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Volume 8 | Issue 2 | 542
Australian COVID-19 pandemic: A Bradford Hill Analysis of Iatrogenic Excess
Mortality
Research Article
Wilson Sy
*Corresponding author
Wilson Sy, Australia
Submitted: 10 Mar 2023; Accepted: 15 Mar 2023; Published: 01 Apr 2023
J Clin Exp Immunol, 2023
Citation: Sy, W. (2023). Australian COVID-19 pandemic: A Bradford Hill Analysis of Iatrogenic Excess Mortality. J Clin Exp
Immunol, 8(2), 542-556.
Abstract
Australian ocial mortality data show no clear evidence of signicant excess deaths in 2020, implying from an older WHO
denition that there was no COVID-19 pandemic. A seasonality analysis suggests that COVID-19 deaths in 2020 were likely
misclassications of inuenza and pneumonia deaths. Australian excess mortality became signicant only since 2021 when
the level was high enough to justify calling a pandemic. Signicant excess mortality was strongly correlated (+74%) with
COVID-19 mass injections ve months earlier. Strength of correlation, consistency, specicity, temporality, and dose-response
relationship are foremost Bradford Hill criteria which are satised by the data to suggest the iatrogenesis of the Australian
pandemic, where excess deaths were largely caused by COVID-19 injections. Therefore, a strong case has been presented
for the iatrogenic origins of the Australian COVID-19 pandemic and therefore, the associated mortality risk/benet ratio for
COVID injections is very high.
Journal of Clinical & Experimental Immunology
ISSN: 2475-6296
Introduction
On 11 March 2020, the World Health Organization (WHO) de-
clared [1] the COVID-19 pandemic based on 4,291 deaths, by
118,000 cases in 114 countries, with an average of about 1,000
cases in each country. Based on this very small sample, the WHO
assumed that the COVID-19 disease is highly infectious and has
an infection fatality rate (IFR) of at least 0.4 percent. Therefore,
the COVID-19 pandemic was declared based on expectation and
not on fact, as the WHO had previously dened for an inuenza
pandemic [2]:
An inuenza pandemic occurs when a new inuenza virus ap-
pears against which the human population has no immunity, re-
sulting in several, and simultaneous epidemics worldwide with
enormous numbers of deaths and illness.
Emphasis added. A pandemic should be justiably declared only
if there are “enormous numbers of deaths”, for otherwise season-
al inuenza or even the common cold of the Rhinovirus could
be declared as pandemics, i.e., just based on numbers of cases of
infection. By now, it is abundantly clear that the number of cases
dened by the PCR tests may be grossly inated (see section 2).
By assuming “cases” would lead to “enormous deaths”, the
WHO declared a pandemic based on supposition, not on scien-
tic fact. The presumption of sound science by governments has
allowed them to justify harsh public health measures which may
have been counter-productive ultimately causing more deaths.
Based on objective data, this paper assesses whether there were
enough excess deaths to warrant declaring a pandemic in Austra-
lia. By investigating those excess deaths, the probable cause of
the Australian pandemic is deduced in this study.
In section 2, it is discussed that assessment of the pandemic
based solely and quantitatively on COVID infection cases and
deaths is questionable, because cases of COVID infection and
deaths attributed to the SARS-CoV-2 virus have not been ade-
quately proven. That is, the pandemic cannot be accurately as-
sessed from COVID-19 data which are scientically awed, see
discussed below. This paper assesses the COVID-19 pandemic
in Australia based on all-cause mortality data, consistent with
the earlier WHO denition of pandemics.
Since accurate and reliable data are critically important as inputs
to the data analysis to draw valid conclusions, data methodology
is discussed in section 3. In 2020, when many Victorian deaths
were attributed to COVID-19, the impact on total mortality was
insucient to declare a pandemic in Australia. Details and pos-
sible explanations are discussed in section 4, to justify calling
2020 as the “pre-pandemic” phase.
Australian excess deaths began to rise to a statistically signi-
cant level in 2021 to warrant the appellation of a “pandemic”.
Early increases in excess deaths accompanied the early rollout
of mass COVID-19 injections. The injections were called “vac-
cines”, but they do not prevent infections, nor were they tested
to inoculate against infections, as admitted recently by Pzer to
the European Parliament [3].
This paper rejects calling the COVID-19 injections “vaccines”
which were never tested to be such. The public has been misin-
formed and misled to accept COVID-19 injections as “vaccines”.
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J Clin Exp Immunol, 2023
When the injections clearly failed to reduce transmissions, the
rhetoric of “vaccine” benet changed to reducing serious illness-
es and deaths. This claim is also proved false in this paper, where
the pandemic phase dened by elevated excess deaths is shown
to be correlated with mass COVID-19 injections in section 5.
In section 5, the strong correlation between doses of injections
administered and increased levels of excess deaths ve months
later suggest iatrogenic causality. This possibility is further
strengthened by aspects of consistency and specicity in section
6 where the evidence of causality is seen by consistency across
time and geography. Also, specicity is evident from the fact
that the “vaccinated” are more likely to die than the “unvacci-
nated”, who are simply dened as those without any injections,
rather ocial denitions where the “unvaccinated” may have
had injections.
The main contributions of this paper, addressed in sections 5 and
6, are what we consider the ve foremost criteria of Bradford
Hill [4] causality for an iatrogenic pandemic. The remaining
four aspects of Bradford Hill analysis are briey reviewed from
existing literature in section 7 on coherence and plausibility and
in section 8 on experiment and analogy.
Essentially, iatrogenesis of the pandemic is coherent with, and
does not violate, existing knowledge of pathology and epidemi-
ology and the biological mechanisms are highly plausible, with
some clinical experiments to validate them. In many ways, the
current pandemic is analogous to the previous “swine u” pan-
demic in 2009, except that the 2009 episode was not a pandemic,
which was without “mass vaccination”.
Section 9 contains a summary of preceding sections, with a tab-
ulated synopsis of all nine Bradford Hill criteria discussed. The
nal section concludes that a strong case has been presented for
the iatrogenic origins of the Australian COVID-19 pandemic.
COVID-19 Data
This section explains why the Australian COVID-19 pandem-
ic cannot be accurately assessed from COVID-19 data, because
COVID-19 cases and infections were poorly dened. Therefore,
COVID-19 data are scientically awed, but nevertheless they
drove and continue to drive erroneous health policies.
A COVID infection has no denitive set of symptoms and was
not detected by the presence of the SARS-CoV-2 virus, but was
dened by a positive PCR test. However, a positive PCR test
does not detect the presence of the SARS-CoV-2 virus which is
the denitive pathogen of the COVID-19 disease. The CDC has
explicitly made clear the following disclaimer [5]:
Since no quantied virus isolates of the 2019-nCoV were avail-
able for CDC use at the time the test was developed and this
study conducted, assays designed for detection of the 2019-
nCoV RNA were tested with characterized stocks of in vitro
transcribed full-length RNA (N gene; GenBank accession:
MN908947.2) of known titer (RNA copies/μL) spiked into a di-
luent consisting of a suspension of human A549 cells and viral
transport medium (VTM) to mimic clinical specimen.
