Integrating genomic and spatial analyses to describe tuberculosis transmission: a scoping review
Bibliographic record
Abstract
Tuberculosis remains a leading cause of infection-related mortality, and efforts to reduce its incidence have been hindered by an incomplete understanding of local Mycobacterium tuberculosis transmission dynamics. Advances in pathogen sequencing and spatial analysis have created new opportunities to map M tuberculosis transmission patterns more precisely. In this scoping review, we searched for studies combining pathogen genetics and location data to analyse the spatial patterns of M tuberculosis transmission and identified 142 studies published between 1994 and 2024. Secular changes in genetic methods were observed, with genome sequencing approaches largely replacing lower-resolution genotyping methods since 2020. The included studies addressed four primary research questions: how are tuberculosis cases and M tuberculosis transmission clusters geographically distributed; do spatially concentrated M tuberculosis clusters exist, and where are these areas located; when spatial concentration occurs, what host, pathogen, or environmental factors contribute to these patterns; and do identifiable relationships exist between the spatial proximity of tuberculosis cases and the genetic similarity of the M tuberculosis isolates infecting these individuals? Collectively, in this Review, we examined the available study data, evaluated the analytical requirements for addressing these questions, and discussed opportunities and challenges for future research. We found that the integration of spatial and genomic data can inform a detailed understanding of local M tuberculosis transmission patterns, but improved study designs and new analytical methods to address gaps in sampling completeness and to integrate additional movement data are needed to fully realise the potential of these tools.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".