Debunking the Imperial College Covid model that led to the lockdowns in US and UK

At the outset of the Covid reaction in the UK and US, myself and many others expressed skepticism of taking away peoples rights and destroying the economy without first answering questions to prove these sorts of actions would not only be warranted, but effective in saving lives from the disease.

Many people, ranging from regular people online to media people and politicians suggested we must trust the experts and do whatever was necessary to save lives, and claimed questioning these experts wanting a lock down would lead to people dying.

Many people including myself also said that because these lockdowns and reactions by governments around the world (particularly for my focus the UK and US) were taken without answering basic questions, this would lead to the results and effectiveness of the lockdowns being non falsifiable, which would mean no matter what the outcome, ranging from almost no deaths to millions of deaths in these countries, the government would declare the lockdowns necessary and successful.

I believe that is exactly what we are seeing.

However, some information has come out shedding some light into how these governments came to their decisions on these lockdowns. Even though there remains much mystery as to exactly what data, models, etc. were used in totality by these countries, one model has came up over and over as having the greatest influence on the UK and perhaps US reactions to Covid; the Imperial College model lead by Neil Ferguson.

This model predicted in a do nothing scenario 500,000 people in the UK would die, and 2.2 million in the US.

I am going to look at this model, show that it played a key role in influencing policy in the UK and US directed towards covid, show that its author has a history of people completely wrong and over stating pandemics, and show that the model not only should have never been used, but data has shown it to be completely wrong.


1. The model was used by the US and UK

The first thing that may be asked is why focus on this model? To me it is scandalous in its own right that the US and UK didn’t specifically announce exactly what models they were using to inform their decisions. However, we can see that major media outlets and the Imperial College themselves have shown the model was instrumental in the policy decisions.

Here is an article by the New York Times, that is behind a paywall. It shows though that the prestige of the Imperial college made the US and UK help trust their models in making policy to react to Covid

www.nytimes.com...

Here is a CNN article discussing how the UK used the model.

The study, which has not been published in a peer-reviewed journal, was released on Monday by London's Imperial College COVID-19 Response Team, which says it is advising the UK government on its response strategy. The study says it used modeling that has informed the approach of the British government in recent weeks; on Monday, the government abruptly called on vulnerable and elderly Britons to isolate themselves for 12 weeks, and introduced a variety of social distancing and quarantine recommendations that days earlier seemed distant prospects.

Sir Patrick Vallance, chief scientific adviser to the UK, confirmed Tuesday that the Imperial College study was among those the UK government was looking at.

"What suppression in that paper talks about is exactly what we are doing," he said.

www.cnn.com...

Here is what it says about the models influence on US policy

Also on Monday, President Donald Trump unveiled a 15-day plan to slow new infections in the United States, including more stringent recommendations about staying home and avoiding groups of 10 people or more, among other steps.

An author of the study, Imperial College Professor Neil Ferguson, said in an email to CNN on Tuesday the study was given to the White House Coronavirus Task Force over the weekend and the US Centers for Disease Control and Prevention on Monday.

"The White House task force received it late Sunday afternoon, CDC yesterday," Ferguson wrote to CNN. "To be honest, I don't know how much it influenced decision making. But I hear Dr Birx cited it. We will be having a much more detailed discussion with the task force tomorrow morning."

During a briefing on Monday, White House coronavirus response coordinator Dr. Deborah Birx said, "We have been working on models, day and night, around the globe ... We've been working with groups in the United Kingdom. So we had new information coming out from a model." She did not specify which model she was referring to.

The fact we know the Imperial college reached out to Trumps team, and Dr. Birx admits they are working with a UK model and this was the most impactful model in the UK, as well as the timing of the US decision to start lockdown policies right after the study was released strongly suggests this is the model they were most focused on.

Given that this model was so impactful in shaping the reaction to the virus in the UK, the next relevant question to ask is why was it used.

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2. Why was this model used in the first place

One of the most troubling aspects of the one size fits all lockdown approaches we have seen in states in the US and the federal government, and in the policies in the UK, is that most honest people, even doctors and epidemiologists, admit they were not certain at all at how bad the virus would be, how much a lockdown would help stop it, etc.

Yet despite this uncertainty, somehow the default position was to take away people’s rights and collapse the economy. It seems odd that the burden of proof should be shifted to proving we shouldn’t take away people’s rights and collapse the economy if we are uncertain; we would think the opposite would be true, that before taking such drastic actions we know will have hugely damaging effects effecting millions of people, we would need to have some degree of certainty that it was necessary.