Emphasis added. Therefore, COVID-19 cases may be cases of
respiratory infections caused by other RNA viruses, which also
implies that COVID cases and deaths may be wrongly attributed
to the SARS-CoV-2 virus, wherever its controversial origin.
Deciency of the PCR test has been acknowledged by the CDC
in mid-2021 when it issued a “Lab Alert” [6] to plan a withdraw-
al of the test:
After December 31, 2021, CDC will withdraw the request to
the U.S. Food and Drug Administration (FDA) for Emergency
Use Authorization (EUA) of the CDC 2019-Novel Coronavirus
(2019-nCoV) Real-Time RT-PCR Diagnostic Panel, the assay
rst introduced in February 2020 for detection of SARS-CoV-2
only.
CDC encourages laboratories to consider adoption of a multi-
plexed method that can facilitate detection and dierentiation
of SARS-CoV-2 and inuenza viruses.
Emphasis added. From 2022, instead of the PCR test which can-
not dierentiate between SARS-CoV-2 and inuenza viruses,
the CDC has suggested the use of a multiplexed method. A quad-
raplex method [7] was not discovered until early 2021, when
the researchers claimed to have simultaneously detected from
clinical specimens two SARS-CoV-2 genes, as well as inuenza
A and inuenza B viruses:
To the authors' knowledge, this is the rst study to report a qua-
druplex rRT-PCR assay for the detection of two SARS-CoV-2
genes, hIAV and hIBV with perfect clinical performance.
Emphasis added. It is unclear whether the research has been
independently veried or whether commercial quantities of the
quadraplex method for detecting SARS-CoV-2 have been pro-
duced or widely used since 2022. It is quite clear that COVID-19
data are scientically awed before 2022 everywhere and very
likely since. Australian data continue to be awed because PCR
tests are still being used. The inability to distinguish between
the detection of the SARS-CoV-2 and inuenza viruses is a fun-
damental scientic uncertainty, which renders COVID-19 data
scientically awed.
Adding to this uncertainty about what is identied in COVID
infections and cases, there is also a signicant uncertainty about
the titer (genetic fragments per unit volume) needed to dene
presence of the infection. Through a sucient number of cycles
of titer amplication, which is variable and not scientically de-
termined, the PCR test can nearly always return a positive result.
Therefore, whether someone has a COVID infection at all is not
clear from a PCR test.
For the rst time in medical history, people who are perfectly
healthy with no symptoms, have been declared COVID cases,
based solely on unreliable positive PCR tests. A person could
have minute amounts of dead inuenza viruses and be declared
a COVID threat to public health.
On top of those fundamental uncertainties, there is a question of
whether a particular COVID death is a death “with COVID” or
Volume 8 | Issue 2 | 544
J Clin Exp Immunol, 2023
“from COVID” in a typical case of the deceased having other
comorbidities. Subjective judgement, distorted at times by -
nancial incentives, creates uncertainties which can be removed
objectively by autopsies, but they were rarely performed.
Therefore, COVID cases and deaths cannot be used to charac-
terize the pandemic, because the division of excess deaths into
COVID and non-COVID causes appears arbitrary and inaccu-
rate. Australian health policy has been based on misinformation
from awed COVID-19 data which are scientically unsound
[9]. This paper focuses on all-cause mortality and excess deaths
rather than COVID deaths as indicators of the severity of the
Australian pandemic.
Data Methodology
Even as unreliable as the COVID raw data are, Australian of-
cial COVID-19 data seen by the public are not even the raw
data which are collated by state health authorities. They control
and publish selected data in weekly and monthly reports without
making available the raw data which are needed to independent-
ly verify the ocial data. These reports from health authorities
may be misleading due to selection and classication biases,
which have rendered invisible adverse events and deaths related
to “vaccines”.
For example, ocial reports allowed the national broadcaster
ABC to claim falsely on prime-time television in July 2022 that
the “unvaccinated” are 16 to 37 times more likely to die than
the doubly “vaccinated” [8]. This misinformation was based
on a key ocial data reporting aw which came from classi-
fying some deaths as “unvaccinated” even though they had had
COVID-19 injections and often multiple times [9].
This paper avoids the processed data of health authority reports
to eliminate their selection and classication biases. The main
reliance is on data [10] from the national collector, the Austra-
lian Bureau of Statistics (ABS), which has the fewest conicts
of interest, but its data and reports are not accepted uncritically
either, as will be illustrated below.
In scientic research the raw data and their sources should be
publicly accessible or available and the methods of data analysis
should be clearly disclosed so that the conclusions of this or any
other study can be reproduced precisely.
This study depends principally on the all-cause mortality data
published by the ABS, from January 2015 to September 2022,
the latest month of full reporting data. The raw data are shown
in Figure 1, where the horizontal green line and the sloping red
line have been added heuristically to suggest a “regime change”.
The horizontal green line (for guidance) suggests that 2020 ap-
pears to be merely a continuation of the previous trend of rela-
tively steady uctuations in all-cause mortality. On a denition
of pandemic based on excess mortality, there was no evidence
of a pandemic in Australia in 2020, which could be called the
pre-pandemic phase, followed by the pandemic phase starting in
2021 (the sloping red line).
The above raw data is used to calculate excess mortality in this
paper, instead of simply accepting the ocial excess mortality
data published by the ABS. The ABS has changed its baseline
denitions (moved the “goal posts”) for calculating 2022 excess
mortality in an inconsistent manner, without providing adequate
justication. Normally, the baseline for calculating excess mor-
tality is the average of the previous ve years, but the baseline
for 2022 has been dened by the ABS as the average of four
years, 2017-2019 and 2021, without adequate reasons [10]:
Throughout this report, counts of deaths are compared to an av-
erage number of deaths for previous years. In this report, data for
2021 is compared to an average number of deaths recorded over
the 5 years from 2015-2019 as was the case in previous publi-
cations. Data for 2022 is compared to a baseline comprising the
years 2017-2019 and 2021. 2020 is not included in the baseline
Volume 8 | Issue 2 | 545
J Clin Exp Immunol, 2023
for 2022 data because it included periods where numbers of
deaths were signicantly lower than expected.
Emphasis added. Note that the arbitrary exclusion of 2020, a
year where “numbers of deaths were signicantly lower than ex-
pected”, raises the baseline and therefore lowers excess mortali-
ty statistics for 2021 and 2022, creating a misleading impression
of a less serious pandemic.
The ve-year averages of 2015 to 2019 are used uniformly as the
baseline throughout this study to assess the impact of COVID-19
on Australian mortality. Therefore, our excess mortality sta-
tistics for 2022 are dierent from ocial ABS statistics. Even
though the dierences are not great, a consistent baseline is used
throughout in this paper for sake of scientic clarity.
