I was wrong. We in the scientific community were wrong. And it cost lives.
I can see now that the scientific community from the CDC to the WHO to the FDA and their representatives, repeatedly overstated the evidence and misled the public about its own views and policies, including on natural vs. artificial immunity, school closures and disease transmission, aerosol spread, mask mandates, and vaccine effectiveness and safety, especially among the young. All of these were scientific mistakes at the time, not in hindsight. Amazingly, some of these obfuscations continue to the present day.
Professor John Ioannidis
Professor John Ioannidis is physician-scientist, writer and Stanford University professor who has made contributions to evidence-based medicine, epidemiology, and clinical research. Ioannidis studies scientific research itself, meta-research primarily in clinical medicine and the social sciences.
Ioannidis’s 2005 essay “Why Most Published Research Findings Are False” was the most-accessed article in the history of Public Library of Science as of 2020, with more than 3 million views. (Profile source)
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Published 20 December 2010
If peer review was a drug it would never be allowed onto the market,’ says Drummond Rennie, deputy editor of the Journal Of the American Medical Association and intellectual father of the international congresses of peer review that have been held every four years since 1989. Peer review would not get onto the market because we have no convincing evidence of its benefits but a lot of evidence of its flaws.
Yet, to my continuing surprise, almost no scientists know anything about the evidence on peer review. It is a process that is central to science – deciding which grant proposals will be funded, which papers will be published, who will be promoted, and who will receive a Nobel prize. We might thus expect that scientists, people who are trained to believe nothing until presented with evidence, would want to know all the evidence available on this important process. Yet not only do scientists know little about the evidence on peer review but most continue to believe in peer review, thinking it essential for the progress of science. Ironically, a faith based rather than an evidence based process lies at the heart of science.
Highlights
∗ Across 31 systematically identified national seroprevalence studies in the pre-vaccination era, the median infection fatality rate of COVID-19 was estimated to be 0.034% for people aged 0–59 years people and 0.095% for those aged 0–69 years.
∗ The median IFR was 0.0003% at 0–19 years, 0.002% at 20–29 years, 0.011% at 30–39 years, 0.035% at 40–49 years, 0.123% at 50–59 years, and 0.506% at 60–69 years.
∗ At a global level, pre-vaccination IFR may have been as low as 0.03% and 0.07% for 0–59 and 0–69 year old people, respectively.
∗ These IFR estimates in non-elderly populations are lower than previous calculations had suggested.
A documentary with epidemiologist Dr. John Ioannidis about the scientific community and government response to Covid.
There are a lot of frightening developments in the world, but I want to take a step back and think positive, think about the good things in our world.
Think about the good things, about what we can achieve, about the younger generations, about our future, about our dreams, about our creativity, about how much we can do, how much we can change our world for the better.
There are threats all over the place. Of course, we have climate change, we have war, we have pandemics, we have disease, we have inequalities, we have hunger, we have poverty, we have all sorts of things to worry about.
But the worst thing would be to just keep threatening people, and putting that ghost of disaster that is coming to us. Because if we do that, disaster will come to us sooner or later. And we will just create it with our own hands
Trailer
Full documentary
Across 31 systematically identified national seroprevalence studies in the pre-vaccination era, the median infection fatality rate of COVID-19 was estimated to be 0.035% for people aged 0-59 years people and 0.095% for those aged 0-69 years.
The median IFR was
* 0.0003% at 0-19 years
* 0.003% at 20-29 years
* 0.011% at 30-39 years
* 0.035% at 40-49 years
* 0.129% at 50-59 years
* and 0.501% at 60-69 years.At a global level, pre-vaccination IFR may have been as low as 0.03% and 0.07% for 0-59 and 0-69 year old people, respectively.
