All good things must come to an end.
Nickolas Roubekas and I had a good run as Managing Editors of the Journal of Cognitive Historiography from early 2017 to December 2021. Factor in three more years as Editorial Assistant, give or take a few months, and you’ll soon realise that I’ve spent a good chunk of my academic career involved with this journal. We had our ups and downs, but, as I’m closing this chapter of my life, to quote the lyrics from Queen’s bittersweet Was It All Worth It (1989), I like to think to myself, “Yes, it was a worthwhile experience.”
If you want to have a glimpse of our final efforts, you may read an excerpt below (which, incidentally, expands and updates some old reflections first published here). You can read the rest of our musings on Covid-19, historiography, and Big Data (and the full list of references) on the JCH website through Open Access. Meanwhile, a ginormous and sincere thank you! from the bottom of our hearts to all our editorial colleagues, contributors, and readers: YOU made it worth it.
Enjoy!
When cognitive science and data modelling fell short: a case study
The outbreak of what initially looked like a nasty pneumonia epidemic in the Wuhan region, China, and its initial diffusion in January 2021 failed to raise widespread international concerns, with different countries preparing differently for the worst-case scenario. While Asian governments mindful of recent epidemics (such as the 2015 MERS outbreak in South Korea) were already implementing preventive measures, most of Europe and the United States adopted a wait and see strategy – if they adopted a strategy at all (Russell 2021). Between 21 February and 19 March, Italy became the first European hotspot of this highly infectious disease, with a grand total of 41,035 recorded cases and 3,405 deaths. The Italian National Healthcare Service was quickly overwhelmed. The lack of PPEs, an insufficient number of medical devices, and the unprecedented pressure on the Italian medical personnel exposed the deficiencies resulting from the past widespread privatisation of the health care system (Armocida et al. 2020). As the situation was quickly spiraling out of control, the Italian government imposed a national lockdown on 9 March 2020 to flatten the rising infection curve. It became painfully clear at that point that the spreading disease, called Covid-19 and caused by the SARS-CoV-2 coronavirus, was far from being the usual seasonal flu, and that severe measures were to be taken while waiting for the development of a vaccine. However, no Western government learnt the lesson and acted in time. In Europe, the UK government was particularly dismissive of the sheer gravity of the virus, advocating instead for a misguided herd immunity policy and suggesting the adoption of lax measures like washing hands and disposing immediately of used handkerchief (cf. Henley 2020).
The scientific data coming from Italy, China, and Japan showed that hospitalization was rising across healthier and younger age cohorts, that no one was immune (not even children), and that infection could occur twice (Armocida et al. 2020; Qiu et al. 2020; Lawton 2020). Meanwhile, on 11 March 2020, the World Health Organization upgraded the disease to global pandemic while expressing “concern” about “the alarming levels of spread and severity, and by the alarming levels of inaction” (Ghebreyesus 2020). One day later, disregarding this wake-up call, UK Prime Minister Boris Johnson addressed the nation to tell its citizens that even though “many more families [were] going to lose loved ones before their time” (mainly referring to the elderly), there was basically nothing to do in terms of prevention (Stewart, Proctor and Siddique 2020). Three days later Sir Patrick Vallance, the government’s chief scientific adviser, explained on live television the decision to do nothing to prevent the epidemiological spread of the virus, saying that the most reasonable thing to do was waiting for 60% of the whole population to get infected and build a national herd immunity (Heffer 2020). The graphs and the modelling provided by the scientists and the UK government itself showed rather clearly that this was enough to avoid future endemic peaks.
The scenario unfolded quite differently. A country-wide lockdown came into force at long last on 26 March, but it was too late. The NHS was overwhelmed. On 30 March, Johnson boasted that the UK was “past the peak” of the pandemic (BBC 2020a). Nine days later, on 8 April 2020, the recorded daily deaths in the UK peaked at 1,445 (Booth and Duncan 2020; Bruce 2020). As of 29 April, the officially recorded deaths in the UK were already 26,097 (BBC 2020b). Having wasted the initial two weeks of advantage over Italy, the UK became the European country most hit by the pandemic on 5 May, with the second highest death toll in the world (Perrigo 2020; Helm, Graham-Harrison and McKie 2020). Despite the claim that the UK government was “following the science”, even more mistakes were made when it came to managing the second epidemiological wave of Covid-19. In January 2021, for instance, the recorded Covid-19 deaths in Britain were 85,000, the “fifth highest figure globally” (Reuters 2021; see Hunter 2020 and Stevens 2020). As historians and managing editors of this journal, we could not help but finding this political use of scientific research at the same time interesting and frankly terrifying. Briefly, we identified three main issues at stake.
The first issue is that herd immunity only works through vaccine coverage. As the title of an article written jointly by a biologist and a biostatistician explains in very basic and immediately comprehensible terms, “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). Apparently, the science was not the UK government’s top priority, as the decision to delay twice the adoption of lockdown measures to contain the spread of the virus, and to relax them too soon, was eminently short-term and economic, and very likely contributed to unnecessary deaths in the country (cf. Elgot 2021).
The second issue is that data modelling is only as good as the data you feed into your software. Since all collected data is data about past events, and because of the presence of human biases and preferences during the creation of dedicated algorithms, the selection process, and the input of data (Ambasciano and Coleman 2019), politically-informed in silico simulations that projected in the immediate future the patterns emerging from the gathered data tended to minimise the fact that the virus was a novelty in the European pathocoenosis (i.e., the local ecology of pathogens; Sallares 2005), that viruses evolve (as it happened with the evolution of nastier Covid-19 variants; Callaway 2021; cf. Russell 2011), and that there might be considerable but yet unknown health complications for those who recover, potentially with huge consequences for a post-pandemic return to normality (as it happened with the long-Covid syndrome; Marshall 2020).
The third and final issue is that data modelling and advocacy for herd immunity in the UK were informed by a theoretical approach known in behavioural cognitive sciences as “nudge theory.” 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 subtle approach proved exceptionally reliable when it came to putting fake flies in urinals to reduce cleaning costs, elaborating opt-in modules for boosting organ donations, and recovering unpaid taxes (Lawton 2013). However, nudge theory has also been abused as one of the main justifications behind the neoliberal deregulation of public sectors such as health care. The nudge theory 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). When the Covid-19 pandemic broke in, 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). However, it takes only one ‘superspreader’ opting not to do the right thing to undo any containment initiative (Adam 2020). When asymptomatic spreaders are factored in (Nogrady 2020), any nudge is nothing more than an epidemiological nightmare. In other words, the UK government relied on a set of cognitive and behavioural precepts prone to misuse and exploited by laissez-faire ideology to try and delay a global pandemic. At the same time, the UK government and its advisers downplayed the problematic cognitive dissonance resulting from the devastating news coming every day from the severely affected European neighbours and the deluge of 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 before the real outbreak of the epidemic in the UK (Parveen 2020; Smithers and Collinson 2020).
KEEP READING… (this Editorial is available through OPEN ACCESS at JCH)
Notes
The “wide-ranging, critical conversation with the authors” of Religious Evolution and the Axial Age & The Emergence and Evolution of Religion by Means of Natural Selection I announced way back in December 2020 and wrote about here, is included in this issue of JCH. The dedicated section (Discussion / 2) hosts my own contribution plus the replies by Petersen et al. and Sanderson.