Cases of COVID-19.  Excerpts in italics with my bolds.

The recent mass gathering in South Dakota for the annual Sturgis Motorcycle Rally seemed like the perfect recipe for what epidemiologists call a “superspreading” event. Beginning Aug. 7, an estimated 460,000 attendees from all over the country descended on the small town of Sturgis for a 10-day event filled with indoor and outdoor events such as concerts and drag racing.

Now a new working paper by economist Dhaval Dave and colleagues is making headlines with their estimate that the Sturgis rally led to a shocking 266,796 new cases in the U.S. over a four-week period, which would account for a staggering 19 percent of newly confirmed cases in the U.S. in that time. They estimate the economic cost of these cases at $12.2 billion, based on previous estimates of the statistical cost of treating a COVID-19 patient.

Modeling infection transmission dynamics is hard, as we have seen by the less than stellar performance of many predictive COVID-19 models thus far. (Remember back in April, when the IHME model from the University of Washington predicted zero U.S. deaths in July?) Pandemic spread is difficult both to predict and to explain after the fact—like trying to explain the direction and intensity of current wildfires in the West. While some underlying factors do predict spread, there is a high degree of randomness, and small disturbances (like winds) can cause huge variation across time and space. Many outcomes that social scientists typically study, like income, are more stable and not as susceptible to these “butterfly effects” that threaten the validity of certain research designs.

While this approach may sound sensible, it relies on strong assumptions that rarely hold in the real world. For one thing, there are many other differences between counties full of bike rally fans versus those with none, and therein lies the challenge of creating a good “counterfactual” for the implied experiment—how to compare trends in counties that are different on many geographic, social, and economic dimensions? The “parallel trends” assumption assumes that every county was on a similar trajectory and the only difference was the number of attendees sent to the Sturgis rally. When this “parallel trends” assumption is violated, the resulting estimates are not just off by a little—they can be completely wrong.

This type of modeling is risky, and the burden of proof for the believability of the assumptions very high.

If thinking through the required transmission dynamics doesn’t raise your alarm bells, consider this: The paper’s results show that the significant increase in transmission was only evident after Aug. 26. That makes sense—it would be consistent with a lag time for infections from the beginning of the rally. Nonetheless, the authors state that their estimate of the total number of cases, 266,796, represents “19 percent of the 1.4 million cases of COVID-19 in the United States between August 2nd 2020 and September 2nd.” (Italics mine.) In reality, these extra cases must have occurred in the second half of the month, meaning these estimates would account for a staggering 45 percent of U.S. cases over those two weeks. This simply doesn’t seem plausible.

The 266,796 number also overstates the precision of the estimates in the paper even if the model is taken at face value. The confidence intervals for the “high inflow” counties seem to include zero (meaning the authors can’t say with statistical confidence that there was any difference in infections across counties due to the rally). No standard errors (measures of the variability around the estimate) are provided for the main regression results, and many of the p-values for key results are not statistically significant at conventional levels. So even if one believes the design and assumptions, the results are very “noisy” and subject to caveats that don’t merit the broadcasting of the highly specific 266,796 figure with confidence, though I imagine that “somewhere between zero and 450,000 infections” would not have been as headline-grabbing.

The paper also estimates the rise in cases in Meade County, South Dakota, the site of the rally, and reports an increase of between 177 and 195 cases compared with a “synthetic control” of similar counties, an approach similar in spirit to the difference-in-difference model. This represents a 100 to 200 percent increase in cases, which also appears to be a serious overestimate. Looking at the raw case data for Meade Country, cumulative cases from Aug. 3 to Sept. 2 increased from by 45 to 74, an increase of only total 29 cases (though a 64 percent increase). With a cumulative case count of only 74 in Meade County by Sept. 2, an estimated increase of 103 more than the total observed over the whole pandemic suggests serious problems with the model.

Again, the authors employ a method that implicitly compares what happened in Meade County to similar hypothetical “twin” counties. Counties from within South Dakota and bordering states were excluded since they may also have been directly affected by the rally. Counties that shared similar urbanicity rates, population density, and pre-rally COVID-19 cases per capita were considered good candidates for this counterfactual group. Finding valid comparisons is key. Upon inspection, one of the counties weighted heavily as a “control” was in Hawaii—I think we can agree that islands during a pandemic are not likely a good control group for what is happening in the lower 48.

None of this means that the rally was probably harmless. Common sense would tell us that such a large event with close contact was risky and did increase transmission. The rise in Meade County was real and noticeable, albeit on the scale of 29 cases. Given the huge inflow to this specific location along with increased testing for the event, a bump was not surprising.

Contact tracing reports have identified cases and deaths linked to the event, but in the range of hundreds.

More broadly, while it’s important for us to understand factors driving COVID-19 transmission, the methodological challenges to identifying these effects at the aggregate level are difficult to overcome. Improved contact tracing and surveys at the individual level are the best way to gain insights into transmission dynamics. (At Dear Pandemic, a COVID-19 science communication effort I run with colleagues, we unfortunately spend much of our time explaining and correcting such misleading statistics.) The authors of this study have used the same study design to estimate the effects of other mass gatherings including the BLM protests and Trump’s June Tulsa, Oklahoma, rally. Each paper has given some part of the political spectrum something they might want to hear but has done very little to illuminate the actual risks of COVID-19 transmission at these events.

Exaggerated headlines and cherry-picking of results for “I told you so” media moments can dangerously undermine the long-term integrity of the science—something we can little afford right now.