Nonetheless, we were told the worst case scenario was so appalling that we had to take away people rights and crush the economy with lockdowns.

So why was the Imperial college model chosen?

Sadly, due to the lack of transparency from both the UK and US government, we don’t know exactly why this model was favored. Perhaps as the NYT article suggests (again apologies it’s behind a pay wall) the prestige of the Imperial college led to them being trusted.

But this raises several questions.

The first of which is, what is the history of the people leading the imperial college study, and their accuracy with predictions on pandemics?

Neil Ferguson was the man at the head of this study. He has been making predictions about pandemics for a long time now. Let’s see what his track record is.

Indeed, Ferguson has been wrong so often that some of his fellow modelers call him “The Master of Disaster.”

Ferguson was behind the disputed research that sparked the mass culling of eleven million sheep and cattle during the 2001 outbreak of foot-and-mouth disease. He also predicted that up to 150,000 people could die. There were fewer than 200 deaths. Charlotte Reid, a farmer’s neighbor, recalls: “I remember that appalling time. Sheep were left starving in fields near us. Then came the open air slaughter. The poor animals were panic stricken. It was one of the worst things I’ve witnessed. And all based on a model — if’s but’s and maybe’s.”

In 2002, Ferguson predicted that up to 50,000 people would likely die from exposure to BSE (mad cow disease) in beef. In the U.K., there were only 177 deaths from BSE.

In 2005, Ferguson predicted that up to 150 million people could be killed from bird flu. In the end, only 282 people died worldwide from the disease between 2003 and 2009.

In 2009, a government estimate, based on Ferguson’s advice, said a “reasonable worst-case scenario” was that the swine flu would lead to 65,000 British deaths. In the end, swine flu killed 457 people in the U.K.


Last March, Ferguson admitted that his Imperial College model of the COVID-19 disease was based on undocumented, 13-year-old computer code that was intended to be used for a feared influenza pandemic, rather than a coronavirus. Ferguson declined to release his original code so other scientists could check his results. He only released a heavily revised set of code last week, after a six-week delay.

www.nationalreview.com...

So time and time again, this man has cried wolf, saying a virus would kill tens of thousands or more people in places like the UK, only to have those predictions be laughably overblown.

So why would the UK and US trust his model to inform their reactions to the virus?

Well perhaps you would think it is because a peer review process confirmed the model to be accurate.

Actually, the model never was peer reviewed at the time it was used by the UK and US.

Lets go back to that CNN article posted above

The study, which has not been published in a peer-reviewed journal, was released on Monday by London's Imperial College COVID-19 Response Team

And more.

Although the ICL model’s main paper has been out for over a month, an odd series of missteps continue to hamper external scrutiny of its predictive claims. In an unusual break from peer review conventions, the ICL team delayed releasing the source code for their model for over a month after their predictions. They finally released their code on April 27, 2020 through the popular code and data-sharing website GitHub, but with the unusual caveat that its “parameter files are provided as a sample only and do not necessarily reflect runs used in published papers.”

www.aier.org...

Why would this very important model take such an unusual step against peer review conventions?
This becomes an even bigger question when we see many of the people defending the lockdown look at more recent studies such as the Santa Clara study that shows the mortality rate of Covid is far less than was feared and suggest it shouldn’t be trusted because it’s not peer reviewed.

Why did the governments of the UK and US rely on a non peer reviewed model to enforce lockdowns, but will not look at non peer reviewed studies to adjust them? And why would they trust a man with such a terrible track record on pandemic predictions without first having peers review his model?

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3. Obvious initial problems with the model

Despite the lack of peer review and Fergusons awful track record, many people claimed we must trust him and his model implicitly because he is an expert. Calls that people like me were making asking the government for proof these dire predictions would come true, or that a lockdown would be helpful were met often times with scorn, with many people going so far as to say to question this model and the lockdown was tantamount to wanting people to die.

Despite this, it became obvious even to regular people that there were significant problems with using the models worst case scenario numbers.

To start with, no one with any shred of credibility in any position of power in any government in the world suggested a “do nothing” scenario. The gambit of options included helping out nursing homes with supplies and education, increasing testing and hospital resources, education for people and voluntary social distancing measures, all the way up to full lockdowns, and many things in between.

