Genomic epidemiology and multilevel genome typing of <i>Bordetella pertussis</i>
Bibliographic record
Abstract
Bordetella pertussis causes pertussis (or whooping cough), a severe respiratory infectious disease in infants, although it can be prevented by whole cell and acellular vaccines. The recent pertussis resurgence in industrialised countries is partly attributed to pathogen adaptation to vaccines, while emergence of antimicrobial resistance, specifically to macrolides in China, has become a concern. Surveillance of current circulating and emerging strains is therefore vital to understand the risks they pose to public health. Although the use of genomics-based typing is increasing a genomic nomenclature for this pathogen has not been well established. Here, we implemented the multilevel genome typing (MGT) system for B. pertussis with five levels of resolution, which provide targeted typing of relevant lineages and discrimination of closely related strains at the finest scale. The lower resolution levels (MGT2 and MGT3) describe the distribution of major vaccine antigen alleles including ptxP, fim3, fhaB and prn, as well as temporal and spatial trends within the B. pertussis global population. Mid-resolution levels (MGT3 and MGT4) enable typing of antibiotic-resistant lineages and Prn deficient lineages within the ptxP3 clade. The high-resolution level (MGT5) can capture finer-scale epidemiology such as outbreaks and local transmission events, with comparable resolution to existing genomic methods of strain-relatedness assessment. The scheme offers stable MGT-type assignments aiding harmonisation of typing and communication between laboratories. The scheme is available at https://mgtdb.unsw.edu.au/pertussis, is regularly updated from global data repositories and accepts public submissions. The MGT scheme provides a comprehensive, robust, and scalable system for global surveillance of B. pertussis.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".