These IFR estimates in non-elderly populations are lower than previous calculations had suggested.
https://www.medrxiv.org/content/10.1101/2022.10.11.22280963v1
Abstract
The present coronavirus crisis caused a major worldwide disruption which has not been experienced for decades. The lockdown-based crisis management was implemented by nearly all the countries, and studies confirming lockdown effectiveness can be found alongside the studies questioning it. In this work, we performed a narrative review of the works studying the above effectiveness, as well as the historic experience of previous pandemics and risk-benefit analysis based on the connection of health and wealth. Our aim was to learn lessons and analyze ways to improve the management of similar events in the future. The comparative analysis of different countries showed that the assumption of lockdowns’ effectiveness cannot be supported by evidence—neither regarding the present COVID-19 pandemic, nor regarding the 1918–1920 Spanish Flu and other less-severe pandemics in the past. The price tag of lockdowns in terms of public health is high: by using the known connection between health and wealth, we estimate that lockdowns may claim 20 times more life years than they save. It is suggested therefore that a thorough cost-benefit analysis should be performed before imposing any lockdown for either COVID-19 or any future pandemic.
Conclusions
While our understanding of viral transmission mechanisms leads to the assumption that lockdowns may be an effective pandemic management tool, this assumption cannot be supported by the evidence-based analysis of the present COVID-19 pandemic, as well as of the 1918–1920 H1N1 influenza type-A pandemic (the Spanish Flu) and numerous less-severe pandemics in the past. The price tag of lockdowns in terms of public health is high: we estimate that, even if somewhat effective in preventing death caused by infection, lockdowns may claim 20 times more life than they save. It is suggested therefore that a thorough cost-benefit analysis should be performed before imposing any lockdown in the future.
A series of aggressive restrictive measures around the world were adopted in 2020-2022 to attempt to prevent SARS-CoV-2 from spreading. However, it has become increasingly clear that an important negative side-effect of the most aggressive (lockdown) response strategies may involve a steep increase in poverty, hunger, and inequalities. Several economic, educational and health
repercussions have not only fallen disproportionately on children, students, and young workers, but also and especially so on low-income families, ethnic minorities, and women, exacerbating existing inequalities. For several groups with pre-existing inequalities (gender, socio-economic and racial), the inequality gaps widened. Educational and financial security decreased, while domestic violence surged. Dysfunctional families were forced to spend more time with each other, and there has been growing unemployment and loss of purpose in life. This has led to a vicious cycle of rising inequalities and health issues. In the current narrative and scoping review, we describe macro-dynamics that are taking place as a result of aggressive public health policies and psychological tactics to influence public behavior, such as mass formation and crowd behavior. Coupled with the effect of inequalities, we describe how these factors can interact towards aggravating ripple effects. In light of evidence regarding the health, economic and social costs, that likely far outweigh potential benefits, the authors suggest that, first, where applicable, aggressive lockdown policies should be reversed and their re-adoption in the future should be avoided. If measures are needed,
these should be non-disruptive. Second, it is important to assess dispassionately the damage done by aggressive measures and offer ways to alleviate the burden and long-term effects. Third, the structures in place that have led to counterproductive policies, should be assessed and ways should be sought to optimize decision-making, such as counteracting groupthink and increasing the
level of reflexivity. Finally, a package of scalable positive psychology interventions is suggested to counteract the damage done and improve future proespects for humanity.
“Following the science” became a mainstay mantra of the pandemic, frequently trotted-out to justify unpalatable policy decisions such as banning hugging or denying fathers the right to attend the birth of a child.
Yet as Britain’s epidemic begins to fade away, it is becoming increasingly clear that many influential scientists were ignored, ridiculed and shunned for expressing moderate views that the virus could be managed in a way which would cause far less collateral damage.
Instead, a narrow scientific “groupthink” emerged, which sought to cast those questioning draconian policies as unethical, immoral and fringe. That smokescreen is finally starting to dissipate.
Anti-lockdown scientists were viewed as having ‘fringe’ ideas because those calling for draconian restrictions had more followers on social media, a study has shown.
