Dynamics and persistence of rabies in the Arctic
Bibliographic record
Abstract
Rabies is a major issue for human and animal health in the Arctic, yet little is known about its epidemiology. In particular, there is an ongoing debate regarding how Arctic rabies persists in its primary reservoir host, the Arctic fox (Vulpes lagopus), which exists in the ecosystem at very low population densities. To shed light on the mechanisms of rabies persistence in the Arctic, we built a susceptible–exposed–infectious–recovered (SEIR) epidemiological model of rabies virus transmission in an Arctic fox population interacting with red foxes (Vulpes vulpes), a rabies host that is increasingly present in the Arctic. The model suggests that rabies cannot be maintained in resource-poor areas of the Arctic, characterized by low Arctic fox density, even in the presence of continuous reintroduction of the virus by infected Arctic foxes from neighbouring regions. However, in populations of relatively high Arctic fox density, rabies persists under conditions of higher transmission rate, prolonged infectious period and for a broad range of incubation periods. Introducing the strong cyclical dynamics of Arctic prey availability makes simulated rabies outbreaks less regular but more intense, with an onset that does not neatly track peaks in Arctic fox density. Finally, interaction between Arctic and red foxes increases the frequency and/or the intensity of rabies outbreaks in the Arctic fox population. Our work suggests that disruption of prey cycles and increasing interactions between Arctic and red foxes due to climate change and northern development may significantly change the epidemiology of rabies across the Arctic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".