COVID-19 is a lack of nutrients, exploited by a virus

Colleen Huber, NMD explains at The Primary Doctor COVID-19 is a lack of nutrients, exploited by a virus. Excerpts in italics with my bolds.

“There already exist numerous ways to reliably prevent, mitigate, and even cure COVID-19, including in late-stage patients who are already ventilator-dependent.”
– Thomas Levy, MD JD

Abstract

COVID-19 disease is alleged to be caused by the RNA coronavirus SARS-CoV-2. However, clinical findings from around the world show a sharp inflection point from morbidity to recovery on supplementation of one or another nutrient. In other cases, severe COVID-19 morbidity is significantly correlated with deficiency of a particular nutrient. Any of the nutrients that are discussed in this paper, when used alone or with a co-factor, has been either sufficient for prompt and complete recovery in a majority of patients treated or highly correlated with low morbidity and high survival from the disease.

If any one of several nutrients is adequate for victory over COVID-19, then logically (the contrapositive), the simultaneous deficiency of all of those same nutrients is the necessary preliminary condition for the subsequent presence of the virus to result in COVID-19 morbidity and mortality. This paper will show which nutrients are lacking in those with severe pathogenesis, and why all of those nutrients must be deficient in order for severe COVID-19 disease to occur in an individual, and that supplementation with any one of these nutrients is likely to result in recovery.

[Note: The full list of nutrients discussed in the paper are as follows:

  • Vitamin D
  • Glutathione
  • Zinc
  • Quercitin
  • Epigallocatechin-gallate (EGCG)
  • Vitamin C
  • Selenium

    Below are excerpts relating to the big 3 in the image above.]

Vitamin D vs COVID19

Vitamin D3 (commonly known simply as “vitamin D,” but formally as cholecalciferol) may be the most potent defense available against COVID-19, from the studies described below. It may also be the most easily acquired COVID-19 treatment, because vitamin D is produced in the skin on exposure to sunlight, with further processing in the liver and then in the kidneys to its fully useful form.

In this large Israeli study of over 7,000 people, “low plasma [vitamin D] levels almost doubled the risk for hospitalization due to the COVID-19 infection in the Israeli studied cohort.” Also, “the mean plasma vitamin D level was significantly lower among those who tested positive than negative for COVID-19.” (1)

In a retrospective cohort study in Indonesia of 780 cases of COVID-19 positive patients, it was found that those with below normal vitamin D levels were associated with increasing odds of death. (2)

The correlation among low serum vitamin D levels and COVID-19 mortality was so high in that study that this nutrient may turn out to be the most decisively valuable against COVID-19. This graph (3) shows the stark contrast found between high and low vitamin D levels and COVID-19 survivability.

​In European countries also, a significant inverse relationship was found between serum vitamin D levels and COVID-19 mortality. Mean levels of vitamin D and COVID-19 mortality in twenty European countries were examined. Also aging populations, which have been the worst affected by COVID-19 were found to have the lowest serum vitamin D levels. (4)

Vitamin D is known to be essential to the maturing of macrophages, which in turn are a necessary tool of the immune system against pathogenic microbes. Macrophages with vitamin D also produce hydrogen peroxide, an important pro-oxidant molecular weapon against microbial pathogens. (5) However, vitamin D also stimulates production of anti-microbial peptides that appear in natural killer cells and neutrophils in respiratory tract epithelial cells, where they are able to protect the lungs from the ravages of infection.

One of the most alarming features of COVID-19 disease in the clinical setting has been the “cytokine storm,” which is itself life-threatening. It is an inflammatory over-reaction to the replicating viral pathogen. The utility of Vitamin D for the COVID-19 patient may best be appreciated in its prevention of excessive inflammatory cytokines, thereby sparing the patient of the body’s most severe reactions to the virus. (6) Vitamin D deficiency is also implicated in acute respiratory distress syndrome. (7)

Respiratory infectious disease prevalence has a strong seasonality through the centuries and around the world. That season peaks in the winter and early spring, after the year’s fewest hours and lowest angle of sunlight on the winter solstice. That lack of sunlight occurs during a time of the least skin surface exposed to freezing weather, and therefore the least endogenous vitamin D production. Supplementation of oral vitamin D through this difficult season may therefore be a prudent prophylaxis.

Zinc vs COVID-19

​Zinc has many functions in the cell. One of these is to inhibit replication of RNA-type viruses. SARS-CoV-2 is such a virus. The mechanism is that zinc blocks the enzyme RNA-dependent RNA polymerase (RdRp). This enzyme is required for replication of the virus. Without this enzyme, copying of the viral RNA cannot occur. The virus’s assault against the body is not merely inhibited. It is stopped with adequate zinc.

Zinc, however, is mostly kept out of the cell by other mechanisms, partly because zinc plays a role in normal cell death. A survival mechanism of a normal cell is to therefore limit the zinc that can enter.

However, in the event of infection with an RNA virus, a useful strategy for medical treatment is to bring enough zinc into cells to block viral replication. What is needed is a substance that can accompany and transport zinc across the cell membrane and into the cell. Such a substance is an ionophore; it transports the zinc ion. The function is to allow more zinc into a cell than would typically enter. For this purpose, zinc ionophore agents are used in clinical settings together with zinc as a combination strategy against an RNA virus infection. I will discuss a few of these zinc ionophores.

It should also be noted that zinc deficiency is characterized by loss of senses of smell and/or taste. (11) These are also known to be common symptoms of COVID-19 patients. (12) This is further evidence that deficiency of zinc may be correlated with COVID-19 morbidity.