Despite this, the model pretended that it was worthwhile to look at a scenario no one was proposing to give the worst numbers possible, and predictably, those were the numbers then touted by those supporting lockdowns.

Even the Imperial College study itself admitted that the do nothing scenario was unlikely.

Although the ICL paper described its own “do nothing” scenario as “unlikely” given that it assumed the virus’s spread in the absence of even modest policy and behavioral responses, its astronomical death toll projections were widely credited at the time with swaying several governments to adopt the harsh lockdown policies that we are now living under.

www.aier.org...

Not only were these worst case scenario numbers extremely unlikely, but this was then used by advocates of lockdowns to suggest people who questioned them such as myself wanted to “do nothing” in reaction to Covid; again a position that no remotely credible person I have seen ever suggested.

But its actually worse than that. At the time the Imperial college and Neil Ferguson were creating their “do nothing” model for the UK and US, both countries had already been taking some actions, meaning before the model was even released, its do nothing scenario was impossible as actions had already been taken.

It’s worth noting that even at the time of its March 16th public release, the conditions of the ICL’s “do nothing” scenario were already violated, rendering its assumptions invalid. Most governments had already started to “do something” by that point, whether it involved public information campaigns about hygiene and social distancing or event cancellations and the early stages of the lockdown, which began in earnest a week earlier. Voluntary behavioral adaptations also preceded government policies by several weeks, with a measurable uptick in hand-washing traceable to at least February and a dramatic decline in restaurant reservations during the first two weeks of March. When read in this context, Ferguson’s decision to hype the extreme death tolls of the “do nothing” scenario to the press in mid-to-late March comes across as irresponsible.

www.aier.org...

Despite this, the do nothing death tolls were widely cited in the UK and US, even though they were already invalid. Why would leaders in both countries cite non peer reviewed predictions from a man who has been wrong over and over with predictions, and the conditions for the prediction already violated?

The Trump administration specifically cited ICL’s 2.2 million death projection on March 16th when it shifted course toward a stringent set of “social distancing” policies, which many states then used as a basis for shelter-in-place orders. In the United Kingdom, where the same model’s “do nothing” scenario projected over 500,000 deaths, the ICL team was directly credited for inducing Prime Minister Boris Johnson to shift course from a strategy of gradually building up “herd immunity” through a lighter touch policy approach to the lockdowns now in place.


…


Nonetheless, the damage from the over-hyped ICL “do nothing” scenario was already done. Indeed, as of this writing, President Trump is still citing the 2.2 million projection in his daily press conferences as the underlying rationale for the lockdowns. The New York Times’s COVID reporter Donald McNeil was also still touting the same numbers as recently as April 18th, and even a month later it remains something of a social media taboo for non-epidemiologists to scrutinize the underlying statistical claims of credentialed experts such as Ferguson.

www.aier.org...

Another problem that was obvious with the study is that despite predicting half a million deaths in the UK, two days after the UK’s lockdown policies were announced, Neil Ferguson and the Imperial College shifted their prediction to only 20 thousand deaths.

What evidence did they have that a lockdown would be so successful? We were told one of the things that warranted such drastic reactions to Covid was it being new and unknown, so how did the Imperial College predict that by following their recommendations almost to the letter, 25 times fewer people would die in the UK? That seemed more successful than most vaccines and medications we have for other infectious diseases.

In addition, although Ferguson claimed this was not a shift in his prediction, only a reevaluation taking lock down polices into account, the fact that he suggested in an interview the best case scenario for the US was 1.1 million dead suggests he did in fact change his stance on how effective a lockdown would be.

On March 20th ICL lead author Neil Ferguson reported the 2.2 million death projection to the New York Times’s Nicholas Kristof as the “worst case” scenario. When Kristof queried him further for a “best case” scenario, Ferguson answered “About 1.1 million deaths” – a projection based on a modest mitigation strategy.*

www.aier.org...

Despite these obvious problems and questions it raises for even non experts, the model was used almost without question by the US and UK, and media professionals, politicians and many others shamed anyone who dare question it.

(continued below)

4. An in depth look at more problems

As time went on, more experts started to do what the could to review this model, even though the code used to get the predictions was not released by the Imperial College until much later (and even then there were problems with it).

Despite not having all of the facts necessary for a total peer review, experts started to see problems with the model.

