She told me she believes people are getting used to being controlled by the government in a similar way. We have gone between lockdown (the extreme abuse) and more freedom (the honeymoon period) but that freedom has become controlled and authorised. The control crept in and the goalposts were moved, again like domestic abuse: ‘Freedom becomes conditional. You wait to be told you are allowed it. And it can be removed from you. The British public are in a coercive control relationship with the government. Most people will say they are not; in fact they will defend the “relationship”. People in an abusive relationship can get very angry when they are called out, if they are not ready to hear what’s going on.’
See if you think Biderman’s Chart of Coercion can be applied to the government’s policies.
BIDERMAN’S CHART OF COERCION
TOOL
EFFECT
LOCKDOWN EFFECT
Isolation
Deprives victim of all social supports and of his ability to resist. Develops an intense concern with self. Makes victim dependent upon interrogator. Solitary confinement and isolation.
‘Stay at home orders’, self-isolation, social distancing, isolation from loved ones.
Monopolisation of perception
Fixes attention upon immediate predicament, fosters introspection. Eliminates stimuli competing with those controlled by captor. Frustrates all actions not consistent with compliance. Physical isolation. Darkness or bright light. Barren environment. Restricted movement. Monotonous food.
The monopolisation of the 24/7 news cycle, social media flooded with government and public health advertising and messaging, censorship of alternative viewpoints. The closure of social, cultural, artistic and leisure venues and events.
Induced debility and exhaustion
Weakens mental and physical ability to resist. Semi-starvation. Exposure. Exploitation of wounds. Induced illness. Sleep deprivation. Prolonged interrogation. Forced writing. Over-exertion.
Isolation and loneliness affect sleep and physical health. Being forced to stay at home and closure of sports affect fitness and health.
Threats
Cultivates anxiety and despair. Threats of death. Threats of non-return. Threats of endless interrogation and isolation. Threats against family. Vague threats. Mysterious changes of treatment.
Fines threatened for activities which were previously normal, like working, travelling, seeing friends and family, dating, worship etc. Vague threats of mandated vaccines and vaccine passports create stress. Threats of repeated lockdowns if measures don’t work and compliance is not high enough.
Occasional indulgences
Provides positive motivation. Occasional favours. Fluctuations of interrogation attitudes.
Reopening of some social and sporting activities and civic functions at certain points, then taken away again.
Demonstrating ‘omnipotence’
Suggests futility of resistance. Demonstrating complete control over victim’s fate. Confrontation. Pretending cooperation taken for granted.
The previously unimaginable situation of basic rights being withdrawn. Claiming omnipotent scientific and medical authority, by ‘following the science’. Proclaiming that we must not act on basic human instincts such as hugging family. Repeating and assuming adherence to the values of collectivism and solidarity and showing you care.
Degradation
Makes cost of resistance appear more damaging to self-esteem than capitulation. Reduces prisoner to ‘animal level’ concerns. Personal hygiene prevented. Filthy, infested surroundings. Demeaning punishments. Insults and taunts. Denial of privacy.
Shaming people for struggling with or refusing to wear masks, which may be impossible with a disability, or feel dehumanising.
Enforcing trivial demands
Develops habit of compliance. Forced writing. Enforcement of minute rules.
Standing on dots in shops and public spaces, queuing, following illogical rules such as entering a restaurant with a mask on, taking it off when seated, but putting it on to go to the toilet.
MAVIS, 35
We can’t do what we normally do. I’m too afraid to even go to the park, I keep my son indoors all the time. We do the Joe Wicks exercise every day and reading. That’s how we manage the lockdown.
It’s better to stay indoors than go outside and catch the virus, but I don’t know how long this can go on. I pray for it to be over soon.
We have lived here for one year. We have one room, we sleep together in the same single bed, there is a cooking area and a fridge. There is no window and no ventilation. There is a cooker hood but it doesn’t suck up the smoke and steam. We have our own bathroom at least. I do my best to make my home nice, but it’s not easy. There is damp and some things are broken. The landlady says she can’t fix things, it will cost too much money.
There is no space for my son to play. If I cook, he is under my feet, I tread on his toys. We have to squeeze around each other. It’s not easy. Sometimes I tread on his feet or something, and he starts crying, and I say, ‘Mummy is so sorry, so sorry, I didn’t mean it.’
My son is three, so normally he goes to nursery. He misses his friends. I miss it too, because I do my shopping and jobs when he is at nursery. He cries a lot at the moment.
I don’t know how online shopping works, but I know a Sainsbury’s delivery man from church and I tell him what I need and he buys it for me, delivers it, and I pay him.
I pray that we will be released quickly from lockdown. Summer is coming and this room gets very hot with no ventilation.
10. THE METRICS OF FEAR
‘Like dreams, statistics are a form of wish fulfilment.’