Zinc and Hydroxychloroquine vs COVID-19

Both hydroxychloroquine (HCQ) and its historical predecessor chloroquine (CQ) are on the World Health Organization’s List of Essential Medicines. The latter was discovered in 1934, and it is still used to manage malaria, although resistant strains of malaria make it less useful these days for that purpose. HCQ has been approved by the US Food and Drug Administration (FDA) for over 65 years. It has been prescribed billions of times throughout the world over the previous decades. The US Centers for Disease Control says that HCQ can be prescribed to adults and children of all ages. It can also be safely taken by pregnant women and nursing mothers. (13) It is among the safest of prescription drugs in the US, which is why it is sold over the counter through much of the world. (14) Both HCQ and CQ are chemically similar to quinine, from the bark of Cinchona trees, which is also a flavoring used in tonic water.

These drugs have been observed to raise the pH of the cell and the endosomes in which entering viruses are packaged. These drugs are easily taken up into the cells of the body. Viruses, however, enter cells packaged in endosomes, and require a low pH acidic environment in the endosome in order to replicate. Once HCQ or CQ are inside cells, they easily enter endosomes, and therefore viruses are stopped from replicating (reproducing) due to this alkalinizing effect. Dr. Peter D’Adamo describes and illustrates these mechanisms in more detail. (15)

So in summary of these functions then, HCQ and CQ not only shepherd zinc into the cell, where zinc blocks the enzyme that is required for replication of RNA viruses, but either of these drugs also raise pH inside the cell to a level where viral replication is impossible.

The combination of HCQ, azithromycin and zinc has shown outstanding results in resolving COVID-19. See: Positive HCQ Treatment Outcomes in 88 International Studies

Veteran virologist Steven Hatfill writes of hydroxychloroquine: The Real HCQ Story: What We Now Know

Yale epidemiology professor Harvey Risch, a highly respected scientist with over 300 published peer-reviewed studies, writes of the contrast between the successful clinical use of HCQ and zinc on the one hand, and its suppression by governments and industry on the other:

Dr. Risch, who has 39,779 citations on Google Scholar, adds that “US cumulative deaths through July 15, 2020 are 140,000. Had we permitted HCQ use liberally, we would have saved half, 70,000, and it is very possible we could have saved 3/4, or105,000.”

There are other zinc ionophores that are also being used together with zinc successfully against COVID-19.  Zinc and Quercitin vs COVID-19;  Zinc and EGCG vs COVID-19 (Epigallocatechin-gallate (EGCG) is a green tea extract)

Vitamin C vs COVID-19

At the Ruijing Hospital in Shanghai, 50 COVID-19 patients were treated with vitamin C. Their hospital stays were 5 days shorter than those COVID-19 patients not treated with Vitamin C. There were no deaths in the Vitamin C group, and no significant side effects were noted either. In the other group of COVID-19 patients, those who did not receive vitamin C, there were 3 deaths. (25)

Dr. Zhiyong Peng conducted the first clinical trial of high-dose intravenous vitamin C with COVID19 patients at Wuhan University in Wuhan, China. His findings were that this treatment of COVID-19 patients reduced their inflammation significantly, and that it reduced their stays in ICU and hospitals. (26) (27)

Vitamin C should be no surprise as an addition to the list of nutrients that provide life-saving help against COVID-19. Dr. Fred Klenner wrote in 1948 about use of intravenous and intramuscular use of vitamin C against viral pneumonia: “In almost every case the patient felt better within an hour after the first injection and noted a very definite change after two hours.” And “three to seven injections gave complete clinical and x-ray response in all of our [42] cases.” (28)

Vitamin C has numerous well-studied and documented mechanisms against viruses. Perhaps the most important of these is the production of Type I interferons. (31) This in turn upregulates natural killer cells and cytotoxic T-lymphocytes for anti-viral activity. (32) However, it has been shown to simply inactivate both RNA and DNA viruses. (33) It also detoxifies viral products that are associated with inflammation and pain. High dose vitamin C and oral doses over 3 grams are established to both prevent and treat a variety of viral infections. (34) (35)

Conclusion

To repeat Dr. Thomas Levy’s memorable quote at the beginning of this paper, “There already exist numerous ways to reliably prevent, mitigate, and even cure COVID-19, including in late-stage patients who are already ventilator-dependent.” Dr. Levy documents many of them in this paper. (38)

Those who were diagnosed and sickened from the most feared viral pathogen of our time fell into several categories. Either they died from the disease, or they healed from one of the interventions discussed in this paper, or fortunately, healed with none of those interventions. The above studies show that it was enough that one or the other of the nutrients discussed herein was adequate to prevent or to vanquish COVID-19, without the need to use all of them. Therefore, because any one of these nutrients proved adequate to heal patients to complete recovery, then the patients who succumbed to COVID-19 disease had likely been deficient in all of these nutrients, and a lack of all of these nutrients was likely a necessary condition for pathogenesis of COVID-19.

Because of the therapeutic impact and success that each of the above nutrients have had in reversing the devastation of COVID-19, they each must be made available immediately and widely throughout the world, for both preventative and prompt therapeutic uses. There is therefore no need or justification for pandemic status of COVID-19. Furthermore, nutritional interventions should be used without hesitation as first-line treatment, as well as prevention, of COVID-19.

