Molecular Epidemiology of <i>Mycobacterium tuberculosis</i> To Describe the Transmission Dynamics Among Inuit Residing in Iqaluit Nunavut Using Whole-Genome Sequencing
Bibliographic record
Abstract
BACKGROUND: In the last decade, tuberculosis (TB) incidence among Inuit in the Canadian Arctic has been rising. Our aim was to better understand the transmission dynamics of TB in this remote region of Canada using whole-genome sequencing. METHODS: Isolates from patients who had culture-positive pulmonary TB in Iqaluit, Nunavut, between 2009 and 2015 underwent whole-genome sequencing (WGS). The number of transmission events between cases within clusters was calculated using a threshold of a ≤3 single nucleotide polymorphism (SNP) difference between isolates and then combined with detailed epidemiological data using a reproducible novel algorithm. Social network analysis of epidemiological data was used to support the WGS data analysis. RESULTS: During the study period, 140 Mycobacterium tuberculosis isolates from 135 cases were sequenced. Four clusters were identified, all from Euro-American lineage. One cluster represented 62% of all cases that were sequenced over the entire study period. In this cluster, 2 large chains of transmission were associated with 3 superspreading events in a homeless shelter. One of the superspreading events was linked to a nonsanctioned gambling house that resulted in further transmission. Shelter to nonshelter transmission was also confirmed. An algorithm developed for the determination of transmission events demonstrated very good reproducibility (κ score .98, 95% confidence interval, .97-1.0). CONCLUSIONS: Our study suggests that socioeconomic factors, namely residing in a homeless shelter and spending time in a gambling house, combined with the superspreading event effect may have been significant factors explaining the rise in cases in this predominantly Inuit Arctic community.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".