av David Crowe. (Min emfas. Källor i slutet av det länkade dokumentet.)
A positive RT-PCR test is used to tell people that they have COVID-19 RNA and are
deemed infected and infectious, despite the technology’s numerous flaws and
known false positives. Antibody tests are now being used under the assumption that
someone who is positive for antibodies for COVID-19 has previously been infected
and, if they have recovered from symptoms, is now immune.
Antibodies are our body’s immune system reaction to viral proteins, known as
antigens. Antibody tests incorporate antigens, and a chemical that allows the
intensity of the reaction to be measured using light. Ideally antigens would come
from pure virus, but COVID-19 virus has never been purified, thus antigens are
created artificially from proteins based on portions of the 30,000 base RNA genome
that is believed to come from the virus.
The major antibody types that are looked for are IgM, believed to be a generic
infection fighting antibody that arises about a week or so after infection, and IgG,
believed to be more specific, and believed by some to take longer for the body to
create. After the infection is resolved, IgM antibodies are believed to gradually
disappear, while IgG remain, providing ongoing immunity.
Unfortunately, this idealized picture is not supported by the available evidence,
either because the evidence does not exist, is insufficient, or because it directly
contradicts the model.
Positive antibody tests should be impossible before the person is first infected (RNA positive). Yet, old blood samples (2019 or before) have tested positive in significant numbers. Almost 14% of saved blood from old donations tested positive in a Dutch study, and in the validation of the Cellex and Chembio tests, 4.4% and 3.6% of old samples were positive.
The idealized antibody model is based on the date of infection as the starting point,
but this date is never known with certainty. Even when someone came into contact
with a COVID-19 RNA positive person on a certain date that is not a guarantee that
this was the date of infection, given that, prior to the lockdown, people could
apparently be infected while playing in the park, eating at a restaurant, walking
down the street, attending a concert, or participating in any other now banned
activity. When antibody surveys are performed, the vast majority of people who test
positive had no idea that they had previously been infected, and cannot possibly be
sure about the date. Thus, the incubation period for the virus is impossible to
determine accurately, as well as the range of days after infection that IgM and IgG
start to develop. This makes an accurate antibody model impossible to construct
based on currently available data, despite numerous beautiful graphs showing this
model in idealized form.
Simple models that illustrate the timing of antibodies show the quantity (titer)
rising smoothly and, for IgM, eventually peaking and declining smoothly. Yet many
studies have found negative tests throughout the symptomatic period. A test
developed by the Wadsworth Centre in New York found 40% of samples negative
for antibodies 11-15 days after symptoms started, and even more between 16-20 days. This indicates that antibodies may come and go randomly and not behave in a smooth and predictable fashion.
No test documentation, antibody surveys or scientific studies showed the
disappearance of IgM antibodies, predicted by the model, perhaps because it does
not happen, or it takes more than 30 days, the maximum examined. This might not
be terribly important in practice, but it is another indication that the beautiful
models shown in the form of graphs are simplistic, if not outright wrong.
Other problems with antibody tests include a significant number of samples testing antibody positive from people who were COVID-19 RNA negative (although some had ‘COVID-like’ symptoms), with no evidence that the person was ever infected. In one Chinese study the positive rate on presumably never infected people was 25%. Antibody tests, like most infectious disease tests, are often reported as ‘Positive’ or ‘Negative’, but the results are really whether the intensity of a color change in the test kit was above or below an arbitrary number. The reliability of this was called into question, inadvertently, by one test manufacturer, who showed that continually diluting samples 50:50 did not result in a halving of the color change at each step. In some cases, less material resulted in significantly more intense color changes. Researchers have tried to connect the antibody titer (in reality, this is just the color change intensity) with the severity of symptoms, but two Chinese papers that studied this had to admit that there was no difference between mildly and severely symptomatic people in the quantity of antibodies, nor between those with or without pre-existing conditions, nor in the duration of symptoms.
