A State of Fear: How the UK government weaponised fear during the Covid-19 pandemic

by Laura Dodsworth

  There have been unqualified assumptions in the media that the differences in case and death rates in different countries are explained by lockdowns and interventions such as masks. But this falls into ‘Illusion of Control’ thinking as well as being, frankly, lazy thinking. And it suits governments to perpetuate the idea that lockdowns work, since they have enacted them.

  I may or may not persuade you that lockdowns do not work. The jury may even still be out, if we’re being generous. I may be a victim of my own ideological prejudice. But I do hope to at least rock your faith in this new orthodoxy.

  The use of universal lockdowns in the event of a new virus has no precedent. Why haven’t they been used before? They are blunt and brutal tools. They breach human rights. (See Chapter 15, ‘Tyranny’.) They are destructive to individuals’ lives and jobs, to businesses and the economy, which ultimately impacts the health of the nation. There was no good evidence that they do work, and in a world which till now has valued evidence-based interventions, that used to be an extremely important consideration. Since the mass quarantining of healthy individuals from 2020, there is a growing body of evidence that this extraordinary measure does not work in achieving its goals.

  After a year of global lockdowns, assumptions behind the modelling – that they successfully control the virus and without them hundreds of thousands more would die – have become accepted truth. This ‘truth’ is close to religious dogma here in the UK, where we have endured one of the strictest lockdowns in the world, according to an analysis by Oxford University’s Blavatnik School of Government.1 Only two governments, Venezuela and Lebanon, have introduced tougher policies. Questioning the efficacy of lockdowns is akin to heresy and might earn you the accusatory title ‘Covid denier’ or ‘covidiot’. Have the UK’s restrictions created a population with Stockholm syndrome?

  A 2019 report2 Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza by the World Health Organization (WHO) warned of the flimsy empirical basis for epidemiology models such as the one developed by Imperial College London. ‘Simulation models provide a weak level of evidence,’ the report noted, and lacked randomised controlled trials to test their assumptions.

  The report is about preparedness for influenza, but there would be much similarity in strategies and effectiveness for coronavirus (this was touched upon on in Chapter 1, ‘Fright Night’, and is referenced further in the endnotes). It said that non-pharmaceutical interventions (which include hand hygiene, respiratory etiquette, face masks for symptomatic individuals, surface and object cleaning, increased ventilation, isolation of sick individuals and travel advice) can be effective: ‘by reducing transmission in the community, the epidemic may be spread out over a longer period, with a reduced epidemic peak. This can be particularly important if the health system has limited resources or capacity (e.g. in terms of hospital beds and ventilators). Also, overall morbidity and mortality can be reduced even if the total number of infections across the epidemic is not reduced.’

  Isolation of ‘sick individuals’ is ‘recommended’ although the evidence is ‘very low’. Note that these are ‘sick’ individuals. But ‘quarantine of exposed individuals’ is ‘not recommended in any circumstances’. Mass quarantine measures – lockdowns – are ‘not recommended’ due to lack of evidence for their effectiveness. Similarly, evidence for closing schools, contact tracing, avoiding crowding, internal travel restrictions, border exit and entry screening is ‘very low’.

  ‘Case isolation’ is defined as ‘separation or restriction of movement of ill persons with an infectious disease at home or in a health care facility, to prevent transmission to others’ [italics my emphasis]. The Covid epidemic is the first time that cases have been accepted to be positive PCR or LFT results without a clinical diagnosis. Normally to qualify as a ‘case’, you must be unwell and have symptoms associated with the disease. In this report, even case isolation has a ‘very low overall quality of evidence’, and that’s for people who are sick, not just in receipt of a positive test result.

  In summarising the section on isolation, this report, which included the same 2006 influenza model that Ferguson adapted to Covid-19, concluded: ‘Most currently available studies on the effectiveness of isolation are simulation studies, which have a low strength of evidence.’

  (Incidentally, nowhere in the chapter ‘Communication for behavioural impact’, in the same report, is the use of fear to encourage adherence to NPIs recommended.)

  It cannot be stressed enough that the WHO and the UK have never included mass quarantining of healthy people – lockdowns – in epidemic or pandemic preparedness planning. There was no evidence that lockdowns would work, and the harms were acknowledged to outweigh the potential and unproven effects.

  Why did we lock down in the UK? SAGE observed the ‘innovative intervention’ out of China, but they initially presumed it would not be an acceptable option in the UK, a liberal Western democracy. In an astonishing interview for The Sunday Times, Professor Neil Ferguson of Imperial College London, a member of SAGE, said, ‘It’s a communist one party state, we said. We couldn’t get away with it in Europe, we thought… and then Italy did it. And we realised we could.’3

  Neil Ferguson’s Imperial simulation model was described in an article in The Telegraph as ‘the most devastating software mistake of all time’.4 The modelling used outdated code and contained multiple flaws. (There is more on the modelling in Chapter 10, ‘The metrics of fear’.)

