Risk assessment of 2024 cattle H5N1 using age-stratified serosurveillance data
Bibliographic record
Abstract
The highly pathogenic avian influenza virus A(H5N1) clade 2.3.4.4b has caused a human outbreak in North America since March 2024. Here, we conducted a serosurveillance study to determine the risk of A(H5N1) clade 2.3.4.4b (2024 cattle H5N1) to general population. In the initial screening of 180 serum specimens encompassing all age groups, 2.2% (4/180) had detectable neutralizing antibody (nAb) titers against reverse genetics-derived 2024 cattle H5N1, with all collected from older adults aged ≥60 years old. Further screening showed that 4.2% (19/450) of adults aged ≥60 years old had detectable nAb titers against the 2024 cattle H5N1. 80% (4/5) serum specimens with nAb titer of ≥40 had detectable HI titer, and there was a positive correlation between nAb titer and HA binding (r = 0.3325, 95% confidence interval 0.2477 to 0.4123; P < 0.0001). The nAb titer against seasonal H1N1 virus was 4.2-fold higher for ≥60 years old individuals with detectable H5N1 nAb titer than those ≥60 years old ones without (geometric mean titer: 89.3 [95% CI 42.9-185.7]) vs 21.3 [95% CI 17.3-26.1], P < 0.0001), but there was no statistically significant difference between H5N1 and H3N2 nAb titer. There was no difference in demographics, comorbidities and clinical frailty scores between individuals with detectable H5N1 nAb and those without. Our findings suggest that most individuals lack nAb response against 2024 cattle H5N1 and there is an urgency to develop and evaluate H5N1 vaccine or prophylactic monoclonal antibodies. Immune imprinting may be responsible for the cross neutralization between H5N1 and H1N1 among older adults.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".