New report: The true covid pandemic was one of policy, not pathology
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, “that first-peak period excess mortality [March-May 2020], where it
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, “that first-peak period excess mortality [March-May 2020], where it occurs, was of institutional and iatrogenic origin, caused by mistreatment of frail and vulnerable people in hospitals and nursing homes,” a new report by Correlation concludes.
In other words, the true pandemic was one of policy, not pathology.
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On 13 June, Correlation, a Canadian non-profit research organisation, published a new report titled ‘Constraints from geotemporal evolution of all-cause mortality on the hypothesis of disease spread during Covid’ 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 Correlation’s report in a question-and-answer format.
In 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 Organisation’s 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 York’s Bronx, served by expanded hospital systems, suffered catastrophic losses.
Rancourt’s work, lauded in ‘Beyond Contagion’ for challenging virological dogma, underscores a grim irony: “88% of patients put on ventilators in New York died,” not from a virus but from protocols like mechanical ventilation and high-dose drug regimens.
Despite such evidence, many, as noted in ‘Rancourt Testimony’, cling to the notion of a “weaponised virus,” 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 Rancourt’s findings, explored further in ‘Was There a Pandemic?’ and ‘No Pandemic’, reframe the entire covid saga as a tale of institutional assault. Lockdowns, fear campaigns and experimental treatments, as critiqued in ‘The Final Pandemic’, 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, “failed spectacularly” 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 “care” turned perilous, challenges the assumption that more medicine equates to better outcomes. Rancourt’s rigorous data, showing “deaths shifted from homes to hospitals” 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: ‘Our latest large study about excess mortality during covid released today: Demonstration that there was no contagion or spread, only unnecessary harm’.
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?
- 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 Analogy
Imagine 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 Explanation
This 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 Summary
1. 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. Poverty Became Dangerous Only Near Large Medical Centres: While socioeconomic vulnerability strongly predicted death rates in areas like New York City and London, many other regions with similar or worse poverty levels experienced minimal excess mortality. This creates a pattern where being poor and a minority became dangerous, specifically when combined with access to large hospital systems implementing aggressive treatments, rather than being general risk factors for the supposed disease.
8. Deaths Shifted from Homes to Institutions: High-mortality areas saw dramatic increases in the percentage of deaths occurring in hospitals and nursing homes rather than at home, while low-mortality areas actually experienced increases in home deaths. This reversal of normal death location patterns suggests that institutional care became dangerous rather than protective, and that proximity to medical facilities increased rather than decreased mortality risk during this period.
9. Stress-Induced Illness May Explain Respiratory Deaths: Lockdown measures, social isolation and fear campaigns created unprecedented biological stress that could suppress immune function and make people vulnerable to bacterial pneumonia from their own respiratory bacteria. This stress-induced pneumonia model explains why deaths were respiratory in nature but followed policy announcements rather than disease transmission patterns, and why they correlated with socioeconomic stress factors rather than infectious disease exposure.
10. Computer Models Failed Spectacularly: Scientific models predicted that virtually all major cities with international airports would experience significant outbreaks by February 2020, with relatively even distribution across connected populations. Instead, many internationally connected cities remained largely unaffected while deaths concentrated in specific hotspots unrelated to predicted spread patterns. This massive failure of predictive models suggests that the underlying assumptions about viral transmission were incorrect.
11. Policy Responses Preceded Rather Than Followed Mortality: The timing of national lockdowns correlates precisely with mortality spikes rather than preceding them, suggesting that policy responses triggered rather than responded to health crises. Italy’s lockdown began on 9 March with deaths spiking that week; Spain’s 15 March lockdown preceded immediate mortality increases, and the UK’s 23 March lockdown coincided with their death spike beginning. This timing indicates that institutional responses created the mortality crisis.
12. The Entire Pandemic Response May Have Been the Problem: The evidence suggests that aggressive medical interventions, experimental treatments, institutional panic, lockdown-induced stress and systematic testing programmes that funnelled vulnerable people into dangerous treatment protocols created the excess mortality attributed to covid-19. Rather than responding to a naturally occurring pandemic, the data indicates that the declared pandemic response itself generated the mortality catastrophe through iatrogenic (treatment-caused) deaths and stress-induced illness.
