Pyser Testing

Modelling

If the Indian Variant Really is 60% More Infectious, Why is it So Tame in Other Countries?

The Government’s favoured modellers appear to have settled on a figure for the greater infectiousness of the Delta (Indian) variant: a spanking 60%. Reuters reports.

Neil Ferguson of Imperial College London told reporters that estimates of Delta’s transmission edge over Alpha had narrowed, and “we think 60% is probably the best estimate”.

Ferguson said that modelling suggested any third wave of infections could rival Britain’s second wave in the winter – which was fuelled by the Alpha variant first identified in Kent, south east England.

But it was unclear how any spike in hospitalisations would translate into a rise in deaths, as more detail was needed on how well the vaccine protects against serious illness from Delta.

“It’s well within possibility that we could see another third wave at least comparable in terms of hospitalisations,” he said.

“I think deaths probably would be lower, the vaccines are having a highly protective effect… still it could be quite worrying. But there is a lot of uncertainty.”

Ferguson isn’t the only one making such doom-laden predictions. The usually more sanguine Philip Thomas of Bristol University is also predicting an “enormous” third wave in the summer. It will be “far bigger than the second”, he says, because of the Delta variant. “There is no hiding place. Either you’ve had the virus or been vaccinated, or you are pretty likely to get Covid this summer.” Ah, so another model that doesn’t factor in prior immunity or T-cells. Professor Thomas writes:

Chief Executive of NHS Providers Criticises Unreliable Covid Modelling

An NHS leader says that the scientific modelling provided to the Government has been “crude” and unreliable through much of the pandemic, warning Boris against relying on it too heavily when he decides whether to extend lockdown past June 21st. The Telegraph has the story.

Chris Hopson, the Chief Executive of NHS Providers, said trusts were “sceptical” about the fitness of models to provide useful forecasts.

It follows heavy criticism of Government modellers, who in February predicted spikes after schools and shops reopened which failed to materialise.

Matt Hancock, the Health Secretary, warned on Sunday that the Indian variant appeared to be 40% more transmissible, a figure which Warwick University modelling has previously suggested could overwhelm the NHS.

The Warwick data suggested that if the variant was found to be 30% more transmissible or higher, then hospital admissions would “exceed that observed in the first wave”.

But Mr Hopson said trusts in Indian variant hotspot areas had not seen huge spikes in admissions and deaths, and had coped well, with many now seeing a decline.

“For the record, trust leaders are sceptical of the value of predictive statistical models here, given their performance of the last 15 months,” he said.

“Leaders point to the crude assumptions that have to be made and the huge shifts in outcome if small changes are made to those assumptions.”

Warwick University is expected to present new modelling data on the Indian variant ahead of the Government announcing whether restrictions will be lifted on June 21st.

Mr Hopson said that it was clear that even areas with the variant had been in no danger of being overwhelmed, as predicted in the earlier models, with admissions and deaths never approaching the levels seen in earlier waves.

Worth reading in full.

Letter in the Telegraph Says Modellers Are Partly to Blame for Care Home Fiasco

There was a good letter in the Telegraph yesterday, pointing out that SAGE and its modellers need to accept some of the blame for the care home fiasco. After all, if the Government and the NHS hadn’t been persuaded by their apocalyptic models that hospitals would be overwhelmed if they didn’t clear out elderly, care home residents, they wouldn’t have been in such a hurry to get rid of them.

SIR – Matt Hancock, the NHS and the Prime Minister have all been blamed by Dominic Cummings for the appalling care-home Covid deaths at the start of the pandemic, but I feel none of these are the real culprits.

At the time, pandemic data modellers were forecasting huge numbers likely to need hospitalisation. So the Government, using the Armed Services, built Nightingale emergency hospitals in double-quick time.

The NHS, spooked by alarmist modellers, cleared the hospitals of all the non-Covid patients they clinically could, anticipating a deluge of Covid patients. Unfortunately, no one knew of non-symptomatic carriers, and the care homes were infected, with devastating results.

The hospital deluge predicted by modellers didn’t materialise, nor anywhere near it. The Nightingale hospitals were hardly used.

So, in apportioning blame for the horrendous care-home deaths, excitable modellers and their statistics, based on unrealistic assumptions, must be the primary culprits. It was a case of “following the science” that led policy-makers astray.

Steve Male
Highampton, Devon

When Will the Evidence From Florida and Texas Break Through the SAGE Groupthink?

