Population-based sequencing of <i>Mycobacterium tuberculosis</i> reveals how current population dynamics are shaped by past epidemics
Bibliographic record
Abstract
Abstract Background Transmission has been proposed as a driver of tuberculosis (TB) epidemics in high-burden regions, with negligible impact in low-burden areas. Genomic epidemiology can greatly help to quantify transmission in different settings but the lack of whole genome sequencing population-based studies has hampered its use to compare transmission dynamics and contribution across settings. Methods We generated an additional population-based sequencing dataset from Valencia Region, a low burden setting, and compared it with available datasets from different TB settings to reveal heterogeneity of transmission dynamics and its public health implications. We sequenced the whole genome of 785 M. tuberculosis strains and linked genomes to patient epidemiological data. We applied a pairwise distance clustering approach and phylodynamics methods to characterize transmission events over the last 150 years, in Valencia, Spain (low burden), Oxfordshire, United Kingdom (low burden) and a high-burden (Karonga, Malawi). Results Our results revealed high local transmission in the Valencia Region (47.4% clustering), in contrast to Oxfordshire (27% clustering), and similar to a high-burden setting like Malawi (49.8% clustering). By modelling times of the transmission events, we observed that settings with high transmission are associated with uninterrupted transmission of strains over decades, irrespective of burden. Conclusions Our results underscore significant differences in transmission between TB settings even with similar burdens, reveal the role of past epidemic in on-going TB epidemic and highlight the need for in-depth characterization of transmission dynamics and specifically-tailored TB control strategies. Funding European Research Council under the European Union’s Horizon 2020 research and innovation program (Grants 638553-TB-ACCELERATE, 101001038-TB-RECONNECT), and Ministerio de Ciencia e Innovación (Spanish Government, SAF2016-77346-R and PID2019-104477RB-I00)
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".