Untangling an AGS Outbreak Caused by the Recombinant GII.12[P16] Norovirus With Nanopore Sequencing
Bibliographic record
Abstract
For a rapidly spreading virus such as NoV (norovirus), pathogen identification, genotype classification, and transmission tracing are urgent for epidemic control. Here, we applied the Nanopore metatranscriptomic sequencing to determine the causative pathogen of a community AGS (Acute gastroenteritis) outbreak. The results were also confirmed by RT-PCR. The NGS (Next Generation Sequencing) library was constructed within 8 hours and sequence analyses were carried out in real-time. NoV positive reads were detected in 13 of 17 collected samples, including two water samples from sewage treatment tank and cistern. A nearly complete viral genome and other genome fragments could be generated from metatranscriptomic sequencing of 13 samples. The NoV sequences from water samples and cases are identical suggesting the potential source of the outbreak. The sequencing results also indicated the outbreak was likely caused by an emerging recombinant GII.12[P16] virus, which was only identified in the United States and Canada in 2017-2018. This is the first report of this emerging variant in mainland China, following the large outbreaks caused by the recombinant GII.17[P17] and GII.2[P16] in 2014 and 2016, respectively. Closely monitoring of the prevalence of this recombinant strain is required. Our data also highlighted the importance of real-time sequencing in emerging pathogens' surveillance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".