The purpose of this systematic review and meta-analysis is to determine the effect of lockdowns, also referred to as ‘Covid restrictions’, ‘social distancing measures’ etc., on COVID-19 mortality based on available empirical evidence. We define lockdowns as the imposition of at least one compulsory, non-pharmaceutical intervention (NPI). We employ a systematic search and screening procedure in which 19,646 studies are identified that could potentially address the purpose of our study. After three levels of screening, 32 studies qualified. Of those, estimates from 22 studies could be converted to standardised measures for inclusion in the metaanalysis.
Covid cases in Singapore and New Zealand have overtaken Australia per capita
Both still have very strict mandates in place unlike Australia where rules eased
Death rates in New Zealand are also higher than in Australia despite masks
Data shared by infectious diseases professor in post saying masks ‘don’t matter’
…The new figures come as it was revealed the median age of those dying from Covid in Australia is now 83 years old, the same age as the nation’s average life expectancy
…The vast majority of those who have caught Covid are under 50, with 3,121,953 cases so far but just 293 of that age have died of the virus since the pandemic began. Most killed by Covid were men over 70 and women over 80, accounting for 7,585 deaths out of the nation’s total virus death toll of 10,582, up to 3pm last Friday
…And even if Covid breaks out among elderly frail residents in aged care centres, more than 95 per cent of those infected will survive.
Imperial College’s death estimates over the years have some things in common: flawed modeling, hair-raising predictions of disaster that missed the mark, and no lessons learned.
The defining event in the history of Western Covid lockdowns occurred on March 16, 2020, with the publication of the now infamous Imperial College London Covid report, which predicted that in the “absence of any control measures or spontaneous changes in individual behaviour,” there would be 510,000 Covid deaths in Great Britain and 2.2 million in the United States. This prediction sent shock waves around the world. The next day, the U.K. media announced that the country was going into lockdown.
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.
A senior epidemiologist who advised the government during the coronavirus pandemic claims he was told to “correct” his views after he criticised what he thought was an “implausible” graph shown at an official briefing.
Professor Mark Woolhouse has also apologised to his daughter, whose generation “has been so badly served by mine”, and believes that closing schools was “morally wrong”.
The Edinburgh University academic is deeply critical of the use of lockdown measures and says “plain common sense” was a “casualty of the crisis”.
Speaking to Sky News, Prof Woolhouse seemed concerned about a possible “big-brother” approach to the control of information about COVID.
He says he was told to watch what he was saying following a briefing given by Chief Scientific Adviser (CSA) Sir Patrick Vallance on 21 September 2020.
Scientists abandoned their objectivity, misled with alarming models and failed to appreciate the damage lockdown would cause, a government adviser has claimed in a damning indictment of Britain’s pandemic response.
In his memoir, The Year The World Went Mad, Prof Woolhouse claimed that lockdowns “had surprisingly little effect” and just “deferred the problem to another day, at great cost”.
He argued that Spi-M was set up to tackle the wrong disease, influenza, and that early models were based on flu dynamics, and so mistakenly thought schools were a major driver while underrepresenting the impact of shielding.
Covid expert Professor Peter Collignon has shared a shocking graph showing why restrictions such as mask-wearing make little difference to case numbers.
The graphs compiled from Johns Hopkins University data compared the seven day rolling case average in Hong Kong and New Zealand.
Both jurisdictions were following a Covid-zero policy with heavy restrictions but have been struck by recent Omicron outbreaks.
The graphs show case numbers have shot up in both countries in the last month from zero to 3,000 new cases per every million people despite a strengthening of face mask and density mandates.
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”.
Google once believed it could use algorithms to track pandemics. People with flu would search for flu-related information, it reasoned, giving the tech giant instant knowledge of the disease’s prevalence. Google Flu Trends (GFT) would merge this information with flu tracking data to create algorithms that could predict the disease’s trajectory weeks before governments’ own estimates.
But after running the project for seven years, Google quietly abandoned it in 2015. It had failed spectacularly. In 2013, for instance, it miscalculated the peak of the flu season by 140 per cent.
According to the German psychologist Gerd Gigerenzer, this is a good example of the limitations of using algorithms to surveil and study society. The 74-year-old has just written a book on the subject, How to Stay Smart in a Smart World. He thinks humans need to remain in charge in a world increasingly filled with artificial intelligence that tries to replicate human thinking.