Enter the new NBER paper, jointly authored by a team of health economists from Harvard University and MIT. Its authors conduct a measured and tactful scrutiny of the leading epidemiology forecasts, including the ICL model at the heart of the lockdown policy decisions back in March. Among their key findings:

“The most important and challenging heterogeneity in practice is that individual behavior varies over time. In particular, the spread of disease likely induces individuals to make private decisions to limit contacts with other people. Thus, estimates from scenarios that assume unchecked exponential spread of disease, such as the reported figures from the Imperial College model of 500,000 deaths in the UK and 2.2 million in the United States, do not correspond to the behavioral responses one expects in practice.”

As the authors explain, human behavior changes throughout the course of an epidemic. Even basic knowledge of the associated risks of infection induces people to take precautionary steps (think increased handwashing, or wearing a mask in public). Expectations about subsequent policy interventions themselves induce people to alter their behavior further – and continuously so. The cumulative effect is to reduce the reliability of epidemiological forecasts, and particularly those that do not account for behavioral changes.

www.aier.org...

In other words, as a virus spreads, people react to it, even without government intervention. And when government gets involved, in further causes people to react. This makes predictions about deaths and mitigation strategies incredibly difficult to achieve. And it makes a “do nothing” scenario touted by Fergusons model to be totally irrelevant and impossible. Yet again, these are the numbers that were cited by leaders to justify the lockdown.

The same review found further problems.

The NBER study thus concludes:

“In sum, the language of these papers suggests a degree of certainty that is simply not justified. Even if the parameter values are representative of a wide range of cases within the context of the given model, none of these authors attempts to quantify uncertainty about the validity of their broader modeling choices.”

www.aier.org...

Translation; the models suggests a degree of certainty that the authors know is not possible. Again, why did we default to destroying the economy and taking away rights based on a model that was not peer reviewed, using a impossible do nothing scenario, and had no way of being certain about its conclusions yet pretended to be?

Lets again look at Fergusons track record with these predictions.

To illustrate the importance of statistical scrutiny, it helps to look to past epidemics and observe what similar debates tell us about the accuracy of competing epidemiological forecasts. In the late 1990s and early 2000s one such example played out in Great Britain concerning Creutzfeldt-Jakob Syndrome, better known by its common moniker of “Mad Cow Disease.”

In 2001 the New York Times ran a story on different epidemiological projections about the spread of Mad Cow Disease, highlighting two competing models.

…

As Cousens told the Times in 2001, “No model came up with a number exceeding 10,000 deaths and most were far lower, in the range of a few thousand deaths”

…

An estimated 177 people died from Mad Cow Disease in the UK in the wake of the 1996 outbreak. Disease mitigation measures persist in an ongoing effort to prevent a future outbreak from cattle-to-human transmissions including import/export restrictions on beef and the slaughter of cattle to contain the infection in livestock, but for the past two decades annual Mad Cow fatalities in humans have remained extremely rare.

When the 2001 Times story ran however, a different model dominated the headlines about the Mad Cow outbreak – one that projected a wide-scale pandemic leading to over 136,000 deaths in the UK. The British government relied on this competing model for its policy response, slaughtering an estimated 4 million cows in the process. The competing model did not stop at cattle either. In an additional study, they examined the disease’s potential to run rampant among sheep. In the event of a lamb-to-human transmission, the modelers then offered a “worst case” scenario of 150,000 human deaths, which they hyped to a frenzied press at the time.

In the 2001 Times article, the lead author of this more alarmist projection responded to the comparatively tiny death toll projections from the LSHTM team. Such numbers, he insisted, were “unjustifiably optimistic.” He laid out a litany of problems with the LSHTM model, describing its assumptions about earlier Mad Cow Disease exposure as “extremely naïve” and suggesting that it missed widespread “underreporting of disease by farmers and veterinarians who did not understand what was happening to their animals.” He conceded at the time that he had “since revised [the 136,000 projection] only very slightly downward,” but expressed confidence it would prove much closer to the actual count.

The lead author of the extreme Mad Cow and Mad Lamb Disease fatality projections in the early 2000s is a familiar name for epidemiological modeling.

It was Neil Ferguson of the ICL team.

He predicted 136,00 would die. 177 did. Why would we trust this man and enforce a devastating lockdown?

In addition, the Imperial College model uses a very interesting data source when touting their predictions of the success of suppression and mitigation numbers.