The annual excess mortality for Australia from 2015 to the pres-
ent is shown in Figure 2.
The annual excess mortality for 2020 was well within the
range of normal statistical uctuations and therefore validates
the proposition that there was no pandemic in Australia, even
though there were about 900 COVID-19 deaths (usually revised
lower by the ABS over time) in 2020.
Clearly, dramatic rises in excess deaths have occurred since
2021, with the last bar (in Figure 2) being an annual estimate
based on nine months of actual data. Relative to excess mortality
in 2020, 2021 was nearly 7-fold and 2022 is already over 14-fold
and potentially more than 19-fold. The data on excess mortali-
ty also validates that the Australian pandemic phase started in
2021, with the 2021 and 2022 total excess death toll likely to
reach over 41,000 or 26 times that of 2020.
Clearly, the demarcation between pre-pandemic phase in 2020
and the pandemic phase since 2021 is the “elephant in the room”
– mass COVID-19 injections for most of the Australian popula-
tion. To study their relationship to excess mortality, raw data on
total national doses of COVID-injections administered over time
have been obtained from a third-party data aggregator CovidBa-
seAU [11], which also supplies data to international data provid-
ers such as Our World in Data. The data is shown in Figure 3:
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J Clin Exp Immunol, 2023
Over 64 million doses have been administered to a population of
25.8 million. The two peaks of mass COVID injections occurred
in September 2021 for the initial drive and in January 2022 for
the rst booster drive. These drives will be seen below to be cor-
related to peaks in excess deaths about ve months later.
The above raw data in Figure 1-3, which are largely free from
data manipulation, are the main sources from which data analy-
sis is performed transparently in the rest of this paper to investi-
gate the iatrogenesis of the Australian COVID pandemic.
The Pre-pandemic Phase
The iatrogenic hypothesis of the Australian pandemic depends
necessarily on objective evidence that there was no signicant
excess mortality before government intervention with mass
COVID-19 injections. The evidence is already apparent in Fig-
ure 2 above, where all-cause mortality in 2020 was well within
normal expectations.
While there was no pandemic in 2020, could the 900 COVID-19
deaths recorded in 2020 presage a pandemic to develop from the
novel coronavirus? A seasonality analysis with Australian mor-
tality data raises serious doubt about how “novel” is the SARS-
CoV-2 virus in Australia. Its closest relative, the 2003 SARS
(now called SARS-CoV-1) was declared an “outbreak”, not even
a pandemic. Respiratory viruses mutate relatively frequently; so
when is a mutation “novel”? COVID-19 viruses had many vari-
ants; why are they not novel "viruses?"
Respiratory diseases are seasonal, with most dying in late win-
ter, which are the months of August and September in the south-
ern hemisphere, when respiratory diseases commonly strike near
the end stages of life. The typical pattern of seasonality is shown
by the blue bars in Figure 4, based on ve-year averages from
2015 to 2019.
By comparison, 2020 was a very odd year, when deaths from
inuenza and pneumonia (red bars in Figure 4) substantially dis-
appeared for several months around their normal peaks in late
winter. The correlation between normal uctuations and 2020
uctuations was negative, at -20%, indicating a signicant sea-
sonal anomaly.
However, COVID-19 is a respiratory disease, with similar symp-
toms to inuenza and pneumonia (I&P) and there were surges in
supposedly COVID deaths around August in 2020, particularly
in Victoria. If the deaths of I&P and COVID-19 are added to-
gether, then the comparison to normally expected seasonality is
shown in Figure 5.
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J Clin Exp Immunol, 2023
In the I&P comparison in Figure 5, the green bars including
COVID deaths are now consistent with the blue bars represent-
ing the expected seasonality pattern of previous years, with a
positive correlation of +70%.
In view of the poorly dened characteristics of COVID-19 in-
fection and the subjective attribution of COVID-19 deaths as
discussed in section 2, there is a strong possibility that COVID
deaths may have been substantially misclassied from I&P
deaths.
The likelihood is very high that COVID deaths were misclas-
sied from I&P deaths, because I&P deaths are themselves not
clinically well-dened [12], as evident in Table 1.
Table 1
CHAPTER X Diseases of the respiratory system (J00-J99) 2019 2020
Diseases of the respiratory system (J00-J99) 15,330 12,721
Inuenza and pneumonia (J09-J18) 3,855 2,287
Inuenza, virus not identied (J11) 249 7
Pneumonia, organism unspecied (J18) 2,721 2,157
Table 1 is a very small and partial extract from an extensive ABS
data table listing detailed causes of doctor-certied deaths for
2019 and 2020 in Australia [12]. Note the codes in the brack-
ets indicate categories and sub-categories (indented). In 2019,
there were 3,855 deaths from inuenza and pneumonia of which
2,970 deaths (77%) had no pathogen identied.
Note that this paper makes no assertion about whether the
COVID-19 virus or disease exists or otherwise. The evidence
suggests that COVID-19 symptoms and diagnosis are so impre-
cise and so much like cases of I&P that they may have been
easily misclassied, as discussed in section 2.
Importantly, there are strong nancial incentives for hospitals to
re-classify I&P patients as COVID-19 patients, because the Aus-
tralian Government had provided $4.8 billion for COVID-19
pandemic response, stating [13]: “The full resources of our
world-class health system – a blend of public and private sys-
tems – are needed to focus on treating COVID-19 patients”, in-
dicating more COVID-19 patients would mean more funding to
hospitals.
Finally, the narrative that Australian public health measures such
as masking and lockdowns were responsible for reducing excess
deaths 2020, has little credibility, for several reasons. Firstly, it
was against the recommendations of the global pandemic pre-
paredness exercise conducted in 2019 Event 201, which not only
did not recommend lockdowns, but instead recommended open
borders [14]:
Countries, international organizations, and global transportation
companies should work together to maintain travel and trade
during severe pandemics. Travel and trade are essential to the
global economy as well as to national and even local economies,
and they should be maintained even in the face of a pandemic.
Emphasis added. Also, tens of thousands of highly credentialed
medical researchers and doctors have signed The Great Bar-
rington Declaration [15] recommending against masking and
lockdowns, in favour of “focused protection”. Overall, large
amounts of research [16] have shown that there is no clear evi-
dence that masking and lockdowns are eective, with countries
such as Sweden ignoring such measures, performing overall
Volume 8 | Issue 2 | 548
J Clin Exp Immunol, 2023
none the worse compared with other countries. If those public
health measures were so good, why do governments even need
“vaccines”?
In summary, on statistics alone, there was no clear evidence of a
new deadly coronavirus in Australia in 2020. Regardless of the
precise nature or cause of COVID deaths, their impact on excess
mortality in 2020 was insucient to characterize that year as a
pandemic.
The Pandemic Phase
The pandemic phase in Australia began in 2021 with rising all-
cause mortality and excess mortality (see Figure 1 and Figure 2).
