A Confidence-based Aberration Interpretation Framework For Outbreak Conciliation
Bibliographic record
Abstract
HEALTH SURVEILLANCE CAN BE VIEWED AS AN ONGOING SYSTEMATIC COLLECTION, ANALYSIS, AND INTERPRETATION OF DATA FOR USE IN PLANNING, IMPLEMENTATION, AND EVALUATION OF A GIVEN HEALTH SYSTEM, IN POTENTIALLY MULTIPLE SPHERES (EX: animal, human, environment). As we move into a sophisticated technologically advanced era, there is a need for cost-effective and efficient health surveillance methods and systems that will rapidly identify potential bioterrorism attacks and infectious disease outbreaks. The main objective of such methods and systems would be to reduce the impact of an outbreak by enabling appropriate officials to detect it quickly and implement timely and appropriate interventions. Identifying an outbreak and/or potential bioterrorism attack days to weeks earlier than traditional surveillance methods would potentially result in a reduction in morbidity, mortality, and outbreak associated economic consequences. Proposed here is a novel framework that takes into account the relationships between aberration detection algorithms and produces an unbiased confidence measure for identification of start of an outbreak. Such a framework would enable a user and/or a system to interpret the anomaly detection results generated via multiple algorithms with some indication of confidence.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".