27 Questions and Answers
1. What was the main purpose of this research study?
This research aimed to examine whether the patterns of excess deaths during early 2020 matched what scientists would expect from a spreading respiratory virus. The researchers analysed detailed death records from Europe and the United States during the period from March to May 2020, looking at when and where people died in unprecedented numbers. They wanted to test whether the official explanation – that a novel coronavirus was spreading from person to person and causing these deaths – actually fit the data.
The study used high-resolution geographic and time-based data to create a comprehensive picture of mortality patterns across two continents. By examining death rates at the level of counties, regions and even city boroughs, the researchers could identify specific patterns that either supported or contradicted the viral spread theory. Their goal was to provide an evidence-based analysis that could inform our understanding of what really happened during those critical early months of the declared pandemic.
2. What is a “P-score” and why is it important for understanding death rates?
A P-score measures how much higher (or lower) the death rate was compared to what would normally be expected based on previous years. Think of it as a percentage showing the increase in deaths above the baseline – if a region normally has 100 deaths per week but suddenly has 150 deaths, that’s a P-score of 50%. This measurement automatically accounts for differences in population age and health between different areas, making it fair to compare a young, healthy population with an older, sicker one.
P-scores are crucial because they reveal the true impact of any event on mortality while filtering out normal seasonal variations and demographic differences. Unlike raw death counts, which can be misleading when comparing differently sized populations, P-scores show the relative intensity of excess mortality. This allows researchers to identify genuine hotspots of unusual deaths and compare the severity of events across completely different geographic regions, from rural counties to major metropolitan areas.
3. What time periods did the researchers focus on and why?
The researchers primarily focused on March through to May 2020, which they called the “first-peak period,” because this is when the largest spikes in deaths occurred immediately after the World Health Organisation declared a pandemic on 11 March 2020. They also examined June through to September 2020 as a “summer-peak period” to see how patterns changed over time. These specific timeframes were chosen because they captured the initial wave of excess mortality that supposedly resulted from the novel coronavirus.
The timing is significant because it allows researchers to examine what happened before any significant medical interventions like vaccines were available, focusing purely on the immediate response to the declared pandemic. By using 2015-2019 as their baseline for comparison, they could identify exactly when unusual death patterns began and how they evolved. This timeframe also lets them analyse whether the deaths followed patterns consistent with a naturally spreading infectious disease or whether other factors might explain the mortality spikes.
4. What did the researchers discover about the timing of death spikes across different countries?
The researchers found that death spikes across Europe and the United States occurred with remarkable synchronisation – all within about three weeks of each other and, notably, none occurred before the WHO’s pandemic declaration on 11 March 2020. Countries as far apart as Italy, Spain, the UK and various US states all experienced their peak death rates within this narrow window, despite having very different geographic locations, climates, and population densities.
This synchronisation was unexpected because if a virus were naturally spreading from person to person across continents, you would expect much more variation in timing as it gradually moved through different populations and transportation networks. The fact that no major death spike occurred anywhere before the official pandemic announcement, combined with the tight clustering of peaks immediately afterwards, suggests that something other than natural viral spread was driving these mortality patterns. This timing pattern is more consistent with a coordinated response or policy change than with organic disease transmission.
5. How did death rates vary between different regions during the first few months of 2020?
The variation in death rates was extreme and geographically patchy rather than following expected patterns of disease spread. Some regions experienced death rate increases of over 200%, while neighbouring areas with similar populations and characteristics had little to no excess mortality. For example, in Europe, western countries like Spain, Italy and the UK saw massive death spikes, while eastern European countries with major airports and urban centres experienced virtually no excess deaths during the same period.
This patchwork pattern extended down to very local levels – some counties in the United States had death rates that were double or triple normal levels, while adjacent counties remained unaffected. The highest death rates often occurred in specific urban areas with large hospitals, particularly those serving economically disadvantaged populations. Rather than showing the gradual geographic spread you’d expect from a contagious disease, the pattern resembled distinct hotspots of mortality that seemed more related to local institutional factors than to natural disease transmission.