 

The Truth About CV Tests

The peoples’ instincts are right, though they have been kept in the dark about this “pandemic” that isn’t.  Responsible citizens are starting to act out their outrage from being victimized by a medical-industrial complex (to update Eisenhower’s warning decades ago).  The truth is, governments are not justified to take away inalienable rights to life, liberty and the pursuit of happiness.  There are several layers of disinformation involved in scaring the public.  This post digs into the CV tests, and why the results don’t mean what the media and officials claim.

For months now, I have been updating the progress in Canada of the CV outbreak.  A previous post later on goes into the details of extracting data on tests, persons testing positive (termed “cases” without regard for illness symptoms) and deaths after testing positive.  Currently, the contagion looks like this.

The graph shows that deaths are less than 5 a day, compared to a daily death rate of 906 in Canada from all causes.  Also significant is the positivity ratio:  the % of persons testing positive out of all persons tested each day.  That % has been fairly steady for months now:  1% positive means 99% of people are not infected. And this is despite more than doubling the rate of testing.

But what does testing positive actually mean?  Herein lies more truth that has been hidden from the public for the sake of an agenda to control free movement and activity.  Background context comes from  Could Rapid Coronavirus Testing Help Life Return To Normal?, an interview at On Point with Dr. Michael Mina.  Excerpts in italics with my bolds. H/T Kip Hansen

A sign displays a new rapid coronavirus test on the new Abbott ID Now machine at a ProHEALTH center in Brooklyn on August 27, 2020 in New York City. (Spencer Platt/Getty Images)

Dr. Michael Mina:

COVID tests can actually be put onto a piece of paper, very much like a pregnancy test. In fact, it’s almost exactly like a pregnancy test. But instead of looking for the hormones that tell if somebody is pregnant, it looks for the virus proteins that are part of SA’s code to virus. And it would be very simple: You’d either swab the front of your nose or you’d take some saliva from under your tongue, for example, and put it onto one of these paper strips, essentially. And if you see a line, it means you’re positive. And if you see no line, it means you are negative, at least for having a high viral load that could be transmissible to other people.

An antigen is one of the proteins in the virus. And so unlike the PCR test, which is what most people who have received a test today have generally received a PCR test. And looking those types of tests look for the genome of the virus to RNA and you could think of RNA the same way that humans have DNA. This virus has RNA. But instead of looking for RNA like the PCR test, these antigen tests look for pieces of the protein. It would be like if I wanted a test to tell me, you know, that somebody was an individual, it would actually look for features like their eyes or their nose. And in this case, it is looking for different parts of the virus. In general, the spike protein or the nuclear capsid, these are two parts of the virus.

The reason that these antigen tests are going to be a little bit less sensitive to detect the virus molecules is because there’s no step that we call an amplification step. One of the things that makes the PCR test that looks for the virus RNA so powerful is that it can take just one molecule, which the sensor on the machine might not be able to detect readily, but then it amplifies that molecule millions and millions of times so that the sensor can see it. These antigen tests, because they’re so simple and so easy to use and just happen on a piece of paper, they don’t have that amplification step right now. And so they require a larger amount of virus in order to be able to detect it. And that’s why I like to think of these types of tests having their primary advantage to detect people with enough virus that they might be transmitting or transmissible to other people.”

The PCR test, provides a simple yes/no answer to the question of whether a patient is infected.
Source: Covid Confusion On PCR Testing: Maybe Most Of Those Positives Are Negatives.

Similar PCR tests for other viruses nearly always offer some measure of the amount of virus. But yes/no isn’t good enough, Mina added. “It’s the amount of virus that should dictate the infected patient’s next steps. “It’s really irresponsible, I think, to [ignore this]” Dr. Mina said, of how contagious an infected patient may be.

We’ve been using one type of data for everything,” Mina said. “for [diagnosing patients], for public health, and for policy decision-making.”

The PCR test amplifies genetic matter from the virus in cycles; the fewer cycles required, the greater the amount of virus, or viral load, in the sample. The greater the viral load, the more likely the patient is to be contagious.

This number of amplification cycles needed to find the virus, called the cycle threshold, is never included in the results sent to doctors and coronavirus patients, although if it was, it could give them an idea of how infectious the patients are.

One solution would be to adjust the cycle threshold used now to decide that a patient is infected. Most tests set the limit at 40, a few at 37. This means that you are positive for the coronavirus if the test process required up to 40 cycles, or 37, to detect the virus.

Any test with a cycle threshold above 35 is too sensitive, Juliet Morrison, a virologist at the University of California, Riverside told the New York Times. “I’m shocked that people would think that 40 could represent a positive,” she said.

A more reasonable cutoff would be 30 to 35, she added. Dr. Mina said he would set the figure at 30, or even less.

Another solution, researchers agree, is to use even more widespread use of Rapid Diagnostic Tests (RDTs) which are much less sensitive and more likely to identify only patients with high levels of virus who are a transmission risk.

Comment:  In other words, when they analyzed the tests that also reported cycle threshold (CT), they found that 85 to 90 percent were above 30. According to Dr. Mina a CT of 37 is 100 times too sensitive (7 cycles too much, 2^7 = 128) and a CT of 40 is 1,000 times too sensitive (10 cycles too much, 2^10 = 1024). Based on their sample of tests that also reported CT, as few as 10 percent of people with positive PCR tests actually have an active COVID-19 infection. Which is a lot less than reported.

Here is a graph showing how this applies to Canada.

