vaxxter--2020-05-09--Is it Junk Science or No Science? Part 1

With recent public announcements by Dr. Judy Mikovits and Dr. Dan Erickson , many of us know that something is wrong with the COVID models and something is wrong with the science .
The gravamen of the debate has two foci : theory and data .
And when it comes to questions of public health or any scientific claims , this should be where we put our attention .
Data without Theory is nonsense , and We are told that COVID-19 is contagious .
What is the logic of the exogenous viral contagion theory ?
What is the data that @ @ @ @ @ @ @ what is the logic and theory behind the purported health benefits of the lockdown ?
And , what data supports or refutes that theory ?
When attempting to analyze the COVID-19 , though we might acknowledge the complexity of having a multiplicity of variables .
Variables such as reverse transcription from injected viruses and proteins , deficiencies of vitamin C , vitamin D3 , selenium , exposure to 5G radiation , comorbidities , and more .
The fundamental questions can be based on cause and effect .
In symbolic terms , we say @ @ @ @ @ @ @ mathematical equation , often used in statistics , the social sciences , and economics to explain any number of phenomena , we write y = a + bx + e. ( Americans might be more familiar with the formula from high school algebra , y = mx + b ) .
For our purposes , we must appreciate that these mathematical formulae , however , expressed , are what the epidemiologists call , the models .
An Exercise in Modeling and Deconstruction The purpose of this article is to discuss the utility of @ @ @ @ @ @ @ is for “ scientific ” papers to make inane pronouncements when the authors don ’ t have to produce any model , adhere to principles of scientific inquiry or present sound statistical methodology .
In the past week , I was asked to analyze some publications from peer-reviewed journals that claim to show statistical evidence about the benefits of certain vaccines .
The first , published in November 2017 in the Journal of Clinical and Infectious Disease , purports to detail the efficacy of Tdap for pregnant women .
Pregnant women receive Tdap @ @ @ @ @ @ @ infants .
See Skoff et al .
2017 .
The second paper was published in the January 2020 issue of the journal Vaccines .
The paper claims that flu shots are good for you , but raises the risk that you ’ ll test positive for a coronavirus .
Though both papers purport to use statistical analysis and claim that certain statistical tests prove the efficacy and benefits of a given vaccine , as always , the devil is in the details .
At the most superficial level , both papers fail to @ @ @ @ @ @ @ between a cause ( x ) and an effect ( y ) .
Arguably this omission , this lack of simplicity , moves me to conclude that the papers are unscientific .
Yes , they have data .
Yes , they present claims about statistical probabilities .
But both publications lack methodological rigor and violate fundamental axioms of statistical modeling , making their conclusions invalid .
For the rest of this essay ( part I ) , I will focus on the Tdap paper .
Part II will examine the article about the @ @ @ @ @ @ @ Among other things , two critical concepts and definitions must be clear in the world of falsifiable , positivistic science .
They are reliability and validity .
Measures are reliable or unreliable .
( Synonyms for reliable include accurate or consistent ) .
Everyday examples of devices that can provide reliable measures include a speedometer or a stopwatch .
Gray areas of measurement , instances with uncertainty and inconsistent determination , often come from the medical world .
Examples include diagnoses for pneumonia , allergies , and/or hay fever .
Though we are @ @ @ @ @ @ @ are caused by different agents , in the 1800s , doctors routinely used the terms to describe what we would call the flu .
No matter the cause , typhoid fever and typhus present with symptoms that are identical .
To claim that a theory is valid , is to declare two things : • There is a logical connection between an independent variable ( cause ) and a dependent variable ( outcome or result ) ; and • The relationship ( the correlation ) is a statistical certainty with a probability in @ @ @ @ @ @ @ – depending on the conventions of that field of science .
In sum , facts are reliable to the degree that they can be measured ; and theories are valid to the degree that the facts are proven to be correlated .
Tdap for Mama ?
GSK says NO , but the CDC says YES !
In their paper on Tdap , Dr. Skoff and others ( 2017 ) insist that babies are less likely to suffer pertussis infection ( by which the authors mean a diagnosis of whooping cough from a licensed @ @ @ @ @ @ @ the Tdap shot during the third trimester of her pregnancy .
There is one big problem with the conclusion : ALL the babies in their sample ( n = 775 ) had pertussis – whether the mothers were vaccinated ( n = 494 ) or not ( n = 281 ) .
Vaccinations occurred regardless of when the shot was received : before , during , or after the pregnancy .
So that paper is not a study , it is not even a statistical evaluation .
It is a mere report .
@ @ @ @ @ @ @ ) paper , was paid for by taxpayers across six states ( California , New York , Oregon , Minnesota , New Mexico , and Connecticut ) .
Tami Skoff , who started working at the CDC in 1999 , led a team of 10 researchers .
For some reason , they decided to construct an argument designed to convince pregnant women to get injected with either Boostrix ( GSK ) or Adacel ( Sanofi ) .
That is , the team did not seek to answer the most important , simple question @ @ @ @ @ @ @ affect the rate of pertussis infection in their babies ?
” That is how a truly scientific study would begin .
It would ask a question and layout some hypotheses .
Let ’ s always remember that the most significant hypothesis is the null hypothesis .
In order to craft the null hypothesis and any alternatives , we need to define the variables and operationalize them .
Meaning , we need to determine how to measure the variables reliably .
Skoff et al .
( 2017 ) defined the independent variable ( x @ @ @ @ @ @ @ , and scored that as Yes ( 1 ) or No ( 0 ) .
Then they defined the dependent variable ( y , baby who had pertussis ) as : “ The onset of cough illness and at least one of the following : ( i ) laboratory-confirmation ( culture or PCR ) of pertussis , epidemiological linkage to a laboratory-confirmed case ; or ( ii ) clinically-compatible illness ( cough [ at least ] 2 weeks with paroxysms , inspiratory whoop or post-tussive vomiting ) in an infant < 2 months @ @ @ @ @ @ @ implicit hypotheses : H = Tdap vaccination status of the mother will not alter the rate of pertussis in their babies ( that is x will have NO effect on y ) ; H = Tdap vaccination status of the mother will decrease the rate of pertussis in their babies ( that is an increase in x will reduce y ) .
With a basic set of hypotheses and concrete parameters for the independent and dependent variables , we could construct a 2 X 2 table .
Using these ideas , I present @ @ @ @ @ @ @ ( 2017 ) this way : Perhaps you notice something curious ?
Despite compiling a list of 775 women and their babies , Skoff et al .
( 2017 ) examined no cases of children without pertussis .
Nevertheless , they had the temerity to conclude : “ Vaccination [ with Tdap ] during pregnancy is an effective way to protect [ sic ] infants , during the early months of life.
” Where was the protection ?
The only infants they examined met their criteria for pertussis infection , and each incident @ @ @ @ @ @ @ when the illness is life-threatening !
No Science Here – Just Faux Epidemiology Obviously Skoff et al .
( 2017 ) did not conduct an experiment .
They did not segregate women into a control and test group .
They did not inject the control group with saline ( a placebo ) and inject others with Tdap .
Instead , they used the idea of statistical control , which if done well , with proper sampling , could be used to evaluate valid hypotheses like those detailed above .
Routinely professors in sociology @ @ @ @ @ @ @ medicine use statistical control to evaluate hypotheses about cause and effect .
The practice is valid , so long as the samples meet certain criteria , the least of which is variation within the variables .
So Skoff et al .
( 2017 ) did the first step .
They divided two groups ( test and control ) according to the defined independent variable , the Tdap status of the mother .
But for some reason , when it came to the dependent variable ( the health of a baby ) , this @ @ @ @ @ @ @ tests : “ variables must vary.
” In this instance , the dependent variable ( y , pertussis illness in an infant ) had only one value , yes .
Normally , to investigate the relationship between two dichotomous variables , we could construct a 2 X 2 table , and apply a chi-square test .
The purpose of the test is to evaluate the null hypothesis that “ the relative proportions of one variable are independent of the second variable.
” ( McDonald 2014 ) .
( Often medical studies report an @ @ @ @ @ @ @ practically identical to the chi-square .
I will discuss the use and limits of statistical tests employing the Odds-Ratio in part II ) .
An honest investigation would want to know if the proportions in one variable ( e.g.
, Tdap status of the mother ) were the same for different values in the second variable ( e.g.
, pertussis illness status in their infant ) .
Please recognize , Dr. McDonald does not label the two variables in a chi-square test as independent ( x ) and dependent ( y ) .
@ @ @ @ @ @ @ not presume to explain causality – rather it is a measure of correlation .
After all , if there is no correlation , there is no causality .
