Herd immunity, or punishing your voters
The response by UK Prime Minister Boris Johnson and his team to the SARS-CoV-2 pandemic has been dismal. On 12 March, I had to endure possibly one of the most cringeworthy political speeches of recent history, when Johnson addressed the nation to tell its citizens that despite the fact that “many more families [were] going to lose loved ones before their time” (meaning the elderly), there was basically nothing to do in terms of prevention (Stewart, Proctor and Siddique 2020). Johnson’s statement was mind-boggling for a variety of reason, the most astounding of which was that the core Tory electorate is currently made up of older people (Inman 2019). You get what you vote for, I guess (Walker 2020), but is a selective culling of the elderly really what elderly Conservative voters voted for during the recent national election?
As if this was not enough, three days later I had the chance to listen to Sir Patrick Vallance, the government’s chief scientific adviser, talking on a news programme about the decision to do nothing to prevent the epidemiological spread of the virus (Heffer 2020). Vallance provided some rather odd justifications for the government’s inaction, saying that they were waiting for a 60% of UK citizens to get sick in order to build herd immunity, protect indirectly the rest of the population, and avoid future endemic peaks. Vallance also said that, ideally, he would take all the individuals in good health and place them in Kent to get sick (sic!), displace all the potentially vulnerable individuals in Scotland, and then wait for the first group to recover to repatriate the first group (sic!). As Vallance and other government’s representatives would go on to repeat in the next few days, banning mass gatherings like sport events, or shutting down universities and commercial activities and the like, was basically futile, adding that the disease apparently spared children and was confined to the elderly (again!), and that all we should care about was merely household transmission among family members. Just wash your hands and remember to “catch it, kill it, bin it”. The graphs and the modelling showed rather clearly that this was enough to avoid future endemic peaks. At least, that’s what they said.
The Battle of BalaCovid-19
What struck me the most was that this health policy did not make any sense from an epidemiological perspective. Like me, specialists were puzzled and worried (Boseley 2020; Hanage 2020). After the pandemic is gone, endemic and recurrent peaks cannot be avoided without a vaccine or without the long-term adoption of preventive measures. If mass exposure to a new pathogen was all that is needed to develop immunity, there would be no need for vaccines at all - which is not the case. And yet, the UK government blatantly ignored what the doctors and the WHO experts had been saying since before the upgrade of the current SARS CoV-2 epidemic to global pandemic (Ghebreyesus 2020), they ignored the fact that viruses evolve, that there might be considerable but yet unknown complications for those who recover, that models are based on the available record of similar previous cases while the presence of this virus in the European pathocoenosis (i.e., the local ecology of pathogens) represents a novelty and thus invalidates the use of any in silico simulation as if the virus’ behaviour was already known and mapped out (cf. Sallares 2005; Smith and Cordes 2019). And they lost precious time. Such poor decisions are likely to cost unnecessary pain and death in this country (Horton 2020).
Now, let’s try not to think too hard about the mental gym behind Vallance’s questionable Scottish displacement hypothesis and let’s focus on the core concept. Was his plea for herd immunity scientifically based? Yes, in a sense. But was it sound? No. Mithridatism also works scientifically, but that’s not a good justification for selling poison over the counter (Mayor 2011). More importantly, herd immunity works along with vaccines when they are available, not as a pandemic-containing policy. In the absence of a vaccine, the worst-case scenario for the outbreak of a new epidemic is a near-extinction event. In the early 2000s, for instance, the non-viral disease DFTD ravaged the populations of Tasmanian devil, in some cases wiping out up to 90% of the entire population from certain sites. In 2015, 60% of the world’s population of saiga antelopes was killed by the bacterium Pasteurella multocida (McCallum 2012; Fereidouni et al. 2019). As to human beings, it takes just a 20% mortality rate to spell the end of entire societies, as it probably happened with the Antonine Plague and the Plague of Cyprian, two possibly related outbreaks of a viral disease tentatively identified with smallpox (caused by variola, gen. Orthopoxvirus) (Ambasciano 2016). The Chinese region of Wuhan, the epicentre of the current Covid-19 pandemic, apparently recorded a similar death rate, while in the rest of the world mean mortality was supposed to have reached 15.2% as of 12 March 2020 (Baud et al. 2020). The key difference between the political management of the Antonine Plague and Covid-19 is science.
