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#instagram #deepfake #holidayseason #pandemic #cartoonistsofinstagram #grandma #virtualreality #covid #coviddeaths #sickhumor https://www.instagram.com/p/CItc9XShCZR/?igshid=1a3vildkybway
Shelter in Place Haiku Series
Casual neglect /
Of the one hundred thousand /
Casualties:Â Trump.
The covid response was not a mistake
The Covid response was not an error, and it was not the result of rushing to address a crisis due to an unknown pathogen.
It was a lot of people, mostly professionals in the field, systematically and collectively doing what they knew was wrong, David Bell writes and systematically lays out the facts.
âWhen laid bare by maths and statistics rather than sponsored modelling, the covid response looks horribly like incompetence that was not completely unintentional,â he says.
The Covid Response Was Not a Mistake â It Was Just Wrong
By David Bell, as published by the Brownstone Institute
Early in 2025, some statisticians from Scotland and Switzerland wrote a discussion paper with a characteristically (for Scots and Swiss) understated, even boring, title: âSome statistical aspects of the Covid-19 responseâ. Good science is stated clearly without fanfare, while âbombshellâ announcements, or similar rants, indicate a need to embellish. Good data speaks for itself. However, it only speaks widely if people read it.
The paper, by Wood and co-authors, was written for presentation at a meeting of the Royal Statistical Society in April 2025 in London. It remains one of the best reviews of the early response to covid - in this case with a United Kingdom focus but relevant globally. However, some people donât avidly read the Journal of the Royal Statistical Society â Series A: Statistics in Society, or attend their London meetings. A pity, as London is nice for three days in summer, and this particular Royal Society seems to have a grasp of reality lacking in some of its siblings.
The paper provides simple statistical truths, as statisticians should. Truths are particularly valuable when applied to subjects where fallacies are more profitable. This is why, in public health, they have become so rare and, therefore, so worth reading. Stating truths dispassionately regarding covid helps to grasp how bad the public health response actually was.Â
Covid and the Economy
Public health has always been highly dependent on economic health, so the authors set the scene by stating the obvious of the economics of the response of Western governments that decided in early 2020 that printing money was simpler than making people work to generate taxes:
Creating money while reducing real economic activity is obviously inflationary.
And consequently:
The subsequent sharp increase in inflation is one path by which the disruption has contributed to increased economic deprivation âĻ of the sort clearly linked to substantially reduced life expectancy and quality of life.
This is important because we knew this long before 2020 (the Romans knew it), and we also knew that the resultant economic deprivation would shorten life expectancy. This is Public Health 101, and every public health physician knew it when covid started.
In public health, we recognise that there is a trade-off between spending money to save one person or allocating it elsewhere to save many more. If we just spend without limit, we all get poor and then we cannot really fund healthcare at all. This is not complicated; people understand it. It is why we donât have MRI scanners in every village. We therefore make estimates of how much can save a life without overly impoverishing society and then losing more. Wood and colleagues looked at the UK standard for this compared to the costs of lockdowns:
âĻany reasonable estimate of the cost per life year saved from covid by non-pharmaceutical interventions substantially exceeds the ÂŖ30K per life year threshold usually applied by NICE (the UK National Institute for Health and Care Excellence) when approving introduction of a pharmaceutical InterventionâĻâĻ Â
 gives a cost per life year saved over 10 times the NICE threshold.Â
Again, this is basic public health. Allocating health resources is a complicated issue as it is (rightly) tied to ethics and emotion but on a societal scale, it is how we manage our health budgets. In this case, the numbers predicted to be saved through the enormous costs of lockdowns never remotely made sense.Â
However, the UK government, like governments elsewhere under the same apparent media-pharmaceutical yoke, simply ignored costs and benefits calculations and ploughed on regardless. Guided by its Scientific Pandemic Influenza Group on Behaviour (âSPI-Bâ), the UK government embarked on a campaign to mislead the public into taking actions they could reasonably expect to be massively harmful on an individual and national level. They knew the campaign to instil fear was unjustified; a campaign of misinformation aimed at the same public who paid them. Wood and colleagues provide âone of the milder examplesâ:
 âĻ a widely displayed government poster picturing a healthy woman in her mid-twenties in a mask with the slogan âI wear this to protect you. Please wear yours to protect me.â
The actual risk profile that the UK government and SPI-B had at that time is shown in the Figure below, provided in the paper.
This is where statisticians are useful - to provide context in place of anecdote and fear. They provide a good one:
âĻthe current best estimate for the return time of a super-volcanic eruption of the civilisation-ending magnitude that city dwellers are unlikely to survive is 17 thousand years (Rougier et al., 2018). Even only considering the two years of the pandemic this is likely larger than the covid risk to the woman pictured.