However , in reference to the Skoff et al .
( 2017 ) data set , I use the labels of independent and dependent because the vaccination event preceded the recorded illness of the child .
That said , for Skoff et al .
( 2017 ) an appropriate null hypothesis could have been articulated in this manner : A real study would then calculate @ @ @ @ @ @ @ vaccinated and unvaccinated mothers – and compare the two .
If the proportions were statistically significantly different ( no matter which was higher or lower ) , we would reject the null hypothesis .
Yet as their sample had no infants who did not have pertussis , the relative proportion of pertussis cases in babies , born either to vaccinated or to unvaccinated mothers , was the same : 100 % !
In statistical terms , the data presented by Skoff et al .
( 2017 ) says that we can not reject @ @ @ @ @ @ @ shot given to mothers , does not reduce pertussis in infants , under 2 months of age.
” The only logical conclusion we can derive from this sample ( n = 775 ) , about pertussis illness in infants ( y ) and the Tdap status of the mothers ( x ) , is that getting the shots makes no difference .
Such is the very opposite of the conclusion Skoff et al .
( 2017 ) offer .
Obviously , we presume that this is not what occurs in the real world @ @ @ @ @ @ @ with Tdap , either before or during pregnancy , or post-partum and when she is breastfeeding , must have some impact on the baby .
But the only way to use statistical analysis in order to reject the null hypothesis would be to have a random sample of infants whereby their scores for pertussis infection ( the dependent variable ) varied ( included both yes = 1 and no = 0 ) .
And Skoff et al .
( 2017 ) did not include those babies in their data .
Is it All @ @ @ @ @ @ @ study did was : • looked at one outcome , “ infants sick with pertussis ” ; • parsed the time when the mother was injected with Tdap ; and • concluded that injection in the last stages of pregnancy reduces the probability that the child would be sick .
But how could they know ?
The had literally no basis of comparison .
They also had no variation in their dependent variable .
I can only conclude that they wrote this paper to serve as a pseudo-scientific , medical advertisement for the @ @ @ @ @ @ @ need to convince doctors , hospital administrators , and mothers to inject pregnant women with Tdap ?
Maybe so , if pregnant women and health care professionals are so foolish as to read the vaccine manufacturer ’ s package insert .
Surely reading the vaccine insert would dissuade women and doctors from giving the shot to anyone !
Here is what GSK says about its Tdap shot : There is more from GSK .
In Section 8 , Use in Specific Populations , subsection 8.1. pregnant women , we read in part : @ @ @ @ @ @ @ see : I guess Skoff and her 10 co-authors , the researchers , PhDs , lawyers , and marketing teams at GSK are putting women and babies at risk because they just do not know the science ?
Apparently , Skoff knows better .
She would tell all women to get an injection of 390 micrograms of aluminum plus the formaldehyde and polysorbate 80 , ( the surfactant that enables the aluminum to enter the brain ) , preferably about three weeks before delivery .
Such advice from Skoff et al .
( @ @ @ @ @ @ @ the babies under evaluation in their review had pertussis anyway .
That means , 494 women were told that a vaccine would help them and their babies by preventing pertussis , but instead , the women ( and 139 babies ) got blasted with aluminum .
Regardless , these women watched their babies struggle with pertussis at the worst time , those critical first weeks of life .
Skoff et al .
( 2017 ) presented a review of 775 women and their sick babies .
They crafted an arbitrary scale for what @ @ @ @ @ @ @ Tdap injection ) and created five sub-categories : no shot , pre-pregnancy , 1st or 2nd trimester , 3rd trimester , or post-partum .
Then they purported to offer a statistical analysis on the proportion of sick babies ( n = 775 ) in relation to the category that they assigned to the mother .
But as I noted above , without variation in both variables , there can be no true statistical test .
Instead , Skoff et al .
( 2017 ) engaged in what is called a fishing expedition : @ @ @ @ @ @ @ had data .
They saw that the fewest number of sick babies were in their artificial category of “ mothers who got the Tdap in the 3rd trimester.
” But that mere association neither proves nor suggests that the vaccine provides any protection against pertussis .
And because we had no variation in the dependent variable , we can not see any pattern or trend – pro or con – from the Tdap vaccination .
Their report is not pseudo-science .
It is not junk science .
It was no science .
We @ @ @ @ @ @ @ like this , and demand better .
Like what you ’ re reading on Vaxxter.com ?
Share this article with your friends .
Help us grow .
Join our list here or text MVI to 555888 John C. Jones received his law degree ( 2001 ) and his Ph.D. is in political science ( 2003 ) from the University of Iowa .
He has over 15 years of research and writing ( both academic and journalistic ) in fields of public policy and law , criminal and Constitutional law , and philosophy of @ @ @ @ @ @ @ of expertise and specialized knowledge include applied statistics , etymology , political communications/public relations , litigation and court procedure .
He has a particular interest in the science and history of vaccines .

By John Jones, JD, PhD, Vaxxter contributor
Science in our daily life
With recent public announcements by Dr. Judy Mikovits and Dr. Dan Erickson, many of us know that something is wrong with the COVID models and something is wrong with the science. The gravamen of the debate has two foci: theory and data. And when it comes to questions of public health or any scientific claims, this should be where we put our attention.
Data without Theory is nonsense, and
Theory without Data is Poppycock
paraphrasing Dr. Tetyana Obukhanych (2013)
We are told that COVID-19 is contagious. What is the logic of the exogenous viral contagion theory? What is the data that supports or refutes it? Additionally, what is the logic and theory behind the purported health benefits of the lockdown? And, what data supports or refutes that theory?
When attempting to analyze the COVID-19, though we might acknowledge the complexity of having a multiplicity of variables. Variables such as reverse transcription from injected viruses and proteins, deficiencies of vitamin C, vitamin D3, selenium, exposure to 5G radiation, comorbidities, and more. The fundamental questions can be based on cause and effect.
In symbolic terms, we say that x causes y. As a mathematical equation, often used in statistics, the social sciences, and economics to explain any number of phenomena, we write y = a + bx + e. (Americans might be more familiar with the formula from high school algebra, y = mx + b). For our purposes, we must appreciate that these mathematical formulae, however, expressed, are what the epidemiologists call, the models.
An Exercise in Modeling and Deconstruction
The purpose of this article is to discuss the utility of modeling and to demonstrate how easy it is for “scientific” papers to make inane pronouncements when the authors don’t have to produce any model, adhere to principles of scientific inquiry or present sound statistical methodology.
In the past week, I was asked to analyze some publications from peer-reviewed journals that claim to show statistical evidence about the benefits of certain vaccines. The first, published in November 2017 in the Journal of Clinical and Infectious Disease, purports to detail the efficacy of Tdap for pregnant women. Pregnant women receive Tdap as a means to prevent pertussis in infants. See Skoff et al. 2017. The second paper was published in the January 2020 issue of the journal Vaccines. The paper claims that flu shots are good for you, but raises the risk that you’ll test positive for a coronavirus.
Though both papers purport to use statistical analysis and claim that certain statistical tests prove the efficacy and benefits of a given vaccine, as always, the devil is in the details. At the most superficial level, both papers fail to offer an equation that models a relationship between a cause (x) and an effect (y). Arguably this omission, this lack of simplicity, moves me to conclude that the papers are unscientific.
Yes, they have data. Yes, they present claims about statistical probabilities. But both publications lack methodological rigor and violate fundamental axioms of statistical modeling, making their conclusions invalid.
For the rest of this essay (part I), I will focus on the Tdap paper. Part II will examine the article about the influenza vaccine (Wolff 2020).
A Quick Detour: Some More Scientific Definitions
Among other things, two critical concepts and definitions must be clear in the world of falsifiable, positivistic science. They are reliability and validity. Measures are reliable or unreliable. (Synonyms for reliable include accurate or consistent). Everyday examples of devices that can provide reliable measures include a speedometer or a stopwatch.
Gray areas of measurement, instances with uncertainty and inconsistent determination, often come from the medical world. Examples include diagnoses for pneumonia, allergies, and/or hay fever. Though we are told today that typhoid fever and typhus are caused by different agents, in the 1800s, doctors routinely used the terms to describe what we would call the flu. No matter the cause, typhoid fever and typhus present with symptoms that are identical.
To claim that a theory is valid, is to declare two things:
There is a logical connection between an independent variable (cause) and a dependent variable (outcome or result); and The relationship (the correlation) is a statistical certainty with a probability in excess of 95% or 99% – depending on the conventions of that field of science.
In sum, facts are reliable to the degree that they can be measured; and theories are valid to the degree that the facts are proven to be correlated.
