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.
I won’t have been the only parent concerned by news last week that the Pfizer vaccine may be approved for use on children as early as June and potentially rolled out to school pupils from September. Healthy children are at almost no serious risk from Covid-19 – the recovery rate for this age group has been calculated at over 99.99 per cent. The argument that children should have the vaccine is not based on a belief that they need or benefit from it but on the logic that it would be good for our communities at large if children were jabbed. In short, those advocating it assume that children have an obligation to protect adults.
It’s worth noting that the UK Government has granted immunity from liability for harms to all Covid-19 vaccine manufacturers. Can we really ask children to accept a greater risk than the manufacturers themselves are prepared to live with?
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.
Some 8.8 million schoolchildren in the UK have experienced severe disruption to their education, with prolonged school closures and national exams cancelled for two consecutive years. School closures have been implemented internationally1 with insufficient evidence for their role in minimising covid-19 transmission and insufficient consideration of the harms to children.
Foot and mouth disease (FMD) is a major threat, not only to countries whose economies rely on agricultural exports, but also to industrialised countries that maintain a healthy domestic livestock industry by eliminating major infectious diseases from their livestock populations. Traditional methods of controlling diseases such as FMD require the rapid detection and slaughter of infected animals, and any susceptible animals with which they may have been in contact, either directly or indirectly. During the 2001 epidemic of FMD in the United Kingdom (UK), this approach was supplemented by a culling policy driven by unvalidated predictive models. The epidemic and its control resulted in the death of approximately ten million animals, public disgust with the magnitude of the slaughter, and political resolve to adopt alternative options, notably including vaccination, to control any future epidemics. The UK experience provides a salutary warning of how models can be abused in the interests of scientiﬁc opportunism.
This is a BMJ Rapid Response letter by Dr Janet Menage, Wales, UK, in response to Covid-19: Social murder, they wrote-elected, unaccountable, and unrepentant, by Kamran Abbasi. You can find the full response in the link below.
From a medical perspective, it was clear early on in the crisis that disregarding clinical acumen in favour of blind obedience to abnormal ventilation measures, reliance on an unsuitable laboratory test for diagnosis and management, and abandoning the duty of care to elderly hospitalised patients and those awaiting diagnosis and treatment of serious diseases, would create severe problems down the line.
Doctors who had empirically found effective pharmaceutical remedies and preventative treatments were ignored, or worse, denigrated or silenced. Information regarding helpful dietary supplements was suppressed.
One in five people in England may have had coronavirus, new modelling suggests, equivalent to 12.4 million people, rising to almost one in two in some areas.
It means that across the country as a whole the true number of people infected to date may be five times higher than the total number of known cases according to the government’s dashboard.
In some areas, however, the disparity may be even greater. Parts of London and the south are estimated to have had up to eight times as many cases as have been detected to date.
The analysis, by Edge Health, reveals that the true number of coronavirus infections in England could be as high as 12.4 million, equivalent to 22% of the population, as of 3 January.
- The ‘new strain’ of coronavirus that put London into Tier 4 was down to more computer modelling from Neil Ferguson.
- The government deliberately resorted to fear.
- The damage done to our standing in the world is permanent.
- The government is doing something it should not do and has no justification.
- The whole notion of the mutant strain is completely constructed.
- NERVTAG is full of psychologists who are experts in frightening people.
- If you don’t get angry, this will never go away.
- There is no evidence that this new variant is any more infection that the old one.
- Historically medical beliefs are often wrong.
- Fighting this thing is probably the most important thing we’ve ever done in our lives.
Wearing a used mask could be more dangerous than not wearing one at all when it comes to warding off COVID-19, a new study has found.
They found that wearing a mask “significantly slows down” airflow, reducing a mask’s efficacy and making a person more susceptible to inhaling aerosols into the nose — where SARS-CoV-2 likes to lurk.
“It is natural to think that wearing a mask, no matter new or old, should always be better than nothing. Our results show that this belief is only true for particles larger than 5 micrometers, but not for fine particles smaller than 2.5 micrometers,” said author Jinxiang Xi.
The researchers found that wearing a mask with low (less than 30%) filtration efficiency can be worse than without.
Results While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproductive number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons.
Conclusions Inferences on effects of NPIs are non-robust and highly sensitive to model specification. Claimed benefits of lockdown appear grossly exaggerated.
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.
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?
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.
Plastic face shields are almost totally ineffective at trapping respiratory aerosols, according to modelling in Japan, casting doubt on their effectiveness in preventing the spread of coronavirus.
A simulation using Fugaku, the world’s fastest supercomputer, found that almost 100% of airborne droplets of less than 5 micrometres in size escaped through plastic visors of the kind often used by people working in service industries.
In addition, about half of larger droplets measuring 50 micrometres found their way into the air, according to Riken, a government-backed research institute in the western city of Kobe.
I knew a second lockdown was on the cards before we’d had the first one. In mid-March my team at the University of Edinburgh modelled a lockdown that ended in June and was followed by a slow, initially imperceptible rise in cases over the summer, culminating in a second lockdown in late September.
The coronavirus pandemic has peaked earlier than expected in many African countries, confounding early predictions, experts have told MPs.
Scientists do not yet know why, but one hypothesis is the possibility of people having pre-existing immunity to Covid-19, caused by exposure to other infections.
Prof Francesco Checchi, a specialist in epidemiology at the London School of Hygiene and Tropical Medicine, told MPs it was “broadly” true that coronavirus had not behaved in expected ways in African countries, including Kenya, Tanzania, Sudan and Somalia.