The Epidemiological Revolution

A History of Epidemiological Reasoning in the 20th Century

Tabulating Epidemics

Tabulation practices and visualisations in the emergence of modern epidemiology (1896-1940)

Modelling in the Media

Communicating Epidemiological Models in the Time of COVID-19

Network model, COO Gerd Altmann

Digital Epidemiology

The Long History of Computational Epidemiology

Epidemiological map of the Hospital Puerta de Hierro in Madrid

Optimizing Antibiotics

Antimicrobial stewardship interventions, medical work and resistant microbes in the Spanish public hospital

A History of Epidemiological Reasoning

The project is concerned with the history of epidemiological reasoning in the 20th century. Epidemiology has radically transformed and reshaped approaches to understanding health and disease in science and society since the early 1900s. But this history is often told too narrowly, or in overly broad brushstrokes.

Epidemiology has historically been a niche field in the medical sciences, side-lined by laboratory scientists and clinicians as just a secondary science. However, over the twentieth century, the field and its expertise gained unprecedented authority and influence. Long before Covid-19, epidemiologists had won the trust of policy makers and the general public to define public health crises brought about by infectious diseases, but also for chronic conditions and ‘unhealthy’ lifestyles.

This history expands far beyond the record of a discipline. The project seeks first to map the multiple influences – the web of causes – from which Epidemiology emerged as a field in the early twentieth century. Second, the project follows the dispersion and spread of epidemiological thought into other fields, domains and disciplines over the course of the century.

What is, or what has been called, epidemiology is not the project’s defining focus. This is instead a history of epidemiological reasoning. This project understands epidemiological reasoning as a discrete set of skills that employ data practices, rely on transdisciplinary expertise and utilise theoretical models to infer the characteristics, dynamics and causes of epidemic phenomena.

Looking at a way of knowing, at how arguments were built and how epidemiological knowledge circulates within and beyond disciplines, empires and thought collectives, will enable the project to broaden the scope of this history. The project gathers a range of impulses from medical statistics, colonial and local administration, anthropology, eugenics and sanitary science (…) that laid the groundwork for epidemiology to become a modern medical science. To understand how epidemiological reasoning assumed scientific, political and social authority, it is necessary to unpack how this unusual scientific project emerged from the flourishing social sciences, how it relied on the evidential power of medical sciences, but also how epidemiology was embedded in late-imperial state craft and driven by a powerful sanitary utopianism.

This historical approach will raise a set of questions: What was the point of concern around which epidemiological reasoning developed? What was and is an epidemic? Or, in the language of some historians of science, what is the epistemic thing around which epidemiological reasoning established experimental systems? It is a curious fact that the noun ‘epidemy’ has all but disappeared from use in English. While French and German – like many other languages – continue to refer to epidemics as a noun (eg. épidémie, Epdiemie) the adjective epidemic has come to stand in as a noun in the English-speaking world. This raises a series of intriguing questions, especially as the noun ‘epidemy’ vanishes precisely in the same period when epidemiology becomes a formal discipline.

With a focus on the epidemic as an object of knowledge, the project asks where the boundaries of this object are in the long twentieth century. Originally defined, modelled and surveyed in communicable diseases, the frame of an epidemic quickly expands into a range of different medical specialties as well as returned into the social sciences. From psychiatric epidemiology in the 1950s over the epidemics of smoking, heart disease and obesity to genetic epidemiology; reasoning about questions of health and disease with an epidemiological lens assumed astonishing prominence.

With the development of data science, epidemiological reasoning becomes supercharged into a leading scientific exercise. But also beyond medical applications, ‘epidemics’ are used to frame, analyse and understand social phenomena. The epidemic seemed to hold insights that the social sciences and humanities could not deliver. Analyses of violence and rioting, considerations of innovations and rumours as well as the timely surveillance of vaccine hesitancy, all became subject to epidemiological reasoning.

The project will trace these multiple translations in epidemiological reasoning and the expansion of the epidemy as a frame of analysis through three discrete themes: modelling, correlation, and configuration. The first theme, Modelling and the history of the epidemiological graph seeks to reconstruct the biographies of models and modelling practices to understand the intellectual currents and theoretical contributions to epidemiological reasoning. The second theme, Correlation, and the Making of Epidemiological Data, will turn to the data practices that established the infrastructures of the thriving field in national and global institutions, to understand how discrete paper technologies structure and inform epidemiological reasoning. The third theme, Configuration, and the Formation of Interdisciplinary Expertise will map out the transdisciplinary networks of the field to understand who at what time and under which circumstances was entrusted with the provision of epidemiological expertise (especially as these were rarely card-carrying epidemiologists)

The Epidemiological Revolution

Funded by an ERC Starting Grant, 2021 - 2025

Tabulating Epidemics - Tables as Tools of Reasoning in the History of Epidemiology 1896-1940

The hypothesis of the project is that tabulation practices, including ways of visualising data through different table forms, have specifically shaped the research, communication, and epistemology of modern epidemiological reasoning. As a form of collecting, organising and presenting epidemiological data, tabulation has enforced processes of standardisation and classification. As visual devices, tables have enabled epidemiologists to communicate the comparison of populations and tables established multi-factorial association as a hallmark of modern epidemiology. Tables and their associated practices were thus a pivotal ingredient in the epoch-defining transformation of epidemiology from a narrative and historical practice into a field based on formal mathematics, models, data, and quantification.