So logically, if they were being logical about covid, the UK government should now be gutting their economy to prepare for the aftermath of a super-volcano. But letâs not suggest that, as they might just do it.
Explaining Covid Burden
The UK governmentâs efforts to mislead the public regarding covid-19 risk were not a case of dealing with an unknown virus, as many are now claiming:
Risk was known early 2020: Diamond Princess, and e.g. Verity et al., 2020; Wood et al., 2020, from Chinese data.
Case fatality data from Figure 3 (B) in Verity et al. published in March 2020 by Imperial College London, noting minimal risk of Covid mortality among young and middle-aged people (i.e. those removed from work and school).
Irrespective, the UK government maintained that covid was severe and debilitating in young, fit people, potentially (as Wood and co-authors note) using actors and fabricated stories and thereby simply lying to people. The UK Office of National Statistics (âONSâ) did its part by, as the authors demonstrate from various studies, also misrepresenting the frequency of long covid.
SPI-B advice on masks was also strange, being at odds with their own citations, thereby grossly exaggerating their impact. This is a strange one - why would a government convince the public to cover their faces, knowing that they are basing their advice on falsehoods, running against previous advice and that it will not significantly help anyone? This is where bad intent starts to look increasingly part of the approach.
The authors then note:
This type of misleading and selective use of statistical evidence was not limited to the media. For example, in 2021, the official online Scottish government advice on face coverings stated thatÂ
âScientific evidence and clinical and public health advice is clear that face coverings are an important part of stopping the spread of coronavirus.â
and provided a link for the scientific evidence. This turned out to be a SPI-B/SAGE advice summary18, which cited two pieces of scientific evidence, apparently suggesting transmission reductions from mask wearing of 6-15%, or up to 45%, respectively. The paper cited as evidence for the first figure was in fact an editorial (Cowling and Leung, 2020), which also pointed out that the paper cited for the 45% figure (Mitze et al., 2020) was flawed (the design appears unable to pick up the case in which mask wearing is actually harmful, for example). The editorialâs figure is quoting a properly conducted meta-analysis (Brainard et al., 2020) which actually concluded
ââĻ wearing a mask may slightly reduce the odds of primary infection with by around 6 to 15% This was low-quality evidence.â
Again, this government was unequivocally misleading their own people into a major behavioural change, whilst having evidence that it would not be of use; either negligence or simply lying.
Mortality
The discussion of Wood and colleagues on quantifying mortality becomes really interesting, demonstrating how difficult this actually is. Firstly, when covid hit in 2020, the babies born immediately after the Second World War were just turning 75. There were 31% more babies born in the UK in the year after the warâs end compared to the previous year, and high birth rates continued in subsequent years. There is nothing magic about 75, but the point is: a mass of the British public, born in the few years after the War, were entering the ages of rapidly increasing mortality.Â
This is a driver of âexcess mortalityâ not widely discussed. It means there should have been an increasing mortality in 2020, and in subsequent years (i.e. above normal compared to pre-2020, but not really an excess if standardised for age). This is important for understanding total excess, whether claiming itâs from âcovid,â vaccination or anything else. It does not, however, account for rising mortality in younger age groups or the rate of death at any age.
The other obvious problem with covid numbers is that, as the authors note, people generally only die once. Thus:
Cumulative excess deaths  much lower than the 212,247 officially considered âcovid.â Many covid would have died anyway , or were not covid deaths. The cumulative excess âĻ are much lower than the total deaths recorded with covid (212,247 with covid mentioned on the death certificate by the end of 2022, according to the UK governmentâs data dashboard). There are a number of mechanisms that are likely to account for this. An obvious one is the fact that only some 17 thousand people had only covid and nothing else recorded on their death certificate.
That was 212,247 with covid on a death certificate - only 17,000 had covid only. But official figures frequently imply that all 212,247 died because of covid. Covid mortality events do not simply add to the mortality caused by the other comorbidities. The viral infection, like other viral infections, often simply hastens the deaths of very sick and dying people.
The equivalent figures for the UK in 2020 was a life expectancy drop of about 1 year and a life loss of about 6 days per head.
