Data from: Differential impacts of vaccination on wildlife disease spread during epizootic and enzootic phases
Bibliographic record
Abstract
1. Dissemination of oral vaccine baits is a cost-effective method to contain and control infectious wildlife diseases. The effectiveness of vaccine barriers in slowing or halting the disease spread depends on host ecology and landscape variability. It is not clear, however, how the success of vaccine barriers to manage disease may change from an epizootic to an enzootic phase of a disease invasion, and if this depends on the quality and configuration of host habitat. 2. We explore these questions using the raccoon variant rabies virus (RRV) as a model system. This zoonotic disease of high concern has been enzootic in eastern North America for decades, pushing into new areas and re-emerging in previously controlled zones. 3. We use a spatially-explicit individual-based model to assess how levels of oral vaccination and habitat fragmentation affect RRV spread across vaccine barriers during epizootic and enzootic phases. Simulated outcomes are compared using space-time characteristics of infection chains (individual-to-individual transmission of RRV). 4. Results indicate that the impact of vaccine barriers on control of RRV is strongest during the epizootic phase. Counterproductively, mid-levels of immunisation during an enzootic phase lead to more rabies-induced mortalities than lower or higher vaccination levels. Infection chains spread faster during the epizootic phase. Landscape effects on chain characteristics were more subtle than effects of invasion phase and vaccination. 5. Synthesis and applications. Space-time patterns of infection chains provide insight into mechanisms of wildlife disease spread. Our results demonstrate the importance of detecting and controlling outbreaks before they become enzootic. We discuss the implications for moving vaccine barriers to push back and decrease the size of enzootic areas.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.008 |
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".