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Posts Tagged ‘Computer Models

Another argument that is no longer off-limits

One of the detailed points of argument during the Great Chinese Lung Rot pandemic was around the definition of what actually constituted a Covid-19 death.

Early in the hysteria it was pointed out that deaths were being recorded as Covid simply because the patient had tested positive for Covid. This included even ridiculous examples such as deaths by car accident.

Naturally the pro-hysteria side, with the aid of the “If It Bleeds, It Leads” MSM, ferociously attacked such arguments. For the MSM it’s quite natural that the more death there is the better the story. That’s been true since the days of William Randolph Hearst and his famous “Sob Sisters” over a hundred years ago.

But even the medical “experts” had motive to push death numbers higher, since the more death there was the more likely they could persuade politicians and The People to undertake the extremist controls they advocated. Some of this was obvious with the pandemic models pushed by the likes of Neil Ferguson (“A spherical cow of uniform density in a frictionless vacuum“).

Naturally their counter-attacks against such critics focused on how you should not argue with medical experts, even though medical experts were among the critics of the Covid-death classifications. The motivations of the likes of Ferguson and company were not to be questioned, only those of their dastardly and uncaring opponents.

My, how things change when the motivations run the other way. In this case the criticism around deaths of people who have been vaccinated for Covid-19. Placed under such pressure, no less than the head of American Center for Disease Control (CDC) backs into …. the precise arguments put forward by critics of the Covid death counters.

Walensky is drawing a distinction between those who died directly because they got COVID and those who may have tested positive, but ultimately died of another comorbidity or condition. Now, to most people, that would seem like common sense. After all, why would you count someone with terminal cancer or an already failing heart as a COVID death – just because they had the virus when they died?

Obviously, what Walensky is saying is true. What we’ve known about COVID from early on from those hit the hardest told us that co-morbidities, including heart problems, lung problems, and morbid obesity, are the top factors, and that very old people (70+) naturally suffered more from the first two factors, hence them suffering a higher Covid-19 death rate than other age groups. If someone is otherwise terminally sick, even a mild case of Covid-19 could expedite matters – just as the Flu or Pneumonia normally does. The latter has long been called the “Old People’s Friend” for that very reason.

But the real point I want made clear here is that what Walensky is saying has previously been declared to be completely off-limits for over a year by the powers that be. In fact, it’s the kind of thing that has often gotten right wing-leaning sites in trouble with the social media censors of FaceTwit and company.

Yet, here is the Biden administration saying what was previously labeled as taboo, just because it now fits their narrative, which is driven by the motivation to reduce the death count rather than increase it because the latter would blow up the vaccination programmes. Meanwhile, the media don’t question it, and the social media overlords just shrug.

Oh, and the CDC has recently and rapidly shifted their positions on masks. Because Science.

This man WILL never be listened to again

My PhotoBack on April 18, I wrote about the British epidemiological “expert”, Neil Ferguson, whose hopeless chicken entrails computer model, had led the British Government a pretty dance on dealing with the Wuhan Flu.

The article pointed out that his models had provided similarly catastrophic predictions of death and destruction over the past twenty years with things like CJD (Mad Cow) disease, Foot and Mouth, Bird Flu, and Swine Flu,

Based on all that, I made the point that:

THIS MAN MUST NEVER BE LISTENED TO AGAIN
 

Well it now turns out that this guy, the “gold standard” of disease modeling, according to the New York Times and Washington Post, never actually followed the rules he lectured everybody else about following:

On Tuesday night, we discovered that the furrowed-browed scientist, who has been at the Prime Minister’s side throughout this crisis, is in fact Austin Powers in a lab coat. He’s been having an affair with a 38-year-old married woman who travels regularly across the capital from her home in south London to spend time with him.

What.
A.
Prick.

He’s resigned now, and with any luck will never be heard from again.

Now I have to admit that the lass making the booty calls – pictured here with Ferguson –  probably is worth breaking a few rules for, and were it anybody else NOT in a position of authority I’d probably be cheering the couple on as they gave the fingers to the Police State.

But of course that’s not who he was. As the article points out, this was Professor Lockdown, the guy telling 66 million Britons they must remain in their homes to protect the NHS and save lives (sob).

I also liked this quote:

Why is it that the most zealous advocates for reining in human behavior, whether it’s in Prohibition-era America or the midst of a public health crisis, always get caught with their pants down? I’m reminded of something the late Christopher Hitchens said:

‘Whenever I hear some bigmouth in Washington or the Christian heartland banging on about the evils of sodomy or whatever, I mentally enter his name in my notebook and contentedly set my watch. Sooner rather than later, he will be discovered down on his weary and well-worn old knees in some dreary motel or latrine, with an expired Visa card, having tried to pay well over the odds to be peed upon by some Apache transvestite.’

But like the writer I’m less concerned about the pathetic double standards than about what this actually means about our society – and who rules us:

It deserves the frontpage treatment it is getting today. For Ferguson’s booty call with his married lover actually reveals a great deal about the 21st-century elites and how they view their relationship with the masses. It’s one rule for them and another for us. They can carry on enjoying sneaky freedoms because their lives and jobs are important; we can’t because we are mere little people, whose silly work lives can casually be disrupted, whose love lives can be turned upside down, and whose families can be ripped apart. The Ferguson affair provides an illuminating insight into the new elitism.

And this regarding “experts”:

The Gospel of Ferguson is really a story of the confused relationship between politicians and experts today. Instead of our elected leaders deciding what is best for the political, economic and social health of the nation, and then employing experts to ensure this vision becomes a reality, we have politicians who bow too cravenly to experts and outsource political authority to them. And so, as some commentators have pointed out, Britain currently feels like it is being run by scientists. That’s bad for politics, which becomes less democratic the more that unelected scientific experts get to make the major decisions, and it’s bad for science, which risks becoming politicised under this pressure to guide the nation. Ferguson bought into the political use of his work. He backed the lockdown. Ferociously.

Fuck them. And the horse they rode into town on.

Written by Tom Hunter

May 7, 2020 at 2:58 am

A picture is worth a thousand graphs

I wish I’d seen this graphic when I published Visible Death vs. Invisible Death a few days ago.