It is evident that increased testing has resulted in more positives, while the positivity rate is unchanged. Doubling the tests has doubled the positives, up from 300 a day to nearly 600 a day presently.  Note these are PCR results. And the discussion above suggests that the number of persons with an active infectious viral load is likely 10% of those reported positive: IOW up from 30 a day to 60 a day.  And in the graph below, the total of actual cases in Canada is likely on the order of 13,000 total from the last 7 months, an average of 62 cases a day.

WuFlu Exposes a Fundamental Flaw in US Health System

Dr. Mina goes on to explain what went wrong in US response to WuFlu:

In the U.S, we have a major focus on clinical medicine, and we have undervalued and underfunded the whole concept of public health for a very long time. We saw an example of this for, for example, when we tried to get the state laboratories across the country to be able to perform the PCR tests back in March, February and March, we very quickly realized that our public health infrastructure in this country just wasn’t up to the task. We had very few labs that were really able to do enough testing to just meet the clinical demands. And so such a reduced focus on public health for so long has led to an ecosystem where our regulatory agencies, this being primarily the FDA, has a mandate to approve clinical medical diagnostic tests. But there’s actually no regulatory pathway that is available or exists — and in many ways, we don’t even have a language for it — for a test whose primary purpose is one of public health and not personal medical health

That’s really caused a problem. And a lot of times, it’s interesting if you think about the United States, every single test that we get, with the exception maybe of a pregnancy test, has to go through a physician. And so that’s a symptom of a country that has focused, and a society really, that has focused so heavily on the medical industrial complex. And I’m part of that as a physician. But I also am part of the public health complex as an epidemiologist. And I see that sometimes these are at odds with each other, medicine and public health. And this is an example where because all of our regulatory infrastructure is so focused on medical devices… If you’re a public health person, you can actually have a huge amount of leeway in how your tests are working and still be able to get epidemics under control. And so there’s a real tension here between the regulations that would be required for these types of tests versus a medical diagnostic test.

Footnote:  I don’t think the Chinese leaders were focusing on the systemic weakness Dr. MIna mentions.  But you do have to bow to the inscrutable cleverness of the Chinese Communists releasing WuFlu as a means to set internal turmoil within democratic capitalist societies.  On one side are profit-seeking Big Pharma, aided and abetted by Big Media using fear to attract audiences for advertising revenues.  The panicked public demands protection which clueless government provides by shutting down the service and manufacturing industries, as well as throwing money around and taking on enormous debt.  The world just became China’s oyster.

Background from Previous Post: Covid Burnout in Canada August 28

The map shows that in Canada 9108 deaths have been attributed to Covid19, meaning people who died having tested positive for SARS CV2 virus.  This number accumulated over a period of 210 days starting January 31. The daily death rate reached a peak of 177 on May 6, 2020, and is down to 6 as of yesterday.  More details on this below, but first the summary picture. (Note: 2019 is the latest demographic report)

Canada Pop Ann Deaths Daily Deaths Risk per
Person
2019 37589262 330786 906 0.8800%
Covid 2020 37589262 9108 43 0.0242%

Over the epidemic months, the average Covid daily death rate amounted to 5% of the All Causes death rate. During this time a Canadian had an average risk of 1 in 5000 of dying with SARS CV2 versus a 1 in 114 chance of dying regardless of that infection. As shown later below the risk varied greatly with age, much lower for younger, healthier people.

Background Updated from Previous Post

In reporting on Covid19 pandemic, governments have provided information intended to frighten the public into compliance with orders constraining freedom of movement and activity. For example, the above map of the Canadian experience is all cumulative, and the curve will continue upward as long as cases can be found and deaths attributed.  As shown below, we can work around this myopia by calculating the daily differentials, and then averaging newly reported cases and deaths by seven days to smooth out lumps in the data processing by institutions.

A second major deficiency is lack of reporting of recoveries, including people infected and not requiring hospitalization or, in many cases, without professional diagnosis or treatment. The only recoveries presently to be found are limited statistics on patients released from hospital. The only way to get at the scale of recoveries is to subtract deaths from cases, considering survivors to be in recovery or cured. Comparing such numbers involves the delay between infection, symptoms and death. Herein lies another issue of terminology: a positive test for the SARS CV2 virus is reported as a case of the disease COVID19. In fact, an unknown number of people have been infected without symptoms, and many with very mild discomfort.

August 7 in the UK it was reported (here) that around 10% of coronavirus deaths recorded in England – almost 4,200 – could be wiped from official records due to an error in counting.  Last month, Health Secretary Matt Hancock ordered a review into the way the daily death count was calculated in England citing a possible ‘statistical flaw’.  Academics found that Public Health England’s statistics included everyone who had died after testing positive – even if the death occurred naturally or in a freak accident, and after the person had recovered from the virus.  Numbers will now be reconfigured, counting deaths if a person died within 28 days of testing positive much like Scotland and Northern Ireland…

Professor Heneghan, director of the Centre for Evidence-Based Medicine at Oxford University, who first noticed the error, told the Sun:

‘It is a sensible decision. There is no point attributing deaths to Covid-19 28 days after infection…

For this discussion let’s assume that anyone reported as dying from COVD19 tested positive for the virus at some point prior. From the reasoning above let us assume that 28 days after testing positive for the virus, survivors can be considered recoveries.

Recoveries are calculated as cases minus deaths with a lag of 28 days. Daily cases and deaths are averages of the seven days ending on the stated date. Recoveries are # of cases from 28 days earlier minus # of daily deaths on the stated date. Since both testing and reports of Covid deaths were sketchy in the beginning, this graph begins with daily deaths as of April 24, 2020 compared to cases reported on March 27, 2020.

