Applications Of Operations Research/Statistics In Infection Outbreak Management
Bibliographic record
Abstract
Operations Research (OR) can be identified as the discipline that uses statistics, mathematics, computer-modelling and similar science methodology for decision making (Luss, Rosenwein, 1997). OR, powered with statistics and models, is a high potential engine for use in many areas that require evidence-based or model-based decision making. One of the most promising areas is specifically the infection outbreak management. Surprisingly, very little OR/statistics research has been aimed at infection outbreak management; usually, other general epidemiology issues were tackled in models. However, OR/statistics models for use in the infection outbreak management exist and can be effectively used in public policy and outbreak management practice. Probably, key reasons for that little involvement of OR/statistics in the infection outbreaks management is low awareness among the specialist community of OR/statistics use and benefits for their decision making. Up to the moment, there is lack of contemporary review of OR/statistics-applied models used for the infection outbreak management decision making. The present paper aimed at filling that gap and providing two benefits to involved health care managers and academics: first, developing awareness on the use and benefits of OR/statistics models for the infection outbreak management decision making, and second, for plotting the current state of affairs to highlight research opportunities for developing the field by academics and epidemic control professionals.
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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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".