(Below find a partial copy of the research paper in the above link. Click on the link for the entire report — mt)
Australian COVID-19 pandemic: A Bradford Hill
analysis of iatrogenic excess mortality
Australian official mortality data show no clear evidence of significant excess deaths in 2020, implying from an older WHO definition that there was no COVID-19 pandemic. A seasonality analysis suggests that COVID-19 deaths in 2020 were likely misclassifications of influenza and pneumonia deaths. Australian excess mortality became significant only since 2021 when the level was high enough to justify calling a pandemic. Significant excess mortality was strongly correlated (+74%) with COVID-19 mass injections five months earlier. Strength of correlation, consistency, specificity, temporality, and dose-response relationship are foremost Bradford Hill criteria which are satisfied 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/benefit ratio for COVID
injections is very high.
On 11 March 2020, the World Health Organization (WHO) declared  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 defined for an influenza pandemic :
An influenza pandemic occurs when a new influenza virus appears against which the
human population has no immunity, resulting in several, simultaneous epidemics
worldwide with enormous numbers of deaths and illness.
A pandemic should be justifiably declared only if there are “enormous
numbers of deaths”, for otherwise seasonal influenza 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 defined by the PCR tests may be
grossly inflated (see section 2).
By assuming “cases” would lead to “enormous deaths”, the WHO declared a pandemic based on supposition, not on scientific fact. The presumption of sound science by governments has
Page 2 of 22
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 Australia. 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 adequately proven. That is, the pandemic cannot be accurately assessed from COVID-19 data which are scientifically flawed, see discussed below. This paper assesses the COVID-19 pandemic in Australia based on allcause mortality data, consistent with the earlier WHO definition 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 insufficient to declare a pandemic in Australia. Details and possible explanations are discussed in section 4, to justify calling 2020 as the “pre-pandemic” phase.
Australian excess deaths began to rise to a statistically significant level in 2021 to warrant the appellation of a “pandemic”. Early increases in excess deaths occurred concomitantly with the early rollout of mass COVID-19 injections. The injections were called “vaccines”, but they do not prevent infections, nor were they tested to inoculate against infections, as admitted recently by Pfizer to the European Parliament .
This paper rejects calling the COVID-19 injections “vaccines” which were never tested to be
such. The public has been misinformed and misled to accept COVID-19 injections as
“vaccines”. When the injections clearly failed to reduce transmissions, the rhetoric of
“vaccine” benefit changed to reducing serious illnesses and deaths. This claim is also proved false in this paper, where the pandemic phase defined 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 five months later suggest iatrogenic causality. This possibility is further strengthened by aspects of consistency and specificity in section 6 where the evidence of causality is seen by consistency across time and geography. Also, specificity is evident from the fact that the “vaccinated” are more likely to die than the “unvaccinated”, who are simply defined as those without any injections, rather official definitions where the “unvaccinated” may have had injections.
The main contributions of this paper, addressed in sections 5 and 6, are what we consider the five foremost criteria of Bradford Hill  causality for an iatrogenic pandemic. The remaining four aspects of Bradford Hill analysis are briefly 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 epidemiology 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 flu” pandemic in 2009, except that the 2009 episode was not a pandemic, which was without “mass vaccination”.
Page 3 of 22
Section 9 contains a summary of preceding sections, with a tabulated synopsis of all nine
Bradford Hill criteria discussed. The final section concludes that a strong case has been
presented for the iatrogenic origins of the Australian COVID-19 pandemic.
2. COVID-19 Data
This section explains why the Australian COVID-19 pandemic cannot be accurately assessed from COVID-19 data, because COVID-19 cases and infection are poorly defined. Therefore, COVID-19 data are scientifically flawed, but nevertheless they drove and continue to drive erroneous health policies.
A COVID infection has no definitive set of symptoms and was not detected by the presence of the SARS-Cov-2 virus, but was defined by a positive PCR test. However, a positive PCR test does not detect the presence of the SARS-Cov-2 virus which is the definitive pathogen of the COVID-19 disease. The CDC has explicitly made clear the following disclaimer :
Since no quantified virus isolates of the 2019-nCoV were available 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 diluent 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.
Deficiency of the PCR test has been acknowledged by the CDC in mid 2021 when it issued a
“Lab Alert”  to plan a withdrawal 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 first
introduced in February 2020 for detection of SARS-CoV-2 only.
CDC encourages laboratories to consider adoption of a multiplexed method that can
facilitate detection and differentiation of SARS-CoV-2 and influenza viruses.
