Estimating Risks of Inapparent Avian Exposure for Human Infection: Avian Influenza Virus A (H7N9) in Zhejiang Province, China
Bibliographic record
Abstract
Inapparent avian exposure was suspected for the sporadic infection of avian influenza A(H7N9) occurring in China. This type of exposure is usually unnoticed and difficult to model and measure. Infected poultry with avian influenza H7N9 virus typically remains asymptomatic, which may facilitate infection through inapparent poultry/bird exposure, especially in a country with widespread practice of backyard poultry. The present study proposed a novel approach that integrated ecological and case-control methods to quantify the risk of inapparent avian exposure on human H7N9 infection. Significant associations of the infection with chicken and goose densities, but not with duck density, were identified after adjusting for spatial clustering effects of the H7N9 cases across multiple geographic scales of neighborhood, community, district and city levels. These exposure risks varied geographically in association with proximity to rivers and lakes that were also proxies for inapparent exposure to avian-related environment. Males, elderly people, and farmers were high-risk subgroups for the virus infection. These findings enable health officials to target educational programs and awareness training in specific locations to reduce the risks of inapparent exposure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 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 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".