Tdap for Mama? GSK says NO, but the CDC says YES!
In their paper on Tdap, Dr. Skoff and others (2017) insist that babies are less likely to suffer pertussis infection (by which the authors mean a diagnosis of whooping cough from a licensed medical practitioner) if the mother gets the Tdap shot during the third trimester of her pregnancy.
There is one big problem with the conclusion: ALL the babies in their sample (n = 775) had pertussis – whether the mothers were vaccinated (n = 494) or not (n = 281). Vaccinations occurred regardless of when the shot was received: before, during, or after the pregnancy. So that paper is not a study, it is not even a statistical evaluation. It is a mere report.
The Skoff et al. (2017) paper, was paid for by taxpayers across six states (California, New York, Oregon, Minnesota, New Mexico, and Connecticut). Tami Skoff, who started working at the CDC in 1999, led a team of 10 researchers. For some reason, they decided to construct an argument designed to convince pregnant women to get injected with either Boostrix (GSK) or Adacel (Sanofi).
That is, the team did not seek to answer the most important, simple question: “Will injecting women with Tdap affect the rate of pertussis infection in their babies?” That is how a truly scientific study would begin. It would ask a question and layout some hypotheses. Let’s always remember that the most significant hypothesis is the null hypothesis.
Null Hypothesis
As professor McDonald reminds us,
“The primary goal of a statistical test is to determine whether observed data, is different from what you would expect, [if] the null hypothesis [were true], that you should reject the null hypothesis.”
In order to craft the null hypothesis and any alternatives, we need to define the variables and operationalize them. Meaning, we need to determine how to measure the variables reliably. Skoff et al. (2017) defined the independent variable (x, mother received a Tdap vaccination), and scored that as Yes (1) or No (0).
Then they defined the dependent variable (y, baby who had pertussis) as:
“The onset of cough illness and at least one of the following:
(i) laboratory-confirmation (culture or PCR) of pertussis, epidemiological linkage to a laboratory-confirmed case; or
(ii) clinically-compatible illness (cough [at least] 2 weeks with paroxysms, inspiratory whoop or post-tussive vomiting) in an infant <2 months old.”
Now we can layout their implicit hypotheses:
H 0 = Tdap vaccination status of the mother will not alter the rate of pertussis in their babies (that is x will have NO effect on y);
H 1 = Tdap vaccination status of the mother will decrease the rate of pertussis in their babies (that is an increase in x will reduce y).
With a basic set of hypotheses and concrete parameters for the independent and dependent variables, we could construct a 2 X 2 table. Using these ideas, I present the data from Skoff et al. (2017) this way:
Perhaps you notice something curious? Despite compiling a list of 775 women and their babies, Skoff et al. (2017) examined no cases of children without pertussis. Nevertheless, they had the temerity to conclude:
“Vaccination [with Tdap] during pregnancy is an effective way to protect [sic] infants, during the early months of life.”
Where was the protection? The only infants they examined met their criteria for pertussis infection, and each incident started between day 15-45 of life – when the illness is life-threatening!
No Science Here – Just Faux Epidemiology
Obviously Skoff et al. (2017) did not conduct an experiment. They did not segregate women into a control and test group. They did not inject the control group with saline (a placebo) and inject others with Tdap. Instead, they used the idea of statistical control, which if done well, with proper sampling, could be used to evaluate valid hypotheses like those detailed above.
Routinely professors in sociology, political science, economics, and medicine use statistical control to evaluate hypotheses about cause and effect. The practice is valid, so long as the samples meet certain criteria, the least of which is variation within the variables.
So Skoff et al. (2017) did the first step. They divided two groups (test and control) according to the defined independent variable, the Tdap status of the mother. But for some reason, when it came to the dependent variable (the health of a baby), this team ignored a fundamental axiom of statistical tests: “variables must vary.” In this instance, the dependent variable (y, pertussis illness in an infant) had only one value, yes.
Normally, to investigate the relationship between two dichotomous variables, we could construct a 2 X 2 table, and apply a chi-square test. The purpose of the test is to evaluate the null hypothesis that “the relative proportions of one variable are independent of the second variable.” (McDonald 2014). (Often medical studies report an Odds-Ratio or Wald test, which is practically identical to the chi-square. I will discuss the use and limits of statistical tests employing the Odds-Ratio in part II).
Investigating Proportions
An honest investigation would want to know if the proportions in one variable (e.g., Tdap status of the mother) were the same for different values in the second variable (e.g., pertussis illness status in their infant).
Remember, real scientific inquiry assumes that any treatment, like a vaccine, or aspirin, or vitamin C has NO effect on the recipients – neither good nor bad. But as we have seen, since the 1950s, the dominant view is that there is no need to prove positive effects of vaccination. Additionally, it seems that practically all vaccine injuries are ignored as an impossibility, coincidence, or non-event.
Please recognize, Dr. McDonald does not label the two variables in a chi-square test as independent (x) and dependent (y). This is because the chi-square test does not presume to explain causality – rather it is a measure of correlation. After all, if there is no correlation, there is no causality. However, in reference to the Skoff et al. (2017) data set, I use the labels of independent and dependent because the vaccination event preceded the recorded illness of the child.
That said, for Skoff et al. (2017) an appropriate null hypothesis could have been articulated in this manner:
“The proportion of infants with pertussis, in vaccinated mothers, is equal to the proportion of sick infants in unvaccinated mothers.”
A real study would then calculate the proportion of sick children of the vaccinated and unvaccinated mothers – and compare the two. If the proportions were statistically significantly different (no matter which was higher or lower), we would reject the null hypothesis. Yet as their sample had no infants who did not have pertussis, the relative proportion of pertussis cases in babies, born either to vaccinated or to unvaccinated mothers, was the same: 100%!
Do shots make no difference?
In statistical terms, the data presented by Skoff et al. (2017) says that we cannot reject the null hypothesis that “the Tdap shot given to mothers, does not reduce pertussis in infants, under 2 months of age.” The only logical conclusion we can derive from this sample (n = 775), about pertussis illness in infants (y) and the Tdap status of the mothers (x), is that getting the shots makes no difference. Such is the very opposite of the conclusion Skoff et al. (2017) offer.
Obviously, we presume that this is not what occurs in the real world. We imagine that injecting a woman with Tdap, either before or during pregnancy, or post-partum and when she is breastfeeding, must have some impact on the baby. But the only way to use statistical analysis in order to reject the null hypothesis would be to have a random sample of infants whereby their scores for pertussis infection (the dependent variable) varied (included both yes = 1 and no = 0).
And Skoff et al. (2017) did not include those babies in their data.
Is it All Just Advertising?
Essentially what the Skoff study did was:
looked at one outcome, “infants sick with pertussis”;
parsed the time when the mother was injected with Tdap; and
concluded that injection in the last stages of pregnancy reduces the probability that the child would be sick.
But how could they know? The had literally no basis of comparison. They also had no variation in their dependent variable.
I can only conclude that they wrote this paper to serve as a pseudo-scientific, medical advertisement for the Tdap vaccine. But was there a need to convince doctors, hospital administrators, and mothers to inject pregnant women with Tdap? Maybe so, if pregnant women and health care professionals are so foolish as to read the vaccine manufacturer’s package insert. Surely reading the vaccine insert would dissuade women and doctors from giving the shot to anyone!
GSK Warnings
Here is what GSK says about its Tdap shot:
Between 1-4% of all people experience serious adverse side-effects – including paralysis, heart attack, seizures, encephalitis, arthritis, and auto-immune disease. (See Boostrix package insert pages 10-13).
There is more from GSK. In Section 8, Use in Specific Populations, subsection 8.1. pregnant women, we read in part:
A developmental toxicity study has been performed in female rats … and revealed no evidence of harm to the fetus. There are no adequate and well-controlled studies in pregnant women. Because animal reproduction studies are not always predictive of human response, BOOSTRIX should be given to a pregnant woman only if clearly needed.” (emphasis added).
And under Section 11, DESCRIPTION we see:
Each 0.5-mL dose contains aluminum hydroxide as adjuvant (not more than 0.39 mg aluminum by assay) … [no more than] 100 mcg of residual formaldehyde, and [no more than] 100 mcg of polysorbate 80 (Tween 80).
I guess Skoff and her 10 co-authors, the researchers, PhDs, lawyers, and marketing teams at GSK are putting women and babies at risk because they just do not know the science? Apparently, Skoff knows better. She would tell all women to get an injection of 390 micrograms of aluminum plus the formaldehyde and polysorbate 80, (the surfactant that enables the aluminum to enter the brain), preferably about three weeks before delivery.
