Surveillance of Respiratory Pathogens Among Rapid Diagnostic Test-Negative Acute Respiratory Infection Patients in Myanmar in 2023, with a Focus on Rhinovirus and Enterovirus Genotyping
Bibliographic record
Abstract
This study explored the distribution and genetic characteristics of respiratory pathogens in outpatients with acute respiratory infections (ARIs) in Yangon, Myanmar, during the 2023 rainy season. Among 267 patients who tested negative for influenza, RSV, and SARS-CoV-2 using rapid diagnostic tests, 84.6% were positive for at least one pathogen according to a multiplex polymerase chain reaction (PCR) assay, the BioFire® FilmArray® Respiratory Panel 2.1. The most common viruses detected were rhinovirus/enterovirus (RV/EV) at 37.8%, respiratory syncytial virus (RSV) at 22.4%, and human metapneumovirus (hMPV) at 10.0%. These pathogens co-circulated mainly from July to September, with RV/EV consistently predominant. Symptom comparison among RV/EV-, RSV-, and hMPV-infected patients showed similar clinical features, though fever was more common in hMPV cases. Among RV/EV-positive patients, 59.3% had single infections, while 40.7% experienced co-infections, especially with RSV and adenovirus. Genotyping identified 28 types from five species, primarily RV-A and RV-C, which were genetically diverse. One EV-D68 case was also found, emphasizing its potential risk. This study underscores the genetic diversity and clinical impact of RV/EV and stresses the importance of ongoing molecular surveillance in Myanmar’s post-COVID-19 context to inform effective public health responses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".