In Popperian terms, Vallance’s statement is still science, because it includes a falsifiable assertion (does herd immunity work?), but it’s a clear case of flawed, outdated, pre-vaccination era bad science. To simplify, it’s pseudoscience (cf. Nickles 2013). And it’s immoral. Let’s just say that herd immunity might work. We have seen the potential costs in terms of mortality rate and long-term social consequences. Assuming that host-pathogen equilibrium is the norm, and that herd immunity will come to save the day, is a very dangerous idea that politicians and their scientific advisers should never be allowed to entertain. Once upon a time, the only sound and intuitive epidemiological answer was to practise social isolation, avoid contacts as much as possible, and hope for the best. Today, however, we do have other institutional superpowers: we can adopt basic hygiene rules, impose a national lockdown, boost research funding, and wait for the development of a vaccine or a cure. Today we have science.
And yet, Vallance was apparently willing to risk the lives of an imprecise but probably staggering number of healthy individuals based on pseudoscientific reasons. At that time, the news coming from the continent was absolutely appalling. Northern Italy was on the verge of becoming a coronavirus wasteland (Armocida et al. 2020). Italy had already seen a sharp rise in the hospitalization of healthy 40-year-old patients, by the way, so it was not like that the virus was confined to the elderly. Indeed, although the disease was far more unforgiving to the elderly, children were not magically immune (Qiu et al. 2020). Also, Japan reportedly recorded the first case of someone catching the virus twice, so post-infection immunity was not a given (Lawton 2020). And why on Earth would you wish for healthy people to get (potentially brutally) sick in 2020 CE and burden an already battered NHS with patients in need of critical care?
Neuroliberalism to the rescue?
In 2016, the then UK Justice Secretary and Brexit supporter, Michael Gove, infamously stated that “people in this country have had enough of experts” (Mance 2016). Four years later, history was repeating itself, for epidemiological expertise was nixed. At the heart of Vallance’s conclusions about the most “scientific” way to avoid future endemic peaks, there’s a single a priori, extra-epistemic decision taken by the Tory government: keep the economy afloat at all costs. Meanwhile, other countries bit the bullet and opted for drastic measures ranging from the gradual stop of certain key activities to a total lockdown for the time needed, while doing everything in their power to prevent the diffusion of the epidemic and avoid running out of critical care beds. This was the right thing to do in order to try and “flatten the curve” of the epidemic, as epidemiologists were tirelessly repeating. There was a clear lesson to learn from the European management of the coronavirus outbreak: “We are now where you will be in a few days. The epidemic’s charts show us all entwined in a parallel dance” (Melandri 2020). And yet, I recall seeing someone on the news calling the Italian decision to impose a lockdown “excessive” and economically self-destructive. The powers that be thought they knew better, so they opted for mild suggestions instead: keep washing your hands, business as usual. No one in the UK bothered to take this seriously and act in time. Just “wash your hands”, repeated ritually, almost magically. Apotropaically.
For almost two weeks, powerless and in constant disbelief, I stared lifeless at idle talks and slothful lucubrations about graphs, math, modelling, and the almost magical “flattening the curve” of the epidemic. Yet, no decision was taken, apart from the blasted mantra of “wash your damned hands” repeated ad nauseam, sometimes accompanied by the “catch it, kill it, bin it” corollary. In hindsight, I think we might have passed peak neoliberal post-truth. I think those debates represented the distilled expression of out-of-touch wishful thinking, right-wing elitism, and insular isolationism with a stiff upper lip, this time supported by what Cathy O’Neil has aptly labelled as Weapons of Math Destruction (WMDs), or the misuse and abuse of Big Data and math to justify dangerous, extra-epistemic, ideological, and anti-democratic positions (O’Neil 2017). In our Covid-19 predicament, the quite literal WMDs were all the graphs and modelling that were being shoved in our faces. However, while the jewel of the WMD crown was obviously (flawed) modelling, the resulting policy was informed by nudge theory (Yates 2020).
Basically, nudge theory “uses insights about our mental processes to change our behaviour through coaxing and positive assertion. Rather than forcing us to do things, nudging tweaks the environments in which we make choices” (Yates 2020). This is all good - if you stick to putting fake flies in urinals, elaborating opt-in modules for organ donations, and recovering unpaid taxes (Lawton 2013). However, nudge theory has been abused as one of the main justification for neoliberal ideology with an eye to deregulation in all matters (including health care). The underlying motto is to avoid forcing people into doing things, let people free to choose the wrong thing, and “find ways of doing ‘more with less’ under austerity” (Quinn 2018; see Whitehead et al. 2018). As to the current pandemic, nudge theorists suggested that should schools and mass gatherings be banned, “‘fatigue’ could set in – meaning people will grow tired of the bans and find ways around them” (Yates 2020). Consider this: it takes only one infected person opting not to do the right thing to undo any containment initiative. When you factor in asymptomatic spreaders, the picture becomes an epidemiological nightmare. In other words, the government relied on a set of cognitive and behavioural precepts prone to misuse and tainted by laissez-faire ideology in order to try and delay a bloody pandemic. Let that sink in for a moment. (They might as well try with homeopathy.) Human lives were deemed expandable, at the very least those without sufficient economic resources, those unable to work from home, or those old enough to be particularly vulnerable. At the same time, the government and its advisers seemingly forgot about the much more problematic cognitive dissonance resulting from the devastating news coming every day from the severely affected European neighbours and the deluge of half-baked conspiracy theories and fake news on social media. As a result of this confusion, people lose trust in the government and went on a panic-buying spree (Parveen 2020; Smithers and Collinson 2020). And all of this occurred before the real outbreak of the epidemic in the UK.