Such advice from Skoff et al. (2017) is bewildering given that all the babies under evaluation in their review had pertussis anyway. That means, 494 women were told that a vaccine would help them and their babies by preventing pertussis, but instead, the women (and 139 babies) got blasted with aluminum. Regardless, these women watched their babies struggle with pertussis at the worst time, those critical first weeks of life.
Conclusions
Skoff et al. (2017) presented a review of 775 women and their sick babies. They crafted an arbitrary scale for what could have been an independent variable (Tdap injection) and created five sub-categories: no shot, pre-pregnancy, 1st or 2nd trimester, 3rd trimester, or post-partum.
Then they purported to offer a statistical analysis on the proportion of sick babies (n = 775) in relation to the category that they assigned to the mother. But as I noted above, without variation in both variables, there can be no true statistical test. Instead, Skoff et al. (2017) engaged in what is called a fishing expedition: They had no theory – but they had data.
They saw that the fewest number of sick babies were in their artificial category of “mothers who got the Tdap in the 3rd trimester.” But that mere association neither proves nor suggests that the vaccine provides any protection against pertussis. And because we had no variation in the dependent variable, we cannot see any pattern or trend – pro or con – from the Tdap vaccination.
Their report is not pseudo-science. It is not junk science. It was no science. We must reject it, and all studies like this, and demand better.
++++++++++++++++++++++++++++++++
Like what you’re reading on Vaxxter.com?
Share this article with your friends. Help us grow.
Join our list here or text MVI to 555888
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John C. Jones received his law degree (2001) and his Ph.D. is in political science (2003) from the University of Iowa. He has over 15 years of research and writing (both academic and journalistic) in fields of public policy and law, criminal and Constitutional law, and philosophy of science and medicine. His additional areas of expertise and specialized knowledge include applied statistics, etymology, political communications/public relations, litigation and court procedure. He has a particular interest in the science and history of vaccines.


vaxxter--2020-05-18--COVID19 Testing: What are we doing? What does it mean?

In 1965 , scientists identified the first human coronavirus ; it was associated with the common cold .
The Coronavirus family , named for their crown-like appearance , currently includes 36 viruses .
Within that group , there are 4 common viruses that have been causing infection in humans for more than sixty years .
In addition , three pandemic coronaviruses that can infect humans : SARS , MERS , and now , SARS-CoV-2 .
As the news of deaths in China , South Korea , Italy , and Iran began to saturate every form of media 24/7 , we @ @ @ @ @ @ @ COVID-19 .
To be clear , the name of the newly identified coronavirus is SARS-CoV-2 , short for Severe Acute Respiratory Syndrome Coronavirus-2 .
This virus is associated with fever , cough , chest pain , and shortness of breath , the complex of symptoms that form the diagnosis of COVID-19 .
The Trump administration declared a public health emergency on January 31 , 2020 , then on February 2 placed a ban on the entry of most travelers who had recently been in China .
On February 4 , Alex Azar , @ @ @ @ @ @ @ ( HHS ) issued a declaration of public health emergency and activated the Public Readiness and Emergency Preparedness Act , otherwise known as the PREP Act .
This nefarious legislation provides complete protection of manufacturers from liability for products , technologies , biologics , or any vaccine developed as a medical countermeasure against COVID-19 .
For those nervously waiting for the vaccine to become available , be sure to understand the PREP Act before rushing to the get in line .
Calls for testing – to see if a person is or isn @ @ @ @ @ @ @ the emergency was declared , but performing those tests was initially slow due to an inadequate number of test kits .
As the kits became available , those developed by the CDC had a defect : The reagents reacted to the negative control sample , making the test inaccurate and the kits unusable .
In various countries , thousands of test kits purchased from China were found to be contaminated with the SARS-CoV-2 viruses .
No one really knows how that happened , but theories spread like wildfire .
Could the test kit @ @ @ @ @ @ @ , did it mean the test would return a false-positive result , driving up the numbers of those said to be infected so those in power could implement stronger lockdowns and accelerate the hockey-stick unemployment rates ?
Neither of those questions has been adequately answered .
Authorities claim that testing is important for public health officials to assess if their mitigation efforts – “ shelter in place ” and “ social distancing ” and “ wearing a mask ” – are making a difference to “ flatten the curve.
” Officials also claim @ @ @ @ @ @ @ many persons are infected within a community and to understand the nature of how coronaviruses spread .
Are these reasons sufficient to give up our health freedom and our personal rights , being tested and shamed in public ?
Despite the challenges with test kits , testing began .
By the end of March 2020 , more than 1 million people had been tested across the US .
By May 9 , the number tested had grown to over 8.7M .
Testing methods include a swab of the nasal passages or by inserting @ @ @ @ @ @ @ nose to scrape the back of the throat .
Specimens have also been obtained bronchoalveolar lavage , from sputum , and from stool specimens .
The call for mandatory testing has been gathering steam and becoming ever more onerous .
In Washington state , Governor Inslee has declared : But what do the results really mean ?
Who Should Be Tested On May 8 , 2020 , the CDC has listed specific priorities for when testing should be done .
As of May 16 , more than 11-million samples have been collected and @ @ @ @ @ @ @ been evaluated .
• Healthcare facility workers , workers in living settings , and first responders with symptoms • Residents in long-term care facilities or other congregate living settings , including prisons and shelters , with symptoms • Persons with symptoms of potential COVID-19 infection , including fever , cough , shortness of breath , chills , muscle pain , new loss of taste or smell , vomiting or diarrhea , and/or sore throat • Persons without symptoms who are prioritized by health departments or clinicians , including but not limited to public @ @ @ @ @ @ @ according to state and local plans .
Read that last priority again : That means virtually everyone can be required to get a test .
Is that a violation of your personal rights ?
And , if you submit to testing , what does a “ positive test ” actually mean ?
PCR , short for polymerase chain reaction , is a highly specific laboratory technique .
The key to understanding PCR testing is that PCR can identify an individual specific virus within a viral family .
However , a PCR test can @ @ @ @ @ @ @ ; the SARS-CoV2 virus is an RNA virus .
Therefore , multiple steps must be taken to “ magnify ” the amount of genetic material in the specimen .
Researchers used a method called RT-PCR , reverse transcription-polymerase chain reaction , to specifically identify the SARS-CoV-2 virus .
It ’ s a complicated process .
To read more about it , go here and here .
If a nasal or a blood sample contains a tiny snip of RNA from the SARS-CoV-2 virus , RT-PCR can identify it , leading to a high @ @ @ @ @ @ @ to the SARS-CoV-2 virus .
However – and this is important – a positive RT-PCR test result does not necessarily indicate a full virus is present .
The virus must be fully intact to be transmitted and cause illness .
Even if a person has had all the symptoms associated with a coronavirus infection or has been closely exposed to persons who have been diagnosed with COVID-19 , the probability of a RT-PCR test being positive decreases with the number of days past the onset of symptoms .
According to a study done @ @ @ @ @ @ @ For a nasal swab , the percentage chance of a positive test declines from about 94 % on day 0 to about 67 % by day 10 .
By day 31 , there is only a 2 % chance of a positive result .
• For a throat swab , the percentage chance of a positive test declines from about 88 % on day 0 to about 47 % by day 10 .
By day 31 , there is only a 1 % chance of a positive result .
In other words , @ @ @ @ @ @ @ onset of symptoms and the time a person is tested , the more likely the test will be negative .
Repeat testing of persons who have a negative test may ( eventually ) confirm the presence of viral RNA , but this is impractical .
Additionally , repeated testing of the same person can lead to even more confusing results : The test may go from negative , to positive , then back to negative again as the immune system clears out the coronavirus infection and moves to recovery .
And what makes @ @ @ @ @ @ @ the FDA admits that “ The detection of viral RNA by RT-PCR does not necessarily equate with an infectious virus.
” You ’ ve had all the symptoms of COVID19 , but your RT-PCR test for SARS-CoV-2 is negative .
• Does that mean you ’ re “ good to go ” – you can go to work , go to school or you can travel ?
OR… • Does that mean your influenza-like illness was caused by some other pathogen , possibly one of the four coronaviruses that have been in circulation @ @ @ @ @ @ @ that mean the result is a false-negative and you still have the infection , but it isn ’ t detectable by current tests ?
OR… • Does that mean it was a sample was inadequately taken due to the faulty technique by the technician ?
OR… • Does that mean you have not been exposed , and you are susceptible to contracting the infection , and you need to stay in quarantine ?
So , what does a “ positive ” test actually mean ?
