Spatial Landscape Structure Influences Cross-Species Transmission in a Rabies-like Virus Model
Bibliographic record
Abstract
In this study, we simulated biologically realistic agent-based models over neutral landscapes to examine how spatial structure affects the spread of a rabies-like virus in a two-species system. We built landscapes with varying autocorrelation levels and simulated disease dynamics using different transmission rates for intra- and interspecies spread. The results were analysed based on combinations of spatial landscape structures and transmission rates, focusing on the median number of new reservoir and spillover cases. We found that both spatial landscape structures and viral transmission rates are key factors in determining the number of infected simulated agents and the epidemiological week when the highest number of cases occurs. While isolated habitat patches with elevated carrying capacity pose significant risks for viral transmission, they may also slow the spread compared to more connected patches, depending on the modelled scenario. This study highlights the importance of spatial landscape structure and viral transmission rates in cross-species spread. Our findings have implications for disease control strategies and suggest that future research should also focus on how landscape factors interact with pathogen dynamics, especially in those locations where susceptible agents could be more in contact with pathogens with high transmission rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".