Cross-border transmissions of delta substrain AY.29 during Olympic and Paralympic Games
Bibliographic record
Abstract
Abstract Tokyo Olympic and Paralympic Games, postponed for COVID-19 pandemic, were finally held in summer of 2021. Just before the games, alpha variant was being replaced with more contagious delta variant (B.1.617.2). AY.4 substrain AY.29, which harbors two additional characteristic mutations of 5239C>T (NSP3 Y840Y) and 5514T>C (NSP3 V932A), emerged in Japan and became the dominant strain in Tokyo by the time of the Olympic Games. As of October 18, 98 AY.29 samples are identified in 16 countries outside of Japan. Phylogenetic analysis and ancestral searches identified 46 distinct introductions of AY.29 strains into those 16 countries. United States has 44 samples with 10 distinct introductions, and United Kingdom has 13 distinct AY.29 strains introduced in 16 samples. Other countries or regions with multiple introductions of AY.29 are Canada, Germany, South Korea, and Hong Kong while Italy, France, Spain, Sweden, Belgium, Peru, Australia, New Zealand, and Indonesia have only one distinct strain introduced. There exists no unambiguous evidence that Olympic and Paralympic Games induced cross-border transmission of the delta substrain AY.29. Since most of unvaccinated countries are also under sampled for genome analysis with longer lead time for data sharing, it will take longer to capture the whole picture of cross-border transmissions of AY.29.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".