Also, beginning in 2021 was the start of mass COVID-19 injec-
tions, which governments called “safe and eective vaccines”,
for a pandemic just shown non-existent in 2020.
The coincidental increases in excess mortality and doses of in-
jections administered (see Figure 3) are investigated here for
possible iatrogenic causality. Essentially, the raw data shown in
Figures 1-3 are reassembled into a new dataset to reveal the re-
lationship between excess deaths and COVID injections as seen
in Figure 6
Page 10 of 22
shown that there is no clear evidence that masking and lockdowns are effective, with countries
such as Sweden ignoring such measures, performing overall none the worse compared with
other countries. If those public health measures were so good, why do governments even need
“vaccines”?
In summary, on statistics alone, there was no clear evidence of a new deadly coronavirus in
Australia in 2020. Regardless of the precise nature or cause of COVID deaths, their impact on
excess mortality in 2020 was insufficient to characterize that year as a pandemic.
5. The Pandemic Phase
The pandemic phase in Australia began in 2021 with rising all-cause mortality and excess
mortality (see Figure 1 and Figure 2). Also, beginning in 2021 was the start of mass COVID-
19 injections, which governments called “safe and effective vaccines”, for a pandemic just
shown non-existent in 2020.
The coincidental increases in excess mortality and doses of injections administered (see Figure
3) are investigated here for possible iatrogenic causality. Essentially, the raw data shown in
Figures 1-3 are reassembled into a new dataset to reveal the relationship between excess deaths
and COVID injections as seen in Figure 6
Figure 6
Overall, there was a negative correlation of -17% between monthly doses of injections and
monthly excess mortality, with best evidence of correlation occurring in January 2022 and
some evidence of correlation in the first half of 2021, when mass injections started.
Contemporaneous correlation should not be expected because there is normally a time-lag
between medication (the cause) and its effects, as will be shown below.
Figure 6
Overall, there was a negative correlation of -17% between
monthly doses of injections and monthly excess mortality, with
best evidence of correlation occurring in January 2022 and some
evidence of correlation in the rst half of 2021, when mass in-
jections started. Contemporaneous correlation should not be ex-
pected because there is normally a time-lag between medication
(the cause) and its eects, as will be shown below.
However, the close correlations observed in some periods sug-
gest the existence of immediate impact of the injections on mor-
tality probably due to anaphylaxis or other pre-conditions as
reported in OpenVAERS in the USA [17]. There may be more
than just a concurrent correlation between mass injection drives
and deaths, which have been discussed in a previous paper [9].
The small peak in excess deaths in the rst half of 2021, when
COVID deaths were largely absent per ABS data [10], has been
attributed to non-COVID deaths, as seen as the rst peak in Fig-
ure 7.
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J Clin Exp Immunol, 2023
As mass injections were rolled out in 2021, there was a surge
in deaths of the elderly, particularly those in the 85+ age group.
Those who were already frail with chronic inammation and nu-
merous comorbidities easily succumbed to the extra challenge
presented by the injections. Whether they had COVID infec-
tions or not, it was probably not unreasonable to assume they
died from pre-conditions, even though an attribution to COVID
deaths would have been inconvenient to the narrative of “vac-
cine protection”.
The excess death peak in January 2022 may be due to the com-
bined eects of both the initial doses of injection in September
2021 and the subsequent boosters in January 2022 due to the
phenomenon of “pathogenic priming” [18]. That is, there may
be a combination of both a concurrent eect of fatal inamma-
tion and a lagged eect of immune suppression, to be discussed
below.
That is, the initial doses of injection may have weakened the
immune system of the recipients to make them more vulnerable
to subsequent challenges introduced e.g. by the boosters, a phe-
nomenon also known as “antibody dependent enhancement” of
disease [19, 20]. Indeed, if the data for total monthly doses were
time shifted forward by ve months, the two datasets (as Figure
6) now overlap well in Figure 8.
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J Clin Exp Immunol, 2023
The rapid rise in excess mortality in January 2022, which coin-
cided with the rst booster campaign, was correlated with the
peak rate of COVID-19 injection, which occurred in September
2021. A secondary injection peak from the rst booster cam-
paign was correlated another ve months later with a secondary
peak in excess mortality in July 2022.
The maximum correlation between COVID-19 injections and
excess deaths at +74% occurs for a ve-month lag. From anal-
ysis, the correlation for a four-month lag is 61%, while for a
six-month lag it is 64%. Therefore, the evidence suggests that
the ve-month lagged eect on excess mortality is stronger than
the concurrent eect or other lagged eects due to the COVID
injections.
The ve-month lag has been observed briey in US and UK
datasets, but has not been supported by more detailed investiga-
tions, as is being done here.
Metaphorically, the high correlation in January 2022 between
the booster injections and deaths is likely to be the result of the
second of a “one-two knockout punch”, where the rst punch
did the most damage ve months earlier by immune suppres-
sion (see discussion below) and then by the second punch of the
boosters which quickly delivered the “coup de grace” to their
victims.
As an example, New South Wales data show [8] that the two-
dose population was dying at a rapid rate of several hundred per
week during the rst booster campaign in January 2022, while
very few deaths were recorded from the boosters. The boosters
were lethal to some of the immune-suppressed two-dose pop-
ulation, but those deaths were wrongly registered as two-dose
deaths due to a awed data reporting convention [9], where in-
jections were recognized only after weeks of delay.
Those who survived the rst boosters would have had their im-
mune system further weakened making them susceptible to viral
infections and harm of the second boosters, which contributed
later to the second peak in excess mortality in July 2022. The
more injections anyone takes the more likely they will sustain
iatrogenic injuries and death. Many Australians have learned
from their actual experience, ignored ocial advice, and have
become more hesitant of repeated injections.
Fortunately, due to falling rates of COVID-19 injections since
July 2022, the empirical evidence may be predicting good news
for lower rates of excess mortality (with data to be released) for
the rest of 2022 per the injection data. Except for a blip in Jan-
uary 2023, excess mortality should continue to fall, as presaged
by the tail-end of the green curve in Figure 8. The prediction has
been conrmed by the data just released [10] for October and
November 2022, after the completion of the research for this
paper.
The data also suggest that the naïve proportional estimate of ex-
cess deaths for the whole of 2022 in Figure 2 is likely to be an
over-estimate because of rapidly falling rates of injection ve
months earlier. The trend of falling excess mortality should con-
tinue, unless ocial advice succeeds in persuading the public to
accept more boosters, which would be the fth dose for many.
The stronger correlation and temporality with the ve-month lag
satisfy two of the main criteria of Bradford Hill causality [4],
which are the “strength” of high correlation and “temporality”
satised by a regular ve-month lag of the excess mortality ef-
fect following the COVID-19 injection cause.