The World Health Organization amplified false Chinese statements about COVID-19 initially, while it dragging its feet on declaring an international emergency. Pandemic experts here clung to flu epidemic plans too, ignoring observable COVID-19 successes in East Asia and so ruling out any similar possibility of test-and-trace containment in the UK from the off.
Most public health experts then pivoted to being extremely pro-lockdown, but stuck rigidly to this even as the context, and so the costs and benefits of restrictions, changed with the vaccines and omicron.
Epidemiologists proved especially stubborn. Their modelling usually ignored the role of voluntary behavioural change entirely, so erred on the side of assuming catastrophic public health outcomes absent government mandates and restrictions. Hence, Freedom Day was dubbed “criminal” by scientists, while the government’s scientific advisers called for more restrictions last Christmas. Both proved wrong in retrospect.
It is more than a rebuke to Medley and the modellers though. This pandemic began, for many, with an announcement from Imperial College, whose study predicted 500,000 deaths if we did nothing. We locked down and never tested the prediction.
This time, in the face of what the public saw as dire predictions, we didn’t lock down and the apocalypse never came. The unspoken — and sometimes spoken — implication is clear: are we all fools?
“There are some scientists who have absolutely loved being media stars for the first time and they don’t want to stop. We don’t hear as much from the paediatricians, disease physicians, academic virologists and the immunologists who really know about these things.” (says Professor Allyson Pollock.)
Paul Hunter, professor of medicine at the University of East Anglia, said many prominent Covid voices have never written papers on infectious diseases. “It’s like me deciding, ‘I did a course on health and economics a year ago: maybe I should set up a group advising the chancellor on how to manage the tax system.’”
Everything the government has got right on Covid-19 in the past 12 months has happened when it ignored ‘the science’. If the modellers hadn’t made such fools of themselves in the summer and autumn of 2021 they might have been taken more seriously by the government in the winter. As it was, their incompetence had seeded enough doubt in Johnson’s mind for him to resist going beyond ‘Plan B’ despite almost every ‘scenario’ modelled telling him that hospitalisations and deaths from the virus would exceed anything England had ever seen before.
The Covid modellers at Imperial College have begun to back down. About time too. Over the past few weeks, they have made extreme claims about the omicron variant that cannot be fully justified by fundamental science, let alone by clinical observation.
These prognosticators of doom have been wrong time after time after time. And not just a little bit wrong – epically wrong, all while morally condemning their more accurate opponents. As cases rose in early July, in the run-up to England’s full reopening on July 19, restrictions advocates said that it was inevitable we would reach 100,000 cases per day. Keir Starmer released a video statement in which he declared that “Boris Johnson’s recklessness means we’re going to have an NHS summer crisis. The Johnson Variant is already out of control.” A set of academics wrote a letter to The Lancet condemning the reopening as a “dangerous and unethical experiment”.
Why haven’t lockdowns worked? There are broadly two types of respiratory virus. There are those that spread person to person – like measles – in a continuous chain of transmission, uninterrupted by season and with every susceptible contact falling ill. Then there are those we do not understand so well, like influenza, which are much more complex. Instead of the simplistic close contact model, which assumes Covid spreads like measles, we should perhaps consider an alternative more sophisticated model based on influenza. The influenza virus model is unusual – it is predicated on the majority being exposed to a particular airborne virus but, oddly, only a minority appear to be susceptible to each year’s variant. To complicate matters further, influenza can also spread person to person.
First major inquiry into the Covid crisis says the tragic losses in care homes were among the highest in Europe
The report finds that deaths could have been prevented but instead elderly were treated as ‘an afterthought’
Finding is just one among catalogue of failings detailed in the inquiry by the health and science committees
The report found test and trace system which cost Government £37billion was also branded ‘chaotic’ fiasco
The ONS antibody studies suggest that nearly half of 16 and 17 year olds have been previously infected. We don’t know the equivalent figure for 12 to fives but it is likely to be similar. That means the vaccine effect relative to all unvaccinated (previously infected and not) will be drastically lower than the figure used in the modelling paper. In turn, even the 15 minutes of prevented lost schooling will be a significant overestimate.
We write as concerned doctors, nurses, and other allied healthcare professionals with no vested interest in doing so. To the contrary, we face personal risk in relation to our employment for doing so and / or the risk of being personally “smeared” by those who inevitably will not like us speaking out.
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.