Population-based sequencing of Mycobacterium tuberculosis reveals how current population dynamics are shaped by past epidemics
Bibliographic record
Abstract
Transmission is a driver of tuberculosis (TB) epidemics in high-burden regions, with assumed negligible impact in low-burden areas. However, we still lack a full characterization of transmission dynamics in settings with similar and different burdens. Genomic epidemiology can greatly help to quantify transmission, but the lack of whole genome sequencing population-based studies has hampered its application. Here, we generate a population-based dataset from Valencia region and compare it with available datasets from different TB-burden settings to reveal transmission dynamics heterogeneity and its public health implications. We sequenced the whole genome of 785 Mycobacterium tuberculosis strains and linked genomes to patient epidemiological data. We use a pairwise distance clustering approach and phylodynamic methods to characterize transmission events over the last 150 years, in different TB-burden regions. Our results underscore significant differences in transmission between low-burden TB settings, i.e., clustering in Valencia region is higher (47.4%) than in Oxfordshire (27%), and similar to a high-burden area as Malawi (49.8%). By modeling times of the transmission links, we observed that settings with high transmission rate are associated with decades of uninterrupted transmission, irrespective of burden. Together, our results reveal that burden and transmission are not necessarily linked due to the role of past epidemics in the ongoing TB incidence, and highlight the need for in-depth characterization of transmission dynamics and specifically tailored TB control strategies.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".