Jean Baudrillard
Logic is slow and fear is fast. Understanding numbers requires logic and sound reasoning. Politicians and the media very often use fear to circumvent our logic, because it slows our thinking. We can be dazzled and alarmed by a big number, or a steep line on a graph, and then we’re less likely to question the nuance and more likely to be suggestible. From the beginning of the epidemic, the government and media reported the daily death tolls with a macabre dedication and, as I have said before, without context, such as comparisons with deaths from other causes, or total deaths, or recovery figures.
Humans can’t sustain fear indefinitely: we get bored, we relax or, some might say, we become complacent. Covid didn’t impact our lives in the ways the Chinese social media videos promised. People didn’t fall over in the streets, to be instantly surrounded by medics in hazmat suits. The weight of rational evidence and experience could have started to outweigh fearful imaginings, and then people’s sense of ‘personal threat’ – as SPI-B put it – might have relaxed. How was the government to sustain the belief in the necessity of restrictions to our lives over the months? One method seemingly favoured by the government was the choice of metrics.
Daily death tolls dominated government press briefings and media reports until they stopped ‘surging’ and were perhaps too low to report, or didn’t seem newsworthy. At that point the focus switched to the reproduction number (R) and then cases. However, hovering just above or below 1, the R is not a very attention-grabbing number, whereas cases have seized headlines, because the absolute totals are large.
I spoke to David Paton, Professor of Industrial Economics at Nottingham University, who has taken a keen interest in the reporting of data during the epidemic. One of his worst data moments came in mid-April 2020 when ‘it was clear deaths were going down, but Chris Whitty, the Chief Medical Officer, said we hadn’t seen the peak yet. That was a big one for me. He was downplaying the downward trend in deaths. I think there were obviously deliberate policy decisions to make people take it seriously, but the data should be presented factually and then interpreted.’
Fear is a depreciating asset and we found ourselves in a time of short selling. Cases which don’t translate into deaths can’t sustain the fear, which is perhaps why Chris Whitty and Patrick Vallance delivered a ‘shock a
nd awe’ presentation on 21 September 2020 about cases and hospitalisations. Metrics selected for maximum impact gave way to fantasy. A red chunk of predicted-but-not-predicted cases loomed like a child’s red crayon drawing of a monster – the mythical monster a rather bad parent might scare their children with if they don’t behave.
Apparently, their decision to present 50,000 cases by mid-October as a possibility was taken carefully, but it was widely suspected that the decision was made because the bigger the number, the greater the fear, and the more compliant the adherence by the population. The anonymous scientist who works within Whitehall told me, ‘You can’t do a pandemic without honesty and trust, but the problem is people don’t trust the government anymore. The Whitty and Vallance press briefing was the ultimate psyop. Even really intelligent people had so much cortisol flooding through them that they couldn’t think rationally. I know people who believed the fantasy graph. Now they’re sold on more lockdown.’
While death tolls and cases dominated the headlines, other metrics were quieter casualties. Discussion of all impacted metrics is essential for people to make cost-benefit analyses. Yet Covid deaths were not balanced against unemployment, the lengthening NHS waiting list, missed cancer screenings, national debt, business closures, or calls to suicide helplines. As Paton told me, ‘There is a lack of critical thinking about parallels. We don’t say no one is allowed to drive a car to prevent all road traffic accidents. We can have different value judgements, but it’s not unhealthy or wrong to discuss tradeoffs and take a cost-benefit approach.’
What is the result of all this fear-mongering? The messaging that was initially designed to help us stay safe by scaring us has been so effective that Britain quickly became the most frightened nation in Europe.1 People significantly over-estimated2 the spread and fatality rate of the disease. The British public thought 6–7% of people had died from coronavirus – around 100 times the actual death rate based on official figures. I tested this out on a neighbour and asked her what percentage of the British people had died. She said 10%. That would have been a very noticeable 6.6 million corpses.
Did using the metrics of fear work? Did it create more compliance in the British public? Whether you think people followed the rules or not will depend on your own experience and attitudes. I asked a dear friend if he and his partner had been compliant. He told me that they had followed the rules very strictly to start with, and they had always followed them in general. It transpired they had had people over to their house, but only a couple at a time and only for drinks, not for dinner, so that was alright in their eyes. Of course for a long time people couldn’t enjoy a drink in a pub without ordering a substantial meal so, although he had the best intentions, my friend was negligent in omitting the life-saving food. I expect most people followed the rules, but they broke them in small ways when it suited them.
One study3 in September set out to investigate adherence to ‘Test, Trace and Isolate’ as it was considered ‘one of the cornerstones’ of the recovery strategy. It found that only 18% of people self-isolated after developing symptoms and only 11% quarantined after being contacted by Test and Trace. People’s reported behaviour fell short of their intended behaviour, but this seems a very unsurprising insight into human nature.
