Canine Distemper Virus: Ecology, Epidemiology and Addressing Challenges in the Wider Wildlife Health Surveillance Context
Bibliographic record
Abstract
This thesis was both an investigation into the ecology of canine distemper virus (CDV) in Ontario wildlife, and an opportunity to use a CDV lens to explore challenges and opportunities associated with the ways in which we learn about wildlife health and disease in Canada. While canine distemper is considered endemic in Ontario raccoons, information on the epidemiology of this important virus is scarce and the genetic identity of circulating variants is unknown. Based on an analysis of a longitudinal serological raccoon data set obtained through a targeted study in southern Ontario, I report that the winter breeding period was a period of high risk of CDV exposure. Furthermore, it was apparent that many wild raccoons survive for extended periods of time after exposure to CDV and that adult raccoons can revert from seropositive on one capture to having a non-detectable titre at a subsequent capture(s). Based on reports of immune amnesia following infection with measles virus (a similar pathogen), it is noteworthy that CDV exposure may be associated with a decrease in parvovirus titre. Using a multi-species dataset of opportunistically collected wildlife, the genetic identities of and genetic relationships between circulating CDV variants were explored. Multiple co-circulating variants were identified including a novel but dominant variant, named Canada-1. Because of the number of different surveillance methodologies used in the CDV literature, I was also interested in exploring how surveillance method can impact the types of data that are obtained and the potential impacts for data integration and interpretation. I found that the number and geographic distribution of reports, proportion of yearly reports classified as CDV positive, and characteristics of CDV positive raccoons differed between passive and enhanced-passive surveillance components. Using regression analyses, I also identified statistically significant associations between the presence of CDV and host and environmental variables that were at times, discordant between the two datasets. Based on some of the challenges and limitations identified, I undertook a scoping review to explore the broader Canadian wildlife surveillance context to identify patterns and inform areas for growth and improvement. The results of the scoping review provide a synopsis of 10-years of published Canadian wildlife health surveillance and identify five primary areas of focus moving forward. Overall, this thesis contributes important knowledge towards a regional understanding of the ecology of CDV in wildlife while at the same time, highlighting the importance of understanding the limitations of our data. The concepts discussed, including challenges, limitations, and opportunities, are broadly applicable across wildlife health surveillance (and research) programs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".