Epidemiological Publics? On the Domestication of Modelling in the era of COVID-19
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Science, Medicine, and Anthropology
http://somatosphere.net
Epidemiological Publics? On the Domestication
of Modelling in the era of COVID-19
2020-04-10 07:35:25
By
The COVID-19 pandemic has been called “a once-in-a-century evidence
fiasco” (Ioannides, 2020), while the editor of the Lancet has declared that
“the handling of the COVID-19 crisis in the UK is the most serious science
policy failure in a generation” (Horton, 2020). At the heart of the science
policy is mathematical modelling, a scientific activity once reserved for
mathematicians, epidemiologists and economists, and now widely
discussed by politicians, journalists and the wider public. The term
“flattening the curve” has gone viral, with DJs, actors and other celebrities
exhorting others to do their bit to achieve epidemiological victory. On
March 17th, Tom Hanks, one of the first high-profile celebrities to be
diagnosed with COVID-19, posted an update to his fans on Instagram,
which has had over 1.6 million likes:
“Hey folks. Good News: One week after testing Positive, in
self-isolation, the symptoms are much the same. No fever but the
blahs. Folding the laundry and doing the dishes leads to a nap on
the couch. Bad news: My wife @ritawilson has won 6 straight
hands of Gin Rummy and leads by 201 points. But I have learned
not to spread my Vegemite so thick. I travelled here with a
typewriter, one I used to love. We are all in this together. Flatten
the curve. Hanx”.
The day before, The Evening Standard ran an editorial headlined “We
must all do our bit to flatten the curve”. In it, Julia Hobsbawm wrote:
“A week is a long time in this new Covid-19 era. Seven days on, I
have cancelled all face-to-face events in my networking business
for the foreseeable future, including my own book launch next
week. It ought to have felt complicated and difficult. It wasn’t. It felt
very straightforward and simple. Why? Because I want to flatten
the curve. Last week that phrase would have meant ‘get a flat
stomach’”. (Hobsbawm, 2020)
Notable in these communications is the entwining of modelling-speak with
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the everyday of the social – playing cards, eating vegemite, doing the
laundry, flattening the curve. As Rhodes and colleagues have observed,
“#FlattenTheCurve entangles science into social practices, calculations
into materialisations, abstracts into affects, and models into
society” (Rhodes et al., 2020).
Beyond simple mantras like “flatten the curve”, whole armies of armchair
epidemiologists have emerged in print and social media, producing their
own graphs and graphics. In the age of citizen science, popular news
articles encourage people to understand the maths and invite them to
engage with interactive graphs and graphics. This ‘democratisation’ of
expertise has been criticised by some professional scientists, such as
Gregg Gonsalves, an Assistant Professor in Epidemiology at Yale, whose
research focuses on the use of quantitative models for improving the
response to epidemic diseases. In a widely liked and re-tweeted thread on
Twitter he wrote, “An epidemic of armchair epidemiology is happening
@NYTimes, first @DrDavidKatz, now @tomfriedman decide to opine on
the dynamics of epidemics and their control, when neither of them (nor
John Ioannidis) work on these topics” (Gonsalves, 2020). Scott Berry, a
respected statistician, was similarly disillusioned with the popularisation of
epidemiological terms, tweeting: “I’m saddened by the lack of
understanding what “exponential growth” means. It’s an adjective without
meaning. Maybe it becomes the next #literally? A word with a very precise
meaning that is lost… “Hey, dude, that car’s speed is
exponential”…” (Berry, 2020).
Historically, epidemiological modelling has been a niche field. First
attempts in formulating mathematical expressions to explain the waxing
and waning of epidemic phenomena reach as far back as William Farr in
the mid-nineteenth century. Even when Ronald Ross, who was credited
with a Nobel Prize for the discovery of mosquitos as the vector of malaria,
suggested the development of a mathematical “theory of happenings” to
explain the dynamics of epidemics (Ross 1916), epidemiologists as well as
policy makers saw little use in these novel tools. At the time, epidemics
were mostly understood to be the result of the invasion of germs into
populations, often associated with global trade, immigration or war efforts.