  The doom-laden modelling grabbed headlines around the world and is credited with some of the responsibility for shifting policies on lockdown. And in circular and fallacious reasoning, the success of lockdown in the UK is measured by deaths ‘saved’ against those predicted by the unsubstantiated simulated forecasts of the modelling.

  Aside from the reportedly dismal coding, was it robust? Models based on assumptions in the absence of data can be over-speculative and open to over-interpretation. Professor John Ioannidis of Stanford University issued a strong warning5 for disease modellers to recognise the deficiencies in reliable data about Covid-19, which in time proved to be transmission, fatality rates and T-cell reactivity (See Chapter 10, ‘The metrics of fear’, for more detail.)

  The modelling also did not take take into account the spread of the virus in hospitals, care homes and prisons. When 40%6 of deaths are care home residents and up to two thirds of infections leading to serious illness are contracted in hospital,7 it cannot be over-stated what a major omission this was.

  Journalists, politicians and pundits state that lockdowns work, as though it is undisputed fact. As a result, politicians tighten and loosen the lockdown screws at will. There is no serious opposition. How have lockdowns become the new orthodoxy, when they were never recommended before 2020 and there was no evidence that they worked? Rather than opposition to the evidential and conceptual framework for lockdown, the problem, as defined by the political opposition, and Boris Johnson himself, is that we didn’t lock down ‘earlier and harder’.8 We appear to be doubling down on false assumptions.

  Regardless, lockdowns cannot be judged solely according to whether they avert death and illness from one virus. If we put aside all consideration of the acceptable reach of government and the imposition on our liberty, there are economic, social and health costs caused by lockdown. The government has shied away from a quantitative assessment of the policy, presumably because the numbers just would not stack up. Civitas9 produced a report using the standard UK government and NHS’s QALY (Quality Adjusted Life Years) calculations and found that the cost per QALY saved ranged from £96,000 to £1.97 million, depending on how successful lockdowns might have been (and the assumptions in the report are quite generous). To contextualise this, the NHS’s upper limit is £30,000 per QALY.

  But did lockdowns save lives?

  Deaths in England and Wales peaked on 8 April 2020, which (given the lag between infection and death), implies that infections peake
d and started to decrease before the lockdown on 23 March. This has been acknowledged by Chief Medical Officer Chris Whitty, who said that the R-number was decreasing before the national lockdown.10 This has been attributed to both voluntary behaviour changes and to the natural bell curve of a virus.

  Simon Wood, a professor of statistics at the University of Edinburgh, wrote for The Spectator that ‘although the estimated fatal infections were in retreat before each lockdown, the daily deaths were surging each time that a lockdown was called. The psychological pressure that this puts on the decision makers is obvious.’11 This is very plausible – when the pressure is at its worst, politicians are under greater pressure to ‘pull a lever’, to do something to slow transmission. Less generously, the lockdowns also happen to have been timed to almost be credited with declines which had just begun.

  It feels counterintuitive that restrictions do not limit the spread and death toll of coronavirus in the way that the Imperial model expected. However, there are now 34 studies and analyses which show that lockdowns do not work, with countries and states with fewer or no restrictions frequently outperforming countries and states with some of the most strict lockdowns. The American Institute for Economic Research12 has listed and summarised the 34 reports, which would be an ideal resource for those interested in learning more.

  In the interest of balance, I should say that the WHO published an article13 on 31 December 2020 which said ‘large scale physical distancing measures and movement restrictions, often referred to as “lockdowns”, can slow COVID-19 transmission by limiting contact between people’. It did not link to any evidence supporting this, but I have collated a few papers which find evidence in favour of lockdowns in the endnotes.14

  Assessing mandatory stay-at-home and business closure effects on the spread of COVID-19,15 from academics at Stanford University, concluded: ‘in summary, we fail to find strong evidence supporting a role for more restrictive NPIs in the control of COVID in early 2020. We do not question the role of all public health interventions, or of coordinated communications about the epidemic, but we fail to find an additional benefit of stay-at-home orders and business closures. The data cannot fully exclude the possibility of some benefits. However, even if they exist, these benefits may not match the numerous harms of these aggressive measures. More targeted public health interventions that more effectively reduce transmissions may be important for future epidemic control without the harms of highly restrictive measures.’16

  Oxford University’s Centre for Evidence-Based Medicine (CEBM) analysed excess mortality for 2020 across 32 countries. They used excess mortality instead of ‘Covid deaths’, to avoid problems with recording and classification of deaths and they used age-adjusted mortality to take into account differences in the average age of populations. It’s a simple matter to look at the table and see that excess mortality does not obviously correlate with the severity of lockdowns.

  To highlight one example, Sweden is often cited as a counter-factual to the UK’s policies because it did not impose strict lockdown measures throughout the year. It kept all retail and hospitality and most schools open and imposed no restrictions on private gatherings. According to CEBM, Sweden only had a 1.5% increase in age-adjusted mortality. England and Wales, with the strictest lockdown in the developed world, saw a 10.5% increase in age-adjusted mortality.