All in all, it’s a textbook case of confirmation bias – and a tragic one at that too: those in charge, imbued with post-truth ideas and still intoxicated by the Brexit victory, have cherry-picked the scientific advisers and the scientific tools and theories they wanted to exploit verbally (in this case, modelling and nudge theory) in order to justify their (in)actions and confer upon them respectability and prestige (contra Jenkins 2020). Already in 2012, a critical review of the neoliberal implementation of behavioural-economic precepts in UK social life warned the readers that nudge theory “encourages no development of capacity to manage problems, contradicting a wider policy intent to build a more responsible and active citizenry” - which is exactly what the Brexiteer politicians did not want to build (Burgess 2012; see D’Ancona 2017). The government policy with regards to the management of the epidemic changed dramatically only when the situation got out of hands, condemning the country to a plague potentially much worse than it could have been, considering that the British Isles had a headstart of almost two weeks (Stewart, Mason, and Dodd 2020; cf. Hanage 2020). In Northern Italy, “the National Healthcare Service is close to collapse - the results of years of fragmentation and decades of finance cuts, privatisation, and deprivation of human and technical resources” (Armocida et al. 2020). Considering the delay in imposing a lockdown, the UK - a country with roughly half the number of critical care beds per capita as Italy - was in for a rude awakening (Lintern 2020; cf. Horton 2020).
Good riddance to nudge theory
The whole herd immunity debacle was the natural consequence of the Tory penchant for truthiness, that is, according to Oxford Dictionaries, “the quality of seeming or being felt to be true, even if not necessarily true.” As it happened with the massive manipulation and post-truth exploitation of computational data from private social media accounts, supported by Dominic Cummings and implemented by Cambridge Analytica during the Brexit campaign, once again the Tories’ Big Data hubris is impacting the lives of UK citizens and residents (The Guardian 2020a; Fry 2020). However, you can concoct all the alternative facts you like and put them on the side of a red double-decker, you can fool all the people you want with your snake oil and brag about it, but ontology – the world out there – is bullshit resistant (I am using the term “bullshit” epistemologically to define a post-truth political environment according to Frankfurt 2005). Ironically, Health Secretary and Brexiteer Matt Hancock made a desperate call for the enrollment of more NHS personnel to help with the management of the epidemic, including retired staff, as a consequence of staff shortages aggravated by the Brexit affaire (The Guardian 2020b; cf. Holmes, Baird and McKenna 2019). Finally, Johnson and Hancock have recently tested positive for SARS-CoV-2, while Cummings has experienced “coronavirus symptoms” (BBC 2020; Merrick 2020; Syal 2020). The neoliberal defunding of both the NHS and the university research system, the disregard for qualitative assessment, and the elite lack of empathy have come back to bite the political clique.
Italian economist Carlo M. Cipolla (1922-2000) famously published his The Basic Laws of Human Stupidity in the late 1980s. Basically, Cipolla’s concept of “stupidity” focuses on the social consequences of this specific cognitive mindset. If “a stupid person is a person who causes losses to another person or to a group of persons while himself deriving no gain and even possibly incurring losses.” (Cipolla 2011: 36), then it might be too easy to conclude that, in terms of cultural ecology, the success of the Brexit campaign had strengthened an already existent bottleneck for short-sighted and ill-advised ideas with potentially dire real-life consequences for everyone (Boudry, Blancke, and Pigliucci 2014; cf. Barber 2020 and Ferraris 2016). However, the situation is far from being this simplistic: this predicament is also our fault. We have seen all the proximate causes that have an immediate bearing on our lives now, but the ultimate causes of the pandemic are to be found in some of the chronic problems of our era. As a consequence of overpopulation, economic delocalization, and deforestation in a global neoliberal economy, “we disrupt ecosystems, and we shake viruses loose from their natural hosts. When that happens, they need a new host. Often, we are it” (Quammen 2020). This time it happened in China; next time, who knows where. Look at the mirror to find the real culprit. As Cipolla’s wisely suggested in his Fourth Basic Law of stupidity, the joke’s on us - we are the stupid who so easily forget the price that comes with our lifestyle (Cipolla 2011: 56). As David Quammen has recently highlighted, the SARS-CoV-2 outbreak “was - it is - part of a pattern of choices that we humans are making”, from the election of our democratic representatives to everyday consumer choices (Quammen 2020). And for better or for worse, we’re all in this together now. Once this is over, the survivors will maybe think twice before cutting budgets destined to the healthcare system and basic research. As for me, I can only hope that the current pandemic will mark the end of both postmodern “alternative truths” on the one hand, and Big Data, quantitative, manipulative WMDs on the other. Whatever the case, good riddance to the neoliberal incarnation of nudge theory.