From the study itself.

Disentangling the relative effectiveness of different interventions from the experience of countries to date is challenging because many have implemented multiple (or all) of these measures with varying degrees of success. Through the hospitalisation of all cases (not just those requiring hospital care), China in effect initiated a form of case isolation, reducing onward transmission from cases in the household and in other settings. At the same time, by implementing population-widesocial distancing, the opportunity for onward transmission in all locations was rapidly reduced. Several studies have estimated that these interventions reduced R to below 115. In recent days, these measures have begun to be relaxed. Close monitoring of the situation in China in the coming weeks will therefore help to inform strategies in other countries.

www.imperial.ac.uk...

(continued below)

That’s right, it is praising the containment strategy of China. A strategy that included “disappearing” sick people, and reportedly welding people into their homes. Not to mention if the success of the lockdown is based off data from China, that prediction would only be accurate if the reporting of deaths was in fact accurate coming out of China. Strong evidence has came out to suggest that this is almost certainly not the case and China is intentionally misreporting their numbers.

Additionally, as shown, this model and its worst case scenario was a driving factor in the lockdowns in the US and UK. So for them to claim such a success for following their advice that it would lead to around 25 times less people dying, these countries would have to follow their suggestions almost perfectly.

The US and UK have suggested they don’t know how long their lockdowns could last, with Trump famously stating it could have been only a little over a month and been done by Easter. So how long did the Imperial College suggest locking down to achieve this best case scenario?

To avoid a rebound in transmission, these policies will need to be maintained until large stocks of vaccine are available to immunise the population–which could be 18 months or more.

If these models were used by governments to issue a lockdown, why didn’t those governments admit to us that the lockdown would need to be 18 months or more to avoid a rebound in transmission?


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5. The nail in the Coffin – Sweden

One of the reasons I suspect their was a lack of transparency by the US and UK, and in fact by Ferguson and the imperial College (regarding withholding the code they used from peer review) is that if there was transparency, people would be able to look at the facts when we come through this to see how correct those making these dire predictions were.

As I said before, by not being transparent, it allows Ferguson and these governments to claim they were right in their prediction, no matter what the outcome is.

There is one wrinkle in that plan though. If a country (or state) was to go against the recommendations of lockdowns, and not show the catastrophic deaths that were predicted, it would go a long way to showing the model was inaccurate.

Enter Sweden. Sweden did not do a nation wide lockdown, instead electing to protect vulnerable populations, and use resources encouraging more voluntary measures.

First, lets look at how Sweden didn’t lock down, and how many people, including Trump, have criticized Sweden as making the wrong decision.

In late March and early April, much of the world’s attention turned to the case of Sweden after its government broke from the lockdown policies being implemented by most other developed world governments. Sweden earned praise early on for keeping its restaurants and businesses open – albeit under moderate social distancing guidelines – in an effort to build up herd immunity rather than delay the disease until a vaccine is developed. Yet by mid-April, its alternative strategy came under a barrage of criticism by epidemiologists, pundits, and even President Trump, who blamed an uptick in COVID-related deaths in Sweden on its failure to impose a lockdown policy similar to the rest of Europe.

The latest numbers from Sweden contain several hints that it has “flattened the curve” and its death rate per capita is consistent with, or below, most other western European nations although also higher than its neighbors Denmark and Norway.

www.aier.org...

If the imperial college model is accurate, despite the demographic, economic and other differences between the UK and Sweden, we would expect the situation in Sweden to be far more dire than that of the UK.

Yet somehow, that is not the case.

As of 5/7/2020

United Kingdom 30,076 (deaths) 66.49 (population in millions) 452.35 (deaths per 100,00)
Sweden 2,941 10.18 288.81

That means per capita, the UK has nearly a 60% higher number of deaths than Sweden.

Defenders of the lockdown caution that we cant compare two countries because of differences in all sorts of factors. But two things; first, the Imperial College model itself was based largely on numbers for other countries such as China and Italy. So if we cant compare data from differing countries, then the model is obsolete anyways.

Secondly, even if there are differences in demographics, economics etc. that make a direct comparison impossible, those differences would be small, and not explain the fact the Imperial College model suggested a lockdown would save a whopping 25 times the amount of people. If the model were accurate, we would expect to see a country that did not lockdown to have a far, far greater death rate per capita than a country that did lockdown, especially the UK which they predicted at a measly 20000 deaths.