  Johan Carlson, Director of the Public Health Agency of Sweden, said: ‘Some believed that it was possible to eliminate disease transmission by shutting down society. We did not believe that and we have been proven right.’17

  How does the Imperial modelling handle this counter-factual? Interestingly, as Simon Woods noted in The Spectator, ‘to accommodate this anomaly their model treats the final March intervention in Sweden (shutting colleges and upper years secondary schools) as if it was lockdown. As many others have pointed out, that’s a strange way to model the set of data that most directly suggests that lockdown might not have been essential.’18

  As economist David Paton pointed out in his article ‘The myth of our “late” lockdown’,19 the November national lockdown in England ‘had no observable impact on hospitalisations or deaths at all. And although both have fallen very significantly over the past couple of months, all the indicators tell us that infections were decreasing well before the third national lockdown in January, even in regions not already placed in the highest Tier 4 restriction level.’ Often, proponents of lockdowns do not take the time lag between infections and hospitalisations and deaths into account, but hope to discern effect where there is none.

  Paton highlights the example of the Czech Republic, a country which, on 16 March 2020, locked down early and hard with border controls and the first national mask mandate in Europe. The early, strict lockdown in Czechia ‘did nothing to stop an autumn surge and second lockdown, then an even bigger December surge and yet another lockdown. Most recently, despite introducing even tougher restrictions at the end of January, Czechia experienced yet another big surge in cases throughout February and early March. As it stands, the Covid-related death rate in Czechia is the highest in the world (excluding the microstates of Gibraltar and San Marino) at 2,245 per million – 20% higher even than the UK.’

  Voluntary behaviour change might impact transmission more than mandatory lockdowns. A first literature review: lockdowns only had a small effect on COVID-1920 from the Centre for Political Studies in Denmark found that ‘Studies which differentiate between the two types of behavioural change find that, on average, mandated behavioural changes accounts for only 9% (median: 0%) of the total effect on the growth of the pandemic stemming from behavioural changes. The remaining 91% (median: 100%) of the effect was due to voluntary behavioural changes. This is excluding the effect of curfew and face masks, which were not employed in all countries.’

  Aside from a possible small effect attributable to mandated lockdowns and the further impact of voluntary behaviour change, what else affects the course of Covid? I would stray too far from the remit of my book and my armchair expertise to offer definitive conclusions, but having immersed myself in articles for the last year, hypotheses include: age of population, number of people in care homes, prevalence of obesity and other co-morbidities, number of nurses per capita, Vitamin D, previous exposure in the population to other coronaviruses, herd immunity, the volume of testing determining how many deaths are attributable to Covid, contact tracing, use of face masks and other NPIs. All of the uncertainties should remove the certainty that people place in one brute intervention.

  Viruses cannot be turned on and off like a tap by governments. This is difficult for the politicians – the enactors of lockdowns – to admit. This is partly due to the sunk cost fallacy, whereby a decision with destructive consequences traps the decision-maker in a cognitive cul-de-sac – they can’t admit the mistake and they keep going. The same is true for governments around the world. Lockdowns don’t work, so they impose more. It is difficult for the opposition to admit as they called for harder, earlier and longer lockdowns. It is hard for the lockdown-cheerleading media to admit. If lockdowns hurt more than they helped, this is a painful truth for us all to admit. Yet it is a truth that must be acknowledged if we want to save ourselves another wave of unnecessary lockdown pain.

  APPENDIX 3

  FIGHT BACK AGAINST THE NUDGE

  This is an excerpt from an essay by behavioural scientist Patrick Fagan.

  There are three solid tactics that you can use to fight back against nudges.

  1. UNCOVER

  Firstly – forewarned is forearmed. There is evidence that education and training can mitigate the effects of cognitive biases. Research by Professor Carey Morewedge, in particular, has found that people can be ‘debiased’ by teaching them about a given nudge through interactions, games, or videos, making them less susceptible to it; Morewedge has demonstrated that this debiasing effect can apply to real-world decisions and can last at least two months.

  In other words, understanding that
your decisions are liable to be nudged, and being able to recognise these nudges in the wild, is key to psychological independence. Being aware, for example, that the government is using fear to manipulate you is the first step towards spotting and resisting that manipulation.

  Additionally, behavioural science insights can be used to manage one’s own environment to reduce persuasibility. For example, one of the biggest psychological manipulators is conformity – that is, feeling pressured to follow and even believe the crowd in defiance of all reason – and research has discovered a number of mediators of this effect. Conformity is reduced, for example, when decisions are made in private; and so we can make an effort to make important decisions away from prying eyes to ensure a degree of rationality.

  2. UNPLUG

  However, although debiasing may be possible, there is also plenty of evidence that biases persist even if you are aware of them. It is a bit like an optical illusion: even though you may rationally know it is an illusion, your brain still cannot unsee it; likewise, even though you may know that Apple use tricks like scarcity and social proof to make their iPhones seem attractive, you still want one. As an experimental example, participants in one study were taught about a nudge called anchoring and adjustment, and they were told that it was about to be used on them – and yet their decisions were still biased by it.