SAGE admitted early virus modelling based on figures from online encyclopedia
Committee of scientists advising PM also had no expert on human coronavirus
Dubious data formed the basis for the group’s calls for first national lockdown
Experts predicted that the peak would be in June – but it actually came in April
Impact of care home staff spreading Covid by working in multiple sites not considered
Scientists failed to consider the impact agency workers would have on spreading Covid in care homes by moving between several different sites to work
There were more than 30,000 excess deaths in care homes because of Covid in 2020
Professor Mark Jit, an epidemiologist at the London School of Hygiene and Tropical Medicine and member of SPI-M, said the group used data from Wikipedia in the UK along with hospitalisations in China and Northern Italy to inform their modelling.
Our mission: save the NHS by neglecting ourselves and the NHS. I received numerous CCG advice and flow-charts on the coronavirus-centric mass processing of patients. Most of it was about whom not to see, and who could pass the pearly gates of the hospitals. Then there was the advice on the parallel IT and video-consultation medical industrial revolution: our new NHS normal.
…For clarity, the “D” in coronavirus means “disease”, the second “S” in SARS-CoV-2 means “syndrome”. In a sense, the WHO had already decided Covid-19 was a distinct disease entity caused by a novel coronavirus before characterising it as a syndrome called SARS-2, and before the naming of the virus as SARS-CoV-2. The importance of scientific syntax and semantics cannot be overemphasised. Such cognitive slip-ups trickle unnoticed into general parlance and may have fatal consequences for us as a species.
Without a definite cause, one cannot definitively conclude to treat anything in particular. Is Covid-19 a syndrome, a mixed bag of symptoms and signs that has been negligently and politically globally fast-tracked to a scientifically wrong conclusion? Is it, in practice, a conflation of different, distinct disease entities including influenzae, rhinoviruses, pneumoniae and other coronaviruses, not to mention other non-infectious phenomena?
Recently, the Government agreed a £161 million deal with a British company called DnaNudge to provide 5.8 million Covid tests, as part of its “Moonshot” programme for mass testing of the population at the point of care. The CovidNudge test is “a rapid, accurate, portable and lab-free RT-PCR test that delivers results at the point of need and in just over an hour”, according to DnaNudge’s own promotional material. DnaNudge is a spinoff company of Imperial College London.
We have consistently (and I’d say flagrantly) over-estimated the threat of Covid-19, starting with the absurd prediction of 500,000 deaths by Imperial College London’s Professor Neil Ferguson. Data experts who later reviewed the computer code used in the professor’s model described it as “a mess which would get you fired in private industry”…
The trashing of the economy, the worst recession in our history, avoidable deaths at home with people too frightened to go to hospital for fear of catching the virus, chaos in education, the explosion in domestic violence, steep rises in anxiety, depression, and heavy drinking?
No. Lockdown will come to be seen as one of the most catastrophic misjudgments a British government has ever made.
The Imperial College study published this morning claiming that 3.4 million people ( six per cent of the UK population) have antibodies indicating that they have been exposed to Covid-19 provides no great revelation. The Office of National Statistics (ONS) has already published similar figures suggesting that 6.5 per cent of the population has been infected. Nevertheless, it is yet more confirmation of how irrelevant are the official statistics for Covid 19 cases – and what a nonsense it is to rely on them for policymaking.
According to the Government’s Covid “dashboard”, updated at 4pm on Wednesday, 313,798 people in Britain have had the disease. This is less than one tenth of the number suggested by the Imperial study. In other words, for all Matt Hancock’s efforts to ramp up testing, the vast majority of cases have not been detected.
But with no sign of a second summer wave nor an autumn eruption reminiscent of 1918, the commentariat has amended the definition. Suddenly, a “second wave” meant Covid’s seasonal return, in winter, a year on. Widespread adoption of a new phrase in the Covid lexicology – “winter wave” – has academically formalised the idea.
But instead of looking us square in the eye, the Tories have chosen Big Brother’s panopticon; No 10’s new Joint Biosecurity Centre, which will drive “whack-a-mole” local lockdowns, is slickness posing as strategy – and, as it happens, reporting into track-and-trace app failure Dido Harding. When the public twigs that the infection is unlikely to be controlled in this way, the sheer panic could send us back into national lockdown. Three scenarios might help avoid the latter: a vaccine comes along; the Government gets its act together with a plan to protect the vulnerable; or we put in place safety valves against mass hysteria.
