Modelling the introduction and transmission of <i>Campylobacter</i> in a North American chicken flock
Bibliographic record
Abstract
Campylobacter is the second leading cause of foodborne illness in the United States. Although many food production animals carry Campylobacter as commensal bacteria, consumption of poultry is the main source of human infection. Previous research suggests that the biology of Campylobacter results in complete flock colonization within days. However, a recent systematic review found that the on-farm prevalence of Campylobacter varies widely, with some flocks reporting low prevalence. We hypothesized that the low prevalence of Campylobacter in some flocks may be driven by a delayed introduction of the pathogen. The objectives of this study were to (a) develop a deterministic compartmental model that represents the biology of Campylobacter, (b) identify the parameter values that best represent the natural history of the pathogen in poultry flocks and (c) examine the possibility that a delayed introduction of the pathogen is sufficient to replicate the observed low prevalence examples documented in the literature. A deterministic compartmental model was developed to examine the dynamics of Campylobacter in chicken flocks over a 56-day time period prior to movement to the abattoir. The model outcome of interest was the final population prevalence of Campylobacter at day 56. The resulting model that incorporated a high transmission rate (β = 1.04) was able to reproduce the wide range of prevalence estimates observed in the literature when pathogen introduction time is varied. Overall, we established that the on-farm transmission rate of Campylobacter in chickens is likely high and can result in complete colonization of a flock when introduced early. However, delaying the time at which the pathogen enters the flock can reduce the prevalence observed at 56 days. These results highlight the importance of enforcing strict biosecurity measures to prevent or delay the introduction of the bacteria to a flock.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".