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Produced for those complaining about articles that have too many words, numbers and graphs.
 

Having said that, here’s a very cool graph from Snopes of the progress of the virus. An interesting way of portraying the data, at least up to early April.

 

 

Written by Tom Hunter

April 28, 2020 at 11:14 pm

Visible Death vs. Invisible Death

One of the most famous economic essays ever written is That Which is Seen, and That Which is Not Seen, by 19th century French economist, Frédéric Bastiat. He introduced what he called the fallacy of the broken window, where the money spent to fix the window – paying the person who made the glass and the glazier who installed it – is seen, but other costs are not:

Frédéric Bastiat


But if, on the other hand, you come to the conclusion, as is too often the case, that it is a good thing to break windows, that it causes money to circulate, and that the encouragement of industry in general will be the result of it, you will oblige me to call out, “Stop there! Your theory is confined to that which is seen; it takes no account of that which is not seen.”


It is not seen that as our shopkeeper has spent six francs upon one thing, he cannot spend them upon another. It is not seen that if he had not had a window to replace, he would, perhaps, have replaced his old shoes, or added another book to his library. In short, he would have employed his six francs in some way which this accident has prevented.

The lockdown of New Zealand society to deal with COVID-1984 is presenting the same problem, except instead of money, the counting is in deaths.

The other day I looked at a report of the results from testing the epidemiological model used by the NZ governments’s health care advisors.

Buried within the report was a short section that made crude sensitivity estimates of the costs and benefits of the Lockdown across just one part of the NZ economy: building and construction. The report estimated the benefits of avoiding deaths and hospitalisations in that industry at $7.6 million and the cost at $3 billion (one month, 250,000 workers at $3000 per week).

The point of this was not to try and calculate precise numbers but to test the ranges and comparisons across different runs and sensitivities to get a handle on the possible cost/benefit. As the report said:

Of course, the benefit cost ratio of .003 is from just model run. Different, and plausible, assumptions can readily generate benefits that are a order of magnitude, say, ten or twenty times, higher than the $7.6 million. But it is very difficult to see how they could be over 300 times higher.

But what I was interested in was the assumptions had purloined from government sources (p. 25):

  • The value of a statistical life is $4.5 million;
  • The life years conversion factor is 0.10 for over 70s and 0.55 for under 70s;
  • The cost of an illness is $4000;
  • The cost of a hospitalisation is $30,000.

The value of a statistical life!

A précis of that report was linked to in an article of Michael Riddell’s Croaking Cassandra blog, Coronavirus economics. But there was another section of Riddell’s article looking at an unpublished (as yet) economic analysis that took a different bite at the cherry.

This analysis was performed by one of New Zealand’s leading academic economists, Professor John Gibson from Waikato University, and he decided to look at the Lockdown policy from the POV of how it might affect population-wide life expectancies in NZ.

The flu kills about 500 New Zealanders a year but it can kill more in a bad season like that of 2015, when 767 died from it. A season worse than that would be “flu shock” and Gibson picked a figure of 875 for that edge-case scenario. That produces a reduction in life expectancy of 0.14 years across the whole population.

Ten such shocks would therefore drop it by 1.4 years: that’s 8,750 dead people, which is in the range of the middle scenario of the Otago model for COVID-19 deaths.

In other words, in saving all those lives the lockdown could be expected to prevent the population life expectancy from dropping by 1.4 years, and more again if the third scenario of 14,000 deaths eventuated.

But at what cost? We’ve allowed ourselves to be bullied by shroud-wavers talking about preferring to make a buck over saving the lives of old people. But that’s a false choice and an emotional weapon wielded by people who don’t want their solution to be questioned. The fact is the lives will be lost as a result of the lockdown.

It turns out that life expectancy in New Zealand is more sensitive to changes in real income than is so for many countries.

In other words, a ten percent decrease in real per capita GDP reduces life expectancy by 1.7 percent. The most recent period life tables for New Zealand report that male life expectancy was 79.5 years and female life expectancy was 83.2 years, so 1.7 percent of the average of those two values is 1.4 years.
 

In other words, if real per capita GDP in New Zealand falls by ten percent due to the lockdown and other effects associated with Covid-19, life expectancy would be predicted to fall by 1.4 years.

And we could be looking at an annual GDP drop of more than 10%. By contrast, even going by the real worst-case death rate of New York City, currently 1,085 deaths per million, we’d be looking at a life expectancy drop of 0.93 years.

So even in that highly unlikely example the lockdown solution would still result in reducing life expectancy by an extra 0.5 of a year. The apparent kindness of doing everything possible to limit deaths due to Covid-19 would, instead, be killing more people by making them poorer.

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And we may not need to dabble in such statistical comparisons of invisible deaths anyway. As this article by six American doctors points out, the US hospital system has been so emptied out that doctors and nurses are being laid off and furloughed in droves:

Almost every hospital outside of the hotspots is empty. The dramatic reduction in healthcare utilization and capacity is by no means limited to small, country hospitals. Mayo Clinic is empty: 65% of the hospital beds at Mayo Clinic are empty, as are 75% of the operating rooms. This is the world’s premier medical center. If Mayo Clinic is empty, imagine how dire the situation is at smaller, community-based healthcare centers. Given the complexity of the patients referred to Mayo Clinic, its emptiness alone will have a significant negative impact on healthcare outcomes.

Same with this article written by Dr Scott W. Atlas, of Stanford University Medical Center:

Most states and many hospitals abruptly stopped “nonessential” procedures and surgery. That prevented diagnoses of life-threatening diseases, like cancer screening, biopsies of tumors now undiscovered and potentially deadly brain aneurysms. Treatments, including emergency care, for the most serious illnesses were also missed. Cancer patients deferred chemotherapy. An estimated 80 percent of brain surgery cases were skipped. Acute stroke and heart attack patients missed their only chances for treatment, some dying and many now facing permanent disability.

