2024 Bangladesh Floodwaters Harbour Regional Hotspots of Pathogens and Antimicrobial Resistance
Bibliographic record
Abstract
Seasonal flooding in Bangladesh poses severe public health risks through waterborne disease, yet a comprehensive, genomic-level understanding of the associated microbial hazards is lacking. This study presents a comprehensive shotgun metagenomic analysis of floodwaters from four districts of Bangladesh (Cumilla, Feni, Lakshmipur, and Noakhali) during the devastating 2024 floods, profiling the distribution of pathogenic bacteria, antimicrobial resistance genes, and virulence factors. A total of 12 samples were collected during peak flooding periods and processed using Illumina sequencing. Taxonomic profiling and resistome analysis were performed using MetaPhlAn4, ABRicate, and MEGAHIT, referencing the NCBI and CZ ID databases. Across all regions, 301 operational taxonomic units were identified. Feni exhibited the highest diversity of pathogenic species, including multidrug-resistant Klebsiella pneumoniae, toxigenic Pseudomonas aeruginosa, and mobilizable resistance plasmids (e.g., IncP1, Col440I). Noakhali samples revealed co-detection of zoonotic and emerging pathogens such as Aliarcobacter spp. and Streptococcus suis, along with key resistance genes like blaOXA and ermB. Microbial community clustering revealed strong spatial heterogeneity. This study provides genomic evidence that floodwaters harbour emerging pathogens and AMR. It strongly advocates for incorporating metagenomic tools into Bangladesh's national flood response and AMR monitoring frameworks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".