Portrait of a Pandemic – The 45 Countries Study

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The Covid-19 Pandemic announced itself in March 2020. Virtually all countries across the globe experienced a surge in mortality. Even countries like Australia, where official estimates of Covid cases were low, became part of that surge. It was the second hottest March on record and coincided with a nadir in the solar cycle (seasonal infections have been linked to solar radiation and levels of Vitamin D in the population). 

Prompted by Deborah Birx and Anthony Fauci, virtually all countries abandoned their Pandemic Plan and the world was plunged into an unprecedented global lockdown. They initially claimed this was necessary for 2 weeks to ‘flatten the curve’, to allow medical services time to gear up and cope with the anticipated demand in emergency services. In her book ‘Silent Invasion’ Deborah Birx openly admits this was a deliberate lie, intended to drive the globe towards a prolonged lockdown based upon her belief that unlike every other strain of coronavirus, asymptomatic individuals were largely responsible spread this virus. 

On reflection it might be fairly said public health services in many countries have been badly damaged under the weight of expectation and the cost of the measures instigated. One might question to what degree was this a rational approach to a ‘once in a century event’ and how much were the measures taken out of a sense of panic, largely fuelled by now discredited modelling by Professor Neil Ferguson at Imperial College London and similar exaggerated estimates of mortality from Covid in other countries? In almost every country healthcare was high-jacked by a global ‘track and trace’ policy that consumed precious resources which along with the lockdown policies, was to contribute to previously inconceivable levels of national debts. These predictions of catastrophic levels Covid deaths never materialized, instead what was witnessed was a slow yet relentless and near-linear rise in excess deaths that appears to be continuing unabated, as depicted in this graph of excess mortality recorded in 28 countries.

Almost 3 years on there is a unique opportunity to analyze the successes and failures of the pandemic response.   Unprecedented amounts of data are freely available at websites such as ‘Our World in Data’ produced by the Mayo Clinic. The benefit of hindsight affords us the chance to determine ‘what worked’ and ‘what failed’. This paper is a portrait of the pandemic painted by numbers from data from 45 different countries and at different times in the response, encompassing 1.8 billion of the world’s population. An unbiased interrogation of the measures employed is essential if we are to learn from the pandemic experience and avoid future mistakes where possible.

It’s also been the most expensive public health exercise in history, with most countries running up unprecedented levels of public debt and an estimated 100 million people being plunged into poverty. So it’s a legitimate and critical question that I now pose, ‘did it work?’

According to the ABS official figures, excess deaths in Australia in 2022 were 17% above their baseline levels recorded from 2015-21. The Australian population is growing at 0.53% per annum, so even correcting for population growth, it still presents us with a 15.7% increase in overall mortality through to August. The following graph charts the history of that increase, which is well above the 95% confidence limits provided in the last column. The chance that this is merely a random event therefore is extremely small and is significantly less than a 1 in 20. 

It’s salient to reflect that little over a year ago virtually every Covid death was being announced on our television sets. Yet as deaths in Australia have continued to increase at an accelerated pace, curiosity seems to have all but disappeared. The silence surrounding the real story (that mortality rates appear to be increasing inexorably) is deafening.

When we then break the Australian findings down by age category and gender, we see that the excess deaths are occurring mainly in the older age groups, and don’t seem to be gender specific.

Clearly excess deaths recorded in Australia since the onset of the pandemic are affecting males and females equally.

Then we can consider disease category and whilst we see a rise in cancer, heart disease and dementia as our population ages, this does not explain the abrupt increase in excess mortality recorded since March 2020;

In fact, the following graph clearly demonstrates that the overwhelming majority of the excess deaths are occurring from Covid and respiratory infections:

The evidence seems unequivocal that the excess mortality witnessed in Australia is largely accounted for by Covid and Respiratory infections. On the face of it, the Pandemic is continuing to take lives unabated. These deaths are largely in the elderly and in individuals with multiple co-morbidities. After almost 3 years of lockdowns, mass vaccinations, the flagrant violation of human rights in the name public safety, unprecedented levels of debt and government borrowing, the social and economic devastation inflicted on communities across Australia and the deprivation of our children to a decent education, the upward trajectory of the pandemic remains unchanged. 