6. What patterns did researchers find when comparing neighbouring countries with similar populations?
The researchers discovered dramatic differences in death rates between neighbouring countries that should have had similar experiences if a virus were spreading naturally. Most striking was the contrast along Germany’s western border, where German regions had very low excess mortality while the adjacent regions in France, Belgium and the Netherlands experienced some of the highest death rates in Europe. These areas are densely populated, have significant daily cross-border traffic for work and commerce, and share similar demographics and healthcare systems.
This pattern defies the logic of infectious disease spread, where you would expect neighbouring regions with high interaction to have similar infection and death rates. The researchers documented that substantial cross-border traffic continued during this period despite some travel restrictions, making it implausible that a virus would respect political boundaries so precisely. Similar patterns appeared along other European borders and between US states, suggesting that political or administrative factors, rather than natural disease transmission, were driving the geographic distribution of excess deaths.
7. Why did the researchers compare Milan to Rome and New York City to Los Angeles?
These city comparisons were designed to test whether factors like international air travel and population characteristics could explain the dramatic differences in death rates. Milan and Rome are both major Italian cities with large international airports, similar demographics, healthcare systems and age structures. However, Rome actually received more direct flights from China and Asia than Milan, yet Milan’s region experienced death rates 18 times higher than Rome’s region during the critical period.
Similarly, Los Angeles and San Francisco received far more flights from China and had larger Asian populations than New York City, yet New York experienced catastrophic death rates while the West Coast cities remained relatively unaffected. This comparison demolishes the theory that the virus arrived via air travel from Asia and spread naturally through communities. If that were true, the cities with the most direct exposure to the supposed source of infection should have been hit hardest, but the opposite occurred. These comparisons strongly suggest that local factors – particularly medical treatment protocols – rather than viral importation determined mortality outcomes.
8. What did the study reveal about where people were dying during the peak periods?
The study found a clear pattern: in areas with high excess death rates, an unusually high percentage of deaths occurred in hospitals and nursing homes rather than at home, while areas with low excess death rates actually saw an increase in deaths at home. This represents a reversal of normal patterns, where most people typically die at home. States and counties with the highest death rates saw dramatic increases in the share of deaths occurring in medical facilities compared to the same months in 2019.
This shift toward institutional deaths in high-mortality areas suggests that medical facilities themselves may have played a role in the excess mortality rather than simply treating victims of an external disease. The correlation between high death rates and high rates of hospital deaths, combined with the increase in home deaths in low-mortality areas, indicates that proximity to and interaction with medical institutions may have been a risk factor rather than a protective factor during this period.
9. How did computer models predict the pandemic would spread and what actually happened?
Computer models used by scientists to predict pandemic spread anticipated that virtually all major cities and regions with international airports would experience significant outbreaks by February 2020, with infection rates ranging from 31-38% across European countries. These models, based on air travel patterns and population mixing, predicted that diseases would spread relatively evenly through connected populations, with rural areas being affected weeks after urban centres but still experiencing substantial mortality.
What actually happened was completely different: many major cities and entire countries with significant international connections experienced minimal excess mortality, while the deaths were concentrated in specific hotspots that didn’t correlate with the predicted spread patterns. The models failed to predict the extreme geographic patchiness, the tight synchronisation of death spikes or the fact that some of the most internationally connected cities would be virtually unaffected. This mismatch between model predictions and reality suggests that the deaths were not caused by a naturally spreading infectious agent.
10. What role did hospitals and intensive care units play in the excess deaths?
Hospitals, particularly their intensive care units, appear to have been central to the excess mortality rather than innocent victims of overwhelming disease. Regions that dramatically expanded ICU capacity and aggressively admitted patients to intensive care experienced the highest death rates, while regions that took more conservative approaches had much lower mortality. For example, Lombardy in Italy created hundreds of new ICU beds and systematically placed covid-diagnosed patients on mechanical ventilators, experiencing catastrophic death rates, while neighbouring regions with similar demographics but different treatment approaches had much lower mortality.