Imperial College’s research needs to be particularly scrutinised, as its international influence grows. Dr Seth Flaxman – the first author in the paper that notoriously claimed lockdowns may have prevented over 3 million deaths in Europe – this week won fresh funding to model the pandemic across several countries.
Revelations that disrupt the narrative also need to find a stronger voice: within 24 hours, the scandal of PHE’s inflated daily death figures was running out of mileage. This week’s London School of Hygiene and Tropical Medicine modelling on the impact of the pandemic on cancer deaths never gathered steam. So too a paper by Oxford’s Prof Sunetra Gupta, which elegantly combined those uneasy epidemiological bedfellows – theory and evidence – to find some parts of the UK may already have reached herd immunity.
In reality many of the people who died from Covid-19 were likely to die this year anyway, so in one respect this estimate is likely to be too high. In another respect it’s likely to be too low, as it will not include ‘lockdown deaths’, that is, the deaths from delayed cancer and heart treatments, and so on, but as I was interested in the effect of Covid-19 I didn’t want those in my graph anyway. (Another complication is that not everyone who is classed as a Covid-19 death actually died from it, but I decided to ignore this.)
The five year average for 2015-19 is 531,355 deaths per year. As of writing this there were 42,462 Covid-19 deaths in the UK. There are likely to be a few more deaths in the next few weeks, but not many more, as the disease is (barring an unlikely second wave in winter), on its way out. Besides, the number we are adding on here is for the whole of the UK, not just England and Wales, so if anything this number is inflated. That gives us 573,817 deaths for 2020. Then I got hold of the historical population figures for England and Wales, and calculated the death rates per 1000 from it, so that population increases are taken account of. Here is the result:
The really concerning thing is that if all the deaths taking place during lockdown are put down as Covid-19 deaths, we are going to miss the fact that the lockdown policies have caused an increase in deaths from many other things. There has been a 50 per cent reduction in people turning up to A&E. It is clear that people just do not want to bother the doctors. And a number of these people will be dying. If we muddle the Covid-19 statistics in with the other statistics, we might think the lockdown has prevented a certain number of deaths, when it has actually caused a large number of deaths.
You hear this idea that all NHS staff have been working 20 times as hard as they have ever done. This is complete nonsense. An awful lot of people have been standing around wondering what the hell to do with themselves. A&E has never been so quiet.
The chances of children dying from COVID-19:
How many people aged 15 or under have died of Covid-19? Four. The chance of dying from a lightning strike is one in 700,000. The chance of dying of Covid-19 in that age group is one in 3.5million. And we locked them all down. Even among the 15- to 44-year-olds, the death rate is very low and the vast majority of deaths have been people who had significant underlying health conditions. We locked them down as well. We locked down the population that had virtually zero risk of getting any serious problems from the disease, and then spread it wildly among the highly vulnerable age group.
It is not clear that getting the virus actually makes you immune to it in the future, and it is not clear a vaccine would either.
The failure to take into account the impact of extreme measures that have become the norm inmany places in the Covid-19 pandemic has been stunning. The destruction of lives and livelihoods in the name of survival will haunt us for decades.
Today’s fear is fueled by four main forces: 1. Mathematical disease modelling – a flexible and highly adaptable tool for prediction, mixing calculations with speculations, often based on codes that are kept secret and assumptions that are difficult to scrutinize from the outside. 2. Neoliberal policies –systematic disinvestments in public health and medical care that have created fragile systems unable to cope with the crisis. 3. Nervous media reporting – an endless stream of information, obsessed with absolute numbers, exploiting the lack of trust in the healthcare infrastructure and magnifying the fear of collapsing systems. 4. Authoritarian longings – a deep desire for sovereign rule, which derives pleasure from destruction and tries to push the world to the edge of collapse so that it can be rebuilt from the scratch.
Flaxman et al. (Nature, 8 June 2020, https://doi.org/10.1038/s41586-020-2405-7, 2020) infer that non-pharmaceutical interventions conducted by several European countries considerably reduced effective reproduction numbers and saved millions of lives. We show that their method is ill-conceived and that the alleged effects are artefacts. Moreover, we demonstrate that the United Kingdom’s lockdown was both superfluous and ineffective.