The line shows the Positivity metric for Canada starting at nearly 8% for new cases April 24, 2020. That is, for the 7 day period ending April 24, there were a daily average of 21,772 tests and 1715 new cases reported. Since then the rate of new cases has dropped down, now holding steady at ~1% since mid-June. Yesterday, the daily average number of tests was 45,897 with 427 new cases. So despite more than doubling the testing, the positivity rate is not climbing.  Another view of the data is shown below.

The scale of testing has increased and now averages over 45,000 a day, while positive tests (cases) are hovering at 1% positivity.  The shape of the recovery curve resembles the case curve lagged by 28 days, since death rates are a small portion of cases.  The recovery rate has grown from 83% to 99% steady over the last 2 weeks, so that recoveries exceed new positives. This approximation surely understates the number of those infected with SAR CV2 who are healthy afterwards, since antibody studies show infection rates multiples higher than confirmed positive tests (8 times higher in Canada).  In absolute terms, cases are now down to 427 a day and deaths 6 a day, while estimates of recoveries are 437 a day.

The key numbers: 

99% of those tested are not infected with SARS CV2. 

99% of those who are infected recover without dying.

Summary of Canada Covid Epidemic

It took a lot of work, but I was able to produce something akin to the Dutch advice to their citizens.

The media and governmental reports focus on total accumulated numbers which are big enough to scare people to do as they are told.  In the absence of contextual comparisons, citizens have difficulty answering the main (perhaps only) question on their minds:  What are my chances of catching Covid19 and dying from it?

A previous post reported that the Netherlands parliament was provided with the type of guidance everyone wants to see.

For canadians, the most similar analysis is this one from the Daily Epidemiology Update: :

The table presents only those cases with a full clinical documentation, which included some 2194 deaths compared to the 5842 total reported.  The numbers show that under 60 years old, few adults and almost no children have anything to fear.

Update May 20, 2020

It is really quite difficult to find cases and deaths broken down by age groups.  For Canadian national statistics, I resorted to a report from Ontario to get the age distributions, since that province provides 69% of the cases outside of Quebec and 87% of the deaths.  Applying those proportions across Canada results in this table. For Canada as a whole nation:

Age  Risk of Test +  Risk of Death Population
per 1 CV death
<20 0.05% None NA
20-39 0.20% 0.000% 431817
40-59 0.25% 0.002% 42273
60-79 0.20% 0.020% 4984
80+ 0.76% 0.251% 398

In the worst case, if you are a Canadian aged more than 80 years, you have a 1 in 400 chance of dying from Covid19.  If you are 60 to 80 years old, your odds are 1 in 5000.  Younger than that, it’s only slightly higher than winning (or in this case, losing the lottery).

As noted above Quebec provides the bulk of cases and deaths in Canada, and also reports age distribution more precisely,  The numbers in the table below show risks for Quebecers.

Age  Risk of Test +  Risk of Death Population
per 1 CV death
0-9 yrs 0.13% 0 NA
10-19 yrs 0.21% 0 NA
20-29 yrs 0.50% 0.000% 289,647
30-39 0.51% 0.001% 152,009
40-49 years 0.63% 0.001% 73,342
50-59 years 0.53% 0.005% 21,087
60-69 years 0.37% 0.021% 4,778
70-79 years 0.52% 0.094% 1,069
80-89 1.78% 0.469% 213
90  + 5.19% 1.608% 62

While some of the risk factors are higher in the viral hotspot of Quebec, it is still the case that under 80 years of age, your chances of dying from Covid 19 are better than 1 in 1000, and much better the younger you are.

Positive HCQ Treatment Outcomes in 88 International Studies

The report is Early treatment with hydroxychloroquine: a country-based analysis at C19study.com.  Excerpts in italics with my bolds.

Many countries either adopted or declined early treatment with HCQ, effectively forming a large trial with 1.8 billion people in the treatment group and 663 million in the control group. As of September 6, 2020, an average of 57.4 per million in the treatment group have died, and 466.4 per million in the control group, relative risk 0.123. After adjustments, treatment and control deaths become 119.6 per million and 694.7 per million, relative risk 0.17. The probability of an equal or lower relative risk occurring from random group assignments is 0.008. Accounting for predicted changes in spread, we estimate a relative risk of 0.24. The treatment group has a 76.2% lower death rate. Confounding factors affect this estimate. We examined diabetes, obesity, hypertension, life expectancy, population density, urbanization, testing level, and intervention level, which do not account for the effect observed.

The treatment group countries generally show significantly slower growth in mortality which may be due to treatment, interventions, differences in culture, or the initial degree of infections arriving into the country. Over time we expect that increasingly similar percentages of people will have been exposed, since it is unlikely that the virus will be eliminated soon.

To account for future spread, we created an estimate of the future adjusted deaths per million for each country, 90 days in the future, based on a second degree polynomial fit according to the most recent 30 days, enforcing the requirement that deaths do not decrease, and using an assumption of a progressively decreasing maximum increase over time. Figure 5 shows the results, which predicts a future relative risk of 0.24, i.e., the treatment group has 76.2% lower chance of death.

Treatment groups.

Entire countries made different decisions regarding treatment with HCQ based on the same information, thereby assigning their residents to the treatment or control group in advance. Since assignment is done without regard to individual information such as medical status, assignment of individuals is random for the purposes of this study.