The correlation between ICU expansion and death rates suggests that the medical response itself may have been deadly. Regions that maintained normal hospital operations and didn’t surge their intensive care capacity generally avoided the massive death spikes. This pattern indicates that aggressive medical intervention, rather than disease severity, may have been the primary driver of excess mortality in hotspot areas. The data shows that interaction with the medical system, particularly intensive care, became a significant risk factor during this period.
11. What is mechanical ventilation and why might it have been dangerous during this period?
Mechanical ventilation involves inserting a tube into a patient’s airway and using a machine to force air into and out of the lungs when they cannot breathe adequately on their own. While this can be life-saving in appropriate circumstances, it carries serious risks, including ventilator-associated pneumonia (infection in the lungs) and ventilator-induced lung injury from the pressure and artificial breathing patterns. Even under normal circumstances, mortality rates for ventilated patients range from 20-76% depending on their condition.
During the early pandemic period, mechanical ventilation was used far more aggressively than normal, with some hospitals placing 88% of their covid-diagnosed patients on ventilators. In New York City hospitals, 88% of patients put on ventilators died, including 97% of elderly patients. The situation was made worse by the use of untested methods due to equipment shortages, including using anaesthesia machines not designed for critically ill patients (which had 70% mortality rates) and splitting single ventilators between multiple patients despite professional warnings against this dangerous practice. This aggressive and often experimental use of mechanical ventilation likely contributed significantly to the excess deaths.
12. What medications were being used to treat patients and what were their risks?
Two main categories of medications appear to have contributed to excess mortality: hydroxychloroquine combined with azithromycin, and various sedatives like midazolam. Hydroxychloroquine, normally used for malaria, has a “narrow therapeutic index,” meaning the difference between a helpful dose and a toxic dose is small. During the pandemic, hospitals used doses that were much higher than normal – sometimes 10 times the usual amount – which could cause fatal heart problems and nervous system damage.
The combination of hydroxychloroquine with the antibiotic azithromycin was particularly dangerous, as both drugs can affect heart rhythm, and combining them significantly increased the risk of heart failure and death. Spain, which used this combination extensively, saw azithromycin consumption increase by more than four times normal levels during March 2020. Additionally, sedatives like midazolam, typically used to calm ventilated patients, were prescribed at much higher rates than normal and have been associated with delayed recovery, increased delirium and higher mortality in critically ill patients. These medications were often used experimentally, without proper safety protocols.
13. How did socioeconomic factors like poverty and race relate to death rates?
The study found strong correlations between socioeconomic vulnerability and death rates, but only in specific geographic hotspots, not everywhere. In the New York City area, death rates increased dramatically with poverty levels, minority population percentages, crowded living conditions and language barriers. The Bronx, the poorest borough of New York City, had the highest death rate in the entire United States. However, many other areas with similar or worse socioeconomic conditions experienced little to no excess mortality.
This creates a puzzle: if poverty and minority status were simply risk factors for the disease itself, you would expect high death rates in poor communities everywhere, but that didn’t happen. Instead, socioeconomic vulnerability only predicted high death rates in areas that also had large hospital systems and aggressive medical interventions. This suggests that being poor and a minority became dangerous specifically when combined with access to certain types of medical care, rather than being protective factors against disease exposure or outcome in general.
14. What is the significance of deaths happening right after the WHO declared a pandemic?
The timing is remarkable because virtually no excess deaths occurred anywhere before 11 March 2020, when the World Health Organisation declared a pandemic, yet massive death spikes began immediately afterwards across multiple continents. If a deadly virus had been spreading naturally through populations for weeks or months, you would expect to see gradual increases in deaths leading up to the declaration, with the announcement simply recognising an already-occurring crisis.