Notes
Unable to cope with the current deluge of pseudoscientific noise, political idiocy, and conspiracy theories online and offline, I decided to stop reading the news some days ago. This is a collation of reworked excerpts from emails I’ve originally sent from 14 March onward with the occasional update (I bit the bullet and reluctantly read a handful of articles). Take care and stay safe.
Please note that this post has been updated on 29, 30 March 2020, and 2 April 2020 (Horton 2020 and Jenkins 2020 added in the refs.). Additional updates can be read below.
ADDENDUM, 13 April 2020 and 5 May 2020: It appears that despite repeated government denials, “by around 23 March, a planning document used by NHSX, the health service’s digital planning department, and Faculty, a British artificial intelligence company contracted to aid the response to the Covid-19 outbreak, included ‘targeted herd immunity’ as one of several government ‘interventions’ whose impact upon the spread of the disease could be examined via computer simulations” (Conn and Lewis 2020). Repetita juvant: herd immunity works only through vaccination coverage. As most clearly outlined in the title of a recent article written by a biologist and a biostatistician, “What the Proponents of ‘Natural’ Herd Immunity Don’t Say: Try to Reach It Without a Vaccine, and Millions Will Die” (Bergstrom and Dean 2020). Despite this basic epidemiological fact, a private contractor with no specific experience in epidemiology and medicine and, more troublingly, an institutional body (self-described in search engine-friendly terms as “a new unit driving forward the digital transformation of health and social care”) included an epidemiologically unsupported and potentially decimating policy suggestion in their simulation modelling and forecasts. This is another instance of the grave danger posed by neoliberal WMDs, if ever there was one (see O’Neil 2017). Moreover, the interaction between the ignorance and hubris of AI/Big-Data companies and the flawed ideological and economic prejudices of neoliberal governments is evident in the chronological sequence of events as they unfolded: “The first revelation that the government might be looking to create ‘herd immunity’ came on 11 March from Dr David Halpern, the chief executive of the governmental behavioural insights team, known as the ‘nudge unit’”. Two days later, “the government’s chief scientific adviser, Sir Patrick Vallance, defended the existing approach and said that building up some form of herd immunity, by having potentially 60% of the population (40 million people) contract Covid-19, was one of the ‘key things we need to do’” (Conn and Lewis 2020). The adoption of such a misguided policy during the very first stages of the epidemic overburdened the NHS and resulted in entirely avoidable deaths and suffering, especially considering that “Not only did the UK have the experience of Italy play out before the virus hit its own shores, illustrating clearly the measures that needed to be taken, it had explicit warnings from Italians spelling out the pitfalls to be avoided” (Malik 2020; cf. Our World in Data 2020). QED.
ADDENDUM, 5 May 2020: Since my last update, it has emerged that Dominic Cummings and Ben Warner, a data scientist who is “said to have worked closely with Cummings on the data modelling used in the Vote Leave campaign for the UK to leave the European Union”, were present “at a crucial convening of the Scientific Advisory Group for Emergencies (Sage) on 23 March, the day Boris Johnson announced a nationwide lockdown in a televised address” (Carrell et al. 2020). Given that Sage is an independent committee, this is a clear evidence of political and ideological meddling, whether directly (by tampering with the report) or indirectly (by exerting psychological pressure on the committee). Additional reports have revealed that “one attendee of [the Sage meeting] said they felt Cummings’ interventions had sometimes inappropriately influenced what is supposed to be an impartial scientific process. A second Sage attendee said they were shocked when Cummings first began participating in Sage discussions, in February, because they believed the group should be providing ‘unadulterated scientific data’ without any political input” (Lawrence, Carrell, and Pegg 2020). Meanwhile, just one day after the United Kingdom “has overtaken Italy as worst-hit European country” (Weaver and Davis 2020), Matt Hancock - who has since recovered from Covid-19 as Boris Johnson himself did after many a tribulation - is reportedly exploiting the exceptional powers he’s been granted to manage the current emergency as a shield to accelerate the privatization of the healthcare system without scrutiny nor open competitions (Neate and Garside 2020). As if there was no lesson to learn from the privatization catastrophe of Italy’s Lombardy region.
Refs.
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