Use of a Digital Health Application for Influenza Surveillance in China
Bibliographic record
Abstract
OBJECTIVES: To examine whether a commercial digital health application could support influenza surveillance in China. METHODS: We retrieved data from the Thermia online and mobile educational tool, which allows parents to monitor their children's fever and infectious febrile illnesses including influenza. We modeled monthly aggregated influenza-like illness case counts from Thermia users over time and compared them against influenza monthly case counts obtained from the National Health and Family Planning Commission of the People's Republic of China by using time series regression analysis. We retrieved 44 999 observations from January 2014 through July 2016 from Thermia China. RESULTS: Thermia appeared to predict influenza outbreaks 1 month earlier than the National Health and Family Planning Commission influenza surveillance system (P = .046). Being younger, not having up-to-date immunizations, and having an underlying health condition were associated with participant-reported influenza-like illness. CONCLUSIONS: Digital health applications could supplement traditional influenza surveillance systems in China by providing access to consumers' symptom reporting. Growing popularity and use of commercial digital health applications in China potentially affords opportunities to support disease detection and monitoring and rapid treatment mobilization.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".