If it was possible to locate a ‘start’ to the pandemic in the UK, then most people would point to the end of January 2020. This was when the first two officially confirmed cases of coronavirus were found in North Yorkshire, a county in northern England. The Guardian covered the contact tracing effort by Jeremy Hunt – a Conservative politician and chair of the Health and Social Care Select Committee – and Public Health England. They led with the headline ‘Hunt begins search for “close contacts” of the two UK coronavirus cases’ (Boseley and Walker, 2020).

There is a capital ‘p’ Political chapter that could be written here. It might emphasize the failures of the UK government to identify the scale of the threat posed by a virus that had forced China to place millions into lockdown measures just over a week before. If this argument seems an unfair one to level at the government at the end of January 2020, then it is one that holds more weight when we consider that widescale government intervention was not enacted until 16 March. But the point about government inaction and ineptitude has been well documented in the popular press, academic literature and investigative journalism (Calvert and Arbuthnott, 2021).

So, this chapter takes a different approach. It focuses on the ‘close contacts’ element of The Guardian headline. It examines international scientific and public health literature up to January 2020. The chapter shows that ‘close contact’ is defined as being within two metres of an infected person for 15 minutes or more. In outlining the history of these two parameters – two metres and 15 minutes – the chapter shows that having such a simple binary of ‘more dangerous’ and ‘less dangerous’ sits at odds with the unimaginable complexity of the phenomenon of transmission. Despite this, this one-sizefits-all definition of ‘close contact’ was consistently used in public health messaging. This speaks to the power of numbers to make the unknown known, establishing quantitative realism even when reality is evasive (Desrosieres, 2002).

Understanding data bounds is not just about data. This chapter marks a break from the previous five by identifying how broader historical norms can help shore up certain data bounds and marginalize others. It does so through a single case study: the way a projection of 90,000 cases per day failed to circulate in the media.

In May 2020, a group of experts in the UK set up the Independent Scientific Advisory Group for Emergencies (Independent SAGE). This operated in opposition to the official Scientific Advisory Group for Emergencies (SAGE) that advised the UK government on the handling of the pandemic. According to Independent SAGE, they are ‘a group of scientists working together to provide independent scientific advice to the Conservative government and public on how to minimize deaths and support Britain's recovery from the COVID-19 crisis’ (Independent SAGE, 2021b). Much of their work on communication centres on influencing the news media's coverage and more direct forms of communication (for example, social media) to pressure the government to change their approach to coronavirus.

As part of this strategy, they held weekly press conferences that were live streamed on YouTube. In one of these press conferences on 2 July 2021, Professor Christina Pagel – a member of Independent SAGE – made the following projection: ‘In mid-July we will have a seven-day average of 90,000 [cases per day]’ (Indie SAGE, 2021). This average of daily cases would far surpass the peaks of any of the previous waves.1 The alarming figure was used by Pagel to underpin her argument that the government should change tack. Instead of opening society on 19 July 2021 – the proposed date – they should wait until the vaccination programme had been rolled out fully. This would mean people gained immunity through vaccination rather than natural infection.

The importance of the 90,000 figure, and the underlying argument, seemed to land with the two main audiences of the broadcast. Members of the public watching the briefing provided some direct reaction to the figure in the live chat:

A lot has changed since the most recent case study from this book, taken from July 2021. After so-called ‘freedom day’ was introduced in mid-July, there was a summer and autumn period of fewer restrictions. The emergence of the Omicron variant in South Africa in November 2021, however, meant the Conservative government introduced a series of measures. These included red listing countries, booster jabs, advising home working and introducing mandatory mask wearing indoors.

For some, these restrictions did not go far enough – a policy closer to lockdowns was called for. A BBC News Online article explained that there were benefits of introducing a lockdown – the delaying of the peak of cases, lower pressure on hospitals, and so on – but this would also cause ‘harm to jobs, mental health and education’ (Triggle, 2021). While the scope of what was traded-off was expanded to include mental health and education, it still operated within the same broader paradigm of Trade-Off.

But the government did not introduce a lockdown and the UK did not experience comparable levels of pressure on hospital systems. It seemed that vaccine coverage enabled England to not lockdown. After the wave of Omicron reduced, so did the restrictions introduced by the government in November 2021. And as the country pushed into 2022, these restrictions have not re-emerged. This has led many to talk of a ‘post-pandemic’ era – one defined by optional lateral flow tests, peeling two-metre stickers on shop windows and dust slowly gathering on face masks. In this world, Trade-Off has become a thing of the past.

For some, this would make the empirical basis of this book less relevant to the ‘post-pandemic’ world. But the stories traced in each of the chapters were not aimed at better understanding the pandemic per se. Rather, they pointed to six key characteristics of ‘data bounds’ and four imperatives for scholars looking to put this concept to work. This means that data bounds outlive the COVID-19 empirical basis upon which they are built – they should be used to understand highly quantified phenomena in the postpandemic world.

