This article is part of the series Governance, in crisis.
By Annabelle Littoz-Monnet
Professor, International Relations/Political Science
Director, Global Governance Centre
Graduate Institute of International and Development Studies
Synopsis: Epidemiological models have played a decisive role from the outset in determining the public policy response to Covid-19, especially in the imposition of quarantines and lockdowns. This emphasis on epidemiology, however, may have resulted in the silencing of alternative voices – from philosophers and anthropologists to general practitioners – and the possibility of alternative solutions for managing the public health emergency.
Keywords: COVID-19, expertise, science, modelling
As policymakers in domestic and global fora of governance have found themselves confronted with governing the rapid spread of a new virus, with severe health effects at a minimum for those whose immunity is less strong, scientific experts have been called in to “do policy”. Governments have appointed special groups of scientists to formulate opinions on the nature of the virus, on how best to cure the sick and on what sanitary and social measures to adopt in order to curb its spread. Experts have also taken centre stage in mediatic spaces, some gaining unprecedented public fame.
In this context, the politics of science quickly came into the limelight. Scientific controversies arose over how and at what pace the virus was spreading, how lethal it was compared to other viruses, how to medicate those affected and whether “social distancing” or more radical lockdowns were effective and ethically justifiable.
Epidemiological models have played a predominant role in determining policy
As these debates unfolded, highly technical forms of knowledge have informed policy, often concealing the more fundamental political and social questions which should have been addressed. Through their mathematical models, simulations and projections, epidemiologists and virologists have held a prominent position in policy debates.
In the UK, an Imperial College team led by Neil Ferguson developed an influential mathematical model whose alarming projections on impending death tolls prompted the government to impose quarantine, after Boris Johnson had first favoured a “herd immunity” approach. But another team led by Sunetra Gupta at Oxford University, which believed that the virus had been spreading for some time before its spread was even noticed, suggested that fewer than one in a thousand of those infected with the virus needs hospitalisation. Of course, different models are based on different assumptions. For instance, some models assume that once people recover from the virus, they become immune, while others assume that this immunity wanes over time. Despite the contingency and widely differing results of such scientific models, governments, at least in Europe, have relied heavily on them, often basing policy on the most alarming projections.
Beyond pointing to what we already know, i.e. that such models are not more objective than any other forms of expertise and that they embody certain theories about nature, it is their startling centrality to current policymaking that calls for greater analysis. How have we come to accept governance modes in which major policy decisions directly affecting peoples’ lives and freedoms are based on such types of expertise?
Other voices have been marginalised
Philosophers, ethicists and even social scientists have hardly been heard in these debates. Even biomedical knowledge from alternate sources, such as experiential evidence collected from countries affected by the spread of the virus before it reached the West or clinical knowledge from doctors who work directly with patients, has hardly been mobilised. To decipher the patterns of the virus, its consequences and how best to address its spread, policymakers reverted to knowledge produced through models and simulations, deflecting their horizons away from past or present realities towards the realm of “virtual” scenarios projected by modelling studies. This is puzzling given that the policy solutions proposed – from partial to complete quarantine scenarios – had implications beyond medicine that deserved broader consideration. In fact, even the health consequences of complete quarantines, due to surgical procedures being postponed, patients delaying visits to the doctor, people living in institutions suffering from isolation or the risk of increased violence in households, were not considered.
“the policy solutions proposed – from partial to complete quarantine scenarios – had implications beyond medicine that deserved broader consideration”
This way of governing became acceptable as the problem at stake was debated nearly exclusively in technical and decontextualised terms. The solutions proposed were framed as sanitary measures that were necessary in a state of emergency. The numbers produced by virologists and epidemiologists were invested with an aura of scientificity which made them appear more authoritative. This is paradoxical, as the scholars who produced such models were themselves aware of the limitations of the knowledge they were producing.
In addition, such knowledge has been perceived as easily actionable. Projections, because they are easily communicable, transportable and seemingly apolitical, can directly be acted upon by policymakers. Ferguson’s model has pulled its weight in policy debates well beyond the UK, as the numbers it projected (both for the number of people who would be contaminated by the virus and for those who would die from it) acted as fast and efficient alarm bells, despite qualifications by Ferguson himself on the lack of certainty of models like his own. Finally, being able to claim that they knew what kind of future lay ahead also invested governments with added authority and competence. This automatically resulted in the exclusion of alternative forms of expertise, and the avoidance of broader debates with the public.
Certain forms of knowledge can appear more “scientific”
Beyond revealing specific forms of knowledge instrumentalisation, such observations point to the dominance of a certain vision of what counts as science in our societies. The delineation of what is “more” or “less” scientific is often made through an appraisal of the methods used by scholars and their particular disciplines. Forms of knowledge produced through statistical analysis, large-scale randomised trials (in medicine) or complex models are valued more highly in policy debates, because they align more closely with current paradigmatic beliefs about what good science is.
“the knowledge from the ‘generalist’, the social scientist or even the general physician does not rank high in contemporary debates”
Knowledge considered as highly scientific and thus superior to other forms of knowledge is also highly specialised. The knowledge from the “generalist”, the social scientist or even the general physician does not rank high in contemporary debates, since governance itself is highly compartmentalised. Inevitably, this has led, in most European countries, to the exclusion from policy debates of people who normally do not portray themselves as health policy specialists, such as anthropologists, sociologists, philosophers or so-called “ethicists”. Notably, this has not been the case in Germany, where ethicists were asked by the Federal Government to issue recommendations on the “coronavirus crisis”. They highlighted the necessity of a discussion that went beyond the realm of medicine and the necessity of a political debate about the legitimacy of strict lockdowns, especially in the event that these could prove long-lasting.
Such reflections have clear implications for health governance, a multi-faceted domain in which problems frequently need to be addressed in multi-disciplinary ways and by actively engaging with the public. The point here is not to argue that models are necessarily wrong or that the decisions taken by politicians on the basis of such projections have necessarily been inadequate. Other forms of knowledge, such as experiential evidence, observations or analysis provided by social scientists have their own biases, of course. But precisely because there are no absolutes upon which policy decisions can be based, this is a call for pluralist and flexible political debates that can answer questions beyond “what works”. Questions such as what counts, what is acceptable and under what circumstances invite us to expand the range of voices consulted in ways that acknowledge both the validity and complementarity of diverse forms of knowledge.
This article was first published in the Graduate Institute’s Global Challenges special issue on “Politics of the Coronavirus Pandemic”.
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