Whilst Australia relinquished most mitigation measures more than a year ago, Covid cases, Covid deaths and excess mortality has continued to rise unabated. In the face of this reality, it might justly be said that the Australian pandemic response was an unmitigated failure and national disaster, with repercussions to our economy and the health of Australians that will last for decades. 

How many of the excess deaths recorded across the globe can explained by the pandemic and deaths from Covid-19 infections and how much is explained by other causes, including the mitigation measures adopted? At first glance it seems a large proportion of these deaths are indeed a consequence of Covid infection, however it is possible that an entirely different explanation exists for what we are witnessing, which comes down to how the data is collected and categorized. For example it’s entirely possible that these deaths are being recorded on patients dying with Covid and not necessarily from Covid. It’s clear from the ABS statistics that the overwhelming majority of these deaths are occurring in the frail and elderly. In the elderly the specific cause of death is almost impossible to determine accurately. Most have co-morbidities, all of which could equally have been the actual attributable reason for that mortality. In a patient with heart failure, renal failure, diabetes and Covid, how does one begin to identify the proximal cause of death? Even with a post mortem the question may be unanswerable.

In such a scenario, where every death ‘with Covid’ becomes registered as ‘from Covid’, the diagnoses becomes a catch-all, offering no more insight as to the actual cause of excess death than the more traditional categorization of frail and elderly. It’s salutary to remember that at last reckoning, 15% of the Australian population being tested were recording a positive test for Covid. That means by default one would expect 15% of people going into hospital would be testing positive and 15% of patients dying would be testing positive, regardless of whether Covid was contributing to morbidity. As such 15% becomes the default or control value. For example, if we were to use another analogy, 100% of us would be carrying Staphylococcal organisms on our skin and 100% of the dying would test positive but that does not mean Staphylococcus was responsible for the death. Similarly 90% of the ‘eligible’ population has been vaccinated, so it’s likely that around 90% of the dying would have a vaccination recorded by random chance, but does that mean vaccination caused these deaths?

This is further complicated in considering what actually constitutes a Covid case. Cases are predicated upon a positive pcr (or RAT) test that is poorly predictive of clinical outcome. Most jurisdictions are using a cycle threshold of 38-45, at which level the test is likely to be detecting fragments of RNA irrelevant of whether any virus is present. NSW pathology pointed out that at current levels of testing it’s likely that as many as 65% of positive results are likely to be ‘false positives’. 

With all things considered, the surge in Covid deaths recorded in the Australian record might largely be reflective of bureaucratic classification rather than a verifiable clinical diagnosis. What is needed is a critical appraisal of these Covid deaths to fully understand their proximal. Furthermore what other factors are contributory and how did the mitigation measures influence outcomes, either positively or negatively?

To attempt to answer those questions I explored data from 45 countries around the globe for comparison. In each of these countries a similar pattern has emerged, clearly demonstrating that increases in excess deaths largely correlate with surges in ‘Covid deaths’.

Pearson’s R is a standardised measure of the strength of correlation between variables and the R squared value is accepted as a reflection of how much a change in one variable contributes to a change in another probabilistically. The R value of the correlation between Covid Deaths and Excess Mortality across these 45 countries is 0.893, yielding an R^2 value of 0.797. This means approximately 80% of the excess deaths recorded across 45 countries since the onset of the pandemic had Covid recorded as their proximal cause. The probability of this being chance finding is < 1/1000.

There are several questions that arise from this analysis. First what actually constitutes a Covid Death? Second do these numbers of excess deaths really arise directly from genuine infections or are they a consequence of how these deaths are being recorded, in which case how do we determine the true causes of these death without the luxury of post-mortem authentication? Third what impact did the various mitigation measures have on the spread of infection and Covid mortality? 

According to Carl Heneghan and his team at the Centre of Evidenced Based Medicine at Oxford, their interrogation of the deaths recorded in Italy at the start of the pandemic suggested that 88% of the deaths recorded as ‘Covid deaths’ were actually attributable to another cause. If we apply that consideration to our previous graphic then we see a very different chart emerges where most of the deaths are reclassified as ‘cause unknown’.

Given the available evidence one can rightly conclude therefore, that since March 2020 mortality rates in most human populations have been increasing in linear fashion and according to current estimates this trend is set to continue into the foreseeable future. These are deaths above and beyond that attributable to an aging and increasing population and according to official national statistics are not easily explained by any other likely causes beyond Covid infection. 

