Whole-Genome Sequencing and Social-Network Analysis of a Tuberculosis Outbreak
Bibliographic record
Abstract
BACKGROUND: An outbreak of tuberculosis occurred over a 3-year period in a medium-size community in British Columbia, Canada. The results of mycobacterial interspersed repetitive unit-variable-number tandem-repeat (MIRU-VNTR) genotyping suggested the outbreak was clonal. Traditional contact tracing did not identify a source. We used whole-genome sequencing and social-network analysis in an effort to describe the outbreak dynamics at a higher resolution. METHODS: We sequenced the complete genomes of 32 Mycobacterium tuberculosis outbreak isolates and 4 historical isolates (from the same region but sampled before the outbreak) with matching genotypes, using short-read sequencing. Epidemiologic and genomic data were overlaid on a social network constructed by means of interviews with patients to determine the origins and transmission dynamics of the outbreak. RESULTS: Whole-genome data revealed two genetically distinct lineages of M. tuberculosis with identical MIRU-VNTR genotypes, suggesting two concomitant outbreaks. Integration of social-network and phylogenetic analyses revealed several transmission events, including those involving "superspreaders." Both lineages descended from a common ancestor and had been detected in the community before the outbreak, suggesting a social, rather than genetic, trigger. Further epidemiologic investigation revealed that the onset of the outbreak coincided with a recorded increase in crack cocaine use in the community. CONCLUSIONS: Through integration of large-scale bacterial whole-genome sequencing and social-network analysis, we show that a socioenvironmental factor--most likely increased crack cocaine use--triggered the simultaneous expansion of two extant lineages of M. tuberculosis that was sustained by key members of a high-risk social network. Genotyping and contact tracing alone did not capture the true dynamics of the outbreak. (Funded by Genome British Columbia and others.).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".