And that ’ s the problem : No @ @ @ @ @ @ @ the nonprofit Foundation for Innovative New Diagnostics ( FIND ) , more than 200 serologic blood tests , to test for antibodies , are either now available or in development .
There are two primary types of antibodies that are assessed for nearly any type of infection : IgM and IgG .
While several new testing devices are being touted as a home test , they are not the same as a home pregnancy test or a glucometer to you ’ re your blood sugar .
The blood spot or saliva specimen can @ @ @ @ @ @ @ must then be sent to a laboratory for analysis .
It can take a few days – or longer – to get the results .
With so many tests in the pipeline , the ability to test at home will be changing over time .
The first antibody to rise is IgM .
It rises quickly after the onset of the infection and is usually a sign of an acute , or current , infection .
The IgM levels diminish quickly as the infection resolves .
The FDA admits they do not know @ @ @ @ @ @ @ SARS-CoV-2 as the infection is being cleared .
The interpretation of an IgG antibody is more difficult .
This antibody is an indicator of a past infection .
The test is often not specific enough to determine if the past infection was caused by the SARS-CoV-2 virus or one of the four common coronaviruses that cause influenza-like illness .
Similarly , the CDC says the following regarding antibody testing : • If you test positive : • A positive test result shows you have antibodies as a result of an infection with SARS-CoV-2 @ @ @ @ @ @ @ if those antibodies can provide protection ( immunity ) against getting infected again .
This means that we do not know at this time if antibodies make you immune to the virus .
• If you have no symptoms , you likely do not have an active infection and no additional follow-up is needed .
• It ’ s possible you might test positive for antibodies and you might not have or have ever had symptoms of COVID-19 .
This is known as having an asymptomatic infection [ ie you have a healthy @ @ @ @ @ @ @ test can not tell if you are currently sick with COVID-19 .
• If you test negative • If you test negative for antibodies , you probably did not have a previous infection .
However , you could have a current infection because antibodies don ’ t show up for 1 to 3 weeks after infection .
• Some people may take even longer to develop antibodies , and some people may not develop antibodies .
• An antibody test can not tell if you are currently sick with COVID-19 .
• Isn @ @ @ @ @ @ @ they assess after you ’ ve had an infection with measles or chickenpox or mumps to determine if you are immune to future infections ?
• Isn ’ t this the marker of induced immunity they are trying to achieve by administering a vaccine ?
If the FDA does not know if an IgG antibody to SARS-CoV-2 after recovering from the infection is protective against a future infection , then they certainly don ’ t know if an antibody caused by a vaccine will prevent infection either .
Doesn ’ t this completely @ @ @ @ @ @ @ and antibodies from vaccines are necessary to keep you from getting sick ?
Illinois U.S. Rep. Bobby L. Rush introduced the H.R .
6666 TRACE Act on May 1 .
On his website , Rush said , H.R.6666 would authorize the Secretary of Health and Human Services ( HHS ) , acting through the Director of the CDC to award grants to eligible entities to conduct diagnostic testing and then to trace and monitor the contacts of infected individuals .
The contact tracers would be authorized to test people in their homes and @ @ @ @ @ @ @ .
Where do they intend to do this testing ?
Besides mobile units to test people in their homes , the bill identifies eight specific locations where the testing and contract tracing could occur : schools , health clinics , universities , churches , and “ any other type of entity ” the secretary of HHS wants to use .
The bill would allocate $ 100 billion in 2020 “ and such sums as may be necessary for fiscal year 2021 and any subsequent fiscal year during which the emergency period continues.
” @ @ @ @ @ @ @ • Is your test supposed to be positive – saying you ’ ve been exposed and you ’ ve possibly recovered ?
• Or is your test supposed to be negative , meaning , you are healthy ?
• Or does a completely negative test – negative RT-PCR test and no IgG antibody mean you ’ re susceptible to infection and you need to stay in quarantine ?
The virus is rapidly mutating , which is rather typical of RNA viruses .
In a study published in April 2020 , researchers have discovered @ @ @ @ @ @ @ at least 30 different genetic variations .
If your RT-PCR test is positive , does this identify exposure to the pandemic virus or exposure to one of the genetic variations ?
The same can be said about the vaccines under development : With each mutation , is the vaccine more likely to be all risk and no benefit when it reaches the market ?
What You Can Do Across the nation , police are being told to not apprehend criminals but instead , to arrest parents at playgrounds , to arrest lone surfers @ @ @ @ @ @ @ and congregation members sitting in their cars listening to a service on the radio , and to restrict movement by creating one-way sidewalks .
People have had enough .
They are beginning to see the huge scam that has been perpetrated on the entire world over a viral infection with a global death rate of 1.4 % ( meaning , 1.4 % of people infected with SARS-CoV-2 have a fatal outcome , while 98.6 % recover ) .
This is far fewer deaths than a severe flu season .
We ’ re already @ @ @ @ @ @ @ our power back : • In Virginia , people went en mass , ignoring social distancing and the orders of the Governor to stay home .
• The central California city of Atwater has declared itself a allowing business owners and churches to open , openly defying Democratic Gov .
Gavin Newsom ’ s coronavirus-related stay-at-home order .
• The truth about is starting to come out and people are voting with their feet .
Retired neurosurgeon , Dr. Russell Blaylock , warns that not only do face masks fail to protect healthy @ @ @ @ @ @ @ they create serious health risks to the wearer .
While they shut us down and held us hostage in our homes , they changed our society , our lives , our world .
I am not willing to accept this is the “ new normal.
” • I will not be afraid of standing next to a friend or family member and will not obey the concept of “ social distancing.
” • I will understand that an asymptomatic carrier is a normal , healthy person and I will not buy into the @ @ @ @ @ @ @ ” from a normal , healthy person .
It ’ s time for Americans to resist with non-violent civil disobedience .
Be brave .
Be bold .
Put on the full armor of God , as found in Ephesians 6:10-20 in the Bible , to stand against the world rulers of this present darkness .
With God on our side , all things are possible .
Like what you ’ re reading on Vaxxter.com ?
Share this article with your friends .
Help us grow .
Join our list here or text MVI @ @ @ @ @ @ @ osteopathic medical doctor , board-certified in three specialties .
She is the founder of Tenpenny Integrative Medical Center , a medical clinic located near Cleveland , Ohio .
Her company , Courses4Mastery.com provides online education and training regarding all aspects of vaccines and vaccination .
Dr. Tenpenny has invested nearly 20 years and more than 40,000 hours documenting and exposing the problems associated with vaccines .
As an internationally known speaker and author , her many articles have been translated into at least 15 languages .
She is a frequent guest on radio @ @ @ @ @ @ @ educate parents on why they should Just.Say.No .
to vaccines

by Dr. Sherri Tenpenny, DO, AOBNMM, ABIHM*
In 1965, scientists identified the first human coronavirus; it was associated with the common cold. The Coronavirus family, named for their crown-like appearance, currently includes 36 viruses. Within that group, there are 4 common viruses that have been causing infection in humans for more than sixty years. In addition, three pandemic coronaviruses that can infect humans: SARS, MERS, and now, SARS-CoV-2.
As the news of deaths in China, South Korea, Italy, and Iran began to saturate every form of media 24/7, we became familiar with a new term: COVID-19. To be clear, the name of the newly identified coronavirus is SARS-CoV-2; short for Severe Acute Respiratory Syndrome Coronavirus-2. This virus’s symptoms include fever, cough, chest pain, and shortness of breath. These are the complex of symptoms that form the diagnosis of COVID-19.
Public Health Emergency
The Trump administration declared a public health emergency on January 31, 2020. Then on February 2, they placed a ban on the entry of most travelers who had recently been in China. On February 4, Alex Azar, the Secretary of Health and Human Services (HHS) issued a declaration of public health emergency and activated the Public Readiness and Emergency Preparedness Act, otherwise known as the PREP Act. This nefarious legislation provides complete protection of manufacturers from liability for all products, technologies, biologics, or any vaccine developed as a medical countermeasure against COVID-19. For those nervously waiting for the vaccine to become available, be sure to understand the PREP Act before rushing to the get in line.
Calls for testing – to see if a person is or isn’t infected – began soon after the emergency was declared. But performing those tests was initially slow due to an inadequate number of test kits. As the kits became available, those developed by the CDC had a defect: The reagents reacted to the negative control sample, making the test inaccurate and the kits unusable.
In various countries, thousands of test kits purchased from China were found to be contaminated with the SARS-CoV-2 viruses. No one really knows how that happened, but theories spread like wildfire. Could the test kit infect the person receiving testing? Or, did it mean the test would return a false-positive result, driving up the numbers of those said to have an infection so those in power could implement stronger lockdowns and accelerate the hockey-stick unemployment rates? There are no adequate answers for any of those questions.