In order test these conclusions and to explore the impact of mitigation measures we can construct plausible hypotheses to interrogate the data systematically. The ‘null hypothesis’ essentially postulates that the outcome of two particular courses of action will be the same and unless that supposition is violated through statistical interrogation, then it can be fairly stated that those actions made no difference to the outcome without contrary available evidence. For example we might hypothesize lockdown policies do not influence the spread of Covid, or perhaps we might suggest they have no impact on survival. We can test these suppositions by examining the outcomes in different communities and if a statistically significant difference is observed, then that provides good evidence that lockdown measures have saved lives. 

In the next section I explore a range of official mitigation measures including lockdown, ‘track and trace’, vaccination and health care provision to consider how they might have influenced the trajectory of the pandemic. It is vital to understand which measures had positive and negative influences if we are to learn from this experience and improve our responses into the future. 

The Effect of Mitigation Measures During the Pandemic

Postulate 1: Lockdowns Measures Save Lives

In March 2020 the World Health Organisation declared a Global Pandemic involving a virus they termed Covid-19. Convinced by ‘worse case’ projections from their public health advisors and influenced by the reported success of the measures observed in China, most countries immediately entered a period of lockdown measures.

However not all countries enacted the exact same policies. Famously Sweden, for example, stayed faithful to it’s pre-pandemic plan. Consequently the range of lockdown measures instigated by various jurisdictions allows us to compare responses in a meaningful way. ‘Our World in Data’ developed its ‘Stringency Index’ in order to quantify the measures recorded. Using this Stringency Index from the graph below, we can see at the start of the Pandemic Excess Mortality seemed to be correlating with the Stringency measures implemented:

Each of these dots is recording the Excess Mortality and Stringency measures adopted in one of 28 countries examined. As can be seen, countries that instigated harsher lockdowns also experienced greater excess mortality at the start of the pandemic. It’s possible to construct a trend line and the strength of the correlation is represented in the R^2 value on the top right hand corner of the graphic, which is 0.127 or 12.7%. Perhaps this correlation is not surprising at the start of the Pandemic, because one might anticipate that those countries hardest hit by Covid would instigate the harshest measures. Therefore it’s entirely possible that in this instance, Covid is driving the lockdown responses. Furthermore there is a 1/13 possibility that this association might have occurred by random chance alone, which is not enough to violate our null hypothesis, namely that there was no significant difference in mortality outcomes between those countries that instigated lockdown measures and those that didn’t. 

The World Health Organisation lamented that if lockdown measures had been instigated earlier, millions of lives could have been saved. It is true to say that lockdown measures at start of the pandemic had no influence on mortality rates, at least in the 28 countries examined. By the end of 2020, mortality rates were rising around the globe. If the WHO were correct in their supposition, one would expect that by December, those countries that locked down hardest would be seeing better mortality rates.

Consequently I did the same analysis for December 2020:

What we see is actually the opposite of what was predicted by the WHO. By December countries that entered harsher lockdowns were actually experiencing greater mortality and in fact the slope of the trend line was increasing. The correlation between lockdown and excess mortality recorded in December 2020 was stronger, suggesting that lockdown were policies were possibly contributing 20% to the excess mortality. This time the possibility that this was a chance association was only 1/71, which is a clear violation of the null hypothesis. 

In statistical terms it provides good evidence that countries that were locking down during the Pandemic were experiencing higher rates of excess mortality and one conclusion to be drawn is that the lockdown policies might have been contributing to these excess deaths. Although we cannot be absolutely certain, this evidence is consistent with that possibility. If lockdowns were having any benefit at all we would expect to see a weakening of the correlation at the very least. In fact throughout the entire period of the pandemic, countries with the harshest lockdowns experienced greater excess mortality, at least in the 28 countries examined. At no point during the pandemic was there a beneficial correlation between the stringency of the policies implemented and mortality. 

The lockdown measures remained firmly in place in most countries analyzed until the end of April 2021. By the end of December 2020 the excess mortality recorded since the start of the pandemic in these 28 countries had reached 22,326,545 in total. The strength of a correlation is classically notated as Pearson’s R and the R^2 value is considered to be an estimate of the contribution that variable made to the outcome measured. The Pearson’s R value for the correlation between lockdown measures and excess mortality at the end of April 2021 was 0.449, so that the R^2 value is 0.202. If so it would imply that lockdown measures to the end of April 2021 possibly accounted for 20.2% of those deaths across the 28 countries examined. 

