Haplotype distribution of SARS-CoV-2 variants in low and high vaccination rate countries during ongoing global COVID-19 pandemic in early 2021
Bibliographic record
Abstract
The widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continuously impacts our economic and public health. The potential of emerging variants to increase transmissibility and evade vaccine-induced immunity lets us put more effort to research on viral mutations and explore the pathogenic haplotypes. In this study, we characterized the haplotype and sub-haplotype diversity of SARS-CoV-2 global variants in January-March and the areas with low and high COVID19 vaccination rates in May 2021 by analyzing viral proteome of complete genome sequences published. Phylogenetic tree analysis of the proteomes of SARS-CoV-2 variants with Neighbor-Joining and Maximum Parsimony methods indicated that haplotype 2 variant with nsp12 P323L and Spike D614G was dominant (98.81%), including new sub-haplotypes 2A_1 to 2A_3, 2B_1 to 2B_3, and 2C_1 to 2C_2 emerged post-one-year COVID-19 outbreak. In addition, the profiling of sub-haplotypes indicated that sub-haplotype 2A_1 with the mutations at N501Y, A570D, D614G, P681H, T716I, S982A, and D118H in Spike was over 58% in May 2021 in the high partly vaccinated rate group (US, Canada, and Germany). Meanwhile, the new haplotype 2C_3 bearing the mutations at EFR156-158del, T19R, A222V, L452R, T478K, and D614G in Spike occupied over 54.8% in May 2021 in the low partly vaccinated rate group (India, Malaysia, Taiwan, and Vietnam). Sub-haplotypes 2A_1 and 2C_3 had a meaningful alternation of ACE2-specific recognition site, neutralization epitopes, and furin cleavage site in SARS-CoV-2 Spike protein. The results discovered the haplotype diversity and new sub-haplotypes of SARS-CoV-2 variants post one-year pandemic in January-March 2021, showing the profiles of sub-haplotypes in the groups with low and high partly vaccinated rates in May 2021. The study reports the emergence of new SARS-CoV-2 sub-haplotypes during ongoing pandemic and vaccination in early 2021, which might help inform the response to vaccination strategies.
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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 | 0.001 |
| 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) | 0.001 | 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".