Digital acoustic surveillance for early detection of respiratory disease outbreaks in Spain: a protocol for an observational study
Bibliographic record
Abstract
INTRODUCTION: Cough is a common symptom of COVID-19 and other respiratory illnesses. However, objectively measuring its frequency and evolution is hindered by the lack of reliable and scalable monitoring systems. This can be overcome by newly developed artificial intelligence models that exploit the portability of smartphones. In the context of the ongoing COVID-19 pandemic, cough detection for respiratory disease syndromic surveillance represents a simple means for early outbreak detection and disease surveillance. In this protocol, we evaluate the ability of population-based digital cough surveillance to predict the incidence of respiratory diseases at population level in Navarra, Spain, while assessing individual determinants of uptake of these platforms. METHODS AND ANALYSIS: Participants in the Cendea de Cizur, Zizur Mayor or attending the local University of Navarra (Pamplona) will be invited to monitor their night-time cough using the smartphone app Hyfe Cough Tracker. Detected coughs will be aggregated in time and space. Incidence of COVID-19 and other diagnosed respiratory diseases within the participants cohort, and the study area and population will be collected from local health facilities and used to carry out an autoregressive moving average analysis on those independent time series. In a mixed-methods design, we will explore barriers and facilitators of continuous digital cough monitoring by evaluating participation patterns and sociodemographic characteristics. Participants will fill an acceptability questionnaire and a subgroup will participate in focus group discussions. ETHICS AND DISSEMINATION: Ethics approval was obtained from the ethics committee of the Centre Hospitalier de l'Université de Montréal, Canada and the Medical Research Ethics Committee of Navarre, Spain. Preliminary findings will be shared with civil and health authorities and reported to individual participants. Results will be submitted for publication in peer-reviewed scientific journals and international conferences. TRIAL REGISTRATION NUMBER: NCT04762693.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".