The coronavirus pandemic has resulted in a deluge of data: deaths from coronavirus, viewing figures for conspiracy theory videos, unequal vaccine coverage, changes in Gross Domestic Product (GDP), the list goes on. While much of this data has been useful in understanding and dealing with COVID-19, there has been a noticeable excess. This has flowed into the so-called ‘post-pandemic’ world, where numbers burst from the seams of public discourse. Experts constantly update us with newfangled metrics, politicians point to the latest iteration of international league tables and journalists report on a dizzying volume of data. We need to step away from this daily churn of the quantitative and ask: what does this data actually mean?

Some approach this effort as a technical exercise. During the pandemic, Tim Harford's More or Less show on BBC Radio 4 provided an excellent weekly dive into salient numbers. The team applied statistical rigour to certain factoids, helping them to detect the pitfalls of small sample sizes, the role of nefarious categorization for political ends and how experts would communicate misleading conclusions from the data (see More or Less (2021) for an example). This was undoubtedly important work. It allowed the public to navigate the sea of data that was flowing their way.

But it often erred on the side of ‘if only they conducted the right statistical test, these numbers would not be a problem’. This meant that they missed a certain something about how numbers gained meaning in society. Quantitative facts are not just the result of mathematical and statistical processes. They are characterized by complexity and paradoxes: at once scientific and ideological, empowering and discriminatory, precise and uncertain, objective and subjective, emotive and informative, truthful and deceptive, fixed and malleable.

To untangle this complexity, I argue that we must begin by accepting two premises. First, we need to pay attention to the way mathematics and statistics combine with politics, culture, technology and economics. The technical process of producing a number – collecting data, cleaning it and analysing it – cannot be divorced from the context within which this occurs.

Whereas the previous three chapters have focused on the relationship between quantitative realism and data bounds in general terms, Chapter 6 and Chapter 7 circle back to the Trade-Off data bound introduced in Chapter 2. This chapter focuses on a data visualization that came to visually represent Trade-Off: the graph showing the number of cases, hospitalizations or deaths per day across the entire pandemic. This ‘humped’ graph – capturing how cases rose and fell across 2020 – provides a way into a discussion about the affective qualities of data visualizations – and by extension, the emotive nature of data bounds themselves. It does so by tracing the story of a particular performance of this graph by a Sky News presenter.

On 11 November 2020, the UK passed the grim landmark of 50,000 deaths within 28 days of a positive test for coronavirus. Later that day, Sky News released a two-and-a-half minute video on YouTube titled ‘COVID-19: How did the UK get to 50,000 deaths?’. The broadcast was relatively simple: a journalist, Roland Manthorpe, stands in front of a large screen containing a succession of data visualizations. He begins on the right of the screen, moves to the left part way through and then comes back to the right again – all the while expressively using his hands, posture and voice to provide his interpretation of the changing images behind him. Nothing about the components of this clip is particularly unusual – presenters will often stand next to, or in front of, a data visualization and explain it to the public.

But it was how Manthorpe performed that underpinned most of the comments below the video. One comment by Jake Jabz read: ‘Why is he so animated, he's talking about deaths in the UK like he's a presenter on Blue Peter’ (Sky News, 2020c).

For Jake Jabz there was too much animation for the severity of death, and this resulted in a performance closer to children's television (Blue Peter) than a serious news broadcast. And there is something true in this comment – the first time I saw this clip, I was struck by the oddness of Manthorpe's approach to telling this data story. It all felt a bit too energetic, lively and affective for graphs about deaths.

As cases were rising in the middle of October 2020, ITV news – a popular commercial television channel – broadcasted a roundtable of coronavirus experts. Some argued for a national lockdown to be introduced, while others called for the UK to live alongside the virus. During the debate Devi Sridhar, Chair of Global Public Health at the University of Edinburgh, explained that, “Right now, we are taking a hit to the economy, major economic damage, without the public health benefit, which, in a way, is kind of a worst of all worlds” (Channel 4, 2020).

Sridhar's argument draws our attention to the two main ways of talking about the effects of the pandemic: public health and the economy. But it also emphasizes the way this conversation is often underpinned by numbers, even if they are not explicitly stated. If we rework her statement using data, we can see the implicit logic: ‘At the moment, in England, GDP growth is negative and cases and deaths linked to COVID-19 are high, which is the worst of all worlds.’ The fact that Sridhar does not need to refer specifically to these indicators demonstrates the way these metrics do not just represent the economy and health during the pandemic – they have come to stand in for these two phenomena. To talk about the economy and health, generally means talking about economic metrics and public health indicators. The numbers have become the phenomena they attempt to represent.

This is not unusual – it has become common to use ‘the economy’ and ‘GDP’ interchangeably. But the pandemic witnessed the rapid establishment of two contradictory data bounds that rested on the ability of numbers to stand in for health and the economy. Trade-Off was the dominant way of thinking of the pandemic in England – it positioned official measures to combat the virus as improving public health but coming at the cost of the economy. In her quote above, Sridhar was talking from the second, less popular, data bound: Protect Both. It emphasized that public health measures could protect both the economy and health, whereas a lack of state intervention would mean both the economy and health would suffer.