In numbers terms it suggests in these 28 countries (accounting for around 25% of the world population) as many as 4,509,962 of these excess deaths may have been attributable to lockdown measures. The possibility that this is a chance finding is 1/36, so whilst it’s not proof that lockdown caused these deaths, without a better alternative explanation it is the most likely explanation. By way of comparison according to the WHO the total deaths in 2020 caused directly or indirectly by Covid was 3 million. This would suggest that lockdown measures caused a 50% greater increase in mortality than the Covid virus itself. 

Returning to our original postulate that lockdowns saved lives, the null hypothesis has indeed been violated. What has been revealed is that far from saving lives, lockdown measures probably cost several million lives globally. The statistical evidence for this statement is robust in the absence of an alternative explanation or hypothesis. The balance of the scientific evidence is in favour of lockdown causing substantial harm during 2020, the first year of the pandemic.

Postulate 2: Poverty Contributed to Excess Mortality

Several environmental factors might be reasonably considered as influential in influencing mortality including access to healthcare, GDP per capita and age of population. According to Jay Bhattacharya around 100 million people worldwide were plunged into poverty as a result of these policies.

Indeed this statement is supported by the evidence as depicted by this graph, which plots GDP per capita along excess mortality recorded at the end of July 2022. Each blue dot represents one of the 28 countries examined and the implication is that poorer countries and individuals faired far more poorly during the pandemic and relative poverty was a substantial contributor to the excess deaths recorded during the pandemic. 

Those with fewer resources were less able to protect themselves against the downturn in circumstances, the poor suffered more, were less resilient and they died in greater numbers. During the first year of the pandemic the world economy shrank by 5.89% according to Macrotrends before rebounding in 2021. According to the UN global income declined by 4.4% over the course of pandemic.

Lower personal income strongly correlated with excess mortality during the pandemic and by July 2022, almost 30% of the excess deaths (R = -0.546) could have been attributable to a lack of financial means. The probability that this is a chance finding is 1/200.  

Returning once again to our original hypothesis that poverty contributed to excess mortality during the pandemic, the null hypothesis is again violated. This is not absolute proof that poverty contributed to mortality, but it is strong and convincing evidence. Without a better explanation of the findings, it is the best available explanation. Accordingly we can surmise that poverty or lack of financial means lead to 18,537,760 excess deaths through to the end of July 2022 in the 28 countries examined.

Postulate 3: Access to Good Healthcare Prevents Unnecessary Deaths

There’s a clear association between access to quality healthcare and excess mortality, furthering the impression that the burden of the pandemic fell unfairly on the disadvantaged, who not only experienced economic hardship but also a likely lack of medical support. This is demonstrated in the graphic above where countries with higher levels of healthcare provision, such as Denmark and France, experienced much better outcomes in terms of mortality than countries like the USA and South Africa. 

There is a strong correlation between the provision of healthcare (according to the Healthcare Index) in the countries examined and excess mortality through the course of the pandemic until the end of July 2022 (R = -0.461). Extrapolating from the trend-line in the above graphic we can estimate that better healthcare provision probably saved many lives during the pandemic and significantly reduced excess mortality. Put another way, a lack of access to good health care may have contributed 21.2% to the total excess mortality by July 2022, or alternatively 13,187,937 lives were lost for want of better health provision in the countries examined.

Once again we cannot say absolutely that 13 million people died because of a lack of access to healthcare, but the evidence available is compelling and the probability of these findings happening by chance is 1/50. So unless a better scientific argument is presented, it strongly supports our hypothesis that better healthcare saved lives and again the null hypothesis is violated.

Postulate 4: Excess Deaths are Due to Covid-19 Infection

During the course of the global pandemic, Covid-19 infections have increased relentlessly and there is a strong correlation excess mortality recorded in the 28 countries examined. In fact 60% of the excess deaths seem to be Covid deaths and the null hypothesis is clearly violated. 