Another important Bradford Hill criterion is “biological gradi-
ent” in medicine, which is the existence of an expected, mono-
tonic dose-response relationship, i.e. higher doses should lead to
stronger responses. This criterion is met statistically in Figure
8, where excess mortality rises and falls with doses adminis-
tered. The dose-response relationship can be made mathemati-
cally more precise by an ordinary-least-squares (OLS) regres-
sion which is statistically signicant with a p-value of 0.0015 as
shown in Figure 9.
Page 13 of 22
Fortunately, due to falling rates of COVID-19 injections since July 2022, the empirical
evidence may be predicting good news for lower rates of excess mortality (with data to be
released) for the rest of 2022 per the injection data. Except for a blip in January 2023, excess
mortality should continue to fall, as presaged by the tail-end of the green curve in Figure 8. The
prediction has been confirmed by the data just released [10] for October and November 2022,
after the completion of the research for this paper.
The data also suggest that the naïve proportional estimate of excess deaths for the whole of
2022 in Figure 2 is likely to be an over-estimate because of rapidly falling rates of injection
five months earlier. The trend of falling excess mortality should continue, unless official advice
succeeds in persuading the public to accept more boosters, which would be the fifth dose for
many.
The stronger correlation and temporality with the five-month lag satisfy two of the main
criteria of Bradford Hill causality [4], which are the “strength” of high correlation and
“temporality” satisfied by a regular five-month lag of the excess mortality effect following the
COVID-19 injection cause.
Another important Bradford Hill criterion is “biological gradient” in medicine, which is the
existence of an expected, monotonic dose-response relationship, i.e. higher doses should lead
to stronger responses. This criterion is met statistically in Figure 8, where excess mortality
rises and falls with doses administered. The dose-response relationship can be made
mathematically more precise by an ordinary-least-squares (OLS) regression which is
statistically significant with a p-value of 0.0015 as shown in Figure 9.
Figure 9
On average, the above dose-response relationship suggests, for example, that five million doses
administered in a month nationally would lead on average to 2,221 excess deaths five months
later, with a standard deviation of 705 excess deaths or a likely range between 1,516 and 2,926.
Figure 9
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J Clin Exp Immunol, 2023
On average, the above dose-response relationship suggests, for
example, that ve million doses administered in a month nation-
ally would lead on average to 2,221 excess deaths ve months
later, with a standard deviation of 705 excess deaths or a likely
range between 1,516 and 2,926.
In summary, in meeting three main Bradford Hill criteria for
causality a strong case, based on statistical data alone, has been
made for the iatrogenesis of excess mortality in the Australian
COVID-19 pandemic.
Consistency and Specicity
As shown above, Australian data have displayed consistency in
causal associations over time. Are similar associations observed
in other places under similar conditions? Consistency is anoth-
er criterion which Bradford Hill [4] thought was important to
consider.
International comparisons of the relationship between
COVID-19 injections and excess mortality are made dicult by
heterogeneity of the data. Some countries, such as those in Afri-
ca, have largely avoided mass injections, while other countries,
such as those in the pacic islands as well as Africa, have irreg-
ular excess mortality statistics. Even for those countries which
have data both on doses of injection and on excess mortality,
some countries report weekly, while others report monthly and
their reporting dates and periods of available data are typically
dierent.
From Our World in Data [22], there are about two dozen coun-
tries, including most of the large developed countries, which
have comparable abundance of data to perform a cross-sectional
analysis. The level of COVID injection for any country is taken
to be the latest reported total doses administered per hundred of
the population. The average monthly excess mortality is calcu-
lated from the increase in cumulative excess mortality per mil-
lion between the earliest injection start date and the latest report
date, which vary between countries.
While the international dataset is far from complete and the data
of selected countries with sucient quantity, are likely incon-
sistent in quality, a positive dose-response relationship appears
discernible across the selected countries as shown in Figure 10.
Country colours refer to their continents. So far in 2022 the Aus-
tralian excess mortality per million population is about double
that of the United Kingdom, but Australians are more highly
“vaccinated”. Australia with its higher dosage also leads its US,
UK and Canadian partners in excess deaths. A clear dose-re-
sponse relationship appears mildly consistent at +31% correla-
tion across 23 countries.
Another useful criterion of Bradford Hill causality is “specic-
ity”, which is related to the question whether there are compet-
ing causes for the excess deaths, with similar strengths of as-
sociation. Note that specicity is not a necessary criterion, but
one which, if satised, helps to draw conclusions for the most
probable cause. Is iatrogenesis the strongest and most specic
explanation for the observed excess mortality?
Bradford Hill [4] gave the example of smoking causing lung
cancer, which has potentially many possible causes, but smok-
ers have statistically signicant higher incidences of lung cancer
than non-smokers. Therefore, smoking is an important specic
cause of lung cancer. The close association between COVID in-
jections and excess deaths shown above suggests a similar argu-
ment prevails for iatrogenesis.
Classication bias has resulted in awed data reported by the
health authorities, which have misled the public to believe most
excess deaths are from the “unvaccinated” [8, 9]. As mentioned
above, the national broadcaster ABC wrongly stated on prime-
time television that the “unvaccinated” are 16-37 times more
likely to die than the “doubly vaccinated”.
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J Clin Exp Immunol, 2023
Australian adults (age 16+) nearly all (97.5%) have had at least
one dose of the injection [11]. Is it likely that the remaining 2.5%
of the adult population are responsible for most of the excess
deaths?
New South Wales Health has COVID death data segregated by
“vaccination status” which is dened by the number of doses [8,
9]. The data permit the “unvaccinated” to be properly dened as
those without any injections. The data show that, by mid-2022,
the “vaccinated” had about double the COVID mortality risk
compared to the “unvaccinated”, as seen in Figure 11.
Page 15 of 22
Country colours refer to their continents. So far in 2022 the Australian excess mortality per
million population is about double that of the United Kingdom, but Australians are more highly
“vaccinated”. Australia with its higher dosage also leads its AUKUS and Canadian partners in
excess deaths. A clear dose-response relationship appears mildly consistent at +31%
correlation across 23 countries.
Another useful criterion of Bradford Hill causality is “specificity”, which is related to the
question whether there are competing causes for the excess deaths, with similar strengths of
association. Note that specificity is not a necessary criterion, but one which, if satisfied, helps
to draw conclusions for the most probable cause. Is iatrogenesis the strongest and most specific
explanation for the observed excess mortality?
Bradford Hill [4] gave the example of smoking causing lung cancer, which has potentially
many possible causes, but smokers have statistically significant higher incidences of lung
cancer than non-smokers. Therefore, smoking is an important specific cause of lung cancer.
The close association between COVID injections and excess deaths shown above suggests a
similar argument prevails for iatrogenesis.
Classification bias has resulted in flawed data reported by the health authorities, which have
misled the public to believe most excess deaths are from the “unvaccinated” [8, 9]. As
mentioned above, the national broadcaster ABC wrongly stated on prime-time television that
the “unvaccinated” are 16-37 times more likely to die than the “doubly vaccinated”.
Australian adults (age 16+) nearly all (97.5%) have had at least one dose of the injection [11].