Professor John Ioannidis, of Stanford University, an expert in data science and the reliability of research, studied the expertise of authors who signed the Great Barrington Declaration (GBD) compared with signatories of the John Snow Memorandum.
…In an article published in BMJ Open Research, he found that both letters were authored by very influential experts, but that the John Snow Memorandum authors had a far greater reach on social media, which made it appear that their view had more support.
…Prof Ioannidis concluded: “Both the Great Barrington Declaration and John Snow Memorandum include many stellar scientists, but JSM has far more powerful social media presence and this may have shaped the impression that it is the dominant narrative.
Results Among the 47 key GBD signatories, 20, 19 and 21, respectively, were top-cited authors for career impact, recent single-year (2019) impact or either. For comparison, among the 34 key JSM signatories, 11, 14 and 15, respectively, were top cited. Key signatories represented 30 different scientific fields (9 represented in both documents, 17 only in GBD and 4 only in JSM). In a random sample of n=30 scientists among the longer lists of signatories, five in GBD and three in JSM were top cited. By April 2021, only 19/47 key GBD signatories had personal Twitter accounts versus 34/34 of key JSM signatories; 3 key GBD signatories versus 10 key JSM signatories had >50 000 Twitter followers and extraordinary Kardashian K-indices (363–2569). By November 2021, four key GBD signatories versus 13 key JSM signatories had >50 000 Twitter followers.
Conclusions Both GBD and JSM include many stellar scientists, but JSM has far more powerful social media presence and this may have shaped the impression that it is the dominant narrative.
http://archive.today/2022.02.16-000944/https://bmjopen.bmj.com/content/12/2/e052891
One of the checks and balances on rampant bad scientific research is to continuously assess how new ideas fit into the framework of the bigger picture. A new piece of information may seem perfectly reasonable and well-documented, but the domino effect of its implications gives you another way to test its validity. When multiple lines of seemingly rock-solid evidence contradict one another, that’s a good sign that something is wrong, even if you don’t yet know why. Whenever a thread seems out of place, it’s time to pull on that thread until you can figure out what exactly is going on.
…”Trusting the science” is not (and never has been) about trusting results or trusting experts. Trusting the scientists is what got us into this mess. For science to function properly, we must NOT trust the scientists. Instead, we must trust in the messy self-correcting process that allows truth to boil to the surface even if every participant in that process is flawed.
“Science is the belief in the ignorance of the Experts”
— Richard P. FeynmanScience is the relentless competition between measurable pieces of evidence, the ruthless gauntlet of debate, the willingness to question even the most “obvious” of assumptions, and the humbleness to test and retest any and all assumptions against hard evidence, most especially when those assumptions are our own.
https://www.juliusruechel.com/2022/01/who-do-you-trust-if-you-cant-trust.html
“Lockdowns,” the mass quarantine of both sick and healthy people, have never before been used for disease mitigation in the modern Western world. Previously, the strategy had been systematically ruled out by the pandemic plans of the World Health Organization (WHO) and by health experts of every developed nation. So how did we get here?
https://www.tabletmag.com/sections/news/articles/masked-ball-cowardice
The scientific literature and publishing scientists have been rapidly and massively infected by COVID-19 creating opportunities and challenges. There is evidence for hyper-prolific productivity.
Results While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproductive number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons.
Conclusions Inferences on effects of NPIs are non-robust and highly sensitive to model specification. Claimed benefits of lockdown appear grossly exaggerated.
https://www.medrxiv.org/content/10.1101/2020.07.22.20160341v3
- We have experience of SARS in 2003 and MERS in 2012, while in the UK there are at least four known strains of coronavirus which cause the common cold.
- Many individuals who’ve been infected by other coronaviruses have immunity to closely related ones such as the Covid-19 virus.
- Multiple research groups in Europe and the US have shown that around 30 per cent of the population was likely already immune to Covid-19 before the virus arrived – something which Sage continues to ignore.