What exactly constitutes a Covid death is difficult to determine even with a post mortem, especially in elderly patients with multiple comorbidities. It is legitimate to ask whether Covid was the proximal cause of death or was it simply identified at the time of hospital admission and recorded on the death certificate. Did the patient die from Covid or die with it. 

What is striking is the strength of the correlation across different jurisdictions with different methods for recording their mortality data. One way to determine how many of these deaths might be directly due to Covid is to compare the rates of Covid in the dying with the rates of Covid in the living! If the rates of Covid identified in the community were the same as the rates of Covid recorded on the death certificates, then the null hypothesis is preserved and one could legitimately say the deaths were with Covid, not caused by it. 

In fact the rates of community infection across these 28 countries was 24.9% and 24% when extended to 45 countries, suggesting that Covid infection was being recorded approximately twice as commonly in those who were terminally ill. This result is remarkably similar to and reminiscent of the graph produced by David Spiegelhalter at the start of the Pandemic (see below).

The graph was published in the British Medical Journal in September 2020 and was produced from data covering the period of the first 16 weeks of the pandemic from 7th March to 26th June 2020. The inescapable conclusion is that between June 2020 and July 2022, when it comes to Covid almost nothing has changed. Despite all the lockdowns, the cost to the global economy, the human toll from the government interventions and the mass vaccination programs, none of it has had any impact on the trajectory of the disease. The outcomes we have observed would almost certainly been exactly the same if we had done none of it! It’s the most compelling evidence that the pandemic response was an unmitigated and complete failure.

BMJ 2020; 370 doi: https://doi.org/10.1136/bmj.m3259 (Published 09 September 2020)

Postulate 5: Vaccination Saved Lives

Much suspicion about the rise in excess deaths has centered on concerns about the safety and effectiveness novel mRNA vaccines. The debate has been fierce, polarizing issues with claim and counter-claim. Arguments that without vaccination millions of people would have died from Covid have been fiercely counter with concerns about lack of safety data in the initial trials, observations of unusual myocardial events in otherwise healthy people and evidence that transmission rates are substantially higher in the vaccinated individuals have been met with either silence or outright denial.

The paucity of available post-mortem data, coupled with suppression of debate has led to a sense that governments are demonstrating lack of transparency and frustration amongst observers. There now seems widespread acceptance that vaccine-related deaths are occurring, the debate seems now to be centred on frequency of events and the degree to which this is contributing to the excess mortality being recorded worldwide. 

A cursory examination of the data would seem to support the impression that excess deaths are resulting from vaccination, but without a careful breakdown of the data, this impression is misleading. Much of the correlation is a function of time, that both deaths and vaccinations are increasing at similar rates over time and that this correlation is simply happenstance. It’s easy to present the data in another way, which shows almost the exact opposite effect;

In this representation of 28 countries vaccination indeed seems to be leading to a substantial reduction in mortality, in this case a 35% in excess mortality in the vaccinated compared to the vaccinated. However again this impression is highly misleading because almost all the effect is coming from one country (South Africa) on the top left hand corner of the graph. We must be very suspicious when 1 outlier exerts such a powerful influence over the whole dataset. Look what happens when we remove South Africa:

Now the trend-line is almost flat, vaccination rates appear to be having no effect on excess mortality in either direction and the null hypothesis is preserved. Without a better argument, from this dataset the science would suggest that vaccination has had no impact on the outcome of the pandemic in terms of excess mortality, either saving or costing lives.

Ostensibly South Africa seems to be an outlier, an exceptional case distorting and biasing the data. However, some might try to argue it’s exceptional because of the low rate of vaccination and should be included in the analysis. To explore this uncertainty I examined the excess mortality in 10 countries with high rates of vaccination and 10 countries with low rates of vaccination. According to the null hypothesis there should be no difference between these groups. If excess mortality were significantly higher in the low vaccination countries then it would be strong evidence that vaccination was saving lives. If mortality were higher in the highly vaccinated countries then it would imply vaccination is contributing to these excess deaths.