We focus here on countries that chose and maintained a clear assignment to one of the groups for a majority of the duration of their outbreak, either adopting widespread use, or highly limiting use. Some countries have very mixed usage, and some countries have joined or left the treatment group during their outbreak. We searched government web sites, Twitter, and Google, with the assistance of several experts in HCQ usage, to confirm assignment to the treatment or control group, locating a total of 225 relevant references, shown in Appendix 12. We excluded countries with <1M population, and countries with <0.5% of people over the age of 80. COVID-19 disproportionately affects older people and the age based adjustments are less reliable when there are very few people in the high-risk age groups. We also excluded countries that quickly adopted aggressive intervention and isolation strategies and consequently have very little spread of the virus to date. This exclusion, based on analysis by [Leffler], favors the control group and is discussed in detail below. We also present results without these exclusions for comparison.

Collectively the countries we identified with stable and relatively clear assignments account for 31.1% of the world population (2.4B of 7.8B). Details of the groups and evidence, including countries identified as having mixed use of HCQ, can be found in Appendix 12.

Case statistics.

We analyze deaths rather than cases because case numbers are highly dependent on the degree of testing effort, criteria for testing, the accuracy and availability of tests, accuracy of reporting, and because there is very high variability in case severity, including a high percentage of asymptomatic cases.

Co-administered treatments.

Several theories exist for why HCQ is effective [Andreani, Brufsky, Clementi, de Wilde, Derendorf, Devaux, Grassin-Delyle, Hoffmann, Hu, Keyaerts, Kono, Liu, Pagliano, Savarino, Savarino (B), Scherrmann, Sheaff, Vincent, Wang, Wang (B)], some of which involve co-administration of other medication or supplements. Most commonly used are zinc [Derwand, Shittu] and Azithromycin (AZ) [Guérin]. In vitro experiments report a synergistic effect of HCQ and AZ on antiviral activity [Andreani] at concentrations obtained in the human lung, and in vivo results are consistent with this [Gautret]. Zinc reduces SARS-CoV RNA-dependent RNA polymerase activity in vitro [te Velthuis], however it is difficult to obtain significant intracellular concentrations with zinc alone [Maret]. Combining it with a zinc ionophore such as HCQ increases cellular uptake, making it more likely to achieve effective intracellular concentrations [Xue]. Zinc deficiency varies and inclusion of zinc may be more or less important based on an individual’s existing zinc level. Zinc consumption varies widely based on diet [NIH]. To the extent that the co-administration of zinc, Azithromycin, or other medication or supplements is important, we may underestimate the effectiveness of HCQ because not all countries and locations are using the optimal combination.

Dr. Fauci’s Hydroxychloroquine Denial

The commentary comes from Mikko Paunio’s article Dr. Fauci’s Hydroxychloroquine Denial.  Excerpts in italics with my bolds.

As an epidemiologist, I believe that America has been profoundly ill-served by the contribution of its public health authorities to the debate on the efficacy of treating vulnerable COVID-19 patients with hydroxychloroquine (HCQ). It is a debate with a direct link to whether America’s schools should reopen next month. Even those who reject the World Health Organization’s misleading comparison of COVID-19 with the horrendous 1918 Spanish flu pandemic and its presumption that humans lack any immunity against SARS-CoV-2 would welcome improvements in our ability to treat patients with COVID-19, in order to reduce the risk in reopening schools.

Distinguished Yale epidemiologist Harvey Risch has written extensively on the meticulous research demonstrating the efficacy of the early administration of HCQ in combination with the antibiotic azithromycin and zinc. Conclusions from this research are based on criteria developed by British epidemiologist Sir Bradford Hill and Sir Richard Doll, two of the first scientists to discover the causal link between tobacco smoking and lung cancer, criteria that laid the foundations of modern epidemiology and that are used to this day to determine whether an observed association can be ascribed to causation.  

Far from exploring this potential breakthrough in the treatment of COVID-19, the National Institutes of Health and the Food and Drug Administration (FDA) were both dismissive, condemning early outpatient treatment with the HCQ triple therapy as ineffective and dangerous. Instead, these agencies state that the only permissible way to determine its efficacy and safety is with randomized clinical trials (RCTs). Virologist Steven Hatfill has described a circle of self-reinforcing media commentary based on flawed, fraudulent, and withdrawn studies and the FDA’s mistaken decision to withdraw its HCQ Emergency Use Authorization, costing thousands of American lives.  See The Real HCQ Story: What We Now Know

Demanding proof when time is short and when there is highly suggestive observational evidence has been condemned by Drs. George Fareed, Michael Jacobs, and Donald Pompan in an open letter to Dr. Fauci. They point out that the FDA has approved many drugs without RCTspenicillin was so efficacious in treating pneumonia that there was no need for RCTs. Moreover, RCTs are not designed to test the efficacy of a therapy in high-risk outpatient settings before a patient is notified of the results of a test for COVID-19. It cannot be ethical for public health bodies to demand impossible standards of proof for potential lifesaving therapies.