Mandatory Testing … of what?
Authorities claim that testing is important for public health officials to assess if their mitigation efforts – “shelter in place” and “social distancing” and “wearing a mask” – are making a difference to “flatten the curve.” Officials also claim that testing is necessary to know how many persons have an infection within a community and to understand the nature of how coronaviruses spread.
Are these reasons sufficient to give up our health freedom and our personal rights, being tested and shamed in public?
Despite the challenges with test kits, testing began. By the end of March 2020, more than 1 million people had been tested across the US. By May 9, the number tested had grown to over 8.7M. Testing methods include a swab of the nasal passages or by inserting a long, uncomfortable swab through the nose to scrape the back of the throat. Specimens have also been obtained bronchoalveolar lavage, from sputum, and from stool specimens.
The call for mandatory testing has been gathering steam and becoming ever more onerous. In Washington state, Governor Inslee has declared:
Individuals that refuse to cooperate with contact tracers and/or refuse testing, those individuals will not be allowed to leave their homes to purchase basic necessities such as groceries and/or prescriptions. Those persons will need to make arrangements through friends, family, or state provided “family support” personnel.
But what do the results really mean?
Who Should Receive Testing
On May 8, 2020, the CDC has listed specific priorities for when testing should be done. As of May 16, more than 11-million samples have been collected and more than 3700 specimens have not yet been evaluated.
High Priority
Hospitalized patients with symptoms
Healthcare facility workers, workers in living settings, and first responders with symptoms
Residents in long-term care facilities or other congregate living settings, including prisons and shelters, with symptoms
Priority
Persons with symptoms of potential COVID-19 infection, including fever, cough, shortness of breath, chills, muscle pain, new loss of taste or smell, vomiting or diarrhea, and/or sore throat
of potential COVID-19 infection, including fever, cough, shortness of breath, chills, muscle pain, new loss of taste or smell, vomiting or diarrhea, and/or sore throat Persons without symptoms who are prioritized by health departments or clinicians, for any reason, including but not limited to public health monitoring, sentinel surveillance, or screening of asymptomatic individuals according to state and local plans.
Read that last priority again: That means virtually everyone can be required to get a test.
Is that a violation of your personal rights? And, if you submit to testing, what does a “positive test” actually mean?
Types of Testing: RT-PCR
PCR, short for polymerase chain reaction, is a highly specific laboratory technique. The key to understanding PCR testing is that PCR can identify an individual specific virus within a viral family.
However, a PCR was created to identify DNA viruses; the SARS-CoV2 virus is an RNA virus. Therefore, multiple steps are necessary to “magnify” the amount of genetic material in the specimen. Researchers used a method called RT-PCR, reverse transcription-polymerase chain reaction, to specifically identify the SARS-CoV-2 virus. It’s a complicated process. To read more about it, go here and here.
If a nasal or a blood sample contains a tiny snip of RNA from the SARS-CoV-2 virus, RT-PCR can identify it. This leads to a high probability that the person has had exposure to the SARS-CoV-2 virus.
However – and this is important – a positive RT-PCR test result does not necessarily indicate a full virus is present. The virus must be fully intact to cross from person-to-person and cause illness.
RT-PCR Testing: The Importance of Timing
Even if a person has had all the symptoms associated with a coronavirus infection or has had exposure to persons who have had a COVID-19 diagnosis, the probability of an RT-PCR test being positive decreases with the number of days past the onset of symptoms.
According to a study done by Paul Wikramaratna and others:
For a nasal swab, the percentage chance of a positive test declines from about 94% on day 0 to about 67% by day 10. By day 31, there is only a 2% chance of a positive result.
the percentage chance of a positive test declines from about 94% on day 0 to about 67% by day 10. By day 31, there is only a of a positive result. For a throat swab, the percentage chance of a positive test declines from about 88% on day 0 to about 47% by day 10. By day 31, there is only a 1% chance of a positive result.
In other words, the longer the time frame between the onset of symptoms and the time a person tests for COVID-19, the more likely the test will be negative.
The Purpose of Repeat Testing
Repeat testing of persons who have a negative test may (eventually) confirm the presence of viral RNA, but this is impractical. Additionally, repeated testing of the same person can lead to even more confusing results. The test may go from negative, to positive, then back to negative again as the immune system clears out the coronavirus infection and moves to recovery.
And what makes this testing even more confusing is that the FDA admits that “The detection of viral RNA by RT-PCR does not necessarily equate with an infectious virus.”
Let’s break that down:
You’ve had all the symptoms of COVID19, but your RT-PCR test for SARS-CoV-2 is negative.
Does that mean you’re “good to go” – you can go to work, go to school or you can travel? OR…
Does that mean your influenza-like illness was caused by some other pathogen, possibly one of the four coronaviruses that have been in circulation for 60 years? OR…
Does that mean the result is a false-negative and you still have the infection, but it isn’t detectable by current tests? OR…
Does that mean it was a sample that was inadequately taken due to the faulty technique by the technician? OR…
Does that mean you have not been exposed, and you are susceptible to contracting the infection, and you need to stay in quarantine?
So, what does a “positive” test actually mean? And that’s the problem:
No one knows for sure.
Another Type of Testing: Antibodies
According to the nonprofit Foundation for Innovative New Diagnostics (FIND), more than 200 serologic blood tests, to test for antibodies, are either now available or in development.
There are two primary types of antibodies that undergo assessment for nearly any type of infection: IgM and IgG. While several new testing devices are being touted as a home test, they are not the same as a home pregnancy test or a glucometer to test your blood sugar. The blood spot or saliva specimen can be collected at home. From there, you’d It send it to a laboratory for analysis. It can take a few days – or longer – to get the results. With so many tests in the pipeline, the ability to test at home will be changing over time.
The first antibody to rise is IgM. It rises quickly after the onset of the infection and is usually a sign of an acute, or current, infection. The IgM levels diminish quickly as the infection resolves. The FDA admits they don’t know how long the IgM remains present for SARS-CoV-2 as the infection clears.
The interpretation of an IgG antibody is more difficult. This antibody is an indicator of a past infection. The test is often not specific enough to determine if SARS-CoV-2 virus was the cause for past infection, or one of the four common coronaviruses that cause influenza-like illness.
Negative or Positive
The FDA says:
Because serology testing can yield a negative test result even if the patient is actively infected (e.g., the body has not yet developed in response to the virus) or maybe falsely positive (e.g., if the antibody indicates a past infection by a different coronavirus), this type of testing should not be used to diagnose an acute or active COVID-19 infection.
Similarly, the CDC says the following regarding antibody testing:
If you test positive: A positive test result shows you have antibodies as a result of an infection with SARS-CoV-2, possibly a related coronavirus. It’s unclear if those antibodies can provide protection (immunity) against getting infected again. This means that we do not know at this time if antibodies to SARS-CoV-2 make you immune to the SARS-CoV-2. If you have no symptoms, you likely do not have an active infection and no additional follow-up is needed. It’s possible you might test positive for antibodies and you might not have, or have ever had, symptoms of COVID-19. This is known as having an asymptomatic infection [ie you have a healthy immune system that eliminated the virus without experiencing the illness.] An antibody test cannot tell if you are currently sick with COVID-19.
If you test negative If you test negative for antibodies, you probably did not have a previous infection. However, you could have a current infection because antibodies don’t show up for 1 to 3 weeks after infection. Some people may take even longer to develop antibodies, and some people may not develop antibodies. An antibody test cannot tell if you are currently sick with COVID-19.
What? Wait!
Doesn’t the vaccine industry call the IgG a “protective antibody”?
Isn’t this the marker assessed after you’ve had an infection with, say, measles or chickenpox or mumps, to determine if you are immune from future infections?
Isn’t this the marker of induced immunity they are trying to achieve by administering a vaccine?
If the FDA doesn’t know if an IgG antibody to SARS-CoV-2 after recovering from the infection is protective against a future infection, then they certainly don’t know if an antibody caused by a vaccine will prevent infection either.
Doesn’t this completely eliminate the theory that antibodies afford protection and antibodies from vaccines are necessary to keep you from getting sick?
Mandatory Testing – New Job Creation
Illinois U.S. Rep. Bobby L. Rush introduced the H.R. 6666 TRACE Act on May 1. On his website, Rush says:
Until we have a vaccine to defeat this dreaded disease, contact tracing in order to understand the full breadth and depth of the spread of this virus is the only way we will be able to get out from under this.