Instead, the data shows that the declaration appears to have triggered the mortality crisis rather than responding to it. This suggests that the announcement itself, and the policies and medical responses it prompted, may have been the primary cause of the excess deaths. The synchronisation of death spikes with the political announcement, rather than with any biological timeline of disease spread, indicates that administrative and institutional factors were driving mortality patterns rather than natural infectious disease processes.
15. Why didn’t the virus seem to spread the way scientists expected it would?
Scientific models of infectious disease spread predict that viruses move through populations based on contact patterns, transportation networks and population density, creating waves of infection that move geographically and temporally in predictable ways. The models expected similar attack rates across connected populations and predicted that all major cities with international airports would be significantly affected by early 2020. They also anticipated that within affected areas, different neighbourhoods would experience peaks at different times based on their connectivity and demographics.
What actually happened violated all these predictions. The mortality patterns showed extreme geographic patchiness without regard to transportation networks, identical timing of peaks across vast distances and uniform timing within regions regardless of local connectivity patterns. For example, all regions within Spain experienced their mortality peaks simultaneously despite vastly different population densities and connection patterns. This type of synchronisation across diverse geographic and demographic conditions is incompatible with natural infectious disease spread and suggests that coordinated policy responses or institutional changes, rather than biological transmission, drove the mortality patterns.
16. What happened in The Bronx that made it have the highest death rate in America?
The Bronx combines several factors that created a perfect storm for high mortality: it’s the poorest borough in New York City with high rates of underlying health conditions like asthma, it has a large minority population with language barriers and it’s served by SBH Health System, a large “safety-net” hospital that dramatically expanded its capacity and aggressively treated patients. SBH increased its patient capacity by 50% and critical care capacity by over 500% within three weeks, while simultaneously making large purchases of hydroxychloroquine, azithromycin, midazolam and other medications associated with high mortality rates.
The Bronx also had the highest covid testing rate among New York City’s boroughs, meaning more residents were diagnosed and subsequently received aggressive hospital treatments. The combination of a vulnerable population, expanded hospital capacity specifically designed to serve that population, aggressive use of dangerous treatments and high testing rates created conditions where seeking medical care became extremely risky. This represents a tragic example of how well-intentioned medical expansion in disadvantaged communities may have inadvertently caused more harm than good during the crisis.
17. How did air travel patterns relate to where high death rates occurred?
Surprisingly, air travel patterns showed little to no correlation with death rates, contradicting the theory that the virus spread through international travel. Rome received significantly more direct flights from China than Milan, yet Milan’s region experienced death rates 18 times higher than Rome’s. Similarly, Los Angeles and San Francisco had more flights from Asia and larger Asian populations than New York City, yet experienced minimal excess mortality while New York was devastated.
This lack of correlation between air travel exposure and mortality outcomes undermines the fundamental assumption that the deaths were caused by a virus imported from Asia. If international travel were the primary vector for disease introduction, the cities and regions with the highest exposure should have been hit hardest and earliest. Instead, the data suggests that local factors – particularly medical treatment protocols and hospital policies – were far more important in determining mortality outcomes than international connectivity or supposed viral exposure.
18. What differences did researchers find between spring and summer death patterns?
The spring (March-May) and summer (June-September) death patterns were dramatically different in both geography and characteristics. Spring deaths were concentrated in northeastern US states and western European countries, particularly in urban areas with large hospital systems. Summer deaths occurred primarily in southern US states, especially counties along the Mexican border and the Mississippi River, areas characterised by rural poverty rather than urban hospital concentration.
The demographic correlations also shifted completely between periods. Spring deaths correlated with urban characteristics like high population density, proximity to major medical centres and certain types of socioeconomic vulnerability. Summer deaths correlated with rural poverty, different ethnic populations and different geographic regions entirely. This dramatic shift in patterns suggests that different causes were operating in each period rather than the same virus affecting different populations. If a single infectious agent were responsible, you would expect more consistent geographic and demographic patterns across time periods.
19. What is “biological stress” and how might it have contributed to deaths?
Biological stress refers to the body’s physical response to psychological, social and environmental pressures, which can suppress immune function and make people more susceptible to infections, particularly bacterial pneumonia. During the early pandemic period, lockdown measures, social isolation, fear campaigns and disrupted medical care created unprecedented levels of stress for many people, especially the elderly and those in institutional settings like nursing homes.
