Pandemics, Politics, and Society

Critical Perspectives on the Covid-19 Crisis

Gerard Delanty

• Publisher: De Gruyter
• Year: 2021
• eBook ISBN: 9783110713404
• Edition: 1
• Topic: Social Sciences
• Subtopic: Politics
• Index: Social Sciences

This volume is an important contribution to our understanding of global pandemics in general and Covid-19 in particular. It brings together the reflections of leading social and political scientists who are interested in the implications and significance of the current crisis for politics and society.

The chapters provide both analysis of the social and political dimensions of the Coronavirus pandemic and historical contextualization as well as perspectives beyond the crisis. The volume seeks to focus on Covid-19 not simply as the terrain of epidemiology or public health, but as raising fundamental questions about the nature of social, economic and political processes. The problems of contemporary societies have become intensified as a result of the pandemic. Understanding the pandemic is as much a sociological question as it is a biological one, since viral infections are transmitted through social interaction. In many ways, the pandemic poses fundamental existential as well as political questions about social life as well as exposing many of the inequalities in contemporary societies. As the chapters in this volume show, epidemiological issues and sociological problems are elucidated in many ways around the themes of power, politics, security, suffering, equality and justice.

This is a cutting edge and accessible volume on the Covid-19 pandemic with chapters on topics such as the nature and limits of expertise, democratization, emergency government, digitalization, social justice, globalization, capitalist crisis, and the ecological crisis.

Contents

Notes on Contributors

Preface

Gerard Delanty
1. Introduction: The Pandemic in Historical and Global Context

Part 1 Politics, Experts and the State

Claus Offe
2. Corona Pandemic Policy: Exploratory Notes on its 'Epistemic Regime'

Stephen Turner
3. The Naked State: What the Breakdown of Normality Reveals

Jan Zielonka
4. Who Should be in Charge of Pandemics? Scientists or Politicians?

Jonathan White
5. Emergency Europe after Covid-19

Daniel Innerarity
6. Political Decision-Making in a Pandemic

Part 2 Globalization, History and the Future

Helga Nowotny
7. In AI We Trust: How the COVID-19 Pandemic Pushes us Deeper into Digitalization

Eva Horn
8. Tipping Points: The Anthropocene and COVID-19

Bryan S. Turner
9. The Political Theology of Covid-19: a Comparative History of Human Responses to Catastrophes

Daniel Chernilo
10. Another Globalisation: Covid-19 and the Cosmopolitan Imagination

Frédéric Vandenberghe & Jean-Francois Véran
11. The Pandemic as a Global Total Social Fact

Part 3 The Social and Alternatives

Sylvia Walby
12. Social Theory and COVID: Including Social Democracy

Donatella della Porta
13. Progressive Social Movements, Democracy and the Pandemic

Sonja Avlijaš
14. Security for Whom? Inequality and Human Dignity in Times of the Pandemic

Albena Azmanova
15. Battlegrounds of Justice: The Pandemic and What Really Grieves the 99%

Index

Part 1: Politics, Experts and the State

Corona Pandemic Policy: Exploratory notes on its “epistemic regime”

Claus Offe

Every disease, I suppose, allows for categorizing people into groups which are specific to it: those more or less likely to be affected, those currently undergoing medical treatment, those chronically ill, etc. The nature and configuration of these groups is the combined outcome of what epidemiologist, pathologists, medical specialists and public health policy makers know about the incidence and progression of the disease, its treatment, and the assumptions and preferences governing its management. Let me start this think piece by describing the groupings created by the Corona pandemic. This exercise can be compared to a model of three Russian dolls, except that they are not put in a spatial but temporal sequence, or flow chart. Figure 1 (p. 30) tries to illustrate what I mean.

The Virus as a Sorting Mechanism: Six categories of people

(1) The first and by far largest category consists of the vast group of those in a territorially defined resident population who are, at a given point in time, not infected by the virus. Yet almost all of its members are, at the beginning of an epidemic, threatened by the disease and thus infectable by (because not yet immune to) the Corona virus. At the beginning of an epidemic, this group is, so to speak, the virgin land into which the virus can spread exponentially. The indicator (R-0) by which the initial rate of its reproduction is measured is commonly estimated to be slightly above 3, meaning that every person infected infects on average three others. As the pandemic runs its course, the great majority of the infected who have survived it and are thus immune for a greater or lesser length of time return to the subset of group (1) that, at least for the time being, is no longer infectable.