On average there were 2.5 million vaccines delivered per million in the highly vaccinated countries and 0.65 million vaccines delivered in the low vaccinated countries. In other words, for every 1 vaccine delivered in the low vaccine group, almost 4 vaccine doses were delivered in the high vaccine group. If vaccination were having an effect on mortality, it should certainly be apparent by comparing outcomes in these 2 groups. However as already demonstrated that income is having a significant effect on excess deaths, so to avoid bias countries were selected with similar GDP per capita:

The following graph compares excess mortality in the highly vaccinated with the low vaccinated groups from January 2021 through to December 2022, reflecting the entire first year of the vaccination program:

As we can see there is virtually no difference in excess mortality in the 2 groups. In the highly vaccinated countries 263 excess deaths per 100,000 population were recorded, compared with 310 excess deaths per 100,000 in the low vaccinated countries. At first glance it appears to support the impression that vaccination is saving lives, but this needs to be tested statistically to determine whether the difference is real or just random variation. This is achieved by comparing the variations within the 2 groups, with the variation between them, using a test called an ANOVA (analysis of variation).

Sum of SquaresdfFSig.
Between the Groups11094.4051.344.565
Within the Groups581218.13218
Excess mortality 2022

This table might appear intimidating but the critical value to observe is in the top far right column, which informs us that the rates of mortality in the high and low vaccinated countries is essentially identical, that Covid vaccination appears to be having no effect on overall mortality and that the null hypothesis is preserved. This statement is based upon sound and classical statistical analysis and is valid unless scientific evidence presents itself to the contrary.

Whilst it is reassuring that vaccination is not appearing to cost lives, given the enormous economic and social cost of the vaccination program, it is disappointing to say the least that on this evidence, it has failed to deliver any benefit.

Some might argue this data relates to all-cause mortality and there may be confounding variables that underscore the protection afforded by vaccinations. A different picture would be obtained if we examined Covid deaths alone. According to official statements the vaccination protects against severe disease and death, whilst perhaps reducing transmissibility albeit being somewhat ‘leaky’. Even if all-cause mortality is unaffected, the vaccine still offers benefits against Covid infection, especially to the elderly and those with multiple comorbidities.

To examine these statements I examined rates of vaccination across 45 countries encompassing almost 1.9 billion of the world’s population using the Our World in Data website, the same one Deborah Birx used when she was issuing directives from the White House. If indeed the vaccination was protecting against Covid death and transmission of the virus, it should be apparent. The findings are summarized in the table below:

What this table demonstrates is that far from preventing transmission of the Covid virus, the vaccination is actually increasing spread of the virus. This is confirmed in the Ro column for transmissibility and the actual number of cases recorded. The evidence is highly significant, with the probability of this being a chance finding being less than 1/1000. The following graph demonstrates that according to this data, receiving a vaccination will likely increase the risk of contracting Covid by approximately 23%.

In the final column of the table, the Covid mortality figures demonstrates that vaccination is having no significant impact on Covid mortality. Consequently according to the postulate, the null hypothesis is preserved that vaccination is not demonstrating benefit in respect of saving lives, nor is it significantly increasing all-cause mortality in this dataset.

However the null hypothesis is violated in respect of transmission of infection, where rates of transmission are increased by about 23% after receiving the vaccination and the probability of this being a chance finding is less than 1/1000.

Postulate 6: Spread of Covid and Covid Mortality is Predicted by Ro (Reproduction number)

R0 (R naught) is the basic reproduction number, also known as basic reproduction ratio or rate which is an epidemiological metric used to measure the transmissibility of infectious agents. R0 is a derivative of the following variables—the duration of infectivity after the patient gets infected, the likelihood of transmission of infection per contact between a susceptible person and an infectious individual, and the contact rate. R0 is usually estimated retrospectively from serial epidemiological data or using theoretical mathematical models. Epidemiologists can calculate R0 using contact-tracing data, the most common method is to use cumulative incidence data. When mathematical models are used, R0 values are estimated using ordinary differential equations. R0 of COVID-19 as initially estimated by the World Health Organization (WHO) was between 1.4 and 2.4. The forecast is of critical importance as it will help the governments to have an estimate as well as strategize quickly to avoid any unfavourable condition.

The SEIR model (Susceptible, Exposed, Infected, Recovered) was developed in the 1920’s and was used as the basis for prediction throughout the pandemic period. R0 value is central to the model prediction and was used by the Doherty Institute in the Pandemic Plan formulated by the Scott Morrison government in July 21. It was used to justify the stringency of the lockdowns and the vaccination targets to halt the spread of the infection. Examination of the subsequent infection rates at 6 months and 12 months after the Doherty Model was delivered demonstrates no correlation between R0 values at a given point in time and subsequent outcome in cases, Covid mortality and Excess mortality. 