Same in Britain:

It’s chilling to know that many hospital wards, waiting rooms and car parks are now empty. Before this country was hit, only 800 of the NHS’s 4,125 critical care beds were vacant at one time. Now it’s 2,300. Even with some of the worst fatality rates in Europe, some hospitals now report being half empty since they paused almost all non-emergency work.

Richard Sullivan, a professor of cancer and global health at King’s College London, says: The number of deaths due to the disruption of cancer services is likely to outweigh the number of deaths from the coronavirus itself over the next five years. Cancer screening services have stopped, which means we will miss our chance to catch many cancers when they are treatable and curable.

And there are almost certainly deaths happening right now because of the focus on saving people from COVID-19:

Accident and Emergency chiefs in London are concerned that more people are dying of non-coronavirus-related illnesses than normal because they are reluctant to leave their homes and be a burden on their local hospital. They believe there has been a ‘sharp rise in the number of seriously ill people dying at home’. They report that dozens more people than normal are dying at home from cardiac arrests, for example, presumably because they do not want to impose upon our locked-down society and what is continually presented to us as a busy, stressed-out health service.

Spain:

In Spain, health investigators found a 40 per cent reduction in emergency procedures for heart attacks at the end of March compared with a normal period.

Australia:

there has been a ‘drastic drop’ in cancer and heart-attack patients presenting to the health services. In Victoria, health officials report a 50 per cent decline in new cancer patients and 30 per cent decline in cardiac emergencies. It is now feared that ‘coronavirus anxiety’ could lead to ‘more deaths from cancer and heart attacks’.

And back to NYC:

The New York Times published a piece on 6 April headlined, ‘Where have all the heart attacks gone?’. It was written by a doctor who likewise described hospitals in the US as being ‘eerily quiet’. He has heard from colleagues who are seeing fewer patients with heart attacks, strokes, acute appendicitis and acute gall-bladder disease than they would normally see.

In Britain at least they appear to have asked the question:

Matt Hancock, the health secretary, refuses to give a figure for the potential non-Covid fatalities from this catastrophe but the cabinet was told it could be up to 150,000 avoidable deaths.

I’ve seen no evidence that we asked that question of our public health experts, either inside government or outside. It cannot be possible that the same deaths are not happening here. We’re just not seeing them widely reported in the MSM or announced in the PM’s press conferences.

The Swedish View on COVID-19

Sweden has been in the news almost more than Italy, France, Britain and the USA when it comes to dealing with Chinese Lung Rot AIDS because it has taken the opposite approach to their nationwide lockdowns or – given the USA’s Federal system, partial lockdowns.

Sweden, almost alone among nations, is using the strategy of what is simplisitically called Herd immunity. This is not quite as crude as the critics would have it, which is that the government simply sits back and lets her rip.

In fact Sweden is pushing testing and tracing of those who have the disease as well as trying to quarantine the most at-risk groups, which are the elderly, especially in hospital, rest-home and aged-care facilities where the disease has proven deadly if it gets in.

The thing is that some of the global criticism has been muted because this has not been done at the behest of Evil Corporates Who Care Only About Money, or fanatical Objectivist political leaders or because it’s a selfish, uncaring society that kicks the poor to the side (aka the Evil USA). It’s Sweden FFS and it’s following this strategy on the advice of its health experts.

One of them is Professor Johan Giesecke who just happens to be one of the world’s most senior epidemiologists. He was the first Chief Scientist of the European Centre for Disease Prevention and Control, and an advisor to the director general of the WHO.

An expert in other words. You know how we must listen to experts – instead of random internet bloggers – and do what the experts say, right?

The following is a 35 minute interview Giesecke and it’s perfect lockdown viewing while we wait for Judge Jacinda to decide if we’re allowed out for good behaviour.

 

As far as I can summarise these are the main points of that interview:

  • The flattening of the curve being seen in countries is due to the most vulnerable dying first as much as any lockdown.
  • The results will eventually be similar for all countries.
  • Covid-19 is a “mild disease” and similar to the flu, and it was the novelty of the disease that scared people.
  • The actual fatality rate of Covid-19 will in all likelihood turn out to be in the region of 0.1%
  • At least 50% of the population of both the UK and Sweden will likely be shown to have already had the disease when mass antibody testing becomes available. (Something already also suspected for California, which was expected to be hit like NYC but has not been).
  • The correct policy is to protect the old and the frail only (he says that Sweden did not do a good job on this, hence its death toll is higher than it should be); employ social distancing with restrictions on crowd size to no more than 50; keep schools open for older kids who know how to maintain social distancing.
  • This will eventually lead to herd immunity as a “by-product”. You can’t stop it spreading anyhow.
  • The initial UK response, before the “180 degree U-turn”, was better than the lockdown now being used by it and other European countries.
  • The theory of lockdown is not evidence-based.
  • The Imperial College paper was poor and far too pessimistic in not accounting for the ability to rapidly increase ICU capacity.
  • The paper was so poor that even in the unlikely scenario of no mitigation measures being implemented he rejects its projection of 510,000 deaths for the UK.
  • He has never seen an unpublished, non-peer-reviewed paper have so much policy impact. 
  • Any such models are a poor basis for public policy anyway, because they take no account of real world specifics.
  • Getting out of the lockdowns will be the big challenge since the question is around which restrictions can be lifted, followed by watching for upticks in cases and deaths at each stage, with increases met by what? Reinstating the restriction?
It’s also nice to see a scientist with a sense of civil liberties, where he talks about the concerns of having the Police stopping and interrogating people on the street to enforce laws, especially when those laws are not based on science.

 

At the moment the NZ Lockdown supporters are winning the public argument because we’ve only had 1431 cases (297 per million popn) and a dozen deaths (2 per million) while Sweden has had 14,385 cases (1,424 per million) and  1540 deaths (152 per million).

 
The thing is that Sweden has not flattened one third or more of it’s businesses and crushed its GDP by some 40% in a month. They’ll come out of this pandemic in much better shape than NZ and then we’ll see if we want to point the finger and say how cruel and uncaring the long respected idol of Democratic Socialists is.
 

Written by Tom Hunter

April 20, 2020 at 1:10 am

More Epidemiology Modelling Problems

“You fucked up. You trusted us.”