On 27 May, WIRED UK online (Temperton, 2020) reported that The Plandemic conspiracy documentary had been ‘viewed more than eight million times across Facebook, Instagram, Twitter and YouTube’. Just over three weeks later, on 18 June, the UK section of Yahoo! News released an article titled ‘COVID conspiracies: 7% of Britons think there is no hard evidence that coronavirus exists, poll suggests’ (Wells, 2020a). Both statistics attempted to capture the public's belief in misinformation.

One number looked to tie belief with views: eight million people had viewed The Plandemic – a documentary that argued SARS-CoV-2 was deliberately released to control the world's population. The other figure was based on a traditional approach to public opinion: a set of survey questions asking people about their belief in misinformation. These two statistics were given their life through the narrative of the ‘infodemic’. This portmanteau of information and pandemic is described in most detail by the World Health Organization (WHO, 2020c):

It is at this point in the chapter that I face a juncture. I could take the section above as contextual – the foundation upon which a piece of research would examine why people believe in misinformation, where people receive disinformation from or how researchers can prevent people from believing in fake news. This is the path taken by much of the research on the infodemic – see the work of Brennen et al (2020) and Roozenbeek et al (2020) for two noteworthy examples from early in the pandemic. The approach by this chapter, however, follows a different tack. To do so, we must begin with the premises of the infodemic itself.

The story of ‘one billion items of PPE’ begins at an English government press conference on Saturday 18 April 2020. These daily briefings, streamed live across radio and television broadcasters, comprised of one elected politician from the Conservative Party and one or more scientific advisers to the government. These officials provided an update on the latest data, the policies being introduced and implemented, and the longer-term strategy to deal with the pandemic.

At this particular press conference in mid-April, Dan Hewitt from the popular television broadcaster ITV asks three questions to Steven Powys, the National Medical Director for National Health Service (NHS) England, and Robert Jenrick, Secretary of State for Housing, Communities and Local Government. It is worth outlining each in full:

• 1. How have we found ourselves in the situation where we are dangerously low on PPE?

• 2. Why hasn't the government had a plan B here, getting small, medium, large manufacturers to produce PPE?

• 3. Do you accept the worries of NHS doctors and nurses that we have spoken to today that by downgrading your PPE guidance, by not providing proper PPE, you are putting their lives at greater risk? (BBC News, 2020a)

Robert Jenrick, after a brief stutter, emphasizes how the public are in awe of social care and NHS staff and how nobody wants to see these people worried about whether they will have the correct equipment. He emphasizes how the government has to do more to get the PPE to the frontline but also stresses how it is a huge challenge given the global demand for equipment. Despite this, he explains that the government is making progress: “Today I can report that a very large consignment of PPE is due to arrive in the UK tomorrow from Turkey … which will include 400,000 gowns” (BBC News, 2020a).

This statement starts a strange ball rolling in government rhetoric around supply and distribution of PPE – one that bounced along from Saturday to Sunday to Monday to Tuesday and, finally coming to a stop, on Wednesday. It is important to provide a day-by-day play of how this panned out.

These six short stories point to the increased importance of the quantitative during the pandemic. While much of the pre-pandemic world was dominated by digital data, often describing individual behaviours online, the pandemic and post-pandemic world has forced numbers about society onto the mainstage. Before the pandemic, people would be familiar with GDP or unemployment rates. But the scale, scope and familiarity with this type of data marks a distinct juncture.

This makes understanding the cause, nature and effect of the quantitative a pressing task in the ‘post-pandemic’ world. To do so, this book argues that we need to engage with data bounds: how data becomes a meaningful way to experience, think about, discuss, react to, engage with and change certain phenomena. These data bounds are complex, made up of an ensemble of technical processes (collecting data, cleaning it, analysing it), contexts (political, economic, cultural, and so on) and media and communication.

But this complexity should not inhibit understanding. Each chapter in this book offers a distinct perspective of data bounds: they are reinforced by policy (Chapter 2), quantitative realism underpins them (Chapter 3), quantitative realism is mathematical and abstract (Chapter 4), desire for them underpins quantitative realism (Chapter 5), they are emotive (Chapter 6) and their boundaries are drawn within historical norms (Chapter 7).

These perspectives allow for data bounds to be broken down into six characteristics. In doing so, it can be used to think through non-pandemic phenomena that are highly quantified. These include – but are not limited to – health and fitness, inflation and cost of living, crime and justice and finance. It is hard to discuss, engage, experience or think about these contexts and for data not to be meaningful. To speak of the cost of living is to speak of wages, prices and profits. It is not to say that the qualitative plays no role, but to argue that the quantitative dominates.

Just as there are highly quantified phenomena, there are also contexts that have not felt the data creep so acutely. We can think of poetry and literature as existing outside data bounds.