Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience
Bibliographic Data
| ID | 5260374 |
|---|---|
| Authors | Lukas Engelmann (0000-0002-2175-0156, University of Edinburgh, corresponding author) |
| Year | 2022 |
| Volume | 9 |
| Issue | 1 |
| Publication date | 2022-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Big Data & Society (JOURNAL) |
| Journal identifiers | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/20539517211066451 |
| OpenAlex | W4206311101 |
| Language | EN |
| Citations received | 10 |
| References cited | 63 |
Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology's methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this article argues, are not just a quantitative expansion of epidemiology's scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to 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 is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future
Data science · Epistemology · Fantasy · Pathology · Sociology · Computer Science · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Ethics in Clinical Research · Medicine · Artificial Intelligence · Epidemiology
Patient trust and professional authority
Contested Care
From genome to voiceome
Old data in new media? Problematic popularity of digital health data and consumer devices
The techno-politics of computing the mind
Digital phantoms in medical research
A Postgenomic Quilt
Biology and Criminology
Short-circuiting biology
Choreographing for public value in digital health
The Politics of Large Numbers
Promising Genomics
Personalized Medicine
Digital Health
The Postgenomic Condition
Digital Disease Detection — Harnessing the Web for Public Health Surveillance
The Parable of Google Flu
The effect of human mobility and control measures on the Covid-19 epidemic in China
Indexing, Coding, Scoring
Sorting Things Out
The Digital Phenotype
Food as exposure
Mining data, gathering variables and recombining information
The total archive
Datafication and accountability in public health
Following the algorithm
Is there a duty to participate in digital epidemiology
National electronic health records and the digital disruption of moral orders
Data as promise
Critical Questions for Big Data
The Death of the Clinic? Emerging Biotechnologies and the Reconfiguration of Mental Health
Big Data, new epistemologies and paradigm shifts
Conceptualizations of Big Data and their epistemological claims in healthcare
The birth of sensory power
Logged out
Disruption and dislocation in post-Covid futures for digital health
| Unique citing works | 10 |
|---|---|
| Citations per year | 3,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 8 |