Epidemiological, clinical and genomic insights into the ongoing diphtheria outbreak in Yemen
Bibliographic record
Abstract
Abstract Background An outbreak of diphtheria, declared in Yemen in October 2017, is still ongoing. Methods. Probable cases were recorded through an electronic diseases early warning system. Microbiological culture, genomic sequencing, antimicrobial susceptibility and toxin production testing were performed. Methods Probable cases were recorded through an electronic diseases early warning system. Microbiological culture, genomic sequencing, antimicrobial susceptibility and toxin production testing were performed. Findings The Yemen diphtheria outbreak developed in three epidemic waves, which affected nearly all governorates (provinces) of Yemen, with 5701 probable cases and 330 deaths (October 2017 - April 2020). The median age of patients was 12 years (range, 0.17-80). Virtually all outbreak isolates (40 of 43 tested ones) produced the diphtheria toxin. We observed low level of antimicrobial resistance to penicillin. We identified six separate Corynebacterium diphtheriae phylogenetic sublineages, three of which are genetically related to isolates from Saudi Arabia and Somalia. The predominant sublineage was resistant to trimethoprim and was associated with unique genomic features, more frequent neck swelling ( p =0.002) and a younger age of patients ( p =0.06). Its evolutionary rate was estimated at 1.67 × 10 −6 substitutions per site year -1 , placing its most recent common ancestor in 2015, and indicating silent circulation of C. diphtheriae in Yemen earlier than outbreak declaration. Interpretation We disclose clinical, epidemiological and microbiological characteristics of one of the largest contemporary diphtheria outbreaks and demonstrate clinically relevant heterogeneity of C. diphtheriae isolates, underlining the need for laboratory capacity and real-time microbiological analyses to inform prevention, treatment and control of diphtheria. Funding This work was supported by institutional funding from the National Centre of the Public Health Laboratories (Sanaa, Yemen) and Institut Pasteur (Paris, France) and by the French Government Investissement d ‘Avenir Program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".