Global dynamics of SARS-CoV-2 clades and their relation to COVID-19 epidemiology
Bibliographic record
Abstract
Expansion of COVID-19 worldwide increases interest in unraveling genomic variations of novel SARS-CoV-2 virus. Metadata of 408,493 SARS-CoV-2 genomes submitted to GISAID database were analyzed with respect to genomic clades and their geographic, age, and gender distributions. Of the currently known SARS-CoV-2 clades, clade GR was the most prevalent worldwide followed by GV then GH. Chronological analysis revealed expansion in SARS-CoV-2 clades carrying D614G mutations with the predominance of the newest clade, GV, in the last three months. D614G clades prevail in countries with more COVID-19 cases. Of them, the clades GH and GR were more frequently recovered from severe or deceased COVID-19 cases. In contrast, G and GV clades showed a significantly higher prevalence among asymptomatic patients or those with mild disease. Metadata analysis showed higher (p < 0.05) prevalence of severe/deceased cases among males than females and predominance of GR clade in female patients. Furthermore, severe disease/death was more prevalent (p < 0.05) in elderly than in adults/children. Higher prevalence of the GV clade in children compared to other age groups was also evident. These findings uniquely provide a statistical evidence on the adaptation-driven evolution of SARS-CoV-2 leading to altered infectivity, virulence, and mortality.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".