H.R.6666 would authorize the Secretary of Health and Human Services (HHS), acting through the Director of the CDC to award grants to eligible entities to conduct diagnostic testing and then to trace and monitor the contacts of infected individuals. The contact tracers would receive authorization to test people in their homes and as necessary, quarantine people in place.
Where do they intend to do this testing? Besides mobile units to test people in their homes, the bill identifies eight specific locations where the testing and contract tracing could occur. Locations include schools, health clinics, universities, churches, and “any other type of entity” the secretary of HHS wants to use.
The bill would allocate $100 billion in 2020 “and such sums as may be necessary for the fiscal year 2021 and any subsequent fiscal year during which the emergency period continues.”
Rapid Mutation
But what are they looking for?
If you’re asymptomatic, is your test supposed to be positive – saying you’ve been exposed and you’ve recovered?
– saying you’ve been exposed and you’ve recovered? Or is your test supposed to be negative , meaning, you’re healthy?
, meaning, you’re healthy? Or does a completely negative test – negative RT-PCR test and no IgG antibody – mean you’re susceptible to infection and you need to stay in quarantine?
The virus is rapidly mutating, which is rather typical of RNA viruses. In a study published in April 2020, researchers discovered that the novel coronavirus has already mutated into at least 30 different genetic variations. If your RT-PCR test is positive, does this identify exposure to the original pandemic virus, or have you had exposure to one of the genetic variations?
The same can be said about the vaccines under development: With each mutation, is the vaccine more likely to be all risk and no benefit when it reaches the market?
What You Can Do
Across the nation, police are receiving orders to not apprehend criminals but instead, to arrest parents at playgrounds, to arrest lone surfers on public beaches, to fine ministers and congregation members sitting in their cars listening to a service on the radio, and to restrict movement by creating one-way sidewalks.
People have had enough. They are beginning to see the huge scam that has been perpetrated on the entire world over a viral infection with a global death rate of 1.4%. Meaning, 1.4% of people infected with SARS-CoV-2 have a fatal outcome, while 98.6% recover). This is far fewer deaths than a severe flu season.
We’re already starting to see the thrust to take our power back:
In Virginia, people went to the beaches ignoring social distancing and the orders of the Governor to stay home.
and the orders of the Governor to stay home. The central California city of Atwater has declared itself a “sanctuary city,” openly defying Democratic California
Democratic The truth about wearing masks
I’m Not/I Will Not
While they shut us down and held us hostage in our homes, they changed our society, our lives, our world. You need to decide where you stand on each of these statements:
I will not submit to testing.
I will not wear a mask.
I will not accept this is the “new normal.”
I will not be afraid of standing next to a friend or family member and will not obey the concept of “social distancing.”
I will understand that an asymptomatic carrier is a normal, healthy person; I will not buy into the fear that I might “catch something” from a normal, healthy person.
I will refuse mandatory vaccination.
It’s time for Americans to resist with non-violent civil disobedience. Be brave. Be bold. Put on the full armor of God, as found in Ephesians 6:10-20 in the Bible, to stand against the world rulers of this present darkness. With God on our side, all things are possible.
*updated August 9, 2020
++++++++++++++++++++++++++++++++
Like what you’re reading on Vaxxter.com?
Share this article with your friends. Help us grow.
Join our list here or text MVI to 555888
+++++++++++++++++++++++++++++++++
Dr. Sherri Tenpenny is an osteopathic medical doctor, board-certified in three specialties. She is the founder of Tenpenny Integrative Medical Center, a medical clinic located near Cleveland, Ohio. Her company, Courses4Mastery.com provides online education and training regarding all aspects of vaccines and vaccination.
Dr. Tenpenny has invested nearly 20 years and more than 40,000 hours documenting and exposing the problems associated with vaccines. As an internationally known speaker and author, her many articles have been translated into at least 15 languages. She is a frequent guest on radio and TV to share her knowledge and educate parents on why they should Just.Say.No. to vaccines
photocredit: ID 175464933 © Mrdoomits | Dreamstime.com


vaxxter--2020-05-18--Coronavirus Pt3 – Testing: What are we doing? What does it mean?

In 1965 , scientists identified the first human coronavirus ; it was associated with the common cold .
The Coronavirus family , named for their crown-like appearance , currently includes 36 viruses .
Within that group , there are 4 common viruses that have been causing infection in humans for more than sixty years .
In addition , three pandemic coronaviruses that can infect humans : SARS , MERS , and now , SARS-CoV-2 .
As the news of deaths in China , South Korea , Italy , and Iran began to saturate every form of media 24/7 , we @ @ @ @ @ @ @ COVID-19 .
To be clear , the name of the newly identified coronavirus is SARS-CoV-2 , short for Severe Acute Respiratory Syndrome Coronavirus-2 .
This virus is associated with fever , cough , chest pain , and shortness of breath , the complex of symptoms that form the diagnosis of COVID-19 .
The Trump administration declared a public health emergency on January 31 , 2020 , then on February 2 placed a ban on the entry of most travelers who had recently been in China .
On February 4 , Alex Azar , @ @ @ @ @ @ @ ( HHS ) issued a declaration of public health emergency and activated the Public Readiness and Emergency Preparedness Act , otherwise known as the PREP Act .
This nefarious legislation provides complete protection of manufacturers from liability for products , technologies , biologics , or any vaccine developed as a medical countermeasure against COVID-19 .
For those nervously waiting for the vaccine to become available , be sure to understand the PREP Act before rushing to the get in line .
Calls for testing – to see if a person is or isn @ @ @ @ @ @ @ the emergency was declared , but performing those tests was initially slow due to an inadequate number of test kits .
As the kits became available , those developed by the CDC had a defect : The reagents reacted to the negative control sample , making the test inaccurate and the kits unusable .
In various countries , thousands of test kits purchased from China were found to be contaminated with the SARS-CoV-2 viruses .
No one really knows how that happened , but theories spread like wildfire .
Could the test kit @ @ @ @ @ @ @ , did it mean the test would return a false-positive result , driving up the numbers of those said to be infected so those in power could implement stronger lockdowns and accelerate the hockey-stick unemployment rates ?
Neither of those questions has been adequately answered .
Authorities claim that testing is important for public health officials to assess if their mitigation efforts – “ shelter in place ” and “ social distancing ” and “ wearing a mask ” – are making a difference to “ flatten the curve.
” Officials also claim @ @ @ @ @ @ @ many persons are infected within a community and to understand the nature of how coronaviruses spread .
Are these reasons sufficient to give up our health freedom and our personal rights , being tested and shamed in public ?
Despite the challenges with test kits , testing began .
By the end of March 2020 , more than 1 million people had been tested across the US .
By May 9 , the number tested had grown to over 8.7M .
Testing methods include a swab of the nasal passages or by inserting @ @ @ @ @ @ @ nose to scrape the back of the throat .
Specimens have also been obtained bronchoalveolar lavage , from sputum , and from stool specimens .
The call for mandatory testing has been gathering steam and becoming ever more onerous .
In Washington state , Governor Inslee has declared : But what do the results really mean ?
Who Should Be Tested On May 8 , 2020 , the CDC has listed specific priorities for when testing should be done .
As of May 16 , more than 11-million samples have been collected and @ @ @ @ @ @ @ been evaluated .
• Healthcare facility workers , workers in living settings , and first responders with symptoms • Residents in long-term care facilities or other congregate living settings , including prisons and shelters , with symptoms • Persons with symptoms of potential COVID-19 infection , including fever , cough , shortness of breath , chills , muscle pain , new loss of taste or smell , vomiting or diarrhea , and/or sore throat • Persons without symptoms who are prioritized by health departments or clinicians , including but not limited to public @ @ @ @ @ @ @ according to state and local plans .
Read that last priority again : That means virtually everyone can be required to get a test .
Is that a violation of your personal rights ?
And , if you submit to testing , what does a “ positive test ” actually mean ?
PCR , short for polymerase chain reaction , is a highly specific laboratory technique .
The key to understanding PCR testing is that PCR can identify an individual specific virus within a viral family .
However , a PCR test can @ @ @ @ @ @ @ ; the SARS-CoV2 virus is an RNA virus .
Therefore , multiple steps must be taken to “ magnify ” the amount of genetic material in the specimen .
Researchers used a method called RT-PCR , reverse transcription-polymerase chain reaction , to specifically identify the SARS-CoV-2 virus .
It ’ s a complicated process .
To read more about it , go here and here .
If a nasal or a blood sample contains a tiny snip of RNA from the SARS-CoV-2 virus , RT-PCR can identify it , leading to a high @ @ @ @ @ @ @ to the SARS-CoV-2 virus .