Is it likely that the remaining 2.5% of the adult population are responsible for most of the
excess deaths?
New South Wales Health has COVID death data segregated by “vaccination status” which is
defined by the number of doses [8, 9]. The data permit the “unvaccinated” to be properly
defined as those without any injections. The data show that, by mid 2022, the “vaccinated” had
about double the COVID mortality risk compared to the “unvaccinated”, as seen in Figure 11.
Figure 11
Figure 11
This COVID injection enhancement of COVID deaths extends
to excess mortality and as fth dose or the third booster rolls out
across Australia from March 2023, excess mortality is expected
to remain elevated. As Bradford Hill noted [4], this is a “speci-
city in the magnitude of association”.
Coherence and Plausibility
On Bradford Hill’s coherence and plausibility, the suggestion of
iatrogenic origin of excess deaths following ve months after
COVID injections does not contradict any research on “vaccine
safety”. The clinical trials conducted were much shorter than
ve months. For example, the Pzer BNT162b2 trial [23] was
between 27 July 2020 and 14 November 2020, with a data cut-
o date 9 October 2020.
That is, the Pzer trial data analysed were conducted over eleven
weeks or 77 days, about half of the time necessary for fatalities
to occur per the above empirical ndings, so the suggested iat-
rogenesis is coherent and not in conict with any known facts.
Are there any plausible biological mechanisms which could ex-
plain the causal impact of COVID-19 injections on the excess
mortality of the young and healthy? In the past three years, there
has been a deluge of research published on how the spike pro-
tein, either from the assumed SARS-CoV-2 virus infection or
generated from the mRNA injections, could lead to inamma-
tion in various organs causing death.
Most of the proposed mechanisms are evidential, plausible and
coherent with existing knowledge on the cutting-edge of med-
ical research. However, the “speed of science” requires many
more years of replication and validation of the research to sort
out the best explanations for the ever-accumulating evidence. It
is beyond our knowledge or the scope of this paper to comment
on the vast literature, except to mention some research ndings
which may be relevant to the statistical observations presented
in this paper.
Most theories of the iatrogenesis of the COVID injections re-
volve around mechanisms for how the spike protein can cause a
suppression of the immune system called “pathogenic priming”
[18] or “antibody dependent enhancement” [19, 20]. Essentially,
after repeated infections or mRNA injections, the body adjusts
to the pathogen or similar ones, by down-regulating the immune
system.
In a recently published clinical study [21] of the mRNA injec-
tions, production of neutralizing IgG3 antibodies against the
spike protein was observed to switch over time to the production
of non-neutralizing IgG4 antibodies. Thus, the class switching
may reduce the rate of clearance of the toxic spike protein which
may accumulate sucient titers to cause pathogenesis and mor-
tality.
The ve-month lag between injections and mortality found in
this paper may be related to the switching time between the
classes of antibodies, which was not the focus of the cited clin-
ical study, but it provides some useful indications. The levels of
IgG antibodies were measured 10 days and 210 days after the
second mRNA dose.
Class switching did not occur at 10 days, but was observed at
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J Clin Exp Immunol, 2023
210 days, which suggests that it is a relatively slow process [21].
However, some cases of breakthrough infection 70 days after the
second dose suggest the immuno-suppression eect may already
occur meaningfully much earlier.
The recommended interval between the rst and second dose of
mRNA injections in Australia is between 8 to 12 weeks. If the
antibody class switching mechanism were responsible for the
excess deaths ve months later, then the mechanism would sup-
press immunity signicantly after about 100 days. In summary,
the class switching to IgG4 antibodies is a plausible, but not a
proven, mechanism to explain the observed immune suppres-
sion of COVID-19 injections, a mechanism worthy of further
research.
Experiment and Analogy
By “experiment”, Bradford Hill [4] refers to any laboratory (in
vitro) or clinical (in vivo) evidence to support the epidemiolog-
ical association between cause and eect. In the current context
of causes of excess mortality, “experiment” should be taken to
mean post-mortems and autopsies to show the connection be-
tween COVID injections and deaths.
Australian governments have deliberately discouraged such
“experiments” because they may lead to ndings which cause
“vaccine hesitancy”. For example, Australian doctors have been
threatened with nes of up to $20,000 for using serological tests
to verify the results of the PCR tests for COVID-19 diagnosis
[24]. Nevertheless, the scientic imperative is strong enough to
have led to several post-mortem studies [26-29] to discover the
“smoking gun” evidence of spike proteins from COVID injec-
tions.
The SARS-Cov-2 virus is dened by a full genome sequence
published by the Wuhan Institute of Virology [25]. Without any
claim having been independently validated, no virus has ever
been isolated from COVID-19 patients which matches exactly
the genome sequence, nor has the spike protein from infections
been exactly matched to that of the SARS-CoV-2 virus. The
messenger RNA which is synthesized and manufactured to go
inside the lipid nanoparticles (LNP) of the mRNA injections, is
presumably conformal to the relevant part of the published se-
quence.
The spike proteins found in tissues from autopsies may originate,
a priori, from infections and/or from injections. In view of how
the PCR test was developed, as discussed in the introduction of
this paper, without genetic analysis, the spike proteins from a
COVID infected person may have come from an inuenza virus,
which diers from a coronavirus mainly in having a segmented,
rather than continuous, genome.
If COVID injections suppress the immune system and hinder
the clearance of the pathogenic spike proteins and indeed, man-
ufacture even more spike proteins by the body’s own cells, then
post-mortems and autopsies should provide the evidence from
signicant quantities of spike proteins.
Indeed, from autopsies, the absence of the nucleocapsid IgG/IgM
and their characteristic morphological features of COVID-19
is the indicator of mRNA injection origin of the spike proteins
[26-29]. The observed time lags after injections of deaths oc-
curring within days to several months are consistent with the
combination of a short-term causality and a long-term causality
discussed above.
The autopsy experiments, where COVID morphologies are
absent, without viral nucleocapsid protein and the antibodies
associated with them, have largely deprecated the explanation
that the COVID disease or “long COVID” is the cause of those
deaths. The young have often died suddenly from myocarditis
and pericarditis, on the sporting elds or in their sleep, after
mRNA injections, but without any signs of infections [29].
An analogy to the current COVID-19 pandemic is the 2009
“Swine u” pandemic due to the H1N1 inuenza virus. Then, as
now, the pandemic was called, based not on fact, but on expec-
tations of a highly infectious and very deadly disease projected
by the Oxford computer models. The main dierence is that the
2009 “pandemic” was never allowed to be transformed to an
iatrogenic pandemic and it quickly died out on its own accord,
amounting ultimately to a weaker form of the seasonal inuenza.
The episode had more cases worldwide, but fewer deaths (about
18,000) and a much lower case fatality rate than a seasonal u
[30]. On an excess mortality denition, the 2009 “Swine u”
season was not a pandemic.
