Model of Prophylactic Efficiency of Influenza Virus Vaccine Corrected to the Antigenic Distance Hypothesis
Bibliographic record
Abstract
The aim of the study is to find a quantitative relationship between antigenic distances (AD) and vaccination effectiveness (VE) and investigate the response of VE to changes in AD. Material and methods. Through the epidemiological data of three influenza seasons in Canada, interpreted within the framework of the antigenic distance hypothesis (ADH), the introduction of the correction factor into the model for estimating VE was substantiated considering the antigenic relationship between the previous season vaccine (V1), the current season vaccine (V2) and the circulating epidemic strain (e). Results. A quantitative relationship between VE and AD was found, reproducing the results of epidemiological observation of two groups of people: vaccinated in the previous and current seasons (V1+V2) and vaccinated only in the current season (0+V2). The difference in the response of VE to different indicators of AD was found and allowing to use only one significant indicator. Conclusion. Тhe model proposed relates the microbiological indicators AD with epidemiological characteristic of VE. The model extends the analysis, allowing to use it as an assessment tool for the expected changes in vaccine effectiveness in various settings of the ADH hypothesis experiment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".