The latest model of doom from Government advisory group SAGE appeared yesterday, predicting a ludicrous 10,000 hospital admissions a day in mid-July in a vaccinated population (nearly three times the January peak) because of the Indian variant – and that’s the central scenario. Furthermore, the researchers don’t even think the Indian variant is more deadly or particularly good at evading vaccines. So how do they conclude it will precipitate such a calamity?

Professor Adam Kucharski, a SAGE modeller from the London School of Hygiene and Tropical Medicine (LSHTM), explains their reasoning:

The issue is that many people have a mental image that we’ve [already] had the biggest possible epidemic waves, whereas we’ve actually had ones that are relatively small compared to what could have happened without control measures in place. Because of these controls, only a fraction of the people who could have got infected in the past year or so have been infected, so they’re still out there. Of course, for many of these people vaccines have now decreased their risk substantially. But a very large number of infections that come with a very small individual level of risk can produce a similar outcome to a smaller epidemic that carries a larger individual level of risk.

Maths whizz Glen Bishop, writing for Lockdown Sceptics, has shown why SAGE’s assumptions are so unrealistic as to produce these highly implausible scenarios. In their central scenario, for example, their assumptions imply that up to half of the UK will be simultaneously infected in one week in mid-July. This is despite the January peak only having around 2% of the population infected at one time, according to the ONS.

Another of the models’ big assumptions, prominent in what Prof Kucharski says above, is that lockdowns and social distancing have successfully suppressed the virus and that it is only because they continue in some form that the flood of infections, hospitalisations and deaths is held back. The latest modelling starkly shows how, even with a high vaccination coverage as in the UK, such an assumption can produce predictions so dire they send twitchy Governments reaching for the lockdown order.

As the SAGE briefing says:

At this point in the vaccine rollout, there are still too few adults vaccinated to prevent a significant resurgence that ultimately could put unsustainable pressure on the NHS, without non-pharmaceutical interventions. … It is a realistic possibility that this new variant of concern could be 50% more transmissible. If [the Indian variant] does have such a large transmission advantage, it is a realistic possibility that progressing with all roadmap steps would lead to a substantial resurgence of hospitalisations.

In fact, there is no evidence (outside models, which are not evidence) that lockdown measures or social distancing have any significant impact on reducing Covid infections or deaths. This is why the states in America which removed their restrictions in March (Texas) or last autumn (Florida) or never imposed them (South Dakota) are doing no worse, and often better, than many states which maintained strict restrictions throughout the winter (see the graph above). Sweden demonstrates a similar point in Europe.

The depressing truth, though, is that sceptics have largely failed to get this basic point across to those in charge and their scientific advisers. It’s not as though the evidence is not there. There are numerous peer-reviewed articles in leading journals that set out the evidence on this, and more keep appearing. Leading scientists have raised their heads to make the evidence-based case.

Graphs like the above, which should by themselves undermine the entire lockdown edifice, are easy to produce. Leading journalists such as Fraser Nelson, writing in one of the leading Tory newspapers, the Telegraph, has pointed repeatedly to the evidence on this. The data is plain for all to see and the voices highlighting it are not marginal or lacking in credibility.

Yet here we are again, with another model built on dubious assumptions and a presumption of lockdown efficacy once more imperilling our liberty. Freedom has never felt so fragile as in these past 14 months, when access to basic liberties has rested on the evidence-free assumptions made by a small group of mathematical modellers whose word seems to be taken as holy writ by those in charge.

Adam Kucharski is on Twitter. So why not ask him (politely!) why, if so many people remain so susceptible to this virus and its variants as to produce such dire predictions, Florida, Texas and South Dakota have fared no worse than places which have imposed or maintained restrictions? I’ve put the graph as the featured image to make it easy to share – just put a link to this article in the tweet and the graph should appear. If you get any answers from him, why not email them to us here.

Not So SAGE After All: A Review of the Latest Models

Glen Bishop, the second year maths student at Nottingham who was the first to spot that none of the modelling teams feeding into SAGE had taken seasonality into account last February, has taken a look at the new, improved models from Imperial, Warwick and the London School of Hygiene and Tropical Medicine that led to headlines earlier this week saying SAGE was no longer predicting an apocalyptic ‘third wave’. (Yipee!) The good news is, the teams have corrected their seasonality mistake when modelling the likely impact of the lifting of restrictions and now graciously allow that summer sunshine will ameliorate the spread of the virus – one of the reasons their latest projections are less gloomy. But there’s also plenty of bad news, as you’d expect.

