Whole-Genome Sequencing and Social-Network Analysis of a Tuberculosis Outbreak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it