Some considered the constitutions of host populations significant, others
favoured explanations based on environmental drivers. The dynamics of
smallpox, cholera, measles, tuberculosis and the plague aligned quite well
with such mono-causal and straight-forward explanations. It took until after
the Spanish Flu in 1918 for the understanding of epidemics to become
much more complex and for epidemiologists to develop appropriate ways
of thinking. Models could convincingly accommodate the interdependence
of pathogen-host-environment, and most importantly, allowed for the
development of a new scientific discourse about epidemics beyond
simplistic cause-and-effect schemata. But even in the mid-1920s, when
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Lowell Reed (biometrics) and Wade Hampton Frost (field epidemiologist)
designed one of the most influential models of epidemic distribution at
Johns Hopkins University, their “simple scheme” remained a tool of
academic illustration, a facet of “epidemic theory.” Mechanical models
with balls and ramps were built to demonstrate typical dynamics of
epidemics to students of epidemiology but were not meant to guide the
epidemiologist’s fieldwork, nor were they entrusted with any role in the
guidance of policy.
In a TV programme in which Lowell Reed presented their modelling work
to the American public, he described mathematical theories of epidemic
distribution and models of epidemic dynamics as the “workbench of the
epidemiologist’s laboratory,” and “as a critical instrument for
experimental epidemiological science”.
In a field which was fundamentally constrained to rely on the
(retrospective) observation of epidemics, modelling allowed
epidemiologists to emulate the experimental traditions of physiology and
bacteriology. However, while modelling remained for Reed and Frost an
academic exercise, confined to the “epidemiologist’s laboratory,” without
impact on public health advice or the public itself, much has changed
since.
In these times of COVID-19, models have left the niche of academic
specialism and have assumed substantial validity in the guidance of public
health policy and apparently gained some trust in the general public. And
while they might have shed associations with the laboratory workbench,
they still operate in the realm of experimentation, speculation
and simulation. However, now, different camps of epidemiologists and
modellers debate models in the mainstream press – for example, Adam
Kucharski, an epidemiologist at the London School of Hygiene & Tropical
Medicine, wrote a piece in The Guardian titled “Can we trust the Oxford
study on Covid-19 infections?” (Kucharski, 2020). In these very public
exchanges, models are assumed to be of public interest – and indeed they
are, because in the UK, the prime minister has openly stressed that the
country’s approach to managing the pandemic is driven by mathematical
modelling.
What are we to make of this domestication of mathematical modelling,
where to domesticate is literally to bring models into the home? As we
have suggested, domestication implies not just representation in the
media, but the active appropriation of epidemiological discourse into
everyday life – whether through hashtags, celebrity endorsements, or
discussions at the breakfast table. During these times of enforced absence
from the public sphere, when citizens are largely confined to their homes,
what role does the domestication of modelling play in shaping people’s
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understanding of themselves, their fellow citizens, and their role in shaping
the pandemic? As sociologists of science, what questions can we
usefully ask, which will help make sense of science-society relations in
times of pandemic and ensure that public health policy is scientifically
informed and democratically accountable?
The widespread uptake of epidemiological discourse by policy makers and
the wider public raises questions about the work that modelling does to
create strategies of pandemic response, galvanise public support, and
build a well-informed populace. The role of modellers in creating
knowledge of COVID-19 and communicating this successfully to different
audiences needs to be understood, as does the domestication of models
in the public imagination. This domestication is not uncontested, as the
open disagreements between modellers and the epistemological
gatekeeping in print and social media show. It thus becomes a question
not only of how the public assume an epidemiological imagination, but
how modellers recruit the public in their work. As Steve Hinchliffe (2020)
underscores, it is important to listen for the other voices and forms of
knowledge in this pandemic, to pay attention to the specificities and
spatialities of local conditions and practices.