This stress-induced immune suppression could have made people vulnerable to developing bacterial pneumonia through their own respiratory bacteria, without requiring transmission from others. The researchers propose that many of the deaths attributed to covid may have actually been bacterial pneumonias triggered by the extreme stress of lockdowns and institutional responses. This would explain why deaths correlated with socioeconomic vulnerability (stress affects poor people more severely) and why they occurred synchronously with policy announcements rather than following natural disease transmission patterns.
20. Why did some areas with large airports have low death rates while others had high death rates?
The lack of correlation between airport size and death rates contradicts the theory that deaths resulted from virus importation through air travel. Many major international airports in cities like Los Angeles, San Francisco, Atlanta and various German cities served regions that experienced minimal excess mortality, while some areas with smaller airports or less international connectivity had catastrophic death rates. This pattern makes no sense if air travel were the primary route of virus introduction.
Instead, the data suggest that local medical policies and institutional responses determined mortality outcomes. Areas that expanded hospital capacity, aggressively used mechanical ventilation, implemented experimental drug protocols and had large safety-net hospitals serving vulnerable populations experienced high death rates regardless of their airport connectivity. Areas that maintained more conservative medical approaches avoided major mortality increases even if they had significant international exposure. This indicates that medical intervention strategies, rather than disease importation, were the key factor determining regional mortality outcomes.
21. What role did lockdown policies play in the timing of death spikes?
The timing of death spikes correlates remarkably with the implementation of national lockdowns rather than with expected disease transmission timelines. Italy implemented its first national lockdown on 9 March 2020, and experienced its mortality peak during the week of that lockdown. Spain’s lockdown began on 15 March, with deaths spiking the following week. The UK’s lockdown started on 23 March, with mortality increases beginning that same week.
This tight correlation between lockdown implementation and mortality spikes suggests that the policy responses themselves may have triggered the death increases rather than responding to an existing health crisis. The stress, disruption of normal medical care, institutional panic and aggressive medical interventions that accompanied lockdowns may have created the conditions for excess mortality. If deaths were simply the result of natural disease spread, you would expect mortality peaks to drive lockdown decisions, not the reverse pattern observed in the data.
22. How did treatment approaches differ between regions with high and low death rates?
Regions with high death rates consistently adopted aggressive, experimental medical approaches including massive ICU expansion, widespread mechanical ventilation and experimental drug protocols. Northern Italy expanded ICU capacity by over 100% and placed 88% of covid-diagnosed patients on ventilators. New York City similarly surged its ICU capacity and used experimental ventilation techniques. Spain extensively used high-dose hydroxychloroquine combinations. These regions also increased usage of sedatives and implemented systematic testing programmes that funnelled more patients into aggressive treatment protocols.
In contrast, regions with low death rates maintained more conservative medical approaches. The neighbouring Veneto region in Italy focused on home care rather than hospital admissions and experienced much lower mortality than Lombardy. Many German regions, Eastern European countries and US states that didn’t surge their hospital capacity or implement aggressive experimental treatments avoided major death spikes despite similar demographics and supposed viral exposure. This pattern strongly suggests that treatment protocols, rather than disease severity, determined regional mortality outcomes.
23. What evidence suggests that hospital treatments may have caused more harm than good?
Multiple lines of evidence point to hospital treatments as a major cause of excess mortality. First, mortality rates for specific treatments were extremely high: 88% of mechanically ventilated patients died in New York hospitals and experimental ventilation methods had mortality rates up to 70%.
Second, the medications used were often at dangerous doses – hydroxychloroquine doses were sometimes 10 times normal levels, and the drug combinations used had known risks for fatal heart complications.
Third, the geographic correlation between aggressive treatment and death rates is striking: every region that dramatically expanded ICU capacity and implemented experimental protocols experienced high mortality, while conservative regions avoided excess deaths.
Fourth, the timing shows deaths beginning after treatment protocols were implemented rather than before.