This is really important to understand. So, people who died of/with covid lost, on average, a year of life. But the vast majority of the population did not die. So, only 6 days were lost on average across the entire UK population. Â
This raises a problem that governments and public health officials knew well before imposing lockdowns: the known impact of poverty and inequality on life expectancy. To quantify, well-accepted UK data from Marmott et al. (2020) show a 5-year gap between life expectancy of the upper decile (rich) and lower decile (poorest) people in the country. Covid caused, in comparison, a 6-day reduction in life expectancy (averaged across the whole population). It is therefore almost inconceivable that an intervention that greatly increases poverty could be less harmful than covid, from a public health viewpoint.Â
Modelling
The paper points out the really basic flaws in modelling by Imperial College London and others in supposedly predicting the covid-19 impact. These models drove many governmentsâ responses, though it was clear at the time, and the modellers would have known, that the models were designed to exaggerate harms. In particular, they failed to adjust for population heterogeneity, which tends to slow spread and reduce harms (the most vulnerable leave the population, leaving a more resilient populace). Failure to account for heterogeneity will overestimate future transmission by design.
Perhaps the most surprising feature of the epidemic models used to justify covid policy was the omission of the fundamental role of person-to-person transmission rate heterogeneity investigated by Novozhilov (2008).
They also ignored the fact that close to half of early infections were hospital-acquired (China, Northern Italy) rather than from the community, leading to falsely high community transmission rates being fed into the models.
The Imperial modelling group, one should remember, was the same group that published in the Lancet in March 2020, showing almost no mortality in young and middle-aged people (second graphic above). They knew, when they pretended that very high mortality was expected, that the true picture was very different.
UK predictions were consequently far above reality - as were predictions of lockdown impact. Lockdown models assumed reproductive rate (R0) would be constant before or after lockdowns without intervention, whereas in reality, it always varies with time, steadily declining from an initial peak as fewer people remain susceptible to being infected per case, as more of the population is immune. Again, this is really, really basic outbreak modelling. Consistent failures (e.g. non-lockdown Sweden having about 6,000 deaths instead of 35,000) failed to stimulate any modification and rectification of these basic errors.
While the actual impact of lockdowns on poverty and economic health is clear, controversy does remain on their impact on covid transmission and mortality. Wood and co-authors address this by noting that nearly all lockdowns started after transmission had already started declining (see figure). It almost looks as if lockdowns were imposed at a time that would make them look effective, rather than with the expectation that they would avert more infections.
Time to stop pretending.
While covid started over 5 years ago, people want to move on, and there are myriad papers arguing one side or the other. However, the paper of Wood and co-authors does stand out. It does not push any advocacy baggage or speculate on political motives, but simply lays out numbers and facts. From the point of view of the pandemic industry, it provides a really strong argument for censoring facts and hammering dogma. When laid bare by maths and statistics rather than sponsored modelling, the covid response looks horribly like incompetence that was not completely unintentional.
Perhaps the modellers whose numbers justified covid hysteria simply did what they were paid for and did not expect politicians and media to take them seriously. Perhaps public health physicians promoting long-term poverty and inequality were just trying to keep their careers on track and mortgages financed.Â
Perhaps politicians are just resigned to a reality that they must represent corporate sponsors before their constituencies to survive. Perhaps we are just not as smart, virtuous and moral as we like to pretend that we are. Whatever the underlying issues, it is time everyone stopped pretending the covid response was anything but a mess, or that we did not know it would be. There is still a place for truth.Â
About the Author
David Bell, Senior Scholar at Brownstone Institute, is a public health physician and biotechnology consultant in global health. He is a former medical officer and scientist at the World Health Organisation (âWHOâ), Programme Head for malaria and febrile diseases at the Foundation for Innovative New Diagnostics (âFINDâ) in Geneva, Switzerland, and Director of Global Health Technologies at Intellectual Ventures Global Good Fund in Bellevue, Washington, USA.