I was rather hard on Epidemiology Professor Neil Ferguson the other day because of his history of repeated false calls over two decades on various disease pandemics. But I also pointed out that his Imperial College model appears to have fed into models in other countries around the world, and this criticism is starting to mount up:

“It’s not a model that most of us in the infectious disease epidemiology field think is well suited” to projecting Covid-19 deaths, epidemiologist Marc Lipsitch of the Harvard T.H. Chan School of Public Health told reporters this week, referring to projections by the Institute for Health Metrics and Evaluation at the University of Washington. 

Others experts, including some colleagues of the model-makers, are even harsher. “That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool,” said epidemiologist Ruth Etzioni of the Fred Hutchinson Cancer Center, home to several of the researchers who created the model, and who has served on a search committee for IHME. “That it is being used for policy decisions and its results interpreted wrongly is a travesty unfolding before our eyes.”

Not that you’d know any of this from following the MSM, especially here in NZ, or Lefties busily masturbating over pictures of Jacinda, like Chris Trotter, (“SO FAR, SO BLOODY FANTASTIC!”),  Martyn Bradbury (“Thanks to our Government’s wisdom and leadership…”) or Frank Macskasy (“Wonder Woman”).

Amusingly they have all taken swipes at China – without noticing the sickening similarity between their local worship of our government and the standard boilerplate praise lavished on Xi Peng by China’s state media.

By contrast it’s been pleasing to see that none other than the Imperial College is starting to do what scientists are supposed to do: comparing models with reality, although they don’t seem to have got stuck into Ferguson’s to the same degree – yet:

According to a critique by researchers at the London School of Hygiene & Tropical Medicine and Imperial College London,  published this week in Annals of Internal Medicine, the IHME projections are based “on a statistical model with no epidemiologic basis.” 

“Statistical model” refers to putting U.S. data onto the graph of other countries’ Covid-19 deaths over time under the assumption that the U.S. epidemic will mimic that in those countries. But countries’ countermeasures differ significantly.

There are other technical reasons to distrust the IHME model, but the bottom line is that it misinforms national leaders.

This appearance of certainty is seductive when the world is desperate to know what lies ahead,” Britta Jewell of Imperial College and her colleagues wrote in their Annals paper. But the IHME model “rests on the likely incorrect assumption that effects of social distancing policies are the same everywhere.” Because U.S. policies are looser than those elsewhere, largely due to inconsistency between states, U.S. deaths could remain at higher levels longer than they did in China, in particular.

Still, those who live by the sword will likely die by it. Right now those who are in love with government lockdowns and mass house arrest are pointing to the models predictions of mass death early on as justification. Computer models! Run by Experts! But what the scenarios show are ranges so wide that the models could equally screwup in the opposite direction, which is not a comforting thought for governments trying to get out of the mess they have created.

Unfortunately it’s not just the IHME or Imperial College models that have got it so wrong. There’s quite the history of fail in epidemiology:

‘The crisis we face is unparalleled in modern times,” said the World Health Organization’s assistant director, while its director general proclaimed it “likely the greatest peacetime challenge that the United Nations and its agencies have ever faced.” This was based on a CDC computer model projection predicting as many as 1.4 million deaths from just two countries.  

So when did they say this about COVID-19? Trick question: It was actually about the Ebola virus in Liberia and Sierra Leone five years ago, and the ultimate death toll was under 8,000.

Oh dear. The article lists a few others, including bring Ferguson into the picture again.

For AIDS, the Public Health Service announced (without documenting) there would be 450,000 cases by the end of 1993, with 100,000 in that year alone. The media faithfully parroted it. There were 17,325 by the end of that year, with about 5,000 in 1993. SARS (2002-2003) was supposed to kill perhaps “millions,” based on analyses. It killed 744 before disappearing. 

Later, avian flu strain A/H5N1, “even in the best-case scenarios” was to “cause 2 (million) to 7 million deaths” worldwide. A British professor named Neil Ferguson scaled that up to 200 million. It killed 440.

As the article points out, if epidemic models were just haphazardly wrong, we would expect about half the time they would be too low. Instead, they’re almost universally vastly too high. It’s so bad that even the experts in charge of public health in the US have begun to express their doubts:

Then Fauci finally said it. “I’ve spent a lot of time on the models. They don’t tell you anything.”

The fuck? They told our governments to shit themselves and put us in lockdown, a phrase previously preserved for controlling prison riots.

A few days later CDC Director Robert Redfield also turned on the computer crystal balls. “Models are only as good as their assumptions, obviously there are a lot of unknowns about the virus” he said. “A model should never be used to assume that we have a number.”

The fuck? As I said before, the numbers are the whole point. Without numbers you don’t persuade anybody, politician or ordinary citizen, to do anything about this.

As far as I know there have only been a couple of analysis of the NZ models. Both are referred to in this post at Croaking Cassandra. The first looks at cost/benefit in terms of life expectancy gains and losses across the whole NZ population due to the disease and the lockdown.

The second analysis is more appropriate here in that the analysts actually ran the model used by the Otago Covid-19 Research Group (OCRG): it’s in the public domain: covidsum.eu. You can read the full report here, but the following are the key points to take away from it.

We found that OSRG’s model runs grossly overstated the number of deaths because they made an assumption about the critical tool in the Ministry’s arsenal. It was assumed that there would be no tracing and isolation of cases. This led to an explosion in the number of cases and deaths. 

The reporting of the range of deaths was also inflated by the simple expedient of excluding the model runs that produced low numbers. One of their six scenarios showed just seven deaths over a year.

That of course was not what the public saw from the MSM. They saw the following (Stuff quote):

Up to 14,000 New Zealanders could die if coronavirus spread is uncontrolled, according to new modelling by the University of Otago, Wellington.

But to be fair to the MSM they were not given the R0 ranges either, just the range of 8560 and 14400 deaths, and naturally because they’re the media the emphasised the BIG SCARY NUMBER on their headlines, since that’s all that most people read.

