SARS-COV-2 δ variant drives the pandemic in the USA through two subvariants
Bibliographic record
Abstract
Abstract Delta variant of SARS-COV-2 has overtaken all other variants and become a dominant pandemic driver aggressively. In India, it has evolved and yielded delta1, delta2, delta3 and delta4 subvariants. Delta1 has also gradually become the dominant pandemic driver there and across Europe, raising the question whether this is true in other regions around the world. Here I demonstrate that delta1 has also become the dominant pandemic driver in the USA. In April and May 2021, alpha variant was the major pandemic driver, with Ida and gamma variants playing minor roles. Delta variant only started to emerge in April and May, but it rose exponentially and became a major driver one month later. By September, it was detected in ~99% COVID-19 cases and emerged as almost the sole pandemic driver. In the country, ~50% of its population was fully vaccinated in the summer of 2021; vaccination may have selected against all other variants and thereby helped delta variant achieve such an alarming status. One puzzling question is what genomic features make delta variant so highly competitive. Related to this, delta1, but not delta2, delta3 and delta4, has risen exponentially after May 2021, suggesting that unique NSP3 and nucleocapsid mutations that delta1 carries make it so competitive as a predominant pandemic driver. These results indicate that it is not delta variant per se , but its offspring, delta1, that makes delta variant a predominant pandemic driver. Alarmingly, delta1 subvariant has evolved further and gained additional mutations to finetune functions of spike, nucleocapsid and NSP3 proteins. Compared to delta1, delta2 subvariant is less important in driving the ongoing pandemic in the USA, but this subvariant has also evolved further and gained extra mutations. These results suggest a continuously branching model on delta variant evolution and reiterate urgent need to track and block the evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.009 |
| 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 teacher head, 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".