(2) The second group are the actually infected, including those who are not (yet) known (to others and even themselves) to be infected, be it because they have not developed any symptoms (which they do in most cases within about five days after infection) or because they were not (yet) diagnosed through testing. The size of this category of the unrecognized/unreported cases is a major unknown – the under-water and invisible part of the iceberg, as it were.

There are a number of reasons for this ignorance. First of all, there are people who are currently in the initial asymptomatic period and thus have no reason to seek diagnostic testing. But their number can also be unknown because of limitations of testing capacity. It can also be due to the incapacity or unwillingness of governments and public health agencies to generate a valid picture of the actual situation through large scale testing. The unwillingness can be motivated by the intention to save the direct costs of testing or to avoid a country suffering damages (e. g. losing foreign tourists) as a consequence of rigorous testing and the publication of its results, or to avoid “frightening the public” and “causing panic” (as the American president has put it). Also, governments are aware that additional testing is bound to increase the number of positive findings and that such increases may have all kinds of negative economic and/or political implications they wish to avoid, including the concern that “too many” positives may overburden available treatment facilities or make people anticipate shortages of such facilities. As a consequence of these and other considerations (including the lack of trust in the validity of testing results) there is a systematic underreporting of cases. Schools of epidemiologists differ in their estimates of the size of the category of the latently infected. A widely shared assumption is that, contingent on particular conditions in time and space, the latently and unknowingly infected can number up to ten times of those who have tested positive; but this number can be validated only with the (typically prohibitively costly) iterated testing of large samples.

So the extent to which a population is actually tested depends on testing strategies of the authorities of a given political entity. Such strategies derives from what I propose to call an “epistemic regime” that imposes, for the reasons just alluded to, limitations on the “desire to know” (and to allow such knowledge to become public) on the part of national and local governments and health-related agencies and professions. The epistemic regime also prescribes more or less inclusive practices of risk-assessment pertaining to particular sub-groups of group (1), such as the elderly, the poor, or health workers. The volume of testing actually taking place also depends on whether the respective population can be persuaded (or coerced or incentivized, e. g. by making tests cost-free to the tested) to actually undergo testing. After all, a “positive” test result involves potentially severe negative consequences (such as having to undergo mandatory isolation) which people confidently trusting in an asymptomatic progression of their infection or a spontaneous healing may be strongly motivated to avoid. There may also be an aversion to having to face bad news or an outright denial of the existence of a pandemic – a belief that would exonerate individuals holding such belief from the inconvenience and ambiguity of undergoing tests. Given all these motivations, even an approximate number of persons arriving in (2) through flow E, the subtotal of people at a particular point in time and territorial space being infected without them (or anyone else) knowing it, seems exceedingly hard to come by. This largely “willful ignorance” shapes national testing strategies to varying extents and thus makes international comparisons and comparative policy evaluations difficult. Moreover, one of the major problems of the Covid-19 disease and its management results from the fact that members of this (presumably) vast group of unreported cases are likely to be infectious and spread the disease to equally unknown others.

(3) The third group consists of the portion of (2) who have ever tested positive since the early stages of the pandemic, the cumulative total of whom indicates the overall incidence of cases that have occurred in a given country or region since the arrival of the pandemic. The size of this category, to the extent we can disregard issues of validity and specificity of the testing procedures, and its day-to-day greater or smaller increments (flow F) indicates, after the estimated number of those who have died from the virus (flow D) and those who have recovered (flow C) are subtracted, the overall dynamic of the epidemic, namely the level of its prevalence (number of actual cases) at a given point in time and its rate of change. Yet this assessment of the situation is valid only if the testing strategy of authorities and the compliance of citizens remain constant over time, which is typically not the case. Instead, we see patterns of often hectic policy experimentation, changes in testing practices and vehement contestation of policies. Yet once the increment of currently active cases is zero and its stock declines for a period of time, the epidemic can be declared defeated – provided, that is, the influx of new infections can be prevented, e. g. by sealing borders, which is not a realistic option though for any length of time. What members of group (3) have in common is merely the fact that, at some point in time, they have all tested positive and thus were diagnosed as infected, including those self-diagnosed as infected due to symptoms. This broad category consists of three sub-groups: Those having recovered after been tested positive (4), those currently ill (5), and the “case fatalities” who have died with or from the virus (6).

