Part of the rush to dismiss women on the basis of little to no evidence comes – no doubt – from a well-meaning, but ultimately misguided effort to reduce vaccine hesitancy in young women, although if anything will drive hesitancy it is surely exactly this kind of medical gaslighting. More broadly, being disbelieved and dismissed by the medical establishment is nothing new for women, who are used to being, for example, prescribed antidepressants when they present to doctors in pain (men who present with similar symptoms are more likely to be prescribed painkillers). Women are simply not considered to be reliable narrators of their own bodies.
No10’s Test and Trace system has had barely any impact on thwarting the spread of Covid, according to official estimates.
The controversial £37billion scheme has been heavily criticised over the past year for being ineffective at breaking the chains of transmission.
New Government modelling found the programme – which critics have described as being the biggest ever waste of taxpayer money – may have only slashed cases by as little as six per cent.
The overall risk of children becoming severely ill or dying from Covid is extremely low, a new analysis of Covid infection data confirms.
Scientists from University College London, and the Universities of York, Bristol and Liverpool say their studies of children are the most comprehensive yet anywhere in the world.
They checked England’s public health data and found most of the young people who had died of Covid-19 had underlying health conditions:
Around 15 had life-limiting or underlying conditions, including 13 living with complex neuro-disabilities
Six had no underlying conditions recorded in the last five years – though researchers caution some illnesses may have been missed
A further 36 children had a positive Covid test at the time of their death but died from other causes, the analysis suggests
Though the overall risks were still low, children and young people who died were more likely to be over the age of 10 and of Black and Asian ethnicity.
Researchers estimate that 25 deaths in a population of some 12 million children in England gives a broad, overall mortality rate of 2 per million children.
25 CYP died of SARS-CoV-2 during the first pandemic year in England, equivalent to an infection fatality rate of 5 per 100,000 and a mortality rate of 2 per million. Most had an underlying comorbidity, particularly neurodisability and life-limiting conditions. The CYP who died were mainly >10 years and of Asian and Black ethnicity, compared to other causes of the death, but their absolute risk of death was still extremely low.
Modelling that helped persuade the Government to delay the June 21 reopening was overly pessimistic and the lockdown lifting should “possibly” have gone ahead on time, a government adviser has admitted.
Dr Mike Tildesley, an epidemiologist from Warwick University, said Britain had been in a “much better situation than we thought” when his group released models suggesting third wave deaths could hit 72,000.
As Sarah Knapton has revealed in these pages, the brutal postponement of Freedom Day coincided with the release of a bunch of alarmist models predicting a huge new wave of deaths. The most pessimistic, inevitably from Imperial College, forecast 203,824 deaths over the next year. It did so by assuming just a 77-87 per cent reduction in hospitalisations following two vaccinations, despite the fact that real world data shows two vaccinations to be between 92 per cent (AstraZeneca) and 96 per cent (Pfizer) effective in preventing hospitalisation. That would cut the Imperial forecast of deaths by a gob-smacking 90 per cent to 26,854.
This keeps happening. In April the modellers assumed a 30 per cent effectiveness for the vaccine at preventing the spread of the virus. This was described as “a pessimistic view – but it is plausible, it’s not extreme”, by Professor Graham Medley, chairman of the SPI-M sub-group of Sage. It turns out it was far from plausible. At the end of March the BBC’s favourite modeller, Imperial College’s Neil Ferguson, was forecasting that by June 21, even with “optimistic” assumptions, less than half of Britain would be protected against severe disease by vaccination. The true figure is over 80 per cent.
It’s amazing how often Sweden still crops up in conversations. It didn’t impose tough lockdown, kept primary schools and core economic activities functioning, issued clear guidelines and relied on voluntary social distancing and personal hygiene practices to manage the crisis. For harsh lockdowns to be justified elsewhere, Sweden had to be discredited. Hence the harsh criticisms of Sweden’s approach last year by the New York Times, Newsweek, USA Today, CBS News and others.
But with Sweden’s demonstrable success, goalposts have shifted. Every time it’s mentioned as a counter to Europe’s high Covid-toll lockdown countries, the response now is: ‘But their Nordic neighbours did much better. Look at Denmark’. Let’s ‘interrogate’ this argument.
Social distancing and wearing face masks should stay forever, a Communist-supporting SAGE scientist has claimed.
Professor Susan Michie, of University College London, said she thinks the draconian restrictions should become part of people’s every day routine.
Just 851.2 people per 100,000 died last month – the lowest figure since the ONS started recording mortality rates in 2001. At the height of the first wave of the Covid pandemic last April, death rates were 1,859 per 100,000.
The latest figures show that 38,899 people died in April – 6.1 per cent fewer than the five-year average.
Just 2.4 per cent of all deaths mentioned Covid on the death certificate, a 77.6 per cent decrease from March and the largest month-on-month decline since the pandemic began.
The new data provide more evidence that the NHS is in little danger of being overwhelmed in the near future, with deaths from most causes lower than normal. Covid is now the ninth most common cause of death in England and Wales, behind conditions including heart disease, dementia, several cancers and influenza.
The case for the prosecution of Johnson is likely to be heard in a parliamentary inquiry (with Dominic Cummings as the star witness) which should bring scrutiny of the Imperial College cliff-edge hypothesis. This suggests that Covid cases surged every day until lockdown, so Prime Ministerial dither cost thousands of lives. Only when he eventually agreed to lock down on March 23, says Imperial, did cases collapse. This theory is one of the most influential ever deployed in government – and now looks as if it could be bunkum.
We don’t have to guess anymore, given how much Covid data exists. The ONS, Zoe/King’s College, the React-2 study run by a different team at Imperial: none support Neil Ferguson’s cliff-edge theory. All show Covid cases falling before lockdowns. So what forced the virus into retreat, if not stay-at-home orders? We can look at another form of contagion: news, spread digitally. People saw how things were getting dangerous and stayed home of their own accord. This is more than theory. Mobile phone data offers rich detail of this worldwide trend.
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)