This book is about pandemic surveillance. So, what is meant by each of these terms, and the two together? As it happens, each part of the word-duo is hard to define and is understood differently by different experts. The first term is “pandemic,” generally referring to the widespread incidence of disease affecting “all” (pan) “people” (demos) and in use – as “pandemick” – since the 1660s. The fourteenth-century Black Death killed more than 100 million people in Asia and Europe – estimates vary – and the misnamed “Spanish Flu” (it did not originate in Spain) of 1918–20 infected half a billion and killed tens of millions. With improved transportation by railways and steamships, and the movement of troops after the First World War, the flu spread over a wider world. The COVID-19 pandemic is often qualified by the word “global,” signaling that, with air transport, cruise ships and complex long-distance haulage, few around the globe are untouched.
But what makes COVID-19 or any other disease a “pandemic?” Is it, for example, its explosive transmissibility, or the severity of infection, or both, perhaps with other features as well? Medical experts disagree and debate this. After the outbreak of the H1N1 influenza virus in 2009, an article in the Journal of Infectious Diseases debated various options, concluding that “simply defining a pandemic as a large epidemic may make ultimate sense in terms of comprehensibility and consistency.”2 Yet the same article makes many points about how pandemics relate to other factors such as urban population size, modes and ease of transportation, the state of medical knowledge, the actions of public health officials and the role of disease in domesticated animals. These point clearly toward social, technical, economic and political aspects of pandemics.
Indeed, one factor that connects “pandemic” with “surveillance” is that pandemics, however widely distributed, are far from evenly distributed. Even a nuanced reading of the Greek word demos hints at this, suggesting a social division between elites and the “common people” or “the crowd.” While in the early 2020s no one in the world is untouched by the pandemic, at least as a social condition, people are affected with differing degrees of severity, often relating to social class, gender, race and other decidedly social factors. This became more marked as surveillance “solutions” appeared.
So, what is meant by “surveillance?” According to the World Health Organization (WHO), surveillance in relation to public health is the “ongoing, systematic collection, analysis and interpretation of health-related data essential to the planning, implementation and evaluation of public health practice.”3 It is undertaken to inform disease prevention and control measures. Understood this way, it has clear human benefit and should thus be a priority among the available tools for confronting a pandemic, especially a global one. As we shall see, however, the WHO also notes the social and other dimensions of such surveillance, and warns that surveillance tools are not neutral and may be used in ways that challenge other priorities such as human rights and civil liberties.
More generally, we may think of surveillance as any purposeful, focused, systematic and routine observation and attention to personal details. Those “personal details” are sought, today, in digital data, made available in multiple formats that can snowball in some contexts. For instance, the data for contact tracing depends on location-tracking possibilities embedded in the smartphone. If, say, police obtain access to the public health data, as has occurred in several places, including Singapore, the same data could be used for crime investigations as well as contagion control.
In the case of public health surveillance, then, the purposes are those mentioned by the WHO: disease prevention and control. Inevitably, this also includes control of people – who and where they are, who they are with and how close, physically. Another way of thinking about this is to say that surveillance occurs to make people visible in specific ways, then to represent them in those ways so that they can be treated appropriately for whatever purposes the surveillor has in mind. Thus, what sorts of data are collected, how they are analyzed, and what assessments and judgments are made from them are matters of moment – especially as the data is so sensitive, touching on matters of health and the body.
Public health data, then, might make people visible in terms of their relative ages – elderly people are generally more likely to become seriously ill or die if they contract COVID-19, for instance – or where they live – postcodes are often used as proxy for lifestyles by any and all of police, marketers and healthcare scientists – so that testing or vaccines can be targeted appropriately. Equally, public health agencies may wish to know who has been in contact with infected people, or whether those people are isolating or quarantining themselves, and surveillance may be sought for that quest.
Of course, several of these schemes turned out to be quite controversial, whether used for contact tracing or quarantine-policing or, on a large scale, for monitoring the progress of the mutating virus through large populations. Understandably, it is smartphone apps or wearables such as wristbands that directly affect individuals which raise most concern. For instance, a cluster of gay men was “outed” in South Korea when a number of COVID-19 cases came to light in a Seoul district well known for its gay bars. Also, a Minnesota law official appeared to claim that the state was using “contact tracing” to identify connections between Black Lives Matter protesters in May 2020.4 But often the technologies used for surveillance have effects that are hard to discern by those whose data is in use, not least because their impact may be indirect.
As an example, in February 2020, South Korean citizens found that the government was publishing on websites and in texts the details of the exact movements of unidentified individuals for all COVID-19 cases. One could read, “Patient No. 12 had booked Seats E13 and E14 for a 5:30 pm showing of the South Korean film, ‘The Man Standing Next.’ Before grabbing a 12:40 pm train, patient No. 17 dined at a soft-tofu restaurant in Seoul.”5 Doubtless, the aim was to see whether undiscovered contacts could be traced and tested. But such data, in the wrong hands, could also be misused.
As well, cultural differences are significant – seen also, for instance, in the willingness to wear masks in public – in relation to allowing authorities to think that they can impose certain behavioral requirements or post personal details publicly. How people respond – for instance, by stigmatizing or even attacking those who fail to wear masks or who appear to have been contagion carriers – is another matter.
Context is critical
Although the H1N1 pandemic occurred in a world of information technology, the COVID-19 pandemic was the first to occur in a context of surveillance capitalism,6 and this is crucially important. So-called Big Data had made its appearance in the early years of the twenty-first century, prompted by developments in distributed computing, data analytics and statistics. But its value for commerce, especially in rapidly expanding platform companies, following its signal success for Google and then several social media giants, was unprecedented.
Surveillance capitalism had discovered how to make profit from apparently inconsequential data exuded by these platforms, prompted by everyday users of platforms like Facebook and WeChat. But, crucially, that data could also be repurposed by, for example, police and security agencies. Governments found ways of using that data, too, and often sought to attract those large corporations to set up shop in their countries. An example is the attempt by Alphabet, Google’s parent company, to plant a smart city in Toronto – “Sidewalk Labs.”7 The “smartness” lay in the data-dependence of the project, a high-tech “utopia” with sensors embedded everywhere. As the Atlantic put it, “The city is literally built to collect data about its residents and visitors.”8 The plan was aborted during the pandemic in May 2020.
It is clear that platforms were seeking yet other openings for obtaining valuable – and sensitive – data. Google’s Deep Mind, an Artificial Intelligence (AI) company, built an app called Streams, for example,