The main dierence between then and now is that mass “vacci-
nation” did not play a signicant role in 2009, thus avoiding an
iatrogenic pandemic, as now. In 2009, production of “vaccines”
and their injections into the population were not fast enough or
widespread enough before the “Swine u” infections died out on
their own accord.
Between 2009 and 2020, governments were “educated” for
“pandemic preparedness”, which meant preparation for legally
declared emergency measures, unimpeded by the “speed of sci-
ence”. For example, lockdowns were enforced everywhere with-
out scientic justication [16], which also had the eect of pre-
venting the development of herd immunity from isolation thus
prolonging the period of infection. In the extended time avail-
able, “vaccines” were developed under “Operation Warp Speed”
and rushed to the market, side-stepping standard procedures of
longer-term testing to ensure safety.
The analogy to the 2009 swine u is that the COVID-19 pan-
demic might not have continued or even existed (e.g. as the 2003
SARS outbreak), had there not been mass mRNA injections to
cause and perpetuate the COVID-19 pandemic.
Bradford Hill Analysis
Austin Bradford Hill suggested [4] his nine “viewpoints” or as-
pects to be considered for causality. He did not call them “cri-
teria”, which have been used in this paper for simplicity and
convenience. Bradford Hill refrained from calling them nine
criteria, because they are neither necessary nor sucient condi-
tions to make hard and fast decisions on causality. They are as-
pects to address when examining alternative causal hypotheses.
In science, the set of available facts at any time determines what
is the best explanation and Bradford Hill has suggested some
Volume 8 | Issue 2 | 554
J Clin Exp Immunol, 2023
objective aspects to help deciding on alternative explanations.
This paper has reported some highly signicant facts which may
not have been recognized yet. The signicant facts have come
from epidemiological data when they have been presented with-
out obfuscation by manipulation and classication, as in ocial
"health authority reports."
Previous sections of this paper have been devoted to addressing
Bradford Hill “criteria” for assessing the iatrogenic hypothesis
for Australian excess mortality since 2021. The analysis in pre-
vious sections is summarized in Table 2.
Table 2
Criterion Evidence Comment
1. Strength Section 4, Figure 8 Monthly correlation between doses of injections and excess
deaths at +74%
2. Consistency Section 4, Figure 9; Section 5, Figure 10 Strong correlations between injections and excess deaths
exist over time and across many countries.
3. Specicity Section 5, Figure 11 Iatrogenic excess deaths have few other competing expla-
nations, with the “vaccinated” having higher mortality risk
than the “unvaccinated”.
4. Temporality Section 4, Figures 6 & 8 Consistent ve-month lag of excess deaths following
COVID injections.
5. Biological gradient Section 4, Figure 9 Consistent dose-response relationship found in data.
6. Plausibility Section 6 Abundant research indicates the injections suppress immu-
nity. Antibody class switching from IgG3 to IgG4 leads to
non-neutralization of spike proteins.
7. Coherence Section 6 Neither the safety signals found here, nor the suggested
underlying pathology contradicts any existing facts.
8. Experiment Section 7 Autopsies show the pathology of spike proteins produced
explicitly by mRNA injections.
9. Analogy Section 7 Swine u 2009 petered out naturally without mass “vacci-
nation”.
The main contributions to existing knowledge of the Australian
COVID-19 pandemic are contained in sections 5 and 6, where
the rst ve Bradford Hill criteria are addressed. These criteria
are probably foremost because they apply equally to “hard
sciences” such as physics. Criteria 6 to 9 are reviewed in Sections
7 and 8 through existing literature, which can be seen to support
generally the iatrogenic hypothesis advanced in this paper.
On the basis that the Australian pandemic is iatrogenic which
caused the observed excess mortality, then it follows also that
harm, or risk of harm, outweighs any benet of the COVID
injections. This can be shown formally by the equation for
mortality risk and benet which is expressed as follows:
Lives lost (L) from side eects of injection
- Lives saved (S) from disease mitigation
= Excess Deaths (X)
or L-S = X. Excess deaths X are known to be large, but L and S
are unknown from the data. Since X >> 0, it follows that L-S >>
0 or L >> S, hence L/S >> 1. A mortality risk/benet ratio which
may be dened by L/S, is very high.
Therefore, due to the very large excess deaths following
Australia’s policy of mass COVID injections, lives lost far
exceed lives saved; the mortality risk/benet ratio is very high.
Further research is needed to quantify this ratio for health
authorities.
Conclusion
Australian health policy has been based on misinformation from
awed COVID-19 data which are scientically unsound. Based
on sound mortality data, the Australian COVID-19 pandemic did
not begin until the advent of mass mRNA injections in 2021. It is
ironic that mass injections which were introduced to mitigate a
non-existent pandemic, created a real iatrogenic pandemic. This
study, backed by a Bradford Hill analysis, has shown that more
injections administered to reduce the pandemic, had the opposite
eect of causing more excess deaths to increase the pandemic.
The very large excess deaths observed from the data imply that
the mortality risk/benet ratio from COVID injections is very
high. That is, the harm or risk realized has far outweighed any
benet from COVID injections.
This study has introduced a very simple, but robust, methodology,
which should be used by other countries, particularly those in
Figure 10 which appear to have adequate data, to replicate and
investigate the likely iatrogenic origins of their own pandemics.
Billions of lives in the world are at stake from the potential
ndings of the research.
References
1. World Health Organization. WHO Director-General’s
opening remarks at the media brieng on COVID-19 - 11
March 2020, News releases. Retrieved from: https://www.
who.int/director-general/speeches/detail/who-director-
general-s-opening-remarks-at-the-media-briefing-on-
Volume 8 | Issue 2 | 555
J Clin Exp Immunol, 2023
covid-19---11-march-2020 (accessed 23 January 2023)
2. World Health Organization. Pandemic Preparedness,
Communicable Disease Surveillance & Response (CSR),
2 February 2003. Retrieved from: http://web.archive.org/
web/20030202200410/http:/www.who.int/csr/disease/
inuenza/pandemic/en/index.html
3. Roos R. Pzer exec laughs when asked whether they knew
their product stopped transmission prior to release. Retrieved
from: https://www.youtube.com/watch?v=2YagXbprzFE
4. Hill, A. B. (1965). The environment and disease: association
or causation? Proceedings of the Royal Society of Medicine
(1965) 58, 295–300.
5. Centers for Disease Control and Prevention. CDC
2019-Novel Coronavirus (2019-nCoV) Real-Time RT-PCR
Diagnostic Panel, FDA News Release. Retrieved from:
https://www.fda.gov/media/134922/download
6. Centers for Disease Control and Prevention. 07/21/2021:
Lab Alert: Changes to CDC RT-PCR for SARS-CoV-2
Testing, CDC’s Laboratory Outreach Communication
System (LOCS). Retrieved from: https://www.cdc.gov/
locs/2021/07-21-2021-lab-alert-Changes_CDC_RT-PCR_
SARS-CoV-2_Testing_1.html
7. Ni, M., Xu, H., Luo, J., Liu, W., & Zhou, D. (2021).
Simultaneous detection and dierentiation of SARS-
CoV-2, inuenza A virus and inuenza B virus by one-
step quadruplex real-time RT-PCR in patients with clinical
manifestations. International Journal of Infectious Diseases,
103, 517-524.
