Analyse d'indicateurs de mesure pour les éclosions de maladies entériques, Centre de contrôle des maladies de la Colombie-Britannique, 2005-2014
Bibliographic record
Abstract
Background: For enteric disease outbreaks, effective control depends on timely intervention.Routine collection of metrics related to outbreak identification, investigation and control can help evaluate and improve interventions and inform further analyses and modelling of intervention effectiveness.Objective: To analyze data from enteric disease outbreaks in British Columbia, generate outbreak metrics and assess their use in evaluating the impact of outbreak interventions.Methods: This descriptive study analyzed data from 57 provincial and national enteric disease outbreak investigations involving the British Columbia Centre for Disease Control from 2005 to 2014.Data were extracted from internal files and the Canadian Network for Public Health Intelligence.Outbreak metrics analyzed included days to initiate investigation, days to intervention, number and type of interventions, duration of investigation, duration of outbreak and the total number of cases.Results: The median time to initiate an outbreak investigation was 36 days and the median duration of investigations was 39 days.The median duration of outbreaks was 40 days and the median time to intervene was 10 days.Identification of the source was associated with use of one or more interventions (P<0.0001).The duration of outbreaks was correlated with the number of days to initiate an investigation (r s =0.72, P<0.0001) and number of days to intervene (r s =0.51, P=0.025).Conclusion: Identification and analysis of outbreak metrics establishes benchmarks that can be compared to other jurisdictions.This may support continuous quality improvement and enhance understanding of the impact of public health activities.Date information for public health actions is essential for evaluating the timing and effectiveness of outbreak interventions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".