Evaluating the Role of Canada Goose Populations in Transmission Dynamics During Peak HPAI Incidence in Iowa, February 2022–December 2023
Bibliographic record
Abstract
Since its emergence in the United States in February 2022, Highly Pathogenic Avian Influenza (HPAI) H5N1 has caused significant losses for poultry operations, particularly in Iowa between February 2022 and December 2023. Branta canadensis (Canada goose), an abundant North American waterfowl species, is considered a potential reservoir host for H5N1. This study examined the relationship between Canada goose abundance and H5N1 occurrence in Iowa counties. Although counties with H5N1 cases comprised 13% of the state’s Canada goose population—and 32% of those counties had high goose abundance—an inverse relationship was observed. Bivariate analysis indicated that counties with high goose abundance were significantly less likely to report HPAI cases (χ2 = 4.29, p = 0.04). Notably, intermediate goose abundance was associated with a 79% lower likelihood of HPAI occurrence (RR = 0.21, 95% CI [0.05, 0.90], p = 0.04). These findings highlight the limitations posed by the lack of accessible, high-resolution poultry farm location data, which hinders a definitive understanding of Canada geese’s role in H5N1 transmission. To address this gap, stakeholders should consider adopting next-generation surveillance tools like the Biothreats Emergence Analysis and Communication Network (BEACON) AI platform, or AI-integrated chemical sensors that generate real-time, actionable data for biosecurity decision-making. Given the uncertainty surrounding Canada goose role transmission dynamics, the species remains a relevant One Health concern.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".