Household Molecular Epidemiology of <i>Streptococcus pyogenes</i> Carriage and Infection in The Gambia
Bibliographic record
Abstract
BACKGROUND: Africa experiences a high burden of Streptococcus pyogenes disease but has limited epidemiological data. We characterized emm types and emm clusters associated with carriage and disease in The Gambia, a setting with a high rheumatic heart disease burden. METHODS: A 1-year household cohort study (2021-2022) recruited 442 participants from 44 households to assess S. pyogenes carriage and noninvasive infection. Pharyngeal and skin swab samples were collected to detect carriage, and pharyngitis and pyoderma swab samples were taken to assess infection. Cultured isolates underwent emm typing and were compared with previous collection from the same region. RESULTS: A total of 221 cultured isolates showed 52 different emm types and 16 emm clusters. Strain diversity was high (Simpson reciprocal index, 29.3 [95% confidence interval, 24.8-36.0]), with the highest diversity seen in pyoderma and the lowest in pharyngitis. Based on available cross-opsonization data, the 30-valent M-protein vaccine candidate would cover 50.8% of the isolates, but cross-opsonization data are unknown for 38.5% of them. The emm clusters showed lower diversity and were stable over time, with 4 clusters defining 65.2% of the isolates; 68% of isolates were collected from skin sites (carriage and pyoderma), with evidence of skin-to-throat transmission in the same host. CONCLUSIONS: This study provides a unique molecular analysis of skin and throat isolates prospectively collected from persons with carriage and noninvasive infection in Africa. Despite high strain diversity, 4 clusters included two-thirds of the isolates, representing antigen priorities for broad vaccine coverage. In this rheumatic fever-endemic setting, pyoderma and skin carriage represent an important S. pyogenes reservoir and should be included in further surveillance studies and public health interventions. CLINICAL TRIALS REGISTRATION: NCT05117528.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".