On-farm Study of Human Contact Networks to Document Potential Pathways for Avian Influenza Transmission between Commercial Poultry Farms in Ontario, Canada
Bibliographic record
Abstract
Human movements associated with poultry farming create contact networks that might facilitate transmission of avian influenza (AI) between farms during outbreaks. In Canada, no information is available about how these networks connect poultry farms. The purpose of this study was to document human contacts between commercial poultry farms in Ontario, Canada, to learn how AI might be transmitted during outbreaks. We used face-to-face interviews with people entering the farm biosecurity perimeter on four layer, one turkey and three broiler breeder poultry farms in Ontario to collect information on between-farm contacts and biosecurity practices. Over a four-day study period on each farm, a median of 10.5 people entered the farm biosecurity perimeter (range 2-31). Ninety-six per cent (111/118) of people consented to be interviewed. Of these, fifty-three per cent (59/111) had contact with one or more (median 2, degree range 1-14) other poultry farms within 72 h. A median of 25 (range 7-65) human contacts linked study farms to other poultry farms. The mean distance of between-farm contacts was 53 km. Eighty-six per cent of people who answered the biosecurity questions (94/109) reported using one or more biosecurity practices. However, on 7/8 farms, at least one person reported that they did not use any biosecurity practices. Fifty per cent of social visitors used biosecurity, whereas 96% of all other people used biosecurity. Ninety-two per cent of people that entered the poultry barns (46/50) used one or more biosecurity practices, whereas 81% of people (48/59) that did not enter the poultry barns used one or more biosecurity practices. Because our study documented farm visitors who did not use any biosecurity practices and moved between commercial poultry farms, we suggest that rapid trace-out of human movements is as important as containment zoning to limiting disease spread during an outbreak of highly pathogenic AI in Ontario.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".