If the OCRG had so little confidence in the 1.5 estimate then they should have replaced it with a more plausible lower estimate, such as a R0 of 2, and then reported that number. Similarly the upper estimate could have been set at a high, but still reasonably possible 3. Instead the public is given a range of between 8560 and 14400 deaths, giving the misleading impression that there is a good deal of certainty around the estimates of high death numbers because the upper and lower bands are relatively close together.

My existence, while grotesque and incomprehensible to you, saves lives!”

Whether our Prime Minister and her Cabinet actually asked any probing questions about all this is not known, but given that she paraphrased “tens of thousands” of New Zealanders dying it’s a good bet that she simply followed the slots and grooves already prepared for her by the experts. And there’s no evidence that anybody else was handed the model to see what they could produce from it. As Tailrisk notes:

When we ran the Covidsim model we found credible paths that could reduce the pace of infections to sustainable levels. Deaths in the range of 50 – 500 over a year are more realistic numbers. 500 deaths is around average for normal seasonal flu [note that Gibson used 870 annual flu deaths].

Credible paths meaning they looked at what other nations had already been doing for two months. But they also did what good modellers are supposed to do; they tested the robustness and sensitivity of the model by altering key variables. One would hope the OCRG did this as well, but perhaps they just didn’t care to think about the life impacts of locking down the economy and simply adopted an autistic view of the disease problem.

Our benchmark model run shows 105 deaths after six months, and 157 after one year. This is broadly consistent with the experiences countries such as South Korea, Hong Kong, Taiwan, which have achieved a good measure of control over their epidemics without the need for harsh lockdowns.

Those are numbers to keep in mind over the winter. They also ran the Te Pūnaha Matatini Model – which produced and publicised even higher death tolls of 80,000 – and found similar problems, although it’s a more sophisticated model.

This is a case of getting out of the model what you put in. In our view, TPM did not use the best available information, and should have either: not released their report until it was updated (and should have told a different story); or released a heavily caveated paper, without any media fanfare.

One month ago the instant reaction to critics of the lockdown was the shroud-waving cry of “You care more for money than old people”, which has natually been very effective in public.

But of course it was never about lives vs. money: it was always about weighing lives vs. lives.

The economy is people’s lives and the next six months are going to be a terrible test of the theories that have paradoxically been pushed for years by groups such as Otago University healthcare experts; that economic decline and increases in poverty translate into more deaths as well as a miserable quality of life for those still living.

We’ll then see how well the macho attitude of “We saved old people’s lives” still stands in front of a weary, scared and miserable public.

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See Also:
A spherical cow of uniform density in a frictionless vacuum.

 

Written by Tom Hunter

April 19, 2020 at 6:00 pm

A spherical cow of uniform density in a frictionless vacuum.

 
THIS MAN MUST NEVER BE LISTENED TO AGAIN
 

Neil Ferguson is the “gold standard” of disease modeling, according to the New York Times and Washington Post. So naturally the British Government turned to him for advice in dealing with the Chinese Bat Eaters Lung Rot Disease.

The following was the chart that scared the living daylights out of various governments around the world, not just Britain’s, when it appeared in March.

 

 
The chart was produced by the Imperial College epidemiological model, for which Ferguson is the spokesman and it portrayed the so-called “worst-case” scenario where US and GB societies did nothing. Not just that their governments did nothing, but that their people also did nothing. Needless to say the latter is not the modern experience of people facing a deadly epidemic: they were always going to change their behaviour, starting with limiting their contact with other people.
 
But the paper, that chart, and those terrifying numbers, forced governments to do something as well, which is how we ended up with our lockdown.
 
The thing is that the model had other scenarios for such reactions as well, and while they were also publicised they didn’t get quite the attention. That’s a pity because those scenarios, including so-called “best-case” ones, also totally over stated the death toll of COVID-19, usually by at least one order of magnitude. In the following series of OpEds I looked at this problem, and that of other related models such as the IHME ones in the US, and ours, which appear to have suffered the same problems.
 

Using the IHME model in the USA led to graphs like this one for New York City.

The reality on April 4 was 15,905 people hospitalized. The IHME model overstated the actual number by 400 percent. The same thing happened the next day, April 5.

This was produced well into the pandemic when input data should have increased in quality.

But it turns out that this is not the first time Mr Ferguson and his models have been off by huge margins.

2001 – CJD or Mad Cow, courtesy of the NYT:

But Dr. Neil Ferguson, an epidemiologist in another group of highly respected researchers led by Dr. Roy Anderson at Imperial College in London, said the new estimates were ”unjustifiably optimistic.” His group published estimates a year ago predicting that the number of variant C.J.D. cases might reach 136,000 in coming decades.

Twenty years later, we can safely conclude that Dr. Ferguson’s model erred on the high side of what “might” happen in subsequent decades.

2005 – Bird Flu, courtesy of The Grundian:

Last month Neil Ferguson, a professor of mathematical biology at Imperial College London, told Guardian Unlimited that up to 200 million people could be killed.“Around 40 million people died in 1918 Spanish flu outbreak,” said Prof Ferguson. “There are six times more people on the planet now so you could scale it up to around 200 million people probably.”

The Bird Flu’s death toll from 2003 to 2020 is 455.

2009 – Swine Flu, courtesy of the Daily Mail:

He told the BBC Radio 4 Today programme: ‘This virus really does have full pandemic potential. It is likely to spread around the world in the next six to nine months and when it does so it will affect about one-third of the world’s population. 

‘To put that into context, normal seasonal flu every year probably affects around ten per cent of the world’s population every year, so we are heading for a flu season which is perhaps three times worse than usual.

To be fair he didn’t shit himself over a death toll on that occasion, which is a good thing because the Swine Flu death toll globally eventually amounted to between 12,000 and 18,000 over a period of years.

The fact is that Mr Ferguson has a record of making stupid – and I’ll repeat that word – STUPID worst-case predictions about the threat of new viruses. And he has done so repeatedly, which indicates that there has been no change in the thinking behind these models or the models themsleves. Ferguson recently revealed that he hasn’t modified the thousands of lines of un-documented code he wrote years ago and it’s never been opened to the community for peer review. Oh – and it’s in “C”, which is damned near obsolete. Could have been worse I guess: COBOL?

