Practical Approach for Evaluating Machine Learning Anomaly Detection Algorithms for Epidemic Early Warning Systems
Bibliographic record
Abstract
Anomaly detection methods in time series data can play a pivotal role in epidemic surveillance Early Warning Systems (EWS). Statistical and rules-based methods have been traditionally employed in such systems, but are challenged by data dynamics and necessitate expert fine-tuning regularly. On the other hand, machine learning methods can handle complex and multidimensional data better, learn and adapt to changing patterns, and improve their performance. However, practical methodologies for their fitting and evaluation relative to gold standard data for infectious diseases epidemic surveillance are still lacking. In this study, a practical evaluation method was presented using an ensemble technique of four traditional statistical models to build the reference gold standard dataset, and results of validation of two machine learning (LSTM and Isolation Forest) relative to four pathogen data series (COVID19, Hepatitis C, Acinetobacter baumannii and Methicillin-resistant Staphylococcus aureus) was reported with promising results. Lessons learned can be useful in the perspective of adapting ML algorithms to epidemic surveillance EWS.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".