[Note 2] What even is a Normal Pandemic?

Graph of normal distributions

It has been a turbulent week in the unending UK Covid-19 story. On Wednesday, the pandemic briefings of the UK government returned, yet without any plan or measures announced to curb the rising numbers, which – once again – place the UK firmly at the helm of mismanaging this pandemic in Europe and beyond. However, I don’t want to dwell here on the reasons, why we ended up here, nor provide further common sense on how we might get out of this. There are plenty competent voices out there to turn to for this.

The events and trends of the last weeks have, however, underlined in a powerful way that epidemics have never been just mechanical phenomena. It is a persistent illusion in the world of modern epidemiology that epidemics can be measured, defined and known through numerical thresholds alone. When a number of cases has exceeded a limit, so the gospel goes, when counting the dead moves beyond a given number, we can speak of and we can see an epidemic. From there on, the dynamics of an infectious disease can be modelled, the curves can be plotted and the epidemic becomes a thing to study, to react to and – ideally – to contain. Revisiting much of the coverage of Covid-19 epidemiology over the last 18 months, one might be tempted to believe that there are regulated thresholds for this purpose. Surely, the WHO, the CDC or other institutions might have defined what amount of a disease in a population is too much, and that some kind of formula is used to differentiate a normal rate of disease circulation in society from what is considered excessive.

Of course, none of this exists. What is and what counts as an epidemic (or pandemic) is always a social and political question and not one that could be possibly left to the quantitative sciences. Nothing makes this clearer than the current political madness in the UK. In the beginning of last week the UK accounted for 20% of registered cases of Covid-19 globally. With around 50,000 cases now every day, an enormous proportion of the pandemic’s global growth literally occurs on this little island. Now, some might argue, that we have broken the link between exorbitant case rates and serious injury and mortality by vaccination. But have we? Just on Wednesday, the death toll reached the highest rate since March 2021 and the UK government maintains that there is no need for contingency measures, and that masks needn’t to be worn if you embrace a “convivial fraternal spirit” among each other. Over the last few days, Covid-19 returns to the headlines in some newspapers and programmes and maybe, if enough pressure is mounted, we might see some coordinated mitigation from Westminster. But the pace, the tone and the notion of emergency at this time is clearly out of sync with earlier periods of this pandemic, where we saw similar numbers.

I remember in the first few weeks of the pandemic, at some point in early April 2020, a medical professional on the radio tried to emphasise the alarming dimensions of the daily deaths by comparison to deaths in aviation. With over 200-500 people dying of Covid-19 in the UK daily at the beginning of April 2020, every day the UK lost a section of its population the size of a plane crash. While – just like all comparisons – not a perfect analogy, it allows us to pause on the conditions of what we do and what we do not perceive as tragic, avoidable, unexpected and preventable deaths. An airplane crash is often seen as a tragic avoidable accident, a failure of human capacity and technology, whereas the preventable dying from a pandemic can seem natural and almost inconspicuous. The history of AIDS/HIV  offers plenty of examples for this, as I argue elsewhere. In April 2020 comparisons like these worked to some extent. Lockdown measures were extended and an epidemic consciousness took hold. In contrast, in October 2021 many seem to accept the death toll equivalent to the daily crash of a mid-size airplane as merely background noise. While this demonstrates quite clearly that the pandemic is not exclusively a thing made up by numbers and arbitrary thresholds, it still is important to ask why things are so different this time around.

Understanding how epidemics end is one the most insightful question when trying to understand what epidemiology is and how epidemiological reasoning works. Epidemics do not end by certification, at least not regularly (with the exception of smallpox). Epidemics also do neither burn out, nor do they fade away. But epidemics tend to normalise. What does that mean? The crucial point is to neither mistake the process of normalisation with the mathematical & statistical description of achieving averages and means, nor with the behaviourists’ psycho-social diagnostics of an epidemic fatigue in the population. Both processes might be significant, but they tend to shroud the political agency required to frame an epidemic, any epidemic as crisis. Instead, to take normalisation serious means to return to the social processes that inform our shared assessment of what counts as normal, what constitutes a critical threshold and to return the question “what is an epidemic” to being a social and historical question. If we give too much credence to a language of normalisation rooted in statistical averages the current mortality rate might seem low, perhaps even normal, when compared to the outliers along the curves of the last 18 months of the pandemic nowcasting spectacle. But the opposite is the case: what is a normal mortality rate of Covid-19 cannot be deduced from the stats (because there is no such thing), rather the normalised numbers reflect what a society has come to define or accept as an expendable mortality from a preventable disease.

 

cover image of le normal et le pathologique

I tend to return to a trusted authority on these matters of averages, norms and normalisation for clarification. Georges Canguilhem, in his seminal Essay on “The Normal and the Pathological” uses the example of longevity of life to discuss the popular misconception that a measured average or mean might constitute a norm and to debunk that a normal longevity of life could be quantified at all. Any such attempt has always been thwarted not only by the uncertainty of defining what exactly dying of old age might mean (as opposed to dying of sickness), but also by the fact that each society has “the mortality that suits it” (Halbwachs, cited in Canguilhem, 161). How old members of a society become, how the number of the dead is distributed among the different age groups of a society is not much more but a reflection of the value that a society attributes to the protraction of life. Canguilhem writes (161):

In short, the techniques of collective hygiene which tend to prolong human life, or the habits of negligence which result in shortening it, depending on the value attached to life in a given society, are in the end a value judgement expressed in the abstract number which is the average human life span. The average life span is not the biologically normal, but in a sense the socially normative, lifespan. Once more the norm is not deduced from but rather expressed in the average.

For many, this might seem obvious and fairly uncontroversial. But it strikes me as a hallmark of this pandemic that numbers and their public display in dashboards, newspapers and government communications are too often displayed as if they speak for themselves. They do not. Perhaps, as Mark Honigsbaum wrote this week, we are indeed Benumbed by Numbers and have lost some crucial critical capacity to look beyond the stats and to reflect on their meaning. But we should also not forget that one of the most convenient ways to normalise a crisis has always been to declare it a natural phenomenon. If that is done by way of pointing to average numbers or by blaming genetic variants of a virus doesn’t matter much. What matters is that there ought to be nothing normal about this and that a normal pandemic does ipso facto not exist.

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