Mortality patterns during March to May 2020 in Europe and the USA are incompatible with having been caused by person-to-person spread of a novel infectious virus. Instead, âthat first-peak period excess mortality , where it occurs, was of institutional and iatrogenic origin, caused by mistreatment of frail and vulnerable people in hospitals and nursing homes,â a new report by Correlation concludes.In other words, the true pandemic was one of policy, not pathology.On 13 June, Correlation, a Canadian non-profit research organisation, published a new report titled âConstraints from geotemporal evolution of all-cause mortality on the hypothesis of disease spread during Covidâ authored by Joseph Hickey, Denis G. Rancourt and Christian Linard. On 18 June, the report was published on Preprints.org. At 400 pages, including hundreds of graphs, it's a long read. Below Lies are Unbekoming summarises Correlationâs report in a question-and-answer format.Hospitals, Not "Viruses": What Really Caused the Covid-19 Death SpikesBy Lies are UnbekomingIn early 2020, the world recoiled as reports of a novel coronavirus, purportedly unleashed from a laboratory or wet market, ignited a global crisis. Official narratives, amplified by the World Health Organisationâs 11 March 2020 pandemic declaration, framed covid-19 as a relentless, contagious pathogen sweeping through populations, overwhelming hospitals, and claiming lives indiscriminately.Yet, as Denis Rancourt and his team meticulously demonstrate in their groundbreaking study, this narrative crumbles under rigorous scientific scrutiny. Their analysis, summarised here in 27 questions and answers, reveals a startling pattern: excess mortality did not align with the expected dynamics of viral spread but instead correlated tightly with aggressive medical interventions. Synchronised death spikes across Europe and North America, defying geographic logic, erupted immediately post-declaration, with no significant excess deaths prior. Cities like Rome, with heavy air traffic from Asia, saw minimal mortality, while New Yorkâs Bronx, served by expanded hospital systems, suffered catastrophic losses.Rancourtâs work, lauded in âBeyond Contagionâ for challenging virological dogma, underscores a grim irony: â88% of patients put on ventilators in New York died,â not from a virus but from protocols like mechanical ventilation and high-dose drug regimens.Despite such evidence, many, as noted in âRancourt Testimonyâ, cling to the notion of a âweaponised virus,â a belief Rancourt dismantles as scientifically untenable. This study forces a reckoning with iatrogenic harm - hospital protocols, not a swarming pathogen, drove the mortality crisis.The implications of Rancourtâs findings, explored further in âWas There a Pandemic?â and âNo Pandemicâ, reframe the entire covid saga as a tale of institutional assault. Lockdowns, fear campaigns and experimental treatments, as critiqued in âThe Final Pandemicâ, induced biological stress and funnelled vulnerable populations into deadly medical pipelines.The geographic patchiness - high death rates in areas like Lombardy but not neighbouring Veneto - defies viral transmission models, which, as Rancourt notes, âfailed spectacularlyâ in predicting uniform spread. Instead, socioeconomic vulnerability, particularly in poor communities near large medical centres, became lethal only when paired with aggressive interventions.This paradox, where access to âcareâ turned perilous, challenges the assumption that more medicine equates to better outcomes. Rancourtâs rigorous data, showing âdeaths shifted from homes to hospitalsâ in high-mortality zones, invites scepticism of centralised health responses. The true pandemic was one of policy, not pathology.With thanks to Joseph Hickey, Denis Rancourt and Christian Linard, see: âOur latest large study about excess mortality during covid released today: Demonstration that there was no contagion or spread, only unnecessary harmâ.Table of Contents- The Aeroplane Analogy- The One-Minute Elevator Explanation- 12-Point Summary- 27 Questions and Answers- 1. What was the main purpose of this research study?- 2. What is a "P-score" and why is it important for understanding death rates?- 3. What time periods did the researchers focus on and why?- 4. What did the researchers discover about the timing of death spikes across different countries?- 5. How did death rates vary between different regions during the first few months of 2020?- 6. What patterns did researchers find when comparing neighbouring countries with similar populations?- 7. Why did the researchers compare Milan to Rome and New York City to Los Angeles?- 8. What did the study reveal about where people were dying during the peak periods?- 9. How did computer models predict the pandemic would spread and what actually happened?- 10. What role did hospitals and intensive care units play in the excess deaths?- 11. What is mechanical ventilation and why might it have been dangerous during this period?- 12. What medications were being used to treat patients and what were their risks?- 13. How did socioeconomic factors like poverty and race relate to death rates?- 14. What is the significance of deaths happening right after the WHO declared a pandemic?- 15. Why didn't the virus seem to spread the way scientists expected it would?- 16. What happened in The Bronx that made it have the highest death rate in America?- 17. How did air travel patterns relate to where high death rates occurred?- 18. What differences did researchers find between spring and summer death patterns?- 19. What is "biological stress" and how might it have contributed to deaths?- 20. Why did some areas with large airports have low death rates while others had high death rates?- 21. What role did lockdown policies play in the timing of death spikes?- 22. How did treatment approaches differ between regions with high and low death rates?- 23. What evidence suggests that hospital treatments may have caused more harm than good?- 24. Why do the researchers believe the deaths were not caused by a spreading virus?- 25. What alternative explanation do the researchers propose for the excess deaths?- 26. What are the implications of these findings for how we understand pandemics?