- Prof. John Ioannidis, professor of epidemiology at Stanford University in California, have concluded that the mortality rate is closer to 0.2 per cent – 1 in 500 infected die.
- Around 45,000 Covid deaths in the UK
- Approximately 22.5million people have been infected – 33.5 per cent of our population – not Sage’s 7 per cent calculation.
- Not every infected individual produces antibodies.
- The human immune system has several lines of defence:
- Innate immunity which is comprised of the body’s physical barriers to infection and protective secretions (the skin and its oils, the cough reflex, tears etc);
- Inflammatory response (to localise and minimise infection and injury), and the production of non-specific cells (phagocytes) that target an invading virus/bacterium.
- Antibodies that protect against a specific virus or bacterium (and confer immunity) and T-cells (a type of white blood cell) that are also specific.
- T-cells that are crucial in our body’s response to respiratory viruses such as Covid-19.
- World Health Organisation says 750million people have been infected by the virus as of October and almost none have been reinfected.
- Mortality in 2020 so far ranks eighth out of the last 27 years.
- The death rate at present is also normal for the time of year – the number of respiratory deaths is actually low for late October.
- Not only is the virus less dangerous than we are being led to believe, with almost three quarters of the population at no risk of infection.
- I am convinced this so-called second wave of rising infections and, sadly, deaths will fizzle out without overwhelming the NHS.
Interview highlights
- COVID-19 is not a dread disease that will kill everyone.
- The initially high case fatality rate of COVID-19 was because the medical community didn’t know how to treat it.
- The fatality rate of flu is 0.1% (1 in every 1,000 who are infected end up dying).
- Ventilators are the wrong option if you do not have an obstructed airway disease.
- Prod. Ioannidis: The infection fatality ratio of COVID-19 is 0.15%. This is pretty much the same as the flu.
- We should just ask people to be careful but otherwise go about your daily life.
- These things pass every year. This is the first ‘social media pandemic.’
- The normal practice for intensive care beds in the NHS is to run them almost full. This is because a lot of intensive care bed assignment is planned.
- ICU use at the height of the pandemic was has very low because the NHS was run as light as possible to cope with a second wave.
- Respiratory viruses don’t do waves.
- This is not opinion but is basic understanding among experts in the field. It is supposrted by the highest quality science. Sir Patrick Vallance knows this.
- COVID-19 follows the Gompertz Curve.
- You have immunity after your body has fought off a respiratory virus. If that was not the case, you’d be dead. Immunity probably lasts decades based on evidence from other viruses.
- Gompertz Curve is identical in all heavily infection regions.
- Something awefull happened in the middle of the year: PCR swab test.
- It is not true that if you test more people you’ll save more lives. A certain percentage of the test will come up positive even if there’s no virus in you.
- False positive rate wasn’t released.
- Kate Barker wrote in a government document on June 3rd, 2020, to SAGE: test has an unknown false positive rate; based on similar tests it may be between 1%-2%. This is a big deal.
- Based on 1%: for every 1,000 people you test, 10 will come back positive, even if they don’t have the virus. If prevalence is only 0.1% as reported by ONS, only 1 in 1,000 will be genuine. This means 9 in 10–in other words 90%–are false.
- Pillar 2 testing would have caused of the most of the positives to be false.
- 1,700 people die normally every day in the UK. During the summer, only about 10 were dying per day of covid.
- More testing, more false positives. We’ll never escape covid if we keep testing because most of the positives will be false. This is immunology 101. Sir Patrick Vallance would have known this.
- Influenza is a high mutation-rate virus. Coronaviruses are relatively stable so once you’ve recovered, you are probably immune for decades.
- COVID-19 kills 0.15%-0.2%, slightly more lethal than the average flu. Once it’s gone through the population, it won’t come back.
- 99.94% survive COVID-19 and will be resistant for a long time.
- COVID-19 is 80% similar to SARS-COV-1.
