Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance
Bibliographic record
Abstract
Haemagglutination inhibition (HI) assays are typically used for comparing and characterizing influenza viruses. Data obtained from the assays (titres) are used quantitatively to determine antigenic differences between influenza strains. However, the use of these titres has been criticized as they sometimes fail to capture accurate antigenic differences between strains. Our previous analytical work revealed how antigenic and non-antigenic variables contribute to the titres. Building on this previous work, we have developed a Bayesian method for decoupling antigenic and non-antigenic contributions to the titres in this paper. We apply this method to a compendium of HI titres of influenza A (H3N2) viruses curated from 1968 to 2016. Remarkably, the results of this fit indicate that the non-antigenic variable, which is inversely correlated with viral avidity for the red blood cells used in HI assays, oscillates during the course of influenza virus evolution, with a period that corresponds roughly to the timescale on which antigenic variants replace each other. Together, the results suggest that the new Bayesian method is applicable to the analysis of long-term dynamics of both antigenic and non-antigenic properties of influenza virus.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".