Here is an extract:

A rational group of scientists would advise that risks are now within the normal accepted range and thus the end of restrictions is nigh and normal life will return. Unsurprisingly, that is not what these three modelling teams have done. Their models have failed to deliver the pessimism and danger craved by scientists clinging on to power, but a new obsession is taking over – the danger of variants. Imperial elaborates: “preventing the importation of variants of concerns (VOC) with moderate to high immune escape properties will be critical as these could lead to future waves orders of magnitude larger than the ones experienced so far.”

Previous Imperial models have made only passing reference to new variants and never tried to model them, yet Imperial’s latest paper, which shows (even with their modelling) the risk from covid to now be incredibly low, is half filled with predictions of theoretical super variants. The most pessimistic of the predictions entails an imaginary ‘high escape’ variant, which, if we stick to the current roadmap, would lead to a peak of over 4,500 deaths per day and a total of 225,000 deaths this summer. To put this into perspective, it would mean a death rate this summer of 3,300 per million, that is double the death rate in Florida since the pandemic began of 1,669 per million despite Florida being near fully open for the last eight months. It’s a higher total than anywhere in the world since the pandemic began. This is void of reality, but even if it weren’t, what is the proposal? Lockdown for another year until a vaccine for this new variant can be distributed, by which time even more variants will have appeared? One might as well include in the modelling a super infectious variant of Ebola or a new improved laboratory leak from our friends in the Wuhan Institute of Virology.

Worth reading in full.

The Maddening Mystery of Imperial’s Invulnerable Reputation Despite its Dire Record of Failed Model Predictions

Phillip W. Magness in AIER has crunched the numbers and shown how poor Imperial College’s modelling has been at predicting the outcomes of the COVID-19 pandemic under different policy responses in every country in the world (well, 189 of them). Yet for some unexplained reason Neil Ferguson and the rest of the Imperial team remain respected authorities on epidemic modelling and management. Magness writes:

COVID-19 has produced no shortage of doomsaying prophets whose prognostications completely failed at future delivery, and yet in the eyes of the scientific community their credibility remains peculiarly intact.

No greater example exists than the epidemiology modelling team at Imperial College-London (ICL), led by the physicist Neil Ferguson. As I’ve documented at length, the ICL modelers played a direct and primary role in selling the concept of lockdowns to the world. The governments of the United States and United Kingdom explicitly credited Ferguson’s forecasts on March 16th, 2020 with the decision to embrace the once-unthinkable response of ordering their populations to shelter in place.

Ferguson openly boasted of his team’s role in these decisions in a December 2020 interview, and continues to implausibly claim credit for saving millions of lives despite the deficit of empirical evidence that his policies delivered on their promises. Quite the opposite – the worst outcomes in terms of Covid deaths per capita are almost entirely in countries that leaned heavily on lockdowns and related nonpharmaceutical interventions (NPIs) in their unsuccessful bid to turn the pandemic’s tide.

Assessed looking backward from the one-year mark, ICL’s modelling exercises performed disastrously. They not only failed to accurately forecast the course of the pandemic in the US and UK – they also failed to anticipate COVID-19’s course in almost every country in the world, irrespective of the policy responses taken.

Time and time again, the Ferguson team’s models dramatically overstated the death toll of the disease, posting the worst performance record of any major epidemiology model.

Magness has put together a table of all the countries with the predictions ICL made for them and their actual outcomes. The results should be fatal for the reputation of anyone whose job it is to make accurate predictions of the future course of events. But not ICL it seems, whose credibility appears to be invulnerable despite repeated and consistent failure. Magness wonders why.

Why is Ferguson, who has a long history of absurdly exaggerated modeling predictions, still viewed as a leading authority on pandemic forecasting? And why is the ICL team still advising governments around the world on how to deal with COVID-19 through its flawed modeling approach? In March 2020 ICL sold its credibility for future delivery. That future has arrived, and the results are not pretty.

Worth reading in full.

SAGE Modellers Admit ‘Third Wave’ Probably Won’t Happen

New SAGE modelling to be presented to ministers ahead of stage three of reopening on May 17th will show the risk of a ‘third wave’ of Covid infections in the UK has diminished dramatically and may not happen at all. The Telegraph has more.