This project advances the potential of recent scholarship in the medical humanities on the material conditions of medical knowledge, including the research team’s developing work on formal practices in epidemiology. The planned work will incorporate and extend crucial insights into the practice and epistemology of tabulation from the history of statistics, astronomy, demography, and actuarial science. This approach significantly revises historical understandings of epidemiology’s modern transformation in a way that informs contemporary questions about authority and tabular reasoning in epidemiology, exemplified by the ubiquitous dashboards of the Covid-19 pandemic

Funded by a British Academy / Leverhulme Small Grant 2022 - 2024

Optimizing Antibiotics – Antimicrobial stewardship interventions, medical work and resistant microbes in the Spanish public hospital

The project investigates antimicrobial stewardship interventions in Spanish public hospitals, which are known as Programas de Optimización de Antimicrobianos (PROAs). Antimicrobial stewardship is one of the main biomedical interventions being implemented to mitigate the problem of antimicrobial resistance (AMR) in diverse healthcare settings across the world. This research is based on a multi-sited ethnography (carried out between 2021-2023) of these medical interventions in public hospital settings within the Spanish context, and aims to investigate PROAs using an experimental mix of historical and ethnographic methods, including the use of archival materials in in-depth ethnographic interviews and ethnographically documenting visits to the archives.

With the project “Optimizing antibiotics” the aim is to produce an extensive understanding of how clinicians, clinical microbiologists, hospital pharmacists and other hospital professionals coordinate their work to design and implement PROAs by using technologies, materials and work relationships available to them in their workplaces. The main theoretical questions that will be addressed include: what antecedents do these interventions have in the history of Spanish biomedicine since the 1970s; how is the objective of optimization understood and achieved in Spanish antimicrobial stewardship interventions; how are existing hospital materials, infrastructures and labour coordinated to that end; and how do ‘resistant’ microbes emerge as knowledgeable and governable in and through PROAs.

The work will extend decisive observations on the practices of hospital epidemiology, and biomedicine’s attempts to know and control the hospital environment and its human and microbial socialities.

Epidemiological map of the Hospital Puerta de Hierro in Madrid
Funded by an Alice Brown PhD Scholarship


  • Cristina Moreno Lozano

Models in the Media - Communicating Epidemiological Models in the Time of Covid-19

During the COVID-19 outbreak, epidemiological models have acquired unprecedented prominence in public and popular discourse. Modelling has been widely used to frame and justify the government’s response to the pandemic, and has been at the centre of public debate about how citizens as well as experts should respond. Traditional and new media have been abuzz with experts and non-experts alike discussing the validity of different models and the need to “flatten the curve”.

The overt adoption of epidemiological models to explain strategies of pandemic response and motivate public action raises urgent questions about how medical science is communicated, particularly via mass media.

  • How clearly and accurately are epidemiological models represented and interpreted in traditional and new media?
  • How do modellers communicate the uncertainties inherent in predictive modelling, and how is this translated in the media?
  • How do policy makers use models during the COVID-19 outbreak to explain and justify their actions and proposals; how closely does this conform to modellers’ own views; and how do modellers respond?
  • How are differences between models and their implications presented and played out in the media, and how are those differences received?
  • How does the public representation and reception of epidemiological models develop over time, as early predictions are confirmed or corrected by the gradual accumulation of contemporary evidence?
  • What does public discourse around epidemiological models tell us about public trust and the role of scientific argument in motivating and directing population-wide behaviour change?=

To answer these questions, this project undertakes a narrative review of the epidemiological debates around predictive modelling of COVID-19, an analysis of UK print and social media, including Twitter. The proposed research brings sociologists of science into dialogue with epidemiologists and science communicators, with a view to understanding the social and cultural impacts of COVID-19 in a robust and theoretically-informed way. Drawing on established approaches in Science & Technology Studies (STS), it will bring this accumulated learning to the service of better understanding the successes and challenges of the UK’s response to COVID-19.

The Long History of Digital Epidemiology

 The project illuminates historical developments in biomedicine and epidemiology that led to the emergence of an epidemiological reasoning based on data and models, rather than doctors’ diagnoses and the mere counting of cases. The project shows what influence practices of abstraction and formalization in the history of epidemiology had on today’s digital health landscape. The project refers to new data practices, organised around the idea of digital phenotyping, but also asks how traditional classification practices are newly mobilised in computational epidemiology. Of particular concern is finally how principles of interoperability have given rise to the field of digital epidemiology, in which all kinds of digital traces are presumed to enable epidemic insight or pandemic intelligence.