SARS-COV-2 δ variant drives the pandemic in India and Europe via two subvariants
Bibliographic record
Abstract
ABSTRACT SARS-COV-2 evolution generates different variants and drives the pandemic. As the current main driver, δ variant bears little resemblance to the other three variants of concern, raising the question what features future variants of concern may possess. To address this important question, I compared different variant genomes and specifically analyzed δ genomes in the GISAID database for potential clues. The analysis revealed that δ genomes identified in India by April 2021 form four different groups (referred to as δ1, δ2, δ3 and δ4) with signature spike, nucleocapsid and NSP3 substitutions defining each group. Since May 2021, δ1 has gradually overtaken all other subvariants and become the dominant pandemic driver, whereas δ2 has played a less prominent role and the remaining two (δ3 and δ4) are insignificant. This group composition and variant transition are also apparent across Europe. In the United Kingdom, δ1 has quickly become predominant and is the sole pandemic driver underlying the current wave of COVID-19 cases. Alarmingly, δ1 subvariant has evolved further in the country and yielded a sublineage encoding spike V36F, A222V and V1264L. These substitutions may make the sublineage more virulent than δ1 itself. In the rest of Europe, δ1 is also the main pandemic driver, but δ2 still plays a role. In many European countries, there is a δ1 sublineage encoding spike T29A, T250I and Q613H. This sublineage originated from Morocco and has been a key pandemic driver there. Therefore, δ variant drives the pandemic in India and across Europe mainly through δ1 and δ2, with the former acquiring additional substitutions and yielding sublineages with the potential to drive the pandemic further. These results suggest a continuously branching model by which δ variant evolves and generates more virulent subvariants.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".