Comparison of routine field epidemiology and whole genome sequencing to identify tuberculosis transmission in a remote setting
Bibliographic record
Abstract
Yukon Territory (YT) is a remote region in northern Canada with ongoing spread of tuberculosis (TB). To explore the utility of whole genome sequencing (WGS) for TB surveillance and monitoring in a setting with detailed contact tracing and interview data, we used a mixed-methods approach. Our analysis included all culture-confirmed cases in YT (2005-2014) and incorporated data from 24-locus Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats (MIRU-VNTR) genotyping, WGS and contact tracing. We compared field-based (contact investigation (CI) data + MIRU-VNTR) and genomic-based (WGS + MIRU-VNTR + basic case data) investigations to identify the most likely source of each person's TB and assessed the knowledge, attitudes and practices of programme personnel around genotyping and genomics using online, multiple-choice surveys (n = 4) and an in-person group interview (n = 5). Field- and genomics-based approaches agreed for 26 of 32 (81%) cases on likely location of TB acquisition. There was less agreement in the identification of specific source cases (13/22 or 59% of cases). Single-locus MIRU-VNTR variants and limited genetic diversity complicated the analysis. Qualitative data indicated that participants viewed genomic epidemiology as a useful tool to streamline investigations, particularly in differentiating latent TB reactivation from the recent transmission. Based on this, genomic data could be used to enhance CIs, focus resources, target interventions and aid in TB programme evaluation.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".