Insights From Flutracking: Thirteen Tips to Growing a Web-Based Participatory Surveillance System
Bibliographic record
Abstract
Flutracking is a weekly Web-based survey of influenza-like illness (ILI) in Australia that has grown from 400 participants in 2006 to over 26,000 participants every week in 2016. Flutracking monitors both the transmission and severity of ILI across Australia by documenting symptoms (cough, fever, and sore throat), time off work or normal duties, influenza vaccination status, laboratory testing for influenza, and health seeking behavior. Recruitment of Flutrackers commenced via health department and other organizational email systems, and then gradually incorporated social media promotion and invitations from existing Flutrackers to friends to enhance participation. Invitations from existing participants typically contribute to over 1000 new participants each year. The Flutracking survey link was emailed every Monday morning in winter and took less than 10 seconds to complete. To reduce the burden on respondents, we collected only a minimal amount of demographic and weekly data. Additionally, to optimize users' experiences, we maintained a strong focus on "obvious design" and repeated usability testing of naïve and current participants of the survey. In this paper, we share these and other insights on recruitment methods and user experience principles that have enabled Flutracking to become one of the largest online participatory surveillance systems in the world. There is still much that could be enhanced in Flutracking; however, we believe these principles could benefit others developing similar online surveillance 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 | 0.063 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".