8. Sy W. Mortality risk of COVID-19 injections: evidence from
New South Wales and England, Academia, 30 July 2022.
Retrieved from: https://www.academia.edu/83924771/
Mortality_risk_of_COVID_19_injections_evidence_from_
New_South_Wales_and_England
9. Sy W. Data reporting aw in plain sight distorting COVID-19
mortality statistics, Academia, 25 August 2022. Retrieved
from: https://www.academia.edu/85597731/Data_
reporting_flaw_in_plain_sight_distorting_COVID_19_
mortality_statistics
10. Australian Bureau of Statistics, Provisional Mortality
Statistics, Released 22/12/2022. Retrieved from: https://
www.abs.gov.au/statistics/health/causes-death/provisional-
mortality-statistics/jan-sep-2022
11. CovidbaseAU, Australia COVID-19 Vaccinations.
Retrieved from: https://covidbaseau.com/vaccinations/
12. Australian Bureau of Statistics, Causes of Death, Australia,
Released 24/06/2020 and 29/09/2021, Retrieved from:
https://www.abs.gov.au/statistics/health/causes-death/
causes-death-australia
13. Department of Health. Supporting our Hospitals –
COVID-19 pandemic response. Australian Government
Budget 2020-2021. Retrieved from: https://www.health.
gov.au/sites/default/les/documents/2020/10/budget-2020-
21-supporting-our-hospitals-covid-19-pandemic-response.
pdf
14. Event 201, Public-private cooperation for pandemic
preparedness and response, Recommendations. Retrieved
from: https://www.centerforhealthsecurity.org/our-work/
exercises/event201/recommendations.html
15. Kulldor M, Gupta S and Bhattacharya J, The Great
Barrington Declaration. Retrieved from: https://
gbdeclaration.org/#read
16. Herby J, Jonung L and Hanke S, A Literature Review
and Meta-Analysis of the Eects of Lockdowns on
COVID-19 Mortality, Johns Hopkins Institute for Applied
Economics, Global Health, and the Study of Business
Enterprise. Retrieved from: https://sites.krieger.jhu.edu/iae/
les/2022/01/A-Literature-Review-and-Meta-Analysis-of-
the-Eects-of-Lockdowns-on-COVID-19-Mortality.pdf
17. OpenVAERS, VAERS COVID Vaccine Mortality Reports.
Retrieved from: https://www.openvaers.com/covid-data/
mortality
18. Lyons-Weiler, J. (2020). Pathogenic priming likely
contributes to serious and critical illness and mortality in
COVID-19 via autoimmunity. Journal of translational
autoimmunity, 3, 100051.
19. Wen, J., Cheng, Y., Ling, R., Dai, Y., Huang, B., Huang, W.,
... & Jiang, Y. (2020). Antibody-dependent enhancement of
coronavirus. International Journal of Infectious Diseases,
100, 483-489.
20. Ricke, D. O. (2021). Two dierent antibody-dependent
enhancement (ADE) risks for SARS-CoV-2 antibodies.
Frontiers in immunology, 443.
21. Irrgang, P., Gerling, J., Kocher, K., Lapuente, D., Steininger,
P., Habenicht, K., ... & Tenbusch, M. (2022). Class switch
towards non-inammatory, spike-specic IgG4 antibodies
after repeated SARS-CoV-2 mRNA vaccination. Science
immunology, eade2798. (2023) 27 January 2023.
22. Mathieu, E., Ritchie, H., Rodés-Guirao, L., Appel,
C., Giattino, C., Hasell, J., ... & Roser, M. (2020).
Coronavirus pandemic (COVID-19). Our World in Data.
OurWorldInData.org. Retrieved from: https://github.com/
owid/covid-19-data/tree/master/public/data
23. Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J.,
Gurtman, A., Lockhart, S., ... & Gruber, W. C. (2020). Safety
and ecacy of the BNT162b2 mRNA Covid-19 vaccine.
New England journal of medicine, 383(27), 2603-2615.
24. Calaore S, GPs face $20k nes for using serology tests
to diagnose coronavirus, AUSTRALIAN DOCTOR
NEWS, 15/04/2020. Retrieved from: https://www.rcpa.
edu.au/Library/COVID-19-Updates/COVID-19-Useful-
Resources/Docs/GPs-face-$20k-nes-for-using-serology-
tests-to-di.aspx
25. Wu F, Zhao S et al. Severe acute respiratory syndrome
coronavirus 2 isolate Wuhan-Hu-1, complete genome,
National Library of Medicine, NCBI Reference Sequence:
NC_045512.2. Retrieved from: https://www.ncbi.nlm.nih.
gov/nuccore/NC_045512
26. Hansen, T., Titze, U., Kulamadayil-Heidenreich, N. S.
A., Glombitza, S., Tebbe, J. J., Röcken, C., ... & Wilkens,
L. (2021). First case of postmortem study in a patient
vaccinated against SARS-CoV-2. International Journal of
Infectious Diseases, 107, 172-175.
27. Hirschbühl, K., Schaller, T., Märkl, B., Claus, R., Sipos,
E., Rentschler, L., ... & Wylezich, C. (2022). High viral
loads: what drives fatal cases of COVID-19 in vaccinees?–
an autopsy study. Modern Pathology, 35(8), 1013-1021
. Publications of Doctors for COVID Ethics, August 19,
2022. Retrieved from: https://doctors4covidethics.org/
Volume 8 | Issue 2 | 556
J Clin Exp Immunol, 2023
Copyright: ©2023 Wilson Sy. is is an open-access article distributed
under the terms of the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium,
provided the original author and source are credited.
vascular-and-organ-damage-induced-by-mrna-vaccines-
irrefutable-proof-of-causality/
28. Palmer, M., & Bhakdi, S. (2022). Vascular and organ
damage induced by mRNA vaccines: Irrefutable Proof of
sality. "Publications of Doctors for COVID Ethics, August
19, 2022. Retrieved from: https://doctors4covidethics.org/
vascular-and-organ-damage-induced-by-mrna-vaccines-
irrefutable-proof-of-causality/"
29. Schwab, C., Domke, L. M., Hartmann, L., Stenzinger, A.,
Longerich, T., & Schirmacher, P. (2022). Autopsy-based
histopathological characterization of myocarditis after anti-
SARS-CoV-2-vaccination. Clinical Research in Cardiology,
1-10.
30. Klemm, C., Das, E., & Hartmann, T. (2016). Swine u and
hype: a systematic review of media dramatization of the
H1N1 inuenza pandemic. Journal of Risk Research, 19(1),
1-20.
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