Slow vaccination and immunity profile homogeny as a cause of SARS-CoV-2 antigenic drift
Bibliographic record
Abstract
Delays in vaccine production for severe respiratory syndrome coronavirus 2 (SARS-CoV-2), have resulted in countries dependent on foreign biomanufacturing capabilities to underperform in terms of their population immunization rates. The implications of slow vaccination and a comprehensive strategy to combat it have yet to be devised. Of particular concern is the exposure of SARS-CoV-2 to pressures created by vaccinated individuals which could promote antigenic drift negating vaccination efforts. Uncertainty also remains around the usage of the novel mRNA vaccines due to limited characterization of vaccinated host immunity profiles. If unable to induce lymphocytes of high diversity and longevity, mRNA vaccination could further accelerate the development of vaccine escape. This paper outlines the potential consequences of a slow vaccination with mRNA-based vaccines and recommends counteracting measures. It discusses (i) the mutable rate of key SARS-CoV-2 spike (S) protein sites that when altered, could lead to a loss of immunity; (ii) the potential of slow vaccination and mRNA vaccines causing antigenic drift; and (iii) methods of improving immunization rates through production-based means. Comparisons to previous pathogen cases suggest high risk of SARS-CoV-2 antigenic drift in response to slow vaccination and novel vaccines. These mutations are predicted to occur at several S protein epitopes. The results call for genomic surveillance initiatives to track development of immune evasion and longitudinal research of vaccinated host immunity profiles to measure durability and diversity of the induced humoral response. Recommendations for improving vaccination rate include the construction of production facilities and improvement of already existing distribution networks, while maintaining communication channels and transparency with the public.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".