The last set of projections, published by SAGE on March 31st, presented ministers with a difficult dilemma because they suggested a third wave of infections could be expected to kill another 15,000 to 20,000 people in the late summer if steps three and four of the exit roadmap were implemented as planned.

Ministers are expected to proceed with step three of the roadmap, with the return of indoor household mixing and indoor hospitality, as modelling teams which provided projections for ministers via the SPI-M subgroup of SAGE are said to be more optimistic.

Professor Adam Kucharski of the London School of Hygiene and Tropical Medicine, who works on modelling provided to SAGE, welcomed the new real world data on vaccine effectiveness.

“There was considerable uncertainty about the impact of vaccines on infection and transmission earlier this year, but recent studies are landing at the more optimistic end of the scale – at least for the dominant B.1.1.7 variant,” he told the Telegraph.

“We could still see some increase in transmission as things reopen, but the resulting impact could be relatively low if the vaccine programme stays on track and we don’t end up with variants that can partially evade immunity.”

New real-world data released last week has allowed SAGE to improve the assumptions which underpin their models on both vaccine effectiveness and rollout. Crucially, a PHE study released last week showed for the first time that vaccines cut “breakthrough transmission” of the virus by about half after a single shot.

“If you look at where they were in early April, compared to where they were in early February, they moved a huge distance,” said James Ward, a mathematician and insurance risk manager, who runs his own Covid model which closely shadows the official ones.

“So actually, it’s not very far for them to move now, from predicting an exit wave of 15,000 to 20,000 deaths to them predicting an exit wave of zero to 5,000, or maybe nothing at all.”

Worth reading in full.

Imperial College’s Modelling is Even Worse Than We Thought

When Professor Neil Ferguson and his team at Imperial College London have been challenged on their model’s miserable failure to predict the pandemic death toll in Sweden they have always pushed back saying they didn’t model Sweden, disavowing the work of the team at Uppsala University which adapted their modelling to the Swedish context. But it turns out this is not exactly accurate. Phillip W. Magness explains on AIER:

In the House of Lords hearing from last year, Conservative member Viscount Ridley grilled Ferguson over the Swedish adaptation of his model: “Uppsala University took the Imperial College model – or one of them – and adapted it to Sweden and forecasted deaths in Sweden of over 90,000 by the end of May if there was no lockdown and 40,000 if a full lockdown was enforced.” With such extreme disparities between the projections and reality, how could the Imperial team continue to guide policy through their modelling?

Ferguson snapped back, disavowing any connection to the Swedish results: “First of all, they did not use our model. They developed a model of their own. We had no role in parameterising it. Generally, the key aspect of modelling is how well you parameterise it against the available data. But to be absolutely clear they did not use our model, they didn’t adapt our model.”

The Imperial College modeller offered no evidence that the Uppsala team had erred in their application of his approach. The since-published version from the Uppsala team makes it absolutely clear that they constructed the Swedish adaptation directly from Imperial’s UK model. “We used an individual agent-based model based on the framework published by Ferguson and co-workers that we have reimplemented” for Sweden, the authors explain. They also acknowledged that their modelled projections far exceeded observed outcomes, although they attribute the differences somewhat questionably to voluntary behavioural changes rather than a fault in the model design.

Ferguson’s team has nonetheless aggressively attempted to dissociate itself from the Uppsala adaptation of their work. After the UK Spectator called attention to the Swedish results last spring, Imperial College tweeted out that “Professor Ferguson and the Imperial COVID-19 response team never estimated 40,000 or 100,000 Swedish deaths. Imperial’s work is being conflated with that of an entirely separate group of researchers.” It’s a deflection that Ferguson and his defenders have repeated many times since.

In fact, though, as Phillip points out, it is not true to say that the Imperial team never estimated 40,000 or 100,000 Swedish deaths. Hidden away in a spreadsheet in the appendix to Report 12, published on March 26th 2020, are the team’s estimates for other countries including Sweden. The projections are expressly intended to encourage those countries to follow suit with social restrictions. They write:

To help inform country strategies in the coming weeks, we provide here summary statistics of the potential impact of mitigation and suppression strategies in all countries across the world. These illustrate the need to act early, and the impact that failure to do so is likely to have on local health systems.