However – and this is important – a positive RT-PCR test result does not necessarily indicate a full virus is present .
The virus must be fully intact to be transmitted and cause illness .
Even if a person has had all the symptoms associated with a coronavirus infection or has been closely exposed to persons who have been diagnosed with COVID-19 , the probability of a RT-PCR test being positive decreases with the number of days past the onset of symptoms .
According to a study done @ @ @ @ @ @ @ For a nasal swab , the percentage chance of a positive test declines from about 94 % on day 0 to about 67 % by day 10 .
By day 31 , there is only a 2 % chance of a positive result .
• For a throat swab , the percentage chance of a positive test declines from about 88 % on day 0 to about 47 % by day 10 .
By day 31 , there is only a 1 % chance of a positive result .
In other words , @ @ @ @ @ @ @ onset of symptoms and the time a person is tested , the more likely the test will be negative .
Repeat testing of persons who have a negative test may ( eventually ) confirm the presence of viral RNA , but this is impractical .
Additionally , repeated testing of the same person can lead to even more confusing results : The test may go from negative , to positive , then back to negative again as the immune system clears out the coronavirus infection and moves to recovery .
And what makes @ @ @ @ @ @ @ the FDA admits that “ The detection of viral RNA by RT-PCR does not necessarily equate with an infectious virus.
” You ’ ve had all the symptoms of COVID19 , but your RT-PCR test for SARS-CoV-2 is negative .
• Does that mean you ’ re “ good to go ” – you can go to work , go to school or you can travel ?
OR… • Does that mean your influenza-like illness was caused by some other pathogen , possibly one of the four coronaviruses that have been in circulation @ @ @ @ @ @ @ that mean the result is a false-negative and you still have the infection , but it isn ’ t detectable by current tests ?
OR… • Does that mean it was a sample was inadequately taken due to the faulty technique by the technician ?
OR… • Does that mean you have not been exposed , and you are susceptible to contracting the infection , and you need to stay in quarantine ?
So , what does a “ positive ” test actually mean ?
And that ’ s the problem : No @ @ @ @ @ @ @ the nonprofit Foundation for Innovative New Diagnostics ( FIND ) , more than 200 serologic blood tests , to test for antibodies , are either now available or in development .
There are two primary types of antibodies that are assessed for nearly any type of infection : IgM and IgG .
While several new testing devices are being touted as a home test , they are not the same as a home pregnancy test or a glucometer to test your blood sugar .
The blood spot or saliva specimen can be collected @ @ @ @ @ @ @ sent to a laboratory for analysis .
It can take a few days – or longer – to get the results .
With so many tests in the pipeline , the ability to test at home will be changing over time .
The first antibody to rise is IgM .
It rises quickly after the onset of the infection and is usually a sign of an acute , or current , infection .
The IgM levels diminish quickly as the infection resolves .
The FDA admits they do not know how long the @ @ @ @ @ @ @ infection is being cleared .
The interpretation of an IgG antibody is more difficult .
This antibody is an indicator of a past infection .
The test is often not specific enough to determine if the past infection was caused by the SARS-CoV-2 virus or one of the four common coronaviruses that cause influenza-like illness .
Similarly , the CDC says the following regarding antibody testing : • If you test positive : • A positive test result shows you have antibodies as a result of an infection with SARS-CoV-2 , • It @ @ @ @ @ @ @ provide protection ( immunity ) against getting infected again .
This means that we do not know at this time if antibodies to SARS-CoV-2 make you immune to the SARS-CoV-2 .
• If you have no symptoms , you likely do not have an active infection and no additional follow-up is needed .
• It ’ s possible you might test positive for antibodies and you might not have , or have ever had , symptoms of COVID-19 .
This is known as having an asymptomatic infection [ ie you have a healthy @ @ @ @ @ @ @ experiencing the illness . ]
• An antibody test can not tell if you are currently sick with COVID-19 .
• If you test negative • If you test negative for antibodies , you probably did not have a previous infection .
However , you could have a current infection because antibodies don ’ t show up for 1 to 3 weeks after infection .
• Some people may take even longer to develop antibodies , and some people may not develop antibodies .
• An antibody test can not tell if you @ @ @ @ @ @ @ Isn ’ t this the marker assessed after you ’ ve had an infection with , say , measles or chickenpox or mumps , to determine if you are immune from future infections ?
• Isn ’ t this the marker of induced immunity they are trying to achieve by administering a vaccine ?
If the FDA does not know if an IgG antibody to SARS-CoV-2 after recovering from the infection is protective against a future infection , then they certainly don ’ t know if an antibody caused by a vaccine will @ @ @ @ @ @ @ this completely eliminate the theory that antibodies afford protection and antibodies from vaccines are necessary to keep you from getting sick ?
Illinois U.S. Rep. Bobby L. Rush introduced the H.R .
6666 TRACE Act on May 1 .
On his website , Rush said , H.R.6666 would authorize the Secretary of Health and Human Services ( HHS ) , acting through the Director of the CDC to award grants to eligible entities to conduct diagnostic testing and then to trace and monitor the contacts of infected individuals .
The contact tracers would @ @ @ @ @ @ @ homes and as necessary , quarantine people in place .
Where do they intend to do this testing ?
Besides mobile units to test people in their homes , the bill identifies eight specific locations where the testing and contract tracing could occur : schools , health clinics , universities , churches , and “ any other type of entity ” the secretary of HHS wants to use .
The bill would allocate $ 100 billion in 2020 “ and such sums as may be necessary for fiscal year 2021 and any subsequent @ @ @ @ @ @ @ continues.
” But what are they looking for ?
• If you ’ re asymptomatic , is your test supposed to be positive – saying you ’ ve been exposed and you ’ ve recovered ?
• Or is your test supposed to be negative , meaning , you are healthy ?
• Or does a completely negative test – negative RT-PCR test and no IgG antibody mean you ’ re susceptible to infection and you need to stay in quarantine ?
The virus is rapidly mutating , which is rather typical of @ @ @ @ @ @ @ in April 2020 , researchers discovered that the novel coronavirus has already mutated into at least 30 different genetic variations .
If your RT-PCR test is positive , does this identify exposure to the original pandemic virus or have you been exposured to one of the genetic variations ?
The same can be said about the vaccines under development : With each mutation , is the vaccine more likely to be all risk and no benefit when it reaches the market ?
What You Can Do Across the nation , police are being @ @ @ @ @ @ @ , to arrest parents at playgrounds , to arrest lone surfers on public beaches , to fine ministers and congregation members sitting in their cars listening to a service on the radio , and to restrict movement by creating one-way sidewalks .
People have had enough .
They are beginning to see the huge scam that has been perpetrated on the entire world over a viral infection with a global death rate of 1.4 % ( meaning , 1.4 % of people infected with SARS-CoV-2 have a fatal outcome , while 98.6 % @ @ @ @ @ @ @ deaths than a severe flu season .
We ’ re already starting to see the thrust to take our power back : • In Virginia , people went en mass , ignoring social distancing and the orders of the Governor to stay home .
• The central California city of Atwater has declared itself a allowing business owners and churches to open , openly defying Democratic Gov .
Gavin Newsom ’ s coronavirus-related stay-at-home order .
• The truth about is starting to come out and people are voting with their feet .
@ @ @ @ @ @ @ warns that not only do face masks fail to protect healthy people from contracting an illness , but they create serious health risks to the wearer .
While they shut us down and held us hostage in our homes , they changed our society , our lives , our world .
• I am not willing to accept this is the “ new normal.
” • I will not be afraid of standing next to a friend or family member and will not obey the concept of “ social distancing.
” • I @ @ @ @ @ @ @ a normal , healthy person and I will not buy into the fear that I might “ catch something ” from a normal , healthy person .
It ’ s time for Americans to resist with non-violent civil disobedience .
Be brave .
Be bold .
Put on the full armor of God , as found in Ephesians 6:10-20 in the Bible , to stand against the world rulers of this present darkness .
With God on our side , all things are possible .
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Join our list here or text MVI to 555888 Dr. Sherri Tenpenny is an osteopathic medical doctor , board-certified in three specialties .
She is the founder of Tenpenny Integrative Medical Center , a medical clinic located near Cleveland , Ohio .
Her company , Courses4Mastery.com provides online education and training regarding all aspects of vaccines and vaccination .
Dr. Tenpenny has invested nearly 20 years and more than 40,000 hours documenting and exposing the problems associated with vaccines .
As an internationally known speaker and author , @ @ @ @ @ @ @ at least 15 languages .
She is a frequent guest on radio and TV to share her knowledge and educate parents on why they should Just.Say.No .
to vaccines