To require an RCT for the HCQ triple therapy is indeed unethical; evidence supporting its use comes from large patient series, controlled trials, and even a natural experiment in the Brazilian state of Pará. In assessing ongoing patient outcomes, keep in mind that observations might be affected should the SARS-CoV-2 virus lose virulence. Spanish authorities report that fatality rates have fallen, even among elderly patients, which would be consistent with reduced SARS-CoV-2 virulence. This, too, should be considered by public authorities in assessing the risks associated with reopening schools.  See Death toll mounts as FDA denies HCQ for outpatient therapy

Yet rather than engage in proper debate, Dr. Fauci has resorted to name-calling. “The pushback has been furious,” Risch writes. Dr. Fauci “has implied that I am incompetent, notwithstanding my hundreds of highly regarded, methodologically relevant publications in peer-reviewed scientific literature.” Dr. Fauci’s position calls to mind that of English statistician Ronald A. Fisher, who in the 1950s vehemently argued against Hill and Doll and their finding that smoking causes lung cancer, on very similar grounds to those used by Dr. Fauci to dispute the efficacy of HCQ—that observational data cannot prove causality. This is an extraordinary position for America’s leading health official to adopt; by the same logic, Dr. Fauci would deny the evidence that tobacco smoking kills.  See  Hydroxychloroquine: A Morality Tale

Dr. Fauci has also waded into the debate on reopening schools, arguing that they should remain closed where the virus is circulating. While the effect of reopening schools on community transmission is uncertain, we know that keeping them closed harms children, especially those in poorer communities. This should not be a matter of politics, left or right. In Britain, chief medical officers have issued a statement on the benefits of school to children and the “exceptionally small risk” of children dying from COVID-19. In my country, Finland, Prime Minister Sanna Marin, a left-of-center Social Democrat, decided in May that Finnish children should return to school, despite opposition from the teachers’ union.

It would be a needless calamity for America’s schools not to reopen at the start of the new school year—and a calamity not to protect the vulnerable with the most efficacious therapies we have.

Clinical evidence strongly supports the use of the HCQ triple therapy at an early stage for the elderly and those with comorbidities. I earnestly hope that Dr. Fauci reconsiders his opposition to HCQ and restores his hitherto considerable reputation.  See HCQ Proven First Responder to SARS CV2

Dr. Mikko Paunio, an epidemiologist, has held positions at the University of Helsinki, Johns Hopkins Bloomberg School of Public Health, the European Commission, the World Bank, and the Ministry of Social Affairs and Health in Finland.

Footnote:  Excerpt below from COVID-19, Orwell, and the media by Samar Razaq writing at the British Journal of General Practice.

The power of the numbers was immense; a nation paralysed despite being unable to put the numbers into any context. Telling patients that on average 10 000 people die every week in the UK2 (approximately 1500 every day) had little impact on them. The incessant 24-hour media coverage had everyone spooked by an invisible enemy, much like the Eurasian army that had spooked the inhabitants of London in Orwell’s novel.

As a degree of normality has begun to be restored, thanks to the persistently low prevalence levels of COVID-19, mundane chats with my patients often end up discussing the ‘second wave’. There seems to be a general preparedness among everyone for the ‘inevitable second wave’. Reading and hearing about the second wave often reminds me of Orwell’s masterful creation. There is a general agreement that there is no scientific consensus on what a second wave actually is; yet it is on the tongue of every doctor and patient I speak to. At what point does one declare that a second wave has begun?

The figure of 120 000 expected deaths in a winter peak makes the headlines,3 but the fact that the number could be as low as 1300 fails to get a mention in some news outlets.4 The Office for National Statistics reported a prevalence of 0.09% on 25 June 20205 (a non-significant rise from the 0.06% reported in the prior week due to overlapping confidence intervals).6 This was picked up by one media outlet as a cause for pessimism and a sign of a probable impending second wave.7 The prevalence dropped to 0.04% the following week.8 I noticed an amendment in the article a few days later, acknowledging this drop in terms of halving the number of cases.7 This received barely two lines hidden in the middle of the article, with no change to the pessimistic title of the article predicting the second wave. Data released on 17 July revealed a persistently low prevalence at 0.04%.9

Well-respected epidemiologists predicted, from the outset, that the societal, economic, and psychological harm from the unprecedented lockdowns were likely to be far greater than the perceived risk of death. However, such views were lost in the narrative of fear that predominated the early discussions on the matter and treated like an Orwellian Thoughtcrime.

As GPs we should reassure our patients and encourage their active participation in bringing forward other health worries that they may have been ignoring over the last few months.

It is important that as the collateral damage of the steps taken in the last few months to curb the virus becomes clearer and the lower than initially expected fatality rate emerges, a sense of responsibility is demonstrated by those charged with informing the public.

Postscript: This update from CDC (here)  H/T William Briggs (here)

Table 3 shows the types of health conditions and contributing causes mentioned in conjunction with deaths involving coronavirus disease 2019 (COVID-19). For 6% of the deaths, COVID-19 was the only cause mentioned. For deaths with conditions or causes in addition to COVID-19, on average, there were 2.6 additional conditions or causes per death. The number of deaths with each condition or cause is shown for all deaths and by age groups. 

Comment by William Briggs:

Six percent.

The other number of interest is the official coronadoom death total, which as of Sunday night is 167,558. Math is easy: 167,558 * 0.06 = 10,054. Rounded up, to be fair.

The remaining 157,504 died of other things—an average 2.6 other things!—with the presence of coronadoom. Some fraction of these poor people, considering false positives, died of just other things.

How many times, early on, did we scream, rant, and, yes, rave about juicing the numbers? The answer is a large positive number. Alas, the screams hit the ears of bureaucrats and politicians, our dumbest and evilest classes.

It must be recalled that many deaths were caused by the unnecessary lockdowns themselves. Lockdowns killed. This is not hyperbole or a guess. It is fact.