Surveillance medicine, crowdsourced public health, and data-driven epidemiology: the privacy implications of digitally tracking and visualizing contagious disease outbreaks
Bibliographic record
Abstract
The app and website SickWeather collects information from across the web, as well as self-reports from users, so that people can see who is sick in their neighbourhood. A future version of the app will even allow users to see which of their friends are talking about being sick on social media, yet surprisingly, few concerns have been raised about potential privacy infringements. Traditional public health methods for tracking contagious diseases are increasingly complemented with these kinds of digital tools, which use data mining, analytics, and crowdsourcing to predict and monitor disease outbreaks. What are the privacy and surveillance implications of digital disease tracking tools, and the dangers of constructing contagious disease outbreaks through data visualization? I draw on concepts of network power, the surveillance assemblage, and Deleuze's 'control societies', where individuals are moved from one node to another and the function of control is to accumulate and direct information.I performed a content and platform analysis of two apps, SickWeather and HealthMap, by using them over the course of three months, taking regular screenshots and keeping a detailed user journal. This analysis was guided by a cultural-historical activity theory (CHAT) framework, taking note of the data visualizations and other content, but also the functionalities of both apps, including the characteristics of membership, 'rules' and parameters of community mobilization and engagement, monetization, and moderation by designers. This allowed me to study HealthMap and SickWeather as modes of governance that allow for (and depend upon) certain actions and particular activity systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".