- People who were exposed to SARS have T-cell immunity 17 years later. Evidence for COVID-19 all point in direction.
- Our bodies have many lines of defense, including innate immunity and T-cells. Antibodies are in the last line of defense.
- Study shows around 30% prior immunity to SARS-COV-2. It was due to exposure to common-cold coronaviruses.
- The claim made by Sir Patrick Vallance that more than 90% are susceptible is a lie.
- Mass testing of the well populating is the worst problem as it generates false positives, fear and control.
- If you’re immune, you can’t be infected or infectious. Herd immunity is already in play in London.
- If SAGE is correct, London should be ‘ablaze’ with deaths.
- Current testing methods are not forensically sound.
- Tests detect common cold and dead virus.
- SARS-COV-2 has never really been a public health emergency.
- We do not need the vaccine to return to normal. Most people are not in danger from COVID-19. More people are in danger from car crashes and we accept that risk.
- Best case scenario is that the vaccine is 50% effective. Natural immunity might be better.
- The most vulnerable often don’t respond well to vaccines and die anyway.
- SAGE is giving lethally wrong advice.
- The reason the pandemic is not over is because SAGE says it’s not.
Results
I included 61 studies (74 estimates) and eight preliminary national estimates. Seroprevalence estimates ranged from 0.02% to 53.40%. Infection fatality rates ranged from 0.00% to 1.63%, corrected values from 0.00% to 1.54%. Across 51 locations, the median COVID-19 infection fatality rate was 0.27% (corrected 0.23%): the rate was 0.09% in locations with COVID-19 population mortality rates less than the global average (< 118 deaths/million), 0.20% in locations with 118–500 COVID-19 deaths/million people and 0.57% in locations with > 500 COVID-19 deaths/million people. In people < 70 years, infection fatality rates ranged from 0.00% to 0.31% with crude and corrected medians of 0.05%.
Conclusion
The infection fatality rate of COVID-19 can vary substantially across different locations and this may reflect differences in population age structure and case-mix of infected and deceased patients and other factors. The inferred infection fatality rates tended to be much lower than estimates made earlier in the pandemic.
- As of October 2020, there are >1 million documented deaths with COVID‐19.
- Many early deaths may have been due to suboptimal management, malfunctional health systems, hydroxychloroquine, sending COVID‐19 patients to nursing homes, and nosocomial infections; such deaths are partially avoidable moving forward.
- About 10% of the global population may be infected by October 2020.
- Global infection fatality rate is 0.15‐0.20%
- Global infection fatality rate in those younger than 70 years old is 0.03‐0.04%.
- Targeted, precise management of the pandemic and avoiding past mistakes would help minimize mortality.
Many clinical research studies, even in the major general medical journals, do not satisfy the identifiable features that make them useful. These features include:
- problem base;
- context placement;
- information gain;
- pragmatism;
- patient centeredness;
- value for money;
- feasibility;
- transparency.
Most clinical research findings false. Further, most of the true findings do not result in huge human benefit. Reform and improvement in the clinical research are overdue.
See also: Peer review: a flawed process at the heart of science and journals by Richard Smith at the Journal of the Royal Society of Medicine
Quoted summary points
Blue-sky research cannot be easily judged on the basis of practical impact, but clinical research is different and should be useful. It should make a difference for health and disease outcomes or should be undertaken with that as a realistic prospect.
Many of the features that make clinical research useful can be identified, including those relating to problem base, context placement, information gain, pragmatism, patient centeredness, value for money, feasibility, and transparency.
Many studies, even in the major general medical journals, do not satisfy these features, and very few studies satisfy most or all of them. Most clinical research therefore fails to be useful not because of its findings but because of its design.
The forces driving the production and dissemination of nonuseful clinical research are largely identifiable and modifiable.
Reform is needed. Altering our approach could easily produce more clinical research that is useful, at the same or even at a massively reduced cost.
https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002049