Relatively rapid evolution rates of SARS-CoV-2 spike gene at the primary stage of massive vaccination
Bibliographic record
Abstract
A series of stringent non-pharmacological and pharmacological interventions were implemented to contain the pandemic but the pandemic continues. Moreover, vaccination breakthrough infection and reinfection in convalescent coronavirus disease 2019 (COVID-19) cases have been reported. Further, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants emerged with mutations in spike (S) gene, the target of most current vaccines. Importantly, the mutations exhibit a trend of immune escape from the vaccination. Herein the scientific question that if the vaccination drives genetic or antigenic drifts of SARS-CoV-2 remains elusive. We performed correlation analyses to uncover the impacts of wide vaccination on epidemiological characteristics of COVID-19. In addition, we investigated the evolutionary dynamics and genetic diversity of SARS-CoV-2 under immune pressure by utilizing the Bayesian phylodynamic inferences and the lineage entropy calculation respectively. We found that vaccination coverage was negatively related to the infections, severe cases, and deaths of COVID-19 respectively. With the increasing vaccination coverage, the lineage diversity of SARS-CoV-2 dampened, but the rapid mutation rates of the S gene were identified, and the vaccination could be one of the explanations for driving mutations in S gene. Moreover, new epidemics resurged in several countries with high vaccination coverage, questioning their current pandemic control strategies. Hence, integrated vaccination and non-pharmacological interventions are critical to control the pandemic. Furthermore, novel vaccine preparation should enhance its capabilities to curb both disease severity and infection possibility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".