The predictions for Sweden are up to 90,157 deaths under “unmitigated” spread (Uppsala projected 96,000) and, under “population-level social distancing” (lockdowns), 42,473 deaths (compared to Uppsala’s 40,000). So, contrary to their repeated denials, Ferguson’s team did make predictions for Sweden very close to those made by the Uppsala team who adapted their model, and those predictions were just as way off. Sweden’s Covid death toll at the end of the first wave, on August 31st, was 5,821.

Phillip summarises further failures of the Imperial modelling in a table showing four non-lockdown countries (Sweden, Taiwan, South Korea, Japan) and the United States (most of whose states imposed a lockdown in the spring) with their one-year death toll and how it compares to Imperial’s projections.

Performance of Imperial College Modelling in Four Non-Lockdown Countries and the United States (AIER)

It’s worth saying, though, that the models for the ‘unmitigated’ scenarios predicted the deaths to occur over the course of a few months, not a whole year including another winter flu season. There will be another ‘wave’ of deaths every winter, possibly from (or with) COVID-19 if it remains the dominant respiratory virus (and if we keep on testing for it). If we keep on adding the deaths over several seasons then of course they will eventually reach the predicted figures. But that wasn’t what the models were claiming to show and would be a case of making the evidence fit the model.

The AIER article is worth reading in full.

The Paralysis of Caution

We’re publishing a new essay by Guy de la Bédoyère about the excessive cautiousness that is preventing the Government from being more bold. Guy traces it to the cautiousness of the modellers and points out how odd that attitude is to the adventurousness of pioneering scientists like Edward Jenner and Marie Curie. Here’s an extract:

Regardless of your views about vaccines, it is a fact that Edward Jenner took the reckless step of infecting his gardener’s eight-year-old son first with cowpox and then with smallpox. As we all know, the experiment was a success. By today’s standards of gibbering caution, it was the most outrageous example of recklessness imaginable. Yet how else was he ever going to find out if it worked? If that happened today, Jenner would probably never have dared try his theory out and if he had he’d have been struck off and imprisoned. I have no doubt that had a predictive modeller been on hand, busy calculating the risk, there wouldn’t have been a virus in hell’s chance of him being allowed to go ahead.

On December 23rd 1750 Benjamin Franklin electrocuted himself when he tried to kill a turkey with electricity, believing the meat would be more tender. He survived, chastened by the experience. In 1839-43, James Clark Ross took two sailing ships, the Erebus and Terror, on an epic voyage of exploration and scientific experiment around Antarctica. Today he wouldn’t have been allowed out of port.

There are so many other examples from those days of early science it would be impossible to list them. But the underlying approach reaches right into more recent times. We have aviation because people were prepared to throw themselves into the air with bizarre pieces of winged equipment or brave their way across the Atlantic in a Vickers Vimy from Newfoundland to Ireland, as Alcock and Brown did in 1919. Imagine the risk assessment if anyone had bothered to think of writing one at the time, and the same applies to Marie Curie’s work on radiation.

Worth reading in full.

South Dakota – the Least Restrictive State in the Western World, Yet Covid Deaths Now Averaging One a Day

As I noted back in March, South Dakota may have taken the least restrictive approach to COVID-19 of anywhere in the Western world. Its conservative governor, Kristi Noem, has been a stalwart opponent of lockdowns: when the state’s epidemic burgeoned at the end of August, there were practically no restrictions in place. Despite this, case numbers fell rapidly after reaching a peak in mid November. And by late February, they were in the low triple digits.

How has it fared since then? Case numbers have remained low, averaging about 170 per day:

And deaths have continued to fall. The last seven days saw an average of just one death per day:

Although South Dakota has the eighth highest death rate among US states, its epidemic retreated without any government intervention. (And no new restrictions were introduced in March or the first week of April.)

What’s more, South Dakota has done better economically than most other states and Western countries. According to the Bureau of Economic Analysis, its GDP fell only 1.7% in 2020 – the seventh lowest among US states. (Britain’s GDP plunged nearly 10% last year.) In addition, South Dakota currently has the lowest unemployment rate out of all 50 states – at just 2.9%.

One might say, “You can’t equate GDP and unemployment with human life”, but that simply isn’t true. The money to pay for the NHS (and programs like Medicare and Medicaid in the US) comes from taxing economic activity: the less economic activity, the less money there is available. And that’s before you even factor in the social costs of unemployment and bankruptcy.

One should be cautious about extrapolating from South Dakota to countries like Britain, given the state’s low population density. However, the trajectory of its epidemic casts serious doubt on the models that led us into lockdown.