So, the third wave is officially no more. New modelling by SPI-M, the government’s committee on modelling for pandemics, has, at a stroke, eradicated the predicted surge in new infections, hospital admissions and deaths which it had pencilled in for the autumn or winter as a result of lockdown being eased.
…As Philip Thomas explained here on Sunday, Imperial College has also assumed strangely low estimates for the number of people in Britain carrying antibodies. If you are going to use assumptions that are far more pessimistic than real world data suggests, it is small wonder that SPI-M keeps predicting waves and surges that turn out to be wide of the mark. The question is: why are these modelling teams using such negative assumptions?
Professor Neil Ferguson struck an unusually optimistic tone this week. With just one Covid death reported on Monday, and infection levels at an eight-month low in the UK, the architect of the original lockdown said: ‘The data is very encouraging and very much in line with what we expected.’ The first half of that statement is certainly true; the second half much less so.
Watson’s response to the easing of lockdown is not all that uncommon, say psychologists. It is not yet known how many people will be affected by residual Covid anxiety after vaccination, but it’s feared a significant minority will struggle to readjust, especially as increased unlocking allows for large groups and big, crowded events to take place again.
A year ago, there was no evidence that lockdowns would protect older high-risk people from Covid-19. Now there is evidence. They did not.
With so many Covid-19 deaths, it is obvious that lockdown strategies failed to protect the old. Holding the naïve belief that shutting down society would protect everyone, governments and scientists rejected basic focused protection measures for the elderly. While anyone can get infected, there is more than a thousand-fold difference in the risk of death between the old and the young. The failure to exploit this fact about the virus led to the biggest public health fiasco in history.
Texas Gov. Greg Abbott announced last week that his state is ending its mask mandate and business capacity limits. While Democrats and many public-health officials denounced the move, ample data now exist to demonstrate that the benefits of stringent measures aren’t worth the costs.
…We have since learned that the virus never spreads exponentially for very long, even without stringent restrictions. The epidemic always recedes well before herd immunity has been reached.
THE Government has been accused of over-relying on pandemic modelling and risking “catastrophe by computer”. Last week Boris Johnson published a cautious ‘roadmap‘ to normality after scientists warned him there could be 91,000 extra deaths if he scrapped curbs completely at the end of April.
These figures were based on Imperial College modelling that has since been challenged by Mark Harper, deputy chair of the Covid Recovery Group of MPs. He argued the model did not account for key factors shown to change the course of the pandemic such as the most up to date evidence on the protective effect of the vaccines as well as the “seasonal effect” as the country moves into summer. Modelling has driven much of the pandemic response. The initial reaction in the UK, the US and other European countries was shaped by the dramatic headlines in March last year, suggesting 550,000 deaths in the UK and 2.2 million in the US if mitigation measures were not put in place.
- German researchers enrolled nearly 2,500 parents and their children in a study
- Found three times as many adults had coronavirus antibodies than children
- Data also shows a previously infected adult and an uninfected child was 4.3 times more common than a previously infected child and an uninfected parent
Children are unlikely to have played a significant role in the spread of coronavirus during the first wave last year, a study shows.
Throughout the pandemic it has become increasingly evident children are less affected by Covid-19; symptoms, severe disease and death figures in children are all much lower than would be expected when compared to the rest of the population.
Figures from Public Health England (PHE) show the current risk of dying from coronavirus if infected is 1,513 per 100,000 people for over-80s, but for children aged five to nine, this is just 0.1 per 100,000.
No traces of coronavirus have been found on surfaces and in the air on the London Underground or on buses in the capital city, scientists have said.
Experts from Imperial College London have been carrying out monthly tests on the network, mimicking a passenger journey and taking swabs from escalators, handrails, bus shelters and Oyster Card readers.
The Imperial model had larger errors, about 5-fold higher than other models by six weeks. This appears to be largely driven by the aforementioned tendency to overestimate mortality. At twelve weeks, MAPE values were lowest for the IHME-MS-SEIR (23.7%) model, while the Imperial model had the most elevated MAPE (98.8%). Predictive performance between models was generally similar for median absolute errors (MAEs)
- 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.
Official data is ‘exaggerating’ the risk of Covid-19 and talk of a second wave is ‘misleading’, nearly 500 academics told Boris Johnson in open letter attacking lockdown.
The doctors and scientists said the Government’s response to the coronavirus pandemic has become ‘disproportionate’ and that mass testing has distorted the risk of the virus.
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?
Covid-19 rates are not surging, researchers at King’s College have said after results from its symptom tracker app showed a far less deadly virus trajectory than Imperial College findings.
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.
Researchers from Edinburgh University reassessed Imperial modelling that showed half a million people would die.
Blanket social distancing and the closure of schools may have cost more lives than if herd immunity had been allowed to build slowly in the community, a study suggests.
But where did this one percent figure come from? You may find this hard to believe, but this figure emerged by mistake. A pretty major thing to make a mistake about, but that’s what happened.
In order to understand what happened, you have to understand the difference between two medical terms that sound the same – but are completely different. [IFR and CFR.]
CFR will always be far higher than the IFR. With influenza, the CFR is around ten times as high as the IFR. Covid seems to have a similar proportion.
Now, clearly, you do not want to get these figures mixed up. By doing so you would either wildly overestimate, or wildly underestimate, the impact of Covid. But mix these figures up, they did.
…we’ve had all the deaths we were ever going to get. And which also means that lockdown achieved, almost precisely nothing with regard to Covid. No deaths were prevented.
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: