Computational investigations of the immune response to repeated influenza infections and vaccinations
Bibliographic record
Abstract
Previous studies have shown that repeated influenza vaccination can enhance susceptibility to subsequent infection with a drifted influenza virus strain. This paper seeks to further understanding of the interactions between influenza viruses and specific immune cells that accompany this phenomenon. The paper argues that repeated vaccination increases susceptibility to infection only in the context of a residual immunity induced by prior vaccination or infection. The results of computational analysis indicate that this is a dynamic consequence of interactions between vaccines, influenza viruses and specific immune cells. In particular, mathematical modelling was used to show that in the presence of residual immunity conferred by a vaccine administered in Canada in the 2013-2014 influenza season, the 2014-2015 season vaccine enhanced susceptibility to infection. Such infection enhancement occurs when the 2014-2015 vaccine boosts suppressive T-regulatory cells induced by the 2013-2014 vaccine, decreasing the strength of antibody responses to the infecting strain. Overall, the study suggests probable characteristics of infecting viruses and vaccines that make repeated influenza infections and vaccinations detrimental.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".