‘Epidemiological publics’ might then refer both to the way in which
population groups are constituted and represented through modelling and
the way in which the production, circulation and use of epidemiological
models in policy making and in the media creates particular forms of public
participation[1]. In other words, it refers both to the production of
knowledge and to that knowledge’s use. It directs us to investigate the
moral and political capacities with which mathematical modelling comes to
be invested in times of pandemic and the forms of participation it
impels. This is important because public health strategies to manage the
pandemic rely on people participating in disease control measures such as
social distancing, hand-washing, self-isolation and lock-down. Without
strong and informed public participation, these measures will fail; without
trust and democratic accountability, the long-term future of science-based
policy is in doubt.
Catherine Montgomery is a Sociologist of Science, Technology & Medicine
at the Centre for Biomedicine, Self & Society at the University of
Edinburgh.
Lukas Engelmann is a Chancellor’s Fellow in History and Sociology of
Biomedicine at the University of Edinburgh.
References
Berry, S. 2020. I’m saddened by the lack of understanding what
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“exponential growth” means. It’s an adjective without meaning. Maybe it
becomes the next #literally ? A word with a very precise meaning that is
lost… “Hey, dude, that car’s speed is exponential”… In:@STATBERRY
(ed.) 3:53pm ed.: Twitter.
Gonsalves, G. 2020. An epidemic of armchair epidemiology is happening
@NYTimes, first @DrDavidKatz, now @tomfriedman decide to opine on
the dynamics of epidemics and their control, when neither of them (nor
John Ioannidis) work on these topics. In:@GREGGGONSALVES (ed.).
Twitter.
Hinchliffe, S. 2020. Model Evidence – the COVID-19
case. Somatosphere[Online]. Available
from: http://somatosphere.net/forumpost/model-evidence-covid-19/
[Accessed 09 April 2020.
Hobsbawm, J. 2020. We must all do our bit to flatten the curve. Evening
Standard [Online].
Available:
https://www.standard.co.uk/comment/comment/we-must-all-do-our-bit-to-fl
atten-the-curve-a4388381.html[Accessed 01/04/20].
Horton, R. 2020. The handling of the COVID-19 crisis in the UK is the
most serious science policy failure in a generation. Last week, the Deputy
CMO said, “there comes a point in a pandemic where that [testing] is not
an appropriate intervention.” Now a priority. Public mesage: utter
confusion. In:@RICHARDHORTON1 (ed.) 07.13 ed.: Twitter.
Ioannides, J. P. A. 2020. A fiasco in the making? As the coronavirus
pandemic takes hold, we are making decisions without reliable
data. STAT[Online].
Available:
https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coron
avirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data
/[Accessed 01/04/2020].
Kucharski, A. 2020. Can we trust the Oxford study on Covid-19
infections? The Guardian [Online].
Available:
https://www.theguardian.com/commentisfree/2020/mar/26/virus-infection-d
ata-coronavirus-modelling [Accessed 01/04/20].
Montgomery, C. M. & Pool, R. 2017. From ‘trial community’ to
‘experimental publics’: how clinical research shapes
public participation. Critical Public Health,27,50-62.
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Rhodes, T., Lancaster, K., et al. 2020. A model society: maths, models
and expertise in viral outbreaks. Critical Public Health [Online].
Available: https://doi.org/10.1080/09581596.2020.1748310.
[1]
We draw here on previous work on ‘experimental publics’ in relation to
clinical trials: Montgomery, C. M. & Pool, R. 2017. From ‘trial community’
to ‘experimental publics’: how clinical research shapes public
participation. Critical Public Health,27,50-62.
AMA citation
. Epidemiological Publics? On the Domestication of Modelling in the era of
COVID-19. Somatosphere. . Available at: . Accessed April 10, 2020.
APA citation
. (). Epidemiological Publics? On the Domestication of Modelling in the era
of COVID-19. Retrieved April 10, 2020, from Somatosphere Web site:
Chicago citation
. . Epidemiological Publics? On the Domestication of Modelling in the era
of COVID-19. Somatosphere. (accessed April 10, 2020).
Harvard citation
, Epidemiological Publics? On the Domestication of Modelling in the era of
COVID-19, Somatosphere. Retrieved April 10, 2020, from
MLA citation
. "Epidemiological Publics? On the Domestication of Modelling in the era
of COVID-19." . Somatosphere. Accessed 10 Apr. 2020.
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