It looks like you're using an Ad Blocker.
Please white-list or disable AboveTopSecret.com in your ad-blocking tool.
Some features of ATS will be disabled while you continue to use an ad-blocker.
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.
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.
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.
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
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.”
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.
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.
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.
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.*
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.
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.”
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.
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.
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.
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.
United Kingdom 30,076 (deaths) 66.49 (population in millions) 452.35 (deaths per 100,00)
Sweden 2,941 10.18 288.81
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.
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.
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.
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.”
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.
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.
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.