By simply separating out weekly reporting variability, the long-term death rate profile becomes clear, and its peak can be located with confidence. Using the distribution of times from disease onset to death, it is possible to extend the model to infer the time course of fatal infections required to produce the later deaths. Because of the wide variability in onset to death times, a quite sharply peaked infection curve produces a death curve that declines only slowly. The inferred infection curve peaks a few days before lockdown, with fatal infections now likely to be occurring at a much-reduced rate.
There was no exponential growth in Covid-19 infections the UK. From the first days of the outbreak growth rates were in decline.
The following chart produced by financial strategist Alistair Haimes should put the above question to rest (compare it with the above chart).
The left hand side starts in March 2020 when the UK had had its first 300 infections and then stops at 10 April when Europe as a whole had reached a growth rate of zero or less. The chart is analogous to the above chart of interest rates. If you cannot distinguish the different colours and European countries don’t worry too much (UK is dark blue) as they all show the same overall pattern. The trends are all downwards, from start to finish.
We spoke to Sunetra Gupta, Professor of Theoretical Epidemiology at the University of Oxford and head of the team that released a study in March which speculated that as much as 50% of the population may already have been infected and the true Infection Fatality Rate could be as low as 0.1%.
In her first major interview since the Oxford study was published, she goes further by arguing that Covid-19 has already passed through the population and is now on its way out. She said:
On antibodies: • Many of the antibody tests are “extremely unreliable” • They do not indicate the true level of exposure or level of immunity • “Different countries have had different lockdown policies, and yet what we’ve observed is almost a uniform pattern of behaviour” • “Much of the driving force was due to the build-up of immunity”
• “Infection Fatality Rate is less than 1 in 1000 and probably closer to 1 in 10,000.” • That would be somewhere between 0.1% and 0.01%
On lockdown policy: • Referring to the Imperial model: “Should we act on a possible worst case scenario, given the costs of lockdown? It seems to me that given that the costs of lockdown are mounting that case is becoming more and more fragile” • Recommends “a more rapid exit from lockdown based more on certain heuristics, like who is dying and what is happening to the death rates”
On the UK Government response: • “We might have done better by doing nothing at all, or at least by doing something different, which would have been to pay attention to protecting the vulnerable”
On the R rate: • It is “principally dependent on how many people are immune” and we don’t have that information. • Deaths are the only reliable measure.
On New York: • “When you have pockets of vulnerable people it might rip through those pockets in a way that it wouldn’t if the vulnerable people were more scattered within the general population.”
On social distancing: • “Remaining in a state of lockdown is extremely dangerous” • “We used to live in a state approximating lockdown 100 years ago, and that was what created the conditions for the Spanish Flu to come in and kill 50m people.”
On next steps: • “It is very dangerous to talk about lockdown without recognising the enormous costs that it has on other vulnerable sectors in the population” • It is a “strong possibility” that if we return to full normal tomorrow — pubs, nightclubs, festivals — we would be fine.
On the politics of Covid: • “There is a sort of libertarian argument for the release of lockdown, and I think it is unfortunate that those of us who feel we should think differently about lockdown” • “The truth is that lockdown is a luxury, and it’s a luxury that the middle classes are enjoying and higher income countries are enjoying at the expense of the poor, the vulnerable and less developed countries.”
Imperial College’s modelling of non-pharmaceutical interventions for Covid-19 which helped persuade the UK and other countries to bring in draconian lockdowns will supersede the failed Venus space probe and could go down in history as the most devastating software mistake of all time, in terms of economic costs and lives lost.
…when a codebase is used to craft scholarly publications that are in turn used to influence public policy, the authors of those publications (and ultimately policy) need to ensure that the science is verifiable in a public sense. The lack of tests makes that an impossibility. So closure of this Issue, by retraction of studies based on it, is meant as a critique of the publication and policy authors, not the contributors to this repo
…for thirteen years, taxpayer funding from the MRC went to Ferguson and his team, and all it produced was code that violated one of the most fundamental precepts of good software development – intelligibility.
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