Afterword

Summary

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.