In fact the outcomes are completely contrary to the model prediction, in the higher levels of R0 are associated with lower future cases, although this isn’t statistically significant. So the null hypothesis is preserved, R0 doesn’t predict the future outcome of the pandemic in the critical areas of case numbers and mortality. The SEIR modelling used by governments around the world to determine economic and public health response appears to be fundamentally flawed in its assumption that R0 predicts future infections and Covid deaths. It’s decision to prolong the lockdown measures based upon the Doherty Institute modelling with the benefit of hindsight, appears to have been one of the key mistakes in the pandemic response and lead to catastrophic consequences for a great many Australians. This modelling seems to have been predicated on system of beliefs about the nature of seasonal viral transmission that were lacking robust scientific evidence. Without a counter-narrative a groupthink emerged that delivered a damaging dogma that formed the justification for some of the most flagrant transgressions witnessed during the pandemic period. The near universal violation of human rights during this period by political leaders fuelled by the illusion of certitude, afforded through clumsy academic modelling demonstrates a truly staggering level of scientific ignorance and moral weakness.

Postulate 7: The Track and Trace Program Stopped the Spread of Covid and Saved Lives

From all corners of the world governments implemented a track and trace policy to mitigate the spread of Covid in the belief it would save lives. Until vaccination became available this was claimed as the main weapon in the fight against the virus. National health systems were commandeered and turned over to Covid detection, sacrificing access to vital services in the process. Cancer treatments were delayed and access to essential clinical services became almost impossible in many jurisdictions, now widely sited as an explanation for the excess mortality rates we have witnessed globally.

The cornerstone to the track and trace program was the rt-pcr tests. Governments were outcompeting each other to demonstrate their commitment to the cause. So if indeed track and trace stopped Covid in its tracks and saved lives, it should be apparent in the data. Countries more committed to the process would have performed more pcr tests and they should therefore have experienced lower rates of infection and Covid mortality. In fact there is no correlation between pcr testing and Covid mortality. A positive pcr test outside the clinical setting has no predictive value in determining Covid mortality:

Furthermore far from being effective at reducing Covid infection, there is evidence that it actually promoted Covid infection:

Taken at face value, this graph of Covid infection from 45 countries around the world suggests that going for a Covid test may have actually substantially increased the risk of contracting Covid. It was recognized very early in the Pandemic that nosocomial spread was a major contributor to Covid case numbers and this graph supports that impression. Where large numbers of infected individuals congregate to be tested the risk of spread increases dramatically. In this dataset it suggests going for a pcr test increased the risk of contracting Covid by over 40%.

The evidence does not support the postulate that ‘track and trace’ policies saved lives and the null hypothesis is therefore preserved. The null hypothesis is violated however when considering the spread of Covid and the available evidence in this dataset supports the idea that nosocomial infection was a major contributor to the spread of infection. Overwhelmingly the evidence suggests the program was an abject failure and lead to delays in vital treatment of other diseases that over time have contributed to disastrous consequences for many seeking access to healthcare.

Postulate 8: We cannot rely on natural (adaptive) immunity for protection during the pandemic

The claimed novelty of Covid 19 was central to government policy decision-making in determining approach to lockdown policies. It was claimed no natural immunity existed to the virus and to allow indiscriminate exposure through community transmission was tantamount to murder. Those advocating more measured responses were accused of wanting ‘to let it rip’, of being ‘granny-killers’. Sweden and supporters of the Great Barrington Declaration were demonized and cast as pariahs.

It’s critical therefore to examine the impact of the spread of Covid cases and Covid mortality. If government policy is correct, then countries with more Covid should experience more Covid mortality and the policy of lockdown finds evidential support. Any mitigation of Covid mortality in areas of higher Covid numbers lends support to the notion adaptive immunity Covid 19 is robust and protective.

