Speaking this week on The Mail on Sunday’s Medical Minefield podcast, Prof Woolhouse said: ‘I think that lockdown will be viewed by history as a monumental mistake on a global scale, for a number of reasons.
‘The obvious one is the immense harm the lockdown, more than any other measure, did in terms of the economy, mental health and on the wellbeing of society.
…[A study published in Science in February 2021] also found something intriguing: lockdowns could, in a worst-case scenario, actually increase transmission of the virus by up to five per cent.
…As Dr Ali puts it: ‘Some people say lockdowns were beneficial, others that they were really terrible.
‘The reality actually is much closer to the idea that it didn’t make much difference either way.’
For those who made painful sacrifices, that won’t be an easy truth to swallow.
Scientists did not have accurate Covid case numbers, and were unsure of hospitalisation and death rates when they published models suggesting that more than 500,000 people could die if Britain took no action in the first wave of the pandemic, it has emerged.
On March 16 2020, Imperial College published its “Report 9” paper suggesting that failing to take action could overwhelm the NHS within weeks and result in hundreds of thousands of deaths.
Before the paper, the UK coronavirus strategy was to flatten the peak rather than suppress the wave, but after the modelling was made public, the Government made a rapid u-turn, which eventually led to lockdown on March 23.
However SPI-M (Scientific Pandemic Influenza Group on Modelling) minutes released to the Telegraph under a Freedom of Information request show that by March 16, modellers were still “uncertain” of case numbers “due to data limitations”.
The minutes show that members were waiting for comprehensive mortality data from Public Health England (PHE) and said that current best estimates for the infection fatality rate, hospitalisation rates, and the number of people needing intensive care were still uncertain.
They also believed that modelling only showed “proof of concept” that lockdowns could help, and warned that “further work would be required”.
The majority of patients who contracted COVID-19 while in hospital did so from other patients rather than from healthcare workers, concludes a new study from researchers at the University of Cambridge and Addenbrooke’s Hospital.
The researchers analysed data from the first wave of the pandemic, between March and June 2020. While a great deal of effort is made to prevent the spread of viruses within hospital by keeping infected and non-infected individuals apart, this task is made more difficult during times when the number of infections is high. The high level of transmissibility of the virus and the potential for infected individuals to be asymptomatic both make this task particularly challenging.
The open letter states that “a good society cannot be created by an obsessive focus on a single cause of ill-health” and states all restrictions should be lifted in June on the final date in Prime Minister Boris Johnson’s ‘roadmap’ out of lockdown. Masks should no longer be worn by schoolchildren after May 17, say the scientists – and they warn the damage to society will be too great if the current Covid control measures continue beyond the June roadmap date.
Vaccine passports should also be scrapped along with mass community testing, they say.
Instead, the government should focus on targeted testing, creating better incentives for staying home if ill and basic hygiene measures, such as handwashing and surface cleaning.
Signatories (in alphabetical order)
Professor Ryan Anderson, Translational Science, Medicines Discovery Catapult
Dr Colin Axon, Mechanical Engineering, Brunel University
Professor Anthony Brookes, Genomics and Bioinformatics, University of Leicester
Professor Jackie Cassell, FFPH, Deputy Dean, Brighton and Sussex Medical School
Professor Angus Dalgleish, FRCP, FRCPath, FMedSci, Oncology, St George’s, University of London
Professor Robert Dingwall, FAcSS, HonMFPH, Sociology, Nottingham Trent University
Professor Sunetra Gupta, Theoretical Epidemiology, University of Oxford
Professor Carl Heneghan, MRCGP, Centre for Evidence Based Medicine, University of Oxford
Professor Mike Hulme, Human Geography, University of Cambridge.
Dr John Lee – formerly Pathology, Hull York Medical School
Professor David Livermore, Medical Microbiology, University of East Anglia.
Professor Paul McKeigue Genetic Epidemiology and Statistical Genetics, University of Edinburgh
Professor David Paton, Industrial Economics, University of Nottingham
Emeritus Professor Hugh Pennington, CBE, FRCPath, FRCP (Edin), FMedSci, FRSE, Bacteriology, University of Aberdeen
Dr Gerry Quinn, Biomedical Sciences, University of Ulster
Dr Roland Salmon, MRCGP, FFPH, former Director of the Communicable Disease Surveillance Centre (Wales).
Emeritus Professor John Scott, CBE, FRSA, FBA, FAcSS, Sociology, University of Essex
Professor Karol Sikora, FRCR, FRCP, FFPM, Medicine, University of Buckingham
Professor Ellen Townsend, Psychology, University of Nottingham
Dr Chao Wang, Health & Social Care Statistics, Kingston University and St George’s, University of London,
Professor John Watkins, Epidemiology, Cardiff University
Professor Lisa White, Modelling and Epidemiology, University of Oxford.
Norman Fenton is Professor in Risk Information Management at Queen Mary University of London and also a Director of Agena, a company that specialises in risk management for critical systems.
One of the major messages currently being pushed everywhere by the UK Government about COVID-19 is the claim that “1 in 3 people who have the virus have no symptoms”. In fact, if we trust the Government’s own data, this claim is massively exaggerated. The true figure – as we explain below – is more like 1 in 38*. Moreover, using data from
an ongoing study at Cambridge University (in which only people without symptoms are tested) we conclude that 96% of such people who test positive do not have the virus (i.e. they are mostly false positives).
Office for National Statistics (ONS) data – which showed soaring coronavirus cases before the second lockdown – has been quietly revised down and now suggests that cases were largely plateauing at the time, it has emerged.
Many experts have complained that the data presented by the Government ahead of the lockdown was “riddled with errors” and exaggerated the need for a second lockdown, while Greg Clark, the chairman of the Commons science and technology committee, said the belated admission of errors was “of great concern”.
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.
The government has been criticised by the official statistics watchdog for the way it presented data to justify England’s second lockdown.
The UK Statistics Authority highlighted the use of modelling at Saturday’s TV briefing showing the possible death toll from Covid this winter.
It said there needed to be more transparency about data and how predictions were being made.
The projections were out of date and over-estimated deaths, it has emerged…
It is understood the graph was used by the two senior advisers in meetings last week where the decision to impose a nationwide lockdown in England was made.
Death toll forecasts used by the government as grounds for another nationwide lockdown are out-of-date and could be four times too high, experts have said.
A Downing Street press conference led by Boris Johnson on Saturday included data suggesting that England could be seeing up to 4,000 deaths each day by early December.
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.
People under 50 are more likely to die suddenly because of an accident or injury than from coronavirus, a leading risk expert has said. Professor Sir David Spiegelhalter said people under 25 are more likely to die from flu or pneumonia, while under 40s have a greater risk of being killed in a road accident. The Cambridge University professor looked at the average risk for different age groups dying after contracting Covid-19 and compared it with the most recent yearly data from 2018.
School children under the age of 15 are more likely to be hit by lightning than die from coronavirus, new figures suggest, amid mounting pressure for the government to get more to get pupils back into classrooms as quickly as possible.
Scientists from the universities of Cambridge and Oxford have called for “rational debate” based on the “tiny” risk to children and have suggested that if no vaccine is found in the future then it may be better for younger people to continue with their lives, while shielding the more vulnerable.
It comes as the government was accused of “losing the plot” after Gavin Williamson, the Education Secretary, scrapped the Government’s target of getting all primary school pupils back in the classroom before the summer holidays
The government’s daily briefings on #Covid_19 are “not trustworthy communication of statistics” says Professor Sir David Spiegelhalter from the University of Cambridge