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.
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.
The number of new infections per day is a key quantity for effective epidemic management. It can be estimated by testing of random population samples. Without such direct epidemiological measurement, other approaches are required to infer whether the number of new cases is likely to be increasing or decreasing: for example, estimating the pathogen reproductive rate, R, using data gathered from the clinical response to the disease. For COVID-19 (SARS-CoV-2) such R estimation is heavily dependent on modelling assumptions, because the available clinical case data are opportunistic observational data subject to severe temporal confounding. Given this difficulty it is useful to reconstruct the time course of infections from the least compromised available data, using minimal prior assumptions. A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the disease duration distribution suggests that infections were in decline before full UK lockdown (24 March 2020), and that infections in Sweden started to decline only a day or two later. An analysis of UK data using the model of Flaxman et al. (2020, Nature 584) gives the same result under relaxation of its prior assumptions on R.
Sky News host Alan Jones says he has warned time and time again the political leaders who are the architects of this coronavirus response will not be able to escape the criticism that is now finding its way into the public place. It comes as an economist in the Victorian Department of Finance and Treasury, Sanjeev Sabhlok, on Wednesday penned an article in the Australian Financial Review announcing his resignation from his position.
- Policies are a sledgehammer to kill a swarm of flies.
- The Spanish Flu killed killed at least 50 million out of 1.8 billion people out of worldwide.
- To compare with Spanish Flu, COVID-19 would need to kill 210 million people. It has only killed 0.9 million.
- 60 million people worldwide normally die each year.
- There are strong scientific arguments against lockdown.
- The data was clear from February that the elderly should be protected but this wasn’t done.
- Epidemiological models have badly exaggerated the risk.
- There was never any reason to mandate measures such as face masks.
- COVID-19 is no worse than the Asian Flu.
- Lockdowns cannot eradicate the virus.
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 authors of the commentary, titled “COVID-19 Transmission and Children: The Child Is Not to Blame,” base their conclusions on a new study published in the current issue of Pediatrics, “COVID-19 in Children and the Dynamics of Infection in Families,” and four other recent studies that examine Covid-19 transmission by and among children.
Such is the quality of decision-making in the process generating our lockdown narrative. An early maintained but exaggerated belief in the lethality of the virus reinforced by modelling that was almost data-free, then amplified by further modelling with no proven predictive value. All summed up by recommendations from a committee based on qualitative data that hasn’t even been peer-reviewed.
- According to Office for National Statistics, this year comes only eighth in terms of deaths in past 27 years.
- The spread of viruses like Covid-19 is not new. What’s new is our response.
- The whole Covid drama has really been a crisis of awareness of what viruses normally do, rather than a crisis caused by an abnormally lethal new bug.
- Modelling is not science, for the simple reason that a prediction made by a scientist (using a model or not) is just opinion.
- To be classified as science, a prediction or theory needs to be able to be tested, and potentially falsified.
- A problem with the current approach: a wilful determination to ignore the quality of the information being used to set Covid policy.
- Most Covid research was not peer- reviewed.
- In medical science there is a well-known classification of data quality known as ‘the hierarchy of evidence’: a seven-level system gives an idea of how much weight can be placed on any given study or recommendation.
- Randomised controlled trials (RCTs) form the highest, most reliable form of medical evidence: Level 1 and 2.
- Virtually all evidence pertaining to Covid-19 policy is found in the lowest levels (much less compelling Levels 5 and 6): descriptive-only studies looking for a pattern, without using controls.
- Level 7 is at the bottom of the hierarchy (the opinion of authorities or reports of expert committees) because ‘authorities’ often fail to change their minds in the face of new evidence.
- Committees often issue compromise recommendations that are scientifically non-valid.
- The advice of Sage (or any committee of scientists) is the least reliable form of evidence there is.
The death rate from COVID-19 (coronavirus) in Europe appears to be linked to low-intensity flu seasons in the past two years as the same people are vulnerable, says a working paper by Dr Chris Hope, Emeritus Reader in Policy Modelling at Cambridge Judge Business School.