Understanding Detection Performance in Public Health Surveillance: Modeling Aberrancy-detection Algorithms
Bibliographic record
Abstract
OBJECTIVE: Statistical aberrancy-detection algorithms play a central role in automated public health systems, analyzing large volumes of clinical and administrative data in real-time with the goal of detecting disease outbreaks rapidly and accurately. Not all algorithms perform equally well in terms of sensitivity, specificity, and timeliness in detecting disease outbreaks and the evidence describing the relative performance of different methods is fragmented and mainly qualitative. DESIGN: We developed and evaluated a unified model of aberrancy-detection algorithms and a software infrastructure that uses this model to conduct studies to evaluate detection performance. We used a task-analytic methodology to identify the common features and meaningful distinctions among different algorithms and to provide an extensible framework for gathering evidence about the relative performance of these algorithms using a number of evaluation metrics. We implemented our model as part of a modular software infrastructure (Biological Space-Time Outbreak Reasoning Module, or BioSTORM) that allows configuration, deployment, and evaluation of aberrancy-detection algorithms in a systematic manner. MEASUREMENT: We assessed the ability of our model to encode the commonly used EARS algorithms and the ability of the BioSTORM software to reproduce an existing evaluation study of these algorithms. RESULTS: Using our unified model of aberrancy-detection algorithms, we successfully encoded the EARS algorithms, deployed these algorithms using BioSTORM, and were able to reproduce and extend previously published evaluation results. CONCLUSION: The validated model of aberrancy-detection algorithms and its software implementation will enable principled comparison of algorithms, synthesis of results from evaluation studies, and identification of surveillance algorithms for use in specific public health settings.
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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 | 0.013 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".