Instead we see the opposite is true; the lockdowned UK has a 60% increase in per capita deaths than Sweden.
Despite this damning evidence showing the model to be wrong, defenders of it suggest as non experts we cant question and expert like Neil Ferguson and his team.

And so now we have other experts that have weighed in on how Sweden proves this model to be terribly wrong.

(continued below)

To begin with, the Imperial College still will not release their full methodology for a proper peer review.

Put another way, they released a heavily reorganized and generic file that would permit others to run their own version of the COVID model. They do not appear to have released the actual version they ran in the March 16th paper that shaped the US and UK government policies, or the results that came from that model (a distinction that was immediately noticed by other GitHub users, prompting renewed calls to release the original code).

As of this writing, the data needed to fully scrutinize the model and results behind the March 16th ICL paper remains elusive. There may be another way though to see how the ICL model’s COVID projections are performing under pressure.

www.aier.org...

This was written nearly a month and a half after the model was publicly released and used as a reason for lockdowns.

Why the secrecy? Why are people so critical of studies that contradict the fear mongering of models like this for not being peer reviewed so accepting of the fact this model still hasn’t been?

Nonetheless, using the info the Imperial College did give out, experts began to run simulations using that model into other countries, such as Sweden. Here is what they found.

Although ICL only released scenarios and associated forecasts for the United Kingdom and United States, its model is theoretically adaptable to any country by changing the inputs to reflect its population, demographics, and the date its specific policies took effect.

In early April around the peak of the academic community’s backlash against the Swedish government’s strategy, a group of researchers at Uppsala University attempted to do just that. They released an epidemiological model for Sweden that adapted the ICL COVID-19 model from Ferguson and his colleagues, and attempted to project the effects of Sweden’s unique response on both hospital capacity and total fatalities.

So the model used the same criteria used in the Imperial Colleges model for the US and UK, but adjusted for Sweden’s demographics, hospital capacity, etc.

The Uppsala team’s presentation appears to closely follow the ICL approach. They presented a projection for an “unmitigated” response (also known as the “do nothing” scenario in the ICL paper), then modeled the predicted effects of a variety of policy interventions. These included staying the course on the government’s alternative approach of remaining open with milder social distancing guidelines, as well as implementing varying degrees of a lockdown.

The model stressed its own urgency as well. Sweden would have to adopt a lockdown policy similar to the rest of Europe immediately if it wished to avert catastrophe. As the authors explained, under “conservative” estimates using their model “the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000)” being realized by the end of June.

Their proposed mitigation scenarios, which followed lockdown strategies similar to those recommended in the ICL paper and adopted elsewhere in Europe, were “predicted to reduce mortality by approximately three-fold” while also averting a catastrophic failure of the Swedish healthcare system.

The authors of the paper expressed sincere concerns for limiting the damage done by a genuinely horrendous disease, and they released their study in the hope that it would better inform the policy response. Its predictions have already failed to play out though – and badly failed at that.

The Swedish model laid out its predicted death and hospitalization rates for competing policy scenarios in a series of graphs. According to their projections (shown below in blue), the current Swedish government’s response – if permitted to continue – would pass 40,000 deaths shortly after May 1, 2020 and continue to rise to almost 100,000 deaths by June.

The most severe of the lockdown strategies they considered was supposed to cut that number to between 10-20,000 by May 1st while preserving hospital capacity – provided that the Swedish government changed course by April 10th and imposed a policy similar to the rest of Europe. In its most optimistic scenario, the model predicted that this change would reduce total deaths from 96,000 to under 30,000 by the end of June.

Did you get that? Without a lockdown, we should have seen 40000 dead in Sweden by May 1st, 100000 by June 1st.

So how accurate was that prediction? We have passed may first, and here today on May 7, the total amount of Covid deaths in Sweden? 2941.

That means 13 times fewer deaths than what the Imperial college model predicted.

As said above, they predicted the strictest lockdown in Sweden would have led to 10000 dead by May 1st. Instead we see with no lock downs, the number is less than a third of that.

This is absolutely devastating for the Imperial College model. It shows their predictions of deaths, and the effectiveness as lockdowns as a solution were not even remotely in the ballpark of being correct.

