Digital technology and disease surveillance in the COVID-19 pandemic: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Infectious diseases pose a risk to public health, requiring efficient strategies for disease prevention. Digital health surveillance technologies provide new opportunities to enhance disease prevention, detection, tracking, reporting and analysis. However, in addition to concerns regarding the effectiveness of these technologies in meeting public health goals, there are also concerns regarding the ethics, legality, safety and sustainability of digital surveillance technologies. This scoping review examines the literature on digital surveillance for public health purposes during the COVID-19 pandemic to identify health-related applications of digital surveillance technologies, and to highlight discussions of the implications of these technologies. METHODS AND ANALYSIS: . We will search Medline (Ovid), PsycInfo, PubMed, Scopus, CINAHL (EBSCOhost), ACM Digital Library, Google Scholar and IEEE Explore for relevant studies published between December 2019 and December 2020. The review will also include grey literature. Data will be managed and analysed through an extraction table and thematic analysis. ETHICS AND DISSEMINATION: Findings will be disseminated through traditional academic channels, as well as social media channels and research briefs and infographics. We will target our dissemination to provincial and federal public health organisations, as well as technology companies and community-based organisations managing the public response to the COVID-19 pandemic.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".