- 27. What does this research suggest about the relationship between wealth, poverty and access to medical care?The Aeroplane AnalogyImagine you're told that aeroplane crashes are causing thousands of deaths across the country. Officials announce that a mysterious "engine failure disease" is spreading from airport to airport, and they implement emergency protocols: dramatically increasing the number of mechanics at major airports, using experimental repair techniques and requiring all planes with even minor issues to undergo aggressive maintenance procedures.Now imagine if, upon investigation, you discovered that plane crashes only occurred at airports that implemented the new emergency protocols, while airports that continued normal maintenance procedures had no crashes. You'd also find that crashes happened immediately after the emergency announcement rather than before it, and that airports with the most international flights often had fewer crashes than smaller regional airports. Most tellingly, you'd discover that planes were crashing during the aggressive maintenance procedures themselves, not during normal flight operations.This is essentially what the researchers found with covid mortality: the "cure" appears to have been far more deadly than any disease, with excess deaths occurring primarily where and when aggressive medical interventions were implemented, rather than following patterns of natural disease spread.The One-Minute Elevator ExplanationThis study analysed death patterns across Europe and North America during early 2020 and found something shocking: the excess deaths didn't follow the patterns you'd expect from a spreading virus. Instead of gradually moving through connected populations, massive death spikes occurred simultaneously across distant regions immediately after the WHO declared a pandemic on 11 March 2020 - with virtually no excess deaths before that date anywhere.Even more revealing, areas that dramatically expanded hospital capacity and used aggressive treatments like mechanical ventilation had catastrophic death rates, while similar areas with conservative medical approaches stayed relatively unaffected. Cities with the most flights from China often had low death rates, while areas with less international exposure suffered massive mortality spikes.The researchers found that 88% of patients put on ventilators in New York died, experimental drug combinations were used at dangerous doses and deaths shifted from homes to hospitals in high-mortality areas. The geographic patterns, timing and correlation with medical interventions suggest the excess deaths were caused by the pandemic response itself - particularly aggressive hospital treatments and lockdown-induced stress - rather than by a spreading virus.This means our entire understanding of what happened in 2020 is wrong, and that well-intentioned medical interventions killed far more people than they saved.{{Elevator dings}}Research threads to follow: Look into iatrogenic deaths in hospitals, the history of mechanical ventilation mortality rates and studies on stress-induced immune suppression during lockdowns.12-Point Summary1. Geographic Impossibility of Viral Spread: The study reveals that excess deaths during early 2020 occurred in an extremely patchy geographic pattern that defies the logic of infectious disease spread. Some regions experienced death rates over 200% above normal, while neighbouring areas with similar populations, airports and demographics remained largely unaffected. This patchwork pattern extended across international borders and between adjacent counties, creating a geographic distribution incompatible with natural viral transmission.2. Synchronised Timing Contradicts Natural Disease Spread: Death spikes across Europe and North America occurred with remarkable synchronisation - all within three weeks of each other and notably after the WHO's 11 March 2020 pandemic declaration. No significant excess mortality occurred anywhere before this date, despite claims that the virus had been circulating for weeks. This timing pattern resembles a coordinated response to a policy announcement rather than the gradual geographic spread expected from infectious disease transmission.3. Air Travel Patterns Don't Match Death Patterns: Cities and regions with the highest volumes of international air travel, particularly from Asia, often experienced minimal excess mortality, while areas with less international connectivity suffered catastrophic death rates. Rome received more flights from China than Milan, yet had death rates 18 times lower. Los Angeles and San Francisco had more Asian connectivity than New York City but avoided the mortality catastrophe that devastated New York. This contradicts the fundamental assumption that the virus spreads through international travel.4. Hospital Interventions Correlate with Death Rates: Regions that dramatically expanded ICU capacity and implemented aggressive medical interventions experienced the highest death rates, while areas maintaining conservative medical approaches avoided excess mortality. The correlation between medical system expansion and death rates suggests that aggressive treatments, rather than disease severity, drove mortality outcomes. Areas that surged hospital capacity and used experimental protocols consistently suffered higher death rates than similar areas with different treatment approaches.5. Mechanical Ventilation Proved Exceptionally Deadly: Hospitals placed unprecedented numbers of patients on mechanical ventilators, often using experimental techniques due to equipment shortages. Mortality rates for ventilated patients reached 88% in New York City hospitals and 97% for elderly patients. Untested methods like using anaesthesia machines as ventilators showed 70% mortality rates compared to 37% for standard equipment. This aggressive use of mechanical ventilation, far exceeding normal medical practice, likely contributed significantly to excess deaths.6. Dangerous Drug Combinations Were Widely Used: Hospitals extensively used hydroxychloroquine combined with azithromycin, often at doses far exceeding safe levels - sometimes 10 times normal amounts. These combinations carried significant risks of fatal heart complications, and Spain's azithromycin consumption increased by over 400% during March 2020. Additionally, sedatives like midazolam were prescribed at much higher rates than normal, contributing to delayed recovery and increased mortality in critically ill patients.7. Read the full article

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