This also makes sense with what any normal person could see; if deaths would be catastrophic without a lockdown, how are countries like Sweden that didn’t have a lock down not only not experiencing deaths many factors higher than countries that did lockdown, but in many cases such as with Sweden compared to the UK, are doing better than countries which did lockdown?

(continued below)

Conclusions

The Imperial College study was touted by everyone from UK officials to Trump to the media in both countries as accurate and worth noticing. It led to lockdowns in both countries.

The study was never peered reviewed, has an author with a terrible track record of inflating deaths of pandemics to laughable numbers, and had obvious problems. Places like Sweden have showed that both the catastrophic predictions of deaths without a lockdown, and the effectiveness of lockdowns predicted by the study are horribly wrong. Here are some more quotes on the study from experts.

Johan Giesecke, the former chief scientist for the European Center for Disease Control and Prevention, has called Ferguson’s model “the most influential scientific paper” in memory. He also says it was, sadly, “one of the most wrong.”

…


Elon Musk calls Ferguson an “utter tool” who does “absurdly fake science.” Jay Schnitzer, an expert in vascular biology and a former scientific direct of the Sidney Kimmel Cancer Center in San Diego, tells me: “I’m normally reluctant to say this about a scientist, but he dances on the edge of being a publicity-seeking charlatan.”

Indeed, Ferguson’s Imperial College model has been proven wildly inaccurate. To cite just one example, it saw Sweden paying a huge price for no lockdown, with 40,000 COVID deaths by May 1, and 100,000 by June. Sweden now has 2,854 deaths and peaked two weeks ago. As Fraser Nelson, editor of Britain’s Spectator, notes: “Imperial College’s model is wrong by an order of magnitude.”

Indeed, Ferguson has been wrong so often that some of his fellow modelers call him “The Master of Disaster.”

www.nationalreview.com...

A discredited study pushed forth by a charlatan; and yet it was the single most influential study to the US and UK response to Covid.

Yet we were told we couldn’t question it; if we did, we wanted people to die. To this day, people hang on to this model as the standard, and say rather than the authors prove their model correct, its up to others to disprove it.

Even Trump cites claims within it, and has just this past week criticized Sweden for not locking down, despite the data showing Sweden is doing better than many countries with the strictest lockdowns.

The governments of the US and UK took away our rights, collapsed the economy, and did so with a shameful lack of transparency. And one of the key reasons for doing it was this study which is a total failure.

Despite proof this study is wrong, the recommendations for lockdowns provided within, and the catastrophic predictions if we don’t lockdown are still followed to this day by the UK and US.

And many people blindly accept the model and lockdowns, saying we must trust experts, while simultaneously dismissing any expert who points out problems with this study.

The ramifications of this would stretch on for years, and I fear the precedent has been set to use bad science by people with terrible track records to take away our rights any time the government sees fit.


Finished

Saying today, 90% of the new cases in NY were not only home but not even walking around for anything outside. Said they never left the house.

It could be decision makers really had no idea what to do or how to do it. Sounds like subject matter experts were leaned on but they really were not SME's since they never dealt with something like it or shutting down the planet for that matter. Maybe they wanted to be super cautious and over react than to be the one that under reacted.

Whatever the case, the lockdown is not working which is clearly evident from NY. In the end, there was no reason to shut it down. Chalk it as a lesson learned for next time. Don't ever shutdown the economy ever again. Futile and serves no purpose.

a reply to: Stupidsecrets

With the politics being played with this virus, and "the boy who cried wolf syndrome" the larger issue if and when a REAL deadly pathogen rises it's ugly head, who is going to believe it?

We might win this war, but will we be vigilant enough to take a serious threat seriously? Somehow I think there might be more conditioning in this scamdemic than we know.

originally posted by: seeker1963
a reply to: Stupidsecrets

With the politics being played with this virus, and "the boy who cried wolf syndrome" the larger issue if and when a REAL deadly pathogen rises it's ugly head, who is going to believe it?

We might win this war, but will we be vigilant enough to take a serious threat seriously? Somehow I think there might be more conditioning in this scamdemic than we know.

I doubt we will win the war.

Millions of people, even so called constitutional conservatives, totally supported the lockdown. Many people still believe the lockdowns were necessary

So the next time the government cries wolf, they will be ready to believe them again.

