Associations between measures of network centrality and Johne's disease among dairy herds in Ontario, Canada
Bibliographic record
Abstract
Mycobacterium avium ssp. paratuberculosis (MAP) is the causative agent for Johne's disease (JD), a chronic, progressive enteritis in ruminants that may lead to substantial weight loss, reduction in milk yield, and eventual death. Due to the very long incubation period of MAP, many cattle are culled before presenting signs of clinical JD infection. Furthermore, poor sensitivity of diagnostic tests results in subclinically infected cattle contributing to the transmission of JD but otherwise going undetected. Therefore, one of the best control measures for JD is preventing MAP from entering the herd altogether. Numerous studies have identified associations between measurements of cattle purchases-referred to in network analysis as measures of ingoing centrality-and the presence of JD in dairy herds, suggesting that prevention of JD can be achieved by limiting cattle purchases. Between 2010 and 2013, the Ontario Johne's Education and Management Assistance Program (OJEMAP) provided JD education and bulk tank milk (BTM) testing for participating dairy producers in Ontario. Part of the OJEMAP education plan included recommendations regarding cattle purchases. These recommendations were to limit cattle purchases and, if necessary, to purchase cattle from a single, test-negative source herd. Self-reported changes in cattle purchasing behavior were included in a pre-, and post-OJEMAP risk assessment. The objectives of this study were (1) to use data provided by Lactanet Canada to create a network of between-herd dairy cow movements during the OJEMAP study period (2010-2013) and of equal timescale post-OJEMAP (2014-2017) to assess changes in movement behavior in response to program recommendations; (2) to determine whether measures of network centrality from the 2014-2017 network are associated with a positive ELISA test sampled from 2017 BTM; and (3) to use a permutation-based approach (network k-test) to determine whether the structure of the cattle movement network is "epidemiologically relevant" to the distribution of high-risk JD herds in Ontario (i.e., whether the distribution of high-risk JD herds was due to the structure of the between-herd dairy cow movement network itself). It was found that OJEMAP participants had a smaller proportion of herds that increased their cow purchases and number of source herds compared with nonparticipant herds. Furthermore, among herds that increased the number of cows purchased, nonparticipants added more cows from more source herds than OJEMAP participant herds. The results from the logistic regression analyses indicated no associations between measures of centrality, including in-degree, ingoing contact chain, and α centrality, and a positive 2017 BTM ELISA test. However, herd size, region of herd, and previous high-risk JD classification were all positively associated with being classified as high-risk for JD. Results from the network k-test suggest that the distribution of high-risk JD herds in 2013 based on BTM ELISA tests were related to the structure of the 2010-2013 between-herd dairy cow movement network. Conversely, the network of between-herd dairy cow movements between 2014 and 2017 was not considered to be epidemiologically relevant to the distribution of high-risk JD herds. The results presented here are at variance with some literature regarding network centrality and JD. It is possible that the effect of JD transmission through movement is masked either by the high prevalence of high-risk JD herds in Ontario in 2017 or by the lag time associated with the progression of disease in MAP-infected cows.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".