Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience

Abstract

Epidemiology is often misunderstood as a field dedicated to mere surveillance and monitoring of disease incidence, mortality rates and vaccination rollout. Since the onset of Covid-19, epidemiological expertise has been identified with dashboards, nowcasting and often-spurious predictions. However, since its establishment as an academic field in the early 20th century, epidemiology has always impacted on how diseases are classified, how knowledge about disease is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this article argues, should be predominantly seen in the light of such epistemological implications. Digital epidemiology is almost entirely based on deep or digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation constitutes not just an expansion of data collection, but should be examined as a significant epistemic shift in the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology seeks to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from STS, the article demonstrates the qualitative transformations built into what is widely perceived as a mere quantitative expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of the implied pathological omniscience, with which this data-driven engineering seeks to capture and resolve illness in the world, past, present and future.

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Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution Non- Commercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/'>https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us. sagepub.com/en-us/nam/open-access-at-sage https://creativecommons.org/licenses/by-nc/4.0/'>https://creativecommons.org/licenses/by-nc/4.0/

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DOI

https://doi.org/10.1177/20539517211066451

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https://www.research.ed.ac.uk/en/publications/e17014ee-e858-48f7-adbc-7ebd0a2861c4