Optimizing anthrax outbreak detection using reinforcement learning
Bibliographic record
Abstract
The potentially catastrophic impact of a bioterrorist attack makes developing effective detection methods essential for public health. In the case of anthrax attack, a delay of hours in making a right decision can lead to hundreds of lives lost. Current detection methods trade off reliability of alarms for early detection of outbreaks. The performance of these methods can be improved by modern disease-specific modeling techniques which take into account the potential costs and effects of an attack to provide optimal warnings. We study this optimization problem in the reinforcement learning framework. The key contribution of this paper is to apply Partially Observable Markov Decision Processes (POMDPs) on outbreak detection mechanism for improving alarm function in anthrax outbreak detection. Our approach relies on estimating the future benefit of true alarms and the costs of false alarms and using these quantities to identify an optimal decision. We present empirical evidence illustrating that the performance of detection methods with respect to sensitivity and timeliness is improved significantly by utilizing POMDPs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".