As can be seen from the chart above as Covid cases increase there is no significant increase in Covid mortality per million of the population. As case numbers increase, the case fatality rate drops dramatically lending compelling support to the idea that community exposure and natural (adaptive) immunity has a substantial role to play in protection during a pandemic. The null hypothesis is violated and the strength of the correlation is significant (R^2 0.368, p<0.001). The data suggests that community exposure leads to an almost 40% reduction in the virulence of the virus and that the likelihood of this being a chance finding is less than 1/1000. In other words, lockdown has no role to play in protecting individuals against seasonal community-acquired infection. In fact lockdown measures likely interfered with natural biological systems that evolved to protect communities against seasonal infections. As infection spreads within communities the virulence of the virus diminishes dramatically as notated by the Case Fatality Rate (CFR) so that there is no net increase in Covid mortality (see below).

Deaths from increasing numbers of Covid cases are offset by a near-linear reduction in Covid virulence. This seems to be a consistent finding across different population groups and across different periods during the pandemic.

The following graphs look at the 40 most populous States in the USA from June 2020 and demonstrate an almost identical phenomenon.

States that experienced higher rates of Covid infection demonstrated no increase in Covid mortality because there was again a near-linear fall in the CFR as cases increased in number.

Once again a similar story unfolded in Europe as demonstrated in this graph plotting mortality against infection rates in July 2021;

European countries experiencing higher rates of Covid infection did not experience significantly higher mortality rates and the explanation for this phenomenon seems to be a Loss of Function (LOF) in terms of virulence of the virus over time. In other words during it’s transit through human communities the virus and humans demonstrate co-adaptive traits that are mutually beneficial. 

Co-adaptation (or co-evolution), the parallel feedback process by which agents continuously adapt to the changes induced by the adaptive actions of other agents, is a ubiquitous feature of complex adaptive systems, from eco-systems to economies.

Savit, R., Riolo, M., & Riolo, R. (2013). Co-adaptation and the emergence of structure. PloS one, 8(9), e71828. https://doi.org/10.1371/journal.pone.0071828

Further evidence for this comes from a study by Solomon et al. at Stanford University, of over 3 million adults either living in close contact with children with those without contact with children. Adults living without children had 15% less Covid infection by comparison, yet their rates of hospitalization was almost 50% higher and their risk of admission to ICU was increased by 75%. 

In recent years it has become recognized that humans possess genetic pathways capable of selectively modifying viral RNA, potentially leading to LOF effects by reducing viral protein production. Not only does this protect the host against the virus by reducing virulence, the viral modifications are enduring so that the next host is the beneficiary of those modifications. And so it goes on, such that LOF modifications lead to an accelerated reduction in lethality beneficial to host and the virus.

In large human societies with increased population density these adaptive processes confer significant survival benefit both to the individual and to the population as a whole. Given children have low susceptibility to severe Covid infection it would be of enormous societal benefit to have kept the schools open during the Pandemic, thereby promoting transmission and accelerated LOF before the virus entered the more vulnerable elderly population.

There is good evidence that adaptive immunity played and continues to play a significant, if not vital role in mitigating the worse effects of Covid-19. The near-linear reduction in CFR seen during community transmission is most convincingly explained by a co-adaptive response between the virus and human. Such a supposition has strong support from other publications and such phenomena are known to be ubiquitous in nature. 

If we accept co-adaptation as the best explanation for the LOF of Covid-19 during the Pandemic, then indeed adaptive immunity was reliably protective both for the individual and the community and null hypothesis is violated.

Conclusion – The Crime of the Century

Karl Popper claimed the key to good science is looking for black swans, discovering exceptions that disprove the rule. This principle is fundamental to the scientific process, for without this possibility to challenge the orthodoxy errors cannot be detected and corrections cannot be made.

During the pandemic scientific discourse was replaced with dogma, leading to disastrous failures on a previously unimaginable scale. Having explored the effect of the lockdown policies in 45 countries encompassing 1.8 billion of the world’s population, it’s hard to escape the conclusion that every conceivable mistake was made on a truly colossal scale. However, that was not the crime, it was the complete refusal to acknowledge the failure, to accept that errors had been made and the steadfast refusal to correct those errors in the face of overwhelming evidence – that was the crime.

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  • Dr David Richards

    Dr David Richards is an Australian General Practitioner and Adjunct Professor at an Australian University in the faculty of medicine. He graduated from London University in 1984, having also completed an Honours Degree in Human Genetics and Immunology. He has peer reviewed papers for a major European Journal and presented at International Conferences on Genetics and Carotid Ultrasound.

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