Molecular epidemiological surveillance of respiratory syncytial virus infection in Myanmar from 2019 to 2023
Bibliographic record
Abstract
To evaluate genetic changes in respiratory syncytial virus (RSV) between 2019 and 2023, we analyzed RSV strains from Myanmar before and after the COVID- 19 pandemic. Real-time polymerase chain reaction (RT-PCR) positive samples from children presenting with acute respiratory infections at outpatient clinics in Yangon were sequenced to determine the genotype. Phylogenetic and molecular evolutionary analyses were conducted using the Bayesian Markov Chain Monte Carlo method to construct the time-scale Maximum Clade Credibility tree. Of 1127 samples, 104 (9.2%) RSV-A and 233 (20.7%) RSV-B were positive by RT-PCR. There was an absence of a notable epidemic in 2020, a temporal shift with a surge of RSV-A in the 2021 outbreak, a lack of expected cases in 2022 and a substantial resurgence of RSV-B in 2023. The genotype of RSV-A was mainly A.D.3 lineage through the study period, while RSV-B were B.D.4.1.1 and B.D.E.1. RSV-A showed that the same lineage persisted within Myanmar throughout the pandemic, leading to a large outbreak post-COVID. In contrast, RSV-B strains appear to have temporarily disappeared during the pandemic, but subsequently, globally circulating strains likely entered Myanmar, resulting in a major outbreak in 2023. The estimated evolutionary rate at the G-ectodomain for RSV-A was 7.76 × 10⁻³ and RSV-B was 5.67 × 10⁻³ substitutions/site/year. Strengthening genomic surveillance will likely support comparisons of circulating strains with those in other countries and facilitate the introduction of vaccines and other interventions.
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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 | 0.001 |
| 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".