Collective Intelligence and Skills Mobilization: The Key of the Outbreak in Trois-Rivières
Bibliographic record
Abstract
In July 2017, 7 cases of Legionnaires' Disease (LD) were declared to the Public Health Department. This was unusually high considering the region has an annual average of 5 declarations of LD. Also, address of residence of cases indicated they lived in a 7 km radius from each other. This spacio-temporal aggregate prompted an outbreak investigation.The infectious disease team questioned patients and family. Questionnaire revealed that all of them spent time outside, in Trois-Rivières (TR), Québec, Canada. As well, a few patients had everyday activities that were almost exclusively in the downtown area.At the beginning of the investigation, the environmental health team rapidly validated cooling tower’s (CT) microbiological water quality. They were under the intervention threshold prescribed by the provincial regulations. Concomitantly, other potential sources of a Legionella were investigated in regards to their outbreak potential.The epidemiological survey highlighted the information on the temporal distribution of this outbreak (critical period between July 4th and 17th) and the concentration of patient's displacements in the city center of TR, confirming spatial distribution, which made it possible to refine environmental investigation. Therefore, culture results of a CT in this area were scrutinized in detail and irregularities were found. This CT had the results of recurrent interfering flora. Thus, the CT was resampled and results showed contamination at 630 000 CFU/l (under the current health standard of 1 000 000 CFU/l). Subsequently, the CT was closed.Ultimately, the Legionella strain found in respiratory specimen of the patients was a genotypic match to the one cultured from the CT and had never been documented in Quebec before.Major surveys require the participation of multiple skills from different horizons and use collective intelligence. This outbreak illustrates the complementary of the epidemiological and environmental investigations.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".