I will not be surprised if, in the end, more people have died from the consequences of the lock down than would have if the virus was just allowed to run its course. There are areas in downtown Austin that look like scenes from a dystopian future sci-fi flick.

a reply to: Grambler

Yea, I hope you are wrong, but it seems many people are willing to lose what freedoms we have left and put their lives in the hands of power hungry tyrants.

On the other hand, the woman who was thrown in jail for opening her salon so she could make some money to survive on has received over half a million dollars in support thru one of those go fund me sites. That was in one day, so that is encouraging to see so many people still willing to fight back.

originally posted by: seeker1963
a reply to: Grambler

Yea, I hope you are wrong, but it seems many people are willing to lose what freedoms we have left and put their lives in the hands of power hungry tyrants.

On the other hand, the woman who was thrown in jail for opening her salon so she could make some money to survive on has received over half a million dollars in support thru one of those go fund me sites. That was in one day, so that is encouraging to see so many people still willing to fight back.

Oh I agree, ill not give up hope.

I will keep fighting against this tyranny even if its an uphill battle, and there are people who give me hope.

Strange how none of the people on this very site pushing the imperial college mode have commented in here

You think itd be easy to prove my thread and the experts I quote in it are wrong

Or maybe they just want to avoid any evidence that counters their narrative

Great thread
You'll be pleased to know that the hideous Prof Ferguson has been sacked/resigned due to his breaking of lockdown. Hypocrite in the extreme

I really dont believe its about facts anymore.

The initial projections accuracy was largely irrelevant. They wanted to set the tone, plant a seed, and little else. They can revise it at will, later down the road. The important part is to get that fear impulse going. Once it is.. anything that comes out after it will be taken in the same behavioral context (good or bad news). Its a system that has been built for years.

You may have noticed one very specific thing: Folks presenting *exactly* the same data, but not using it to instill fear, are frequently rejected and censored. Its quite interesting.. It can literally be the same data, but if it doesnt cause that rush of fear/extreme emotion that people are addicted to, it will be seen as lies. A "false drug," if you will..

Inculcated authority worship coupled with full blown addiction to extreme emotional states might be the most effective control mechanism we have seen as a civilization.

I really believe the only way to successfully address it is to bypass the framework that has been set up. Systems, tools, technology, and knowledge to build a foundation for self-sufficiency and autonomy while retaining all the connectivity that modern society provides.

Doing so runs a route around the acceptance/rejection programming seeds and appeals directly to other parts of the brain and perception.

S&F. great thread, well stated and researched.

a reply to: Grambler

All those words and absolutely no mention of how the two organizations that initially produced the incredibly flawed projection models (IHME and ICL) received incredibly large sums of money from the Bill and Melinda Gates foundation?

originally posted by: Stupidsecrets
It could be decision makers really had no idea what to do or how to do it. Sounds like subject matter experts were leaned on but they really were not SME's since they never dealt with something like it or shutting down the planet for that matter. Maybe they wanted to be super cautious and over react than to be the one that under reacted.

Imagine spending your whole life studying, preparing, and working to becoming an expert in a field of study where your expertise is not often (if ever) really needed. Constantly working on "what if's" but never putting your theories into practice.

On the eve of the big dance are you going to be subdued and sober? Are you going move forward with rational sceptisum that might cause the dance to be called off? Or are you going to hit the ground running; show people what it is you have spent your whole life preparing for; be the superhero that the world needs because you are the only one who can do it?

How much of the information that has come from "experts", and they are experts there is no doubt about that, has been born from vanity and hubris? Because while they may be experts they are also human.

You don't ask the barber if you need a haircut; maybe its also not a good idea to place the decision to close down the world due to pandemic solely on the shoulders of people who have a vested interest and preoccupation in assuming the worst.



I think when the dust settles, and if we look back on this time with real introspection, what we will find is that one of our biggest mistakes was putting the fate of the world into the hands of people who were ill equipped to lead us through a disaster of this magnitude.

The medical experts have important information and thoughts that must be listened to and addressed as part of any disaster mitigation planning but they do not have the expertise or the experience to lead the disaster mitigation planning. No man is an island and it is responsible of us to assume that medical experts are all seeing and all knowing. These mitigations far out reach their area of study; they are experts in disease research, control and treatment... they are not experts in economics, infrastructure, education, business, food supply and ect all areas very much impacted by the decisions we are asking them to make.

"We have to listen to the experts" yes most certainly we must listen to the experts; but it is gravely irresponsible of us to place all the decision making on their shoulders.