Test manufacturers always run their test on blood samples from people with
unrelated medical conditions as a check. Even though only a small number of
samples were examined, for a small number of conditions, different manufacturers
found a significant percentage of samples positive for COVID-19 antibodies, that
were known not to have COVID-19, but instead contained other viruses, bacteria or
mycoplasma, or were from people with auto-immune conditions, indicating that the
antibodies are not specific. For example, 10% of Hepatitis B samples were positive,
33% of Respiratory Synctitia Virus, 10% of auto-antibodies and 17% of
Streptococcus.
A large number of population surveys have been compiled by Dean Beeler and they
reveal a wide range of percentages of populations antibody positive, from less than
1% in many cases to 32% in a poor part of Boston. This is generally seen as an
indication of how far through the population that the virus has rampaged. One flaw
of most of these surveys is that the population is chosen non-randomly, and does
not represent the general population. The group may be a household survey,
volunteers, high school students and staff, health care workers, blood donors, or
people going for blood tests at a lab.
But a far bigger problem is that the number produced is impossible to validate.
When 1.5% of Santa Clara volunteers tested positive, it was assumed that that was
truth. This ‘truth’ asserts that all of these people were RNA-positive at some point in
the recent past. But there is absolutely no evidence for this. The ‘truth’ assumes that all the people were negative for COVID-19 antibodies prior to the assumed period of RNA-positivity. But there is absolutely no evidence for this.
It assumes that the 98.5% who tested negative were never RNA-positive. But there
is absolutely no evidence for this. It assumes that the 98.5% never had the
antibodies being looked for before. But there is absolutely no evidence for this.
I could assert that the real fraction positive in Santa Clara was 98.5%, not 1.5%, and
there is no less evidence for my assertion than for the results from antibody testing.
These surveys often ask if people who tested antibody positive had ‘COVID-like’
symptoms in the last few weeks or months (and most say that they did not). But
these symptoms (fever, cough, loss of smell or taste, fatigue) are so generic that they are absolutely not evidence that the people were previously COVID-19 RNA positive.
One solution would be a time series survey of a large number of people currently
negative on both RNA and antibody tests (uninfected and never infected). Every few
days these people would give a drop of blood and a nasal swab. Some would become
RNA positive, and then could be examined more frequently for the exact pattern of
antibody development, through to the disappearance of IgM antibodies. This
experiment would be time consuming, intrusive, inefficient (as most people may
never become infected) and expensive. But considering the vast sums of money
spent on COVID-19 research, quarantining and treatment, and the even more
tremendous sums of money lost by a hobbled economy, and the assertion of our
politicians that they follow the science (not the head lemming), this would surely be
worthwhile.
Antibody tests might be fatally flawed, but they can be used in highly destructive
ways. If the number of people who are antibody positive remains below the level of ‘herd immunity’ (90% or so) it will be an excuse to promote or even mandate vaccination, after a vaccine is rushed onto the market. Antibody tests could also be used to indefinitely quarantine people who do not test positive, asserting that they are at danger of becoming infected, and then spreading it to others. They could be used to separate families, arguing that the children must be put in foster homes because the parents are at risk of an infection at any time.
Faulty tests have been used to indefinitely quarantine Chinese citizens. But now, do
we have more civil rights in the UK, United States, Canada or other modern, once
democratic countries?
We have been here before. A BBC story from 2008, “Life Sentence”, always makes
me cry. Starting in 1907 nearly 50 women were locked in an asylum within the Long
Grove insane asylum in Surrey because they were deemed carriers of typhoid. They
were sane and healthy when they entered, but most were driven mad by the solitary
confinement, by humiliations like toilets that flushed boiling water, warmly
reminding them that even their excrement was a danger to the world, by the nurses
wearing PPE. After they stopped imprisoning such women in the 1950s, the
prisoners remained. In 1992, when the asylum closed for good, the three remaining
women were deemed insane and relocated to other institutions, their entire lives
destroyed by an infectious panic. Despite this, the UK Department of Health told the
BBC that there never had been a policy of incarcerating people deemed carriers of
an infectious disease [32].
This document is based on an examination of all antibody test documentation
submitted to the US FDA (Food and Drug Administration) and a series of antibody
surveys of groups of people from around the world.
