How can we use whole genome sequencing and mathematical modelling to understand tuberculosis transmission and inform our public health practices?
Bibliographic record
Abstract
Tuberculosis (TB) remains a public health problem in cities in high-income, low-incidence countries, such as London, where it disproportionately affects particular population groups and, as such, more effective intervention strategies are needed. With whole genome sequencing (WGS) data being increasingly used for TB epidemiology, I investigated how WGS data alongside statistical inference and mathematical modelling can improve our understanding of transmission in these population groups. By reviewing the literature on WGS in TB epidemiology studies, I concluded that whilst genomic data can improve our understanding of TB transmission, including epidemiological data alongside is helpful for mitigating uninformative genomic data or strengthening conclusions. I then employed a statistical inference method on sequencing data from a Canadian outbreak and used the inferred transmission network to determine that the outbreak had ended, demonstrating the use of genomic epidemiology in public health. As we must analyse genomic data using bioinformatics and sometimes phylogenetic methods before we can interpret it for epidemiological purposes, I undertook bioinformatics analysis of 415 genomes from a London TB outbreak and attempted to create a timed-phylogenetic tree that could be used for genomic epidemiology inferences. However, the data proved difficult to interpret resulting in a tree with little confidence, potentially due to little variation amongst the sequences. Finally, I constructed a novel mathematical transmission model to recapitulate the London outbreak and investigate public health interventions to conclude that despite loss-to-follow-up being considered an important factor amongst the cohort anecdotally, focusing interventions on reducing loss-to-follow-up or increasing re-engagement does not significantly reduce the number of outbreak cases. Finding infectious cases early achieves the most impact. In conclusion, combining epidemiological and sequencing with novel quantitative analysis using statistical inference and transmission modelling, provides useful insight into the spread of TB in urban outbreaks and illustrates the limitations of new approaches and data.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".