Finally, the shift from home deaths to hospital deaths in high-mortality areas suggests that seeking medical care became dangerous rather than protective.
The evidence indicates that well-intentioned but aggressive medical interventions likely killed more people than they saved during this period.
24. Why do the researchers believe the deaths were not caused by a spreading virus?
The researchers identified multiple patterns that are incompatible with viral disease spread but consistent with institutional causes.
First, the extreme geographic patchiness – some areas experiencing massive death spikes while similar neighbouring areas remained unaffected – cannot be explained by natural disease transmission.
Second, the synchronisation of death peaks immediately after political announcements rather than following biological transmission timelines suggests policy-driven rather than disease-driven mortality.
Third, the lack of correlation between supposed exposure factors (air travel, population density, international connectivity) and mortality outcomes contradicts viral spread theory.
Fourth, the correlation between aggressive medical interventions and death rates suggests iatrogenic (treatment-caused) rather than infectious causes.
Finally, the demographic patterns showing that vulnerability factors only predicted death in specific geographic locations with particular medical approaches indicate that institutional factors, not infectious disease, determined outcomes.
The totality of evidence shows a pattern of institutional and medical system-induced mortality rather than natural disease spread.
25. What alternative explanation do the researchers propose for the excess deaths?
The researchers propose that the excess deaths were primarily iatrogenic – caused by medical treatments and institutional responses rather than by a spreading virus. They suggest that the combination of aggressive and often experimental medical interventions (mechanical ventilation, high-dose drug combinations, sedatives), institutional panic leading to dangerous treatment protocols and stress-induced bacterial pneumonias created the mortality catastrophe.
Specifically, they propose that lockdown measures and fear campaigns created severe biological stress that suppressed immune systems, making people vulnerable to bacterial pneumonias from their own respiratory bacteria. When these stressed individuals sought medical care, they encountered aggressive treatment protocols including dangerous ventilation techniques and experimental drug combinations that often proved fatal. This explains why death rates correlated with hospital interactions, socioeconomic stress factors and institutional capacity rather than with expected disease transmission patterns. The deaths were real and respiratory-related, but caused by the response to the declared pandemic rather than by a spreading virus.
26. What are the implications of these findings for how we understand pandemics?
These findings suggest that current pandemic models and responses may be fundamentally flawed and potentially more dangerous than the diseases they purport to address. If the excess mortality was primarily caused by institutional responses rather than natural disease spread, then aggressive medical interventions, lockdown policies and panic-driven treatment protocols may have created the very catastrophe they were supposed to prevent.
The research implies that epidemiological thinking needs a major paradigm shift away from assuming that reported disease outbreaks represent natural infectious processes toward examining how institutional and medical responses may create or exacerbate mortality crises. It suggests that factors like healthcare system capacity, treatment protocols, socioeconomic stress and policy responses may be more important determinants of mortality outcomes than infectious disease characteristics. This has profound implications for future pandemic preparedness, suggesting that more conservative, less aggressive responses might save more lives than dramatic expansions of medical interventions.
27. What does this research suggest about the relationship between wealth, poverty and access to medical care?
The research reveals a disturbing paradox: the highest death rates occurred in poor communities that were located near wealthy areas with well-funded, large-capacity hospital systems. The Bronx (next to wealthy Manhattan) and London boroughs like Brent and Westminster demonstrate this pattern. These areas had large “safety-net” hospitals funded by philanthropic wealthy residents intended to serve disadvantaged populations, but these same hospitals became sites of aggressive experimental treatments that proved deadly.
This suggests that well-intentioned efforts to provide medical access to disadvantaged communities may have inadvertently created additional risks during the crisis. Poor people near wealthy areas with expansive medical facilities faced greater danger than poor people in areas without such medical infrastructure. The data indicate that during this period, having access to aggressive medical intervention was more dangerous than beneficial, meaning that the usual assumption that more medical access equals better health outcomes was reversed. This raises profound questions about medical “equity” and whether expanding medical system capacity always serves the interests of vulnerable populations.
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