Given how badly these models have screwed up, defenses of them have already begun to appear, of which this Atlantic Magazine article is one of the better ones:

At the beginning of a pandemic, we have the disadvantage of higher uncertainty, but the advantage of being early: The costs of our actions are lower because the disease is less widespread. As we prune the tree of the terrible, unthinkable branches, we are not just choosing a path; we are shaping the underlying parameters themselves, because the parameters themselves are not fixed. If our hospitals are not overrun, we will have fewer deaths and thus a lower fatality rate. That’s why we shouldn’t get bogged down in litigating a model’s numbers. Instead we should focus on the parameters we can change, and change them.

This asshole is just going to shrug off the social and economic devastation inflicted across the globe with talk of paring branches and how it could have been much worse. Jesus, it’s as though the writer is scripting Being There II:

As long the roots are not severed all is well. And all will be well…  in the garden.

 

I’ve picked it because it’s a great example of how small seeds of truth can be spun into defending bullshit. The models do not actually account for the “costs” outside of their frame of reference. They’re not supposed to, but the projections of huge death tolls caused decision makers to make equally huge decisions. Not litigate the numbers? The numbers are the whole fucking point. Without those numbers the models are meaningless.

That sort of blinkered shit is not acceptable. It is not acceptable that a model consistently blows out its best-case projections time and time again. It is not acceptable when that model is being used by governments to make society-impacting decisions that will damage the lives of millions of people. It would not be acceptable in engineering and most sciences, including much of medicine.

In recent interviews Ferguson himself has started to sound a bit more thoughtful as to the non-disease impacts and exactly how we’re going to get out of this mess, beyond the mere mechanics of how to lift a lockdown. That’s nice, but frankly he’d be the last person on Earth that I’d turn to for advice at this point.

I think he’s begun to sense that whereas all those older predictions were forgotten because nothing much happened as a result of he and his fellow geeks running computer simulations, this time they’ve had huge and terrible real-world effects. These models were given a high profile by people like him and massive coverage by the MSM. They were clearly represented as predictions of what would happen.

 

Fuck that. This is no longer an exercise in onanistic computer modelling. People like Ferguson and the IHME crowd and the Otago University chappies and their models are going to be investigated and people beyond the politicians are going to be held accountable.

Bonus feature: Mr Ferguson’s Headlines

2001 – CJD or Mad Cow Disease

2005 – Bird Flu disaster in America

 

2009 – Swine Flu 

 

 

Written by Tom Hunter

April 17, 2020 at 1:20 pm

Models vs. Reality


“And please: speak as you might to a young child. Or a Golden Retriever. 
It wasn’t brains that got me here I can assure you of that.”

By now most people should be familiar with this basic graph that plots the progress of a pandemic disease and how it looks against the capacity of a nation’s healthcare system to cope with the forecast loads of infected people who need to be hospitalised. The following is courtesy of The Economist.


This graph shows the by-now-classic requirement to “flatten the curve” of the number of cases so the healthcare system is not overwhelmed, resulting in a lot of deaths. Aside from all the other factors involved in forecasting something like this, the phrase “protective measures” that you see in this graph could involve many things itself, from the now equally-famous “social distancing” to the sort of “lockdown” that New Zealand and other countries have imposed on their entire population.

But it should not be forgotten that there are other curves in that model, notably the one created by temporarily boosting the capacity of the healthcare system. There are many ways that can be done. Taking over warehouses, halls, indoor stadiums and other large spaces for hospital beds – Chicago is using McCormick Place, the largest convention centre in the USA – emergency manufacturing of ventilators and other ICU equipment, calling on retired doctors and nurses to return to work, the military’s medical capabilities.

But the central question with any model is how it measures up to reality. Let’s take a look at the real thing, courtesy of the IHME Model that has become the default in the USA and which seems to be based on the Imperial College model from Britain – as it would seem is New Zealand’s. The following is for New York city and the prediction is for April 4.

So how did the model do compared to the reality of April 4, as sent out in a Tweet by NY Governor, Andrew Cuomo:

The reality on April 4 was 15,905 people hospitalized. The IHME model overstated the actual number by 400 percent. The same thing happened the next day, April 5.

As of today (April 8, EST) it has downscaled a lot, predicting a peak of 25,486 beds needed on April 8, the expected peak date for hospitalisations. The peak for deaths is expected to be April 9, EST, with 878 deaths.

When you’re planning for hospital capacity that sort of forecast is worse than useless. It can even be dangerous since you will be diverting resources from other hospital and healthcare needs. What happens to them? There are already many reports out of NYC of huge drops in the numbers of people reporting to NY hospitals with heart attacks, strokes and other dangerous medical issues. How many of those will have died in their homes because they were too scared to report to a hospital or because they simply could not get through the phones lines for an ambulance?

Their deaths should be counted against the deaths by COVID-19.

And understand that this can’t be excused away because the model had factored in the NYC lockdown. The figures were already for the best-case scenario of such a lockdown – and yet they were still wildly off.

And they’re still off: compare and contrast the IHME daily new hospitalisation projections for April 7 with the actual numbers (586).

If the expert’s projections are off by that much, then how much do you think they’ll be off down the line as time passes? NY Governor Andrew Cuomo must be thinking much the same as a press conference included the following graph showing that the total deaths for NYC now look like they’re going to vastly undershoot even the best-case scenario – and NYC is the worst-case situation in the USA.

NYC “is not just flattening the curve – it’s squashing it“.

Source: NY Governor Andrew Cuomo, April 4 press conference.

Something similar is happening in the state of Minnesota, where its Governor ordered a widespread shutdown on March 27, based on a projected 74,000 deaths from COVID-19 and then – a week later in the same manner as GB –  said it would save 24,000 lives. And now:

As of the moment I write this morning, Minnesota has experienced a total of 30 deaths attributable to COVID-19. That is up approximately one since Sunday. 

Minnesota “is not just flattening the curve – it’s squashing it.

And of course in New Zealand, where we are almost certainly relying on the same Imperial Model, we currently have 1,160 cases and 1 death as of April 9.

