The story of COVID-19

COVID-19 resembles these other outbreaks and pandemics, but the news stories about it seem to have a distinctively numerical quality. The number of known infections in a particular place over a period of time gives an index of the transmissibility of an infection. Mortality rates – a measure of the proportion of people that die with an infection – are also woven into pandemic stories, in general. But COVID-19’s numbers seem to be its story. News feeds continually update the counts of diagnoses and deaths, and have numerically tracked the contagion from a supposed source in China to other countries, notably South Korea, Iran and Italy, and then to most other parts of the world. This pandemic by numbers approach charts the particular transmissibility and severity of this virus, but also lends itself to our increasingly complex small-screen media diet, where numbers and graphs alongside images and videos more easily convey the COVID-19 story than do long form narrative texts.

COVID-19 also comes into a context where public health systems have increasingly turned to mathematical models to make decisions about how to manage various infections, notably HIV and influenza. Models are used to decide how to apply resources and strategies to best effect. For example, modelling has shown how HIV can be prevented by HIV treatments, because they reduce the likelihood of the transmission of the virus. For influenza, models are used to calculate what proportion of the population needs to be vaccinated to protect the whole population from an emerging infection. These models can be adjusted to fit what is known about the biosocial properties of a particular infection, the efficacy of treatments and vaccines, and cost. In the case of COVID-19, for example, models may be indicating that containment will reduce the speed and scope of transmission and therefore reduce mortality, the burden on the health system, and ultimately the cost for governments. COVID-19 may be a fully-fledged algorithmic pandemic in the sense that its storyline is transparently about its numbers and because it is managed in terms of the models that combine numerical indices of transmission, mortality, burden, and cost.

Mathematical models and narratives also have a point of contact in relation to futures and pasts: they both offer means of trying to shape what might happen through action in the present guided by understandings of what has happened. Decision-makers want to know how to act now to reduce the future impact of a pandemic and models support these decisions by analysing pre-existing data about transmission, mortality, the benefits and costs of treatments, vaccines and containment, for example. Modelling, then, looks to what the future might be by assessing a pandemic’s past numbers. This is strictly true even during the course of a particular pandemic. Narratives on pandemic futures also recall the past, for example, through reference to the 1918-1919 influenza pandemic, swine flu in 2009, and other outbreaks, and on that basis orient the narrator and their interlocutors to possible futures. Public policy statements about containment requirements and news media vision of empty supermarket shelves both reflect action in the present that is predicated on pasts and aims to shape futures.

For all its life threatening, anxiety-making potential, though, the numerical narrative on COVID-19 is surprisingly impersonal. Stories of the many lives affected by the pandemic and the full tenor of its corporeal horrors are crowded out by governmental advice and updating of the numbers. One reason for this separation of pandemic knowing and lived experience is their incommensurability. Pandemics are constituted in the sheer scale and spread of their numbers, so much so that no one person can experience a pandemic without recourse to some signification of this calculated, collective plight. Infections, in contrast, are visceral and personal, including for those who devote themselves to providing care. Moreover, narrative arguably meets its limits in the experience of an infection because, like traumatic experience, living through a debilitating one is not an easy story to tell. Some accounts of how people experienced the 1918-1919 pandemic remark on the lack of affect, giving the impression that emotion was bracketed aside as irresponsible, impossible or even a luxury.

Numerical narrative, then, may not only be one of the ways we can know a pandemic is happening; it may provide the means of living with emerging threat. As they say, time – and numbers – will tell how things turn out for COVID-19.

Mark Davis is an associate professor in the School of Social Sciences at Monash University. He has published and co-edited books on the socio-cultural aspects of epidemics, including Sex, Technology and Public Health (Palgrave), HIV Treatment and Prevention Technologies in International Perspective (Palgrave), and Disclosure in Health and Illness (Routledge). His most recent book published by Oxford University Press, Pandemics, Publics, and Narrative is based on extensive research and interviews with those who lived through the 2009 H1N1 influenza (swine flu) outbreak.

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