Emphasis added. From 2022, instead of the PCR test which cannot differentiate between
SARS-Cov-2 and influenza viruses, the CDC has suggested the use of a multiplexed method. A quadraplex method  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 influenza A and influenza B viruses:
To the authors’ knowledge, this is the first study to report a quadruplex rRT-PCR
assay for the detection of two SARS-CoV-2 genes, hIAV and hIBV with perfect
Emphasis added. It is unclear whether the research has been independently verified or whether commercial quantities of the quadraplex method for detecting SARS-CoV-2 have been
Page 4 of 22
produced or widely used since 2022. It is quite clear that COVID-19 data are scientifically
flawed before 2022 everywhere and very likely since. Australian data continue to be flawed
because PCR tests are still being used. The inability to distinguish between the detection of the SARS-CoV-2 and influenza viruses is a fundamental scientific uncertainty, which renders COVID-19 data scientifically flawed.
Adding to this uncertainty about what is identified in COVID infections and cases, there is also a significant uncertainty about the titer (genetic fragments per unit volume) needed to define presence of the infection. Through a sufficient number of cycles of titer amplification, which is variable and not scientifically determined, 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 first time in medical history, people who are perfectly healthy with no symptoms, may have been declared COVID cases, based solely on unreliable positive PCR tests. A person could have minute amounts of dead influenza 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 “from COVID” in a typical case of the deceased having other comorbidities. Subjective judgement, distorted at times by financial incentives, creates uncertainties which can be removed objectively by autopsies, but they were rarely performed.
Therefore, COVID cases and deaths cannot be used to characterize the pandemic, because the division of excess deaths into COVID and non-COVID causes appears arbitrary and
inaccurate. Australian health policy has been based on misinformation from flawed COVID-19 data which are scientifically unsound.
This paper focuses on all-cause mortality and excess deaths rather than COVID deaths as
indicators of the severity of the Australian pandemic.
3. Data Methodology
Even as unreliable as the COVID raw data are, Australian official 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 independently verify the official data. These reports from health authorities may be misleading due to selection and classification biases, which have rendered invisible adverse events and deaths related to “vaccines”.
For example, official reports allowed the national broadcaster ABC to claim falsely on primetime television in July 2022 that the “unvaccinated” are 16 to 37 times more likely to die than the doubly “vaccinated” . This misinformation was based on a key official data reporting flaw which came from classifying some deaths as “unvaccinated” even though they had had COVID-19 injections and often multiple times .
This paper avoids the processed data of health authority reports to eliminate potential selection and classification biases. The main reliance is on data  from the national collector, the Australian Bureau of Statistics (ABS), which has the fewest conflicts of interest, but its data and reports are not accepted uncritically either, as will be illustrated below.
Page 5 of 22
In scientific 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 appears to be merely a continuation of the previous trend of relative steady fluctuations in all-cause mortality. On a definition 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 official excess mortality data published by the ABS. The ABS has changed its
baseline definitions (moved the “goal posts”) for calculating 2022 excess mortality in an
inconsistent manner, without providing adequate justification.
Normally, the baseline for calculating excess mortality is the average of the previous five
years, but the baseline for 2022 has been defined by the ABS as the average of four years,
2017-2019 and 2021, without adequate reasons :
Throughout this report, counts of deaths are compared to an average 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
publications. Data for 2022 is compared to a baseline comprising the years 2017-2019
and 2021. 2020 is not included in the baseline for 2022 data because it included
periods where numbers of deaths were significantly lower than expected.
(see original link for graph)
Page 6 of 22
Emphasis added. Note that the arbitrary exclusion of 2020, a year where “numbers of deaths were significantly lower than expected”, raises the baseline and therefore lowers excess mortality statistics for 2021 and 2022, creating a misleading impression of a less serious pandemic.
The five-year averages of 2015 to 2019 are used uniformly as the baseline throughout the study to assess the impact of COVID-19 on Australian mortality. Therefore, our excess mortality statistics for 2022 are different from official ABS statistics. Even though the differences are not great, a consistent baseline is used throughout in this paper for sake of scientific clarity.
The annual excess mortality for Australia from 2015 to the present is shown in Figure 2.
The annual excess mortality for 2020 was well within the range of normal statistical
fluctuations 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 mortality 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 population.
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 CovidBaseAU , which also supplies data to international data providers such as Our World In Data. The data is shown in Figure 3: