Vaccination and immigration rates influence raccoon rabies elimination and recolonization in simulated urban-suburban landscapes
Bibliographic record
Abstract
The raccoon variant of the rabies virus (RRV) is managed in the eastern United States and Canada via distribution of oral rabies vaccine (ORV) baits. The goal of ORV distribution is to reach seroprevalence rates (an index of population immunity) of at least 60%, the threshold thought to eliminate RRV. Seroprevalence rates in urban areas rarely reach target levels, predictably leading to rabies outbreaks. However, many urban areas have spent several years rabies-free, aligning with previous work suggesting RRV can be eliminated from urban areas at below-target seroprevalence rates. Using an agent-based model to simulate raccoon populations in urban landscapes, we examined 1) whether RRV can be eliminated at vaccination thresholds below 60% and 2) whether landscapes with below-target vaccination rates are vulnerable to RRV recolonization, and 3) whether the rate and timing of immigration influences elimination and recolonization. Vaccination and immigration rates influenced elimination probability: elimination was more likely and occurred more quickly in landscapes with higher vaccination rates and less likely in landscapes with higher immigration rates. All immigration variables (immigration rate, immigrant disease prevalence, and immigration timing) influenced the probability of recolonization after rabies was eliminated: recolonization was more likely in landscapes with high immigration rates and when immigrants had higher disease prevalence, but less likely when immigration occurred seasonally rather than continuously. Vaccination did not have a clear effect on recolonization probability but reduced the number of rabies cases during a recolonization event. Although elimination was highly likely in our simulated landscapes due to their small spatial extent, our results suggest that vaccination rates of at least 50% result in timely rabies elimination (median 1.5 years). After elimination is achieved, strategies for preventing infected individuals from entering the rabies-free area are essential for preventing recolonization events, as vaccination rates had a much smaller effect on recolonization that immigration rates and timing. Understanding long-distance movements of host individuals is crucial for managing diseases such as rabies which likely persist at the regional scale.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".