Understanding the Effects of Fox Movement on the Spread of Sarcoptic Mange in Urban Settings – An Individual-based Modelling Approach
Bibliographic record
Abstract
The threat of disease spread to humans is greater in urban settings where contact between wildlife and human populations occurs frequently. Movement of host species plays a key role in maintaining transmission of direct-contact disease. Understanding how wildlife hosts move in fragmented urban landscapes is therefore imperative for disease control efforts. In cities, disease spread can be affected by the ability of a host to move through urban features, or by behavioural changes that are pathogen induced. Using the urban-adapted red fox (Vulpes vulpes) and its associated disease sarcoptic mange (Sarcoptes scabiei) in the city of Toronto, I ask: How does movement of foxes according to landcover type affect the spread of mange? And how does variable movement of susceptible and infected foxes influence mange transmission? These questions are addressed using an individual-based modeling approach, where two movement behaviours of foxes in a city are compared: random and least-cost path. To assess the effects of movement ability according to disease status (here, susceptible-biased, and infected-biased movement), I compare a range of movement probabilities. For each scenario, the number of effective contacts and the effective reproduction number (Re) are estimated. Findings suggest that mange spread may be accelerated when movement is based on landcover types and when there is equal movement ability of susceptible and infected foxes. This study emphasizes the importance of including realistic movement behaviours when modelling the dynamics of mange and disease spread in urbanized landscapes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".