Network analysis of dairy cattle movements in Ontario to support livestock disease simulation modelling
Bibliographic record
Abstract
This thesis was an investigation of the movement of dairy cattle in Ontario in order to improve the realism of simulated epidemics of foot-and-mouth disease (FMD). Canada does not have a livestock movement database to develop parameters for simulations. We evaluated the Dairy Herd Improvement (DHI) database, which stores cow lactation records, as a source of movement information for adult milking cows in Ontario. We used social network analysis techniques to study the structure of the networks formed among DHI farms in Ontario resulting from the movements of adult milking cows in 2004-2006. We showed how the topology of such networks influenced how diseases could spread among farms. We used existing network analysis measures to estimate potential epidemic size at first detection of FMD and compared these estimates to those provided by the infection chain. The infection chain is a modified breadth-first search that we consider the most biologically sensible measure for estimating potential epidemic size in both connected and fragmented networks of direct animal movements. The DHI network information was then used to validate the contact structure represented in the North American Animal Disease Spread Model (NAADSM). Our findings showed that the current parsimonious approach in NAADSM would represent up to 99% of monthly farm-to-farm networks. However, the current approach does not represent extreme situations in which some farms might have a high number of contacts. We proposed alternatives to consider for future versions of NAADSM. We also studied the movements of dairy cattle through two large markets in Ontario in the year 2004. Markets played a central role in the dairy industry as they are the main point for selling slaughter cows and calves. Our study demonstrated the need for quickly closing market sales at first detection of FMD in the Province. In addition, we proposed that tracing efforts be focussed upon determining if infected animals have gone through markets. This thesis shows the importance of obtaining and maintaining livestock movement information and the benefits of using network analysis. However, the rules that define certain network analysis measures must be clearly understood to be useful.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".