New Zealand “is not just flattening the curve – it’s squashing it.

Now of course the leaders in each place are going to claim that the only reason these numbers are so much lower than projected is thanks to their various forms of “lockdown”. But that reason does not fly when the numbers are so much lower than even the best-case, lockdown scenarios of those models. There’s really one explanation.

The models were wrong from the start.

Whether they were wrong because of inadequate data or faulty assumptions or a combination of both will be the questions asked in the next few months. As an example of the problems around calculating just one of the numbers fed into the models, even the death counts from places like Italy could be wrong:

“On re-evaluation by the National Institute of Health, only 12 per cent of death certificates have shown a direct causality from coronavirus, while 88 per cent of patients who have died have at least one pre-morbidity – many had two or three.”

Those questions will be asked of these experts and their models in the wake of the gigantic amount of economic destruction wrought by governments in trying to solve the COVID-19 problem.

And this is where things could get tricky for the IMHE / Imperial College model and its chief spokesman Neil Ferguson. The London Times took a hard look into the tiny world of infectious disease experts and the track record of them and their models in the case of two previous epidemics.

First with the 2001 outbreak of Foot and Mouth disease in the UK:

it was from Imperial that Ferguson and Anderson dominated the government response to foot and mouth.

A subsequent government inquiry was damning of the general approach. It said: “The FMD epidemic in UK in 2001 was the first situation in which models were developed in the ‘heat’ of an epidemic and used to guide control policy

analyses of the field data, suggest that the culling policy may not have been necessary to control the epidemic, as was suggested by the models produced within the first month of the epidemic. If so it must be concluded that the models supporting this decision were inherently invalid.”

Second with the dreaded Swine Flu in 2009:

Britain was, however, left with 34 million doses of unused and expensive vaccines. Again there was an inquiry — which concluded that ministers had once again treated modellers as “astrologers”, asking them to provide detailed forecasts when they had too little data.

“Modelling did not provide early answers,” it concluded. “The major difficulty with producing accurate models was the lack of a relatively accurate idea of the total number of cases . . . This is not to reject the use of models, but to understand their limitations: modellers are not ‘court astrologers’.”

And now here we are again, courtesy of the same people and the same models it would seem. Models that in hindsight don’t stand up well but have nevertheless been used again in a new pandemic – and again have predicted fantastic healthcare damage and death if we did not take the most extreme actions that the experts wanted us to.

And of course this time Ferguson has revealed that their initial dire predictions were supplied to the UK government but when it did not react the way Ferguson and company thought it should they released the data publically to force the government’s hand.

Oxford professor of epidemiology Sunetra Gupta is already on to this:

I decided to publish and speak out because the response to this pandemic is having a huge effect on the lives of vulnerable people with a profound cost and it seems irresponsible that we should proceed without considering alternative models. Imperial has a long history of involvement with government and its epidemiological models can have huge importance and translational impact but it’s tricky to use them to forecast what’s going to happen. We need to also consider alternatives.

Remember that when a government tells you it’s listening to “experts” what they mean is the selected experts they already employ – the “court astrologers”. It’s like a nation that goes to war with generals promoted during peacetime. The ruthless encounter with reality winnows out those who cannot cope. How often does that happen with bureaucrats outside of war?

In the great Asian Flu pandemic of 1957/58, some 150,000 Americans eventually died of the disease. Scale that up to today and that’s about 250,000. Yet in that season, the USA did not shut down its economy at all, let alone to the extent it has now. Nasty epidemics are a natural thing. Turning a country into East Germany circa 1960 isn’t.

In Britain, New Zealand and other places, any alternatives were brushed aside in favour of as complete a lockdown of society as was possible. TINA reigned.

See also:

Supermodels, Dangerous Curves and Experts – Part 1
Supermodels, Dangerous Curves and Experts – Part 2
Supermodels, Dangerous Curves and Experts – Part 3

Written by Tom Hunter

April 8, 2020 at 11:00 pm

Chinese Coronavirus / Wuhan Flu: Good News from the USA!

In the worst-hit part of the USA, New York State, led by New York City, COVID-19 hospitalisations are now showing a declining trend. April 6 was up a bit but the 3-day moving average is definitely down.

Similar data is appearing globally also, even though the death tolls of places like Spain will continue to lag at high levels before dropping, days after the peak in hospitalisations – which itself is lagging the peak in infections.

Other places in the USA are moving at different rates, which is typical given the different factors. But the overall trends are looking like a plateau, according to the CDC. Although the data from last week is incomplete any exponential growth would swamp such data noise.

NOTE: That graph is based on Date of Illness Onset, which is a different way of measuring the daily new case count than the Worldometer USA count, included for comparison:

But even it’s showing much the same trend.

Overall for the US (from the IHME models):

  • Numbers of deaths projected decreased from 93,531 to 81,766
  • Projected total bed shortage went from 87,674 to 36,654
  • Peak dates(April 15 for resource needed peak, 16th for peak daily death toll) unchanged
  • Under 200 deaths a day: Moved from June 3 to May 18

And finally the NY data on death by age-group shows the same results that we’ve known about since China’s initial data in January.

Written by Tom Hunter

April 8, 2020 at 12:59 am

Supermodels, Dangerous Curves and Experts – Part 3

Defeating viral pandemics like COVID-19 is more a matter of logistics than medicine until a vaccine is invented, which is months away. The key to making logistics work is accurate information. 

And that is a real problem at the moment. We don’t know the fatality rate. We don’t know the infection rate. We don’t even know how many people the virus has actually killed yet, given the widespread national differences in determining whether COVID-19 is the primary cause of death or underlying conditions that it worsened. This is like trying to solve the equation “x + y = z” where you don’t know what x, y, or z are.

One of the biggest questionmarks is around the size of the population that’s infected, given that most people with COVID-19 won’t even show any symptoms or that if they did they might just dismiss it as the symptoms of standard cold or flu attacks.

The Imperial College model seems to be the one that’s been producing the highest figures for deaths – such as 80,000 in NZ if no action is taken – and it does not explicitly try to calculate that population.

