Genomic attributes of Vibrio cholerae responsible for a massive cholera outbreak in Bangladesh, 2022
Bibliographic record
Abstract
Abstract Bangladesh is currently experiencing one of the worst cholera outbreaks in its history. The icddr,b hospital has treated a record number of patients, more than 1400 per day and ca. 40,000 diarrheal cases from the end of March through April 20221. A recent genomic study showed temporal progression of two lineages, BD-1 and BD-2, with the former linked to the 7th pandemic wave-3 global clade and the latter predominant in endemic cholera in Dhaka during 2013 and 20172. Here, we present genomic attributes of V. cholerae O1 responsible for the 2022 Dhaka cholera epidemic and genome phylogeny of 960 7th pandemic El Tor strains from 88 countries. Results show the Dhaka cholera etiological agent clustered with the 7th pandemic El Tor wave-3 global clade, but comprises a new subclade, BD-1.2, for which the most recent common ancestor appears to be of the globally distributed sublineage predominantly associated with recent endemic cholera in India. Results also suggest BD-1.2 was present in Bangladesh since 2016. However, it was not until 2018 that strains of the subclade successfully established dominance over the BD-2 during an expansion of the wave-3 global clade. It is concluded that the recent shift in predominant lineage and the observed genetic changes including serotype switch in BD-1.2 from Ogawa to Inaba may explain the increasing number of infections and massive outbreak of cholera during 2022 in Bangladesh.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".