The government and media whipped people into a sustained and at times hysterical fear. Then frightened people voted for harder lockdown measures in public opinion polls. Government then obliged the people with more restrictions. The restrictions didn’t allow the fear to subside, then people voted for more restrictions, and so on in a self-perpetuating doom-loop. Public health policy became a sick dog chasing its own tail.
There is another reason that the government focused on particular metrics during the epidemic. Key Performance Indicators (KPIs) are to civil servants what the Ten Commandments were to Moses.
KPIs enshrine the work of all professionals in a department towards improving those goals and evidencing how they are doing so. For Covid, the KPIs were ‘cases’ (positive PCR test results), hospitalisations and deaths. Once established, the KPIs would have superseded the metrics for the economy, or other aspects of public health, or anything else. Staggeringly, the Treasury revealed it did not forecast the economic impact of the second lockdown.4
The clearer and more easily measurable the KPI, the more powerful it is in rallying activity and obscuring contexts outside the KPI. These KPIs overtly act to blind the government and all civil society to anything else, and then effectively confuse journalists and the public, which in turn creates more fear. The Covid KPIs sit like greedy little gods on thrones made of skulls, demanding obsequious worship.
It gets worse. The Covid KPIs are not reported as they are actually defined. I noticed throughout 2020 that journalists, doctors and politicians would misuse them. For instance, ‘Patients admitted’ actually includes ‘people admitted to hospital who tested positive for COVID-19 in the 14 days prior to admission, and those who tested positive in hospital after admission. Inpatients diagnosed with COVID-19 after admission are reported as being admitted on the day prior to their diagnosis.’5 So, patients admitted with Covid aren’t just patients admitted with Covid – which is what anyone would assume unless they have read the small print – they are also patients who were admitted for something completely different, but tested positive routinely for Covid, as well as patients who were infected with Covid while in hospital. The overall total will be a useful measure when planning how to manage wards and healthcare, but as a public-facing metric it is misleading, and as a KPI it cannot solely respond to restrictions designed to reduce community transmission, when it is also driven by nosocomial infection.
Similarly, deaths were frequently reported using the figure that the UK government dashboard pushes to the top: death ‘by date reported’. Due to time lags in death registration those figures could be lumpy, subject to sharp ups and downs. There was always a dip on a Monday (after weekend delays) and then a higher figure on a Tuesday. On a day with a big number you could guarantee certain MPs and journalists would grimly tell us that ‘x’ people died today, when in fact they didn’t die ‘today’, they died at some point over the recent days and weeks. On 30 December there were 981 deaths by date reported. This figure was bandied around the media and social media, with the incorrect interpretation that the people had died that day. However, the figure included a lag of death registrations over Christmas. In fact 588 people died on 30 December, which was not confirmed until 6 January. (That total will continue to rise over time as late death registrations are added.)
These people all died, so it does it matter how and when the deaths are reported? Am I splitting hairs? Lumpy data produces ‘hot spot’ days, creating panic and driving (or maybe justifying?) policy decisions. And maybe that is the plan. After all, the low days were not reported the same way.
The government’s cost-benefit analysis of lockdown published on 30 November didn’t include the use of QALYs (Quality Adjusted Life Years). This was a strange omission, as they are the bread and butter metric of the NHS. The average age of death with Covid is 82.3 years6 – one year more than the average life expectancy in Britain. The NHS normally allows up to £30,000 for each QALY that a treatment could save. Depending on how many QALYs lockdown saved, the cost is £96,000 to £1.97 million per QALY according to a report by Civitas.7 And that’s quite generous because it might be that lockdown saved no lives at all.
The precautionary principle behind lockdown has been ideological and may be proven, in the end, to be more harmful than doing nothing, because the cost-benefit analysis didn’t contain the numbers.
Here are some examples of the dodgy data and mendacious metrics wielded during the Covid epidemic.
THE IMPERIAL COLLEGE MODEL
Neil Ferguson’s Imperial model predicted that there would be over 500,000 deaths in the UK and 2.2 million deaths in the US if there were no measures to suppress the virus. Described in an article in The Telegraph as ‘the most devastating
software mistake of all time’,8 the modelling used outdated code. However, that insight was not gained easily. At a time when there had probably never been such a need for scrutiny, there was remarkably little. The paper by Imperial was not peer-reviewed and calls by scientists to inspect the code were ignored for weeks. When the code was released it was not the original, but had been modified by teams from Microsoft and Github. The delay was unacceptable given the level of public importance and interest.
The doom-laden modelling grabbed headlines around the world and is credited with some of the responsibility for shifting policies on lockdown.
Aside from the 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 warning9 to disease modellers to recognise the severe deficiencies in reliable data about Covid-19, including assumptions about its transmission and its essentially unknown fatality rates. For instance, the model assumed no existing immunity to Covid. Since then, six studies have shown T-cell reactivity (which gives protection) from previous coronaviruses in 20% to 50% of people with no known exposure to Covid.10
A State of Fear: How the UK government weaponised fear during the Covid-19 pandemic Page 16