But that is exactly what the Oxford Model does factor in (“Fundamental principles of epidemic spread highlight the immediate need for large-scale serological surveys to assess the stage of the SARS-CoV-2 epidemic“). The study thinks there was community spread in the UK as early as January 2020, infiltrating the susceptible but also vaccinating the herd:

Our simulations are in agreement with other studies that the current epidemic wave in the UK and Italy in the absence of interventions should have an approximate duration of 2-3 months, with numbers of deaths lagging behind in time relative to overall infections. Importantly, the results we present here suggest the ongoing epidemics in the UK and Italy started at least a month before the first reported death and have already led to the accumulation of significant levels of herd immunity in both countries.

Shown here is one of the now familar graphs of the progress of a pandemic, showing the famous “curves” with and without protective actions being taken – and of course the now equally famous flat-line of healthcare system capacity. The Oxford model suggests that we may not be looking at the right curve.

In support of that model’s assumption here are two articles of interest by non-epidemiologists that provide some evidence suggesting the same thing.

First up is this article. Many Americans were getting sick with flus and colds during their Winter months of December and January, as usual, and they were being treated for that without any testing for COVID-19, which has similar symptoms.

since the disease originated in China in December at the latest, it’s highly unlikely the number of reported cases in the United States between January 1 and late February is accurate.

an average of 14,000 people per day traveled between [China and the USA] in 2019.
Therefore, how could a highly-contagious virus remain nonexistent in a free-moving society for several weeks?

All anecdotal. But it turns out that the CDC tracks a category called “influenza-like illness,” or ILI from all over the USA, and the underlying cause could be any number of undetected respiratory viruses. And what the CDC data shows is that there was a significant spike in January.

Some 70% of people with these flu-like symptoms did not test positive for the flu. Now flu stats can jump around a bit but as the CDC data makes clear, this was the second highest in a decade, excepting the 2017/18 season – and that one was known to be almost all flu.

To summarise the article:

  • The current coronavirus “curve” cannot be accurate since it does not include suspected cases of the illness before late February.
  • A big increase in symptoms very similar to coronavirus occurred a few weeks after the first case was recorded, a timeline in accordance with the estimated trajectory of the illness’ spread.
  • Roughly 70 percent of those expressing flu-like symptoms did not have the flu. So what was it?
The article sums up to the same conclusion as the Oxford Model:

It’s not unreasonable, in fact, it’s necessary and responsible, to consider that COVID-19 has been in the [USA] since the first of the year; that people suffering similar symptoms to the flu actually had COVID-19; and that the peak of the outbreak occurred last month. The number of people now testing positive for the virus does not mean that the outbreak is accelerating because the data is incomplete.

The second article comes from historian Victor Davis Hanson who lives and works in California, and I found this particularly interesting since I wrote a few weeks ago about the dangerous possibility of the virus getting into the huge community of California homeless (When COVID-19 meets Shithole), who have already produced a number of outbreaks of literally “medieval diseases“.

But as Hanson points out California so far has not suffered the problems of New York City, with only 181 deaths at the time of my writing, out of a population of 40 million, despite the same amount of time having passed since the disease was first detected in the country. California Governor Newsom, taking the models at their word, has said that 56 percent of the state will soon be infected: more than 25 million cases, translating into 250,000–500,000 dead Californians. But if that was the case, even taking geometric growth into account the state surely would have seen the numbers climbing rapidly already as they have in NYC, Italy and Spain.

Yet a number of California’s top doctors, epidemiologists, statisticians, and biophysicists — including Stanford’s John Ioannides, Michael Levitt, Eran Bendavid, and Jay Bhattacharya — have expressed some skepticism about the bleak models predicting that we are on the verge of a statewide or even national lethal pandemic of biblical proportions.

Most people thought that California would be far worse because even in a normal year it usually has the highest number of flu deaths in the USA, and sits in the middle of US states in flu deaths per capita. Aside from the homeless population it also has the highest poverty rate in the USA, one quarter of all illegal (and often undocumented) aliens in the USA, ranks low in terms of general health (diabetes, etc), hospital beds, doctors and nurses per head of population. And then there’s the huge traffic between the state and California, at least up until Trump stomped on that on January 29. And yet:

Here in Fresno County (1.1 million people), we are warned daily that we are the next hot spot. But as of late March, we’ve had no recorded deaths and only 41 known cases. The figure will no doubt multiply rapidly and geometrically, but it still seems incomprehensible that not a single death was attributed to the virus in its first 60 days of visitation.

As Fresno goes, so goes the rest of the state. So what on earth is going on? Hanson looks at several possibilities, niether accepting nor dismissing them because we just don’t know enough yet:

  • Simple statistical anomaly (perhaps like Germany?)
  • A lag in gathering data on testing and deaths across a huge state – something that will change. soon.
  • The state’s typically warmer weather.
But Hanson also suggests the same possibility suggested by the Mid-Western writer and the Oxford Model:

Of the nearly 15,000 passengers who were estimated to be arriving every day in the U.S. on flights from China in 2019 and 2020, the majority flew into California.

So given the state’s unprecedented direct air access to China, and given its large expatriate and tourist Chinese communities, especially in its huge denser metropolitan corridors in Los Angeles and the Bay Area, it could be that what thousands of Californians experienced as an unusually “early” and “bad” flu season might have also reflected an early coronavirus epidemic, suggesting that many more Californians per capita than in other states may have acquired immunity to the virus.

Hanson also points to a news article about Italy that I had missed:

Adriano Decarli, an epidemiologist and medical statistics professor at the University of Milan, said there had been a “significant” increase in the number of people hospitalized for pneumonia and flu in the areas of Milan and Lodi between October and December last year…

Decarli and others are reviewing records now to try to understand whether the viral epidemic had already spread to Italy back then.
But of course one of the main points about the Italian and Iranian outbreaks are their heavy connections to China. Why did herd immunity not happen in those nations – if it happened in California?
And of course, given our heavy people traffic with China, has herd immunity already happened here in New Zealand? The only answer will be testing, and we continue to be woeful at that.

Written by Tom Hunter

April 2, 2020 at 2:14 am