Spatial and temporal aberration detection methods for disease outbreaks in syndromic surveillance systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Early surveillance of notifiable infectious diseases is a key element for their control by public health agencies. The goal of syndromic disease surveillance is to identify emerging infectious risks to public health in real or near real time as a method of early detection, trend monitoring, and false-alarm avoidance. This article reviews temporal, spatial, and spatial–temporal aberration detection techniques that can be used to facilitate the early detection of infectious disease outbreaks that can occur in nonrandom yet clustered distributions in geographic information systems (GIS)-based syndromic surveillance systems. The focus is on the approaches appropriate for prospective surveillance data. In addition, this article discusses the impact of data privacy, security, and data quality on detection algorithms and explores what the future GIS-based syndromic surveillance systems may hold.
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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 | 0.000 |
| 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 it