(4) This sub-group of (3) comprises those who have recovered from an infection and are now considered immune (with the incidence and durability of such immunity apparently being, for the time being, an unsettled question of epidemiological research). Should immunity turn out to be temporary or if the virus undergoes relevant mutation that undercuts immunity and unless it can be boosted through large scale and iterated vaccination, those in (4) are bound to return to (1) (flow A). Due to the suspected possibility of long term negative health effects, members of (4) need not return to the health status they enjoyed prior to their infection but may suffer chronically from various kinds of physical or mental illness. The notion of “herd immunity” envisages that, once a sufficient number of members of (1) (estimates suggest shares of 40 to up to 70 per cent) have contracted the virus, recover and thus end up in (4) and then return to (1), the pandemic will end as the virus is deprived, as it were, of the fertile ground of never-infected and hence non-immune human organisms that it needs in order to thrive and spread. Such a notion tends to under-emphasize, however, to put it cautiously, the number of years needed to reach this happy outcome as well as the number of fatalities (category (6)) to be expected along the road to herd immunity. Not included here is the (again unknown) number of spontaneous recoveries of those who recover without having suffered any, or any severe, symptoms, thus having moved directly, bypassing the stage of manifestation through testing, from (2) to (4) (flow B). Entirely asymptomatic cases are unrecognized and unknown (even to the subject affected); they return to category (1) via flow A where they merge indistinguishably with the immunized portion of the population, the size of which can only be detected by antibody tests. Yet both the duration of immunity and its ability to withstand mutations of the virus seem to be unknowns, both of which stand in the way of the idea to provide people with “immunity certificates” which would exempt them from corona-related hygiene rules

(5) This sub-group consists in the percentage of (3) who request (on their own initiative) and require (according to medical or administrative judgment and contingent upon the capacity of medical and other facilities available to them) some kind of treatment (ranging from physical (self‐) isolation to ICU treatment, as long as pharmaceutical remedies are unavailable) and eventually recover (flow C). These are the currently active cases (“prevalence”), which normally make up just a tiny fraction of both (estimates) of (2) and the aggregate numbers of (3). A metaphor is that of a bus line, where the number of those actually riding on a bus is a positive difference between those who have ever stepped on board minus those who have ever left the vehicle at stops (4) or (6).

(6) This last group is the total of those who have died because of (or just with) the virus (plus, arguably, those who have died from other diseases which they could have survived if the health system had not been overwhelmed by Corona cases or if they had not shied away from seeking timely medical treatment out of fear of getting infected by corona in doctor's offices or hospitals). The validity of data concerning causes of death is contingent upon, among other things such as the administrative capacity of reporting agencies, the capacity for autopsies and the registration of those who have died not medical institutions but at home. Another measure is the size of a spike in the overall mortality statistic that coincides with the virus pandemic, the so-called excess mortality that measures the positive difference between observed and expected deaths during some period of time.

To summarize the argument so far, the presence of a pandemic such as Covid-19 is bound to divide a population into three categories as depicted in Fig. 1. Category (1) is the resident population of a defined space (usually a national territory, federal state or administrative district), the members of which are currently not known to be infected or even (via flow A) known to be immune but to (strongly) varying degrees at risk of being infected. Category (2) is the segment of the population that is, at a particular point in time, infected and includes those not recognized to be infected (be it by themselves or to the health authorities). Such ignorance can be partly attributed to the nature of the epistemic regime and testing strategy in place and partly also to features of the virus which makes it exceedingly difficult, due to its patterns of transmission, to find out who is actually infected and who isn't. Category (3), subdivided in three sub-groups, comprises all those who are known (at least to themselves) to be infected, with the pragmatic consequence of some active convalescence and symptom-suppressing treatment (5). Those with whom such treatment succeeds, fully or partially, end up in group (4) (flow C) and continue, more or less durably, to the immunized subset of group (1) (flow A). The rest ends up, contingent upon the effectiveness of treatment and the (changing) lethality of the disease, in the group of fatalities (6). Needless to state, numerous subgroups within (2) and (3) can be thought of. They would represent groups according to demographic and health-related criteria and thus provide a more complex and informative picture.

Figure 1: Flow chart describing three groups of the population.

This rough flow chart describes how the Corona pandemic generates groups of people with distinctive pandemic-related features as they are observed and reported by the epistemic regime in place. It also demarcates strategic points of policy intervention. Pharmaceutical points of intervention are at the interface between (1) and (2) where infection can be prev...

Table of contents

• Title Page
• Copyright
• Contents
• Introduction: The Pandemic in Historical and Global Context
• Part 1: Politics, Experts and the State
• Part 2: Globalization, History and the Future
• Part 3: The Social and Alternatives
• Notes on Contributors
• Index