Modelling the effect of landscape heterogeneity on the efficacy of vaccination for wildlife infectious disease control
Bibliographic record
Abstract
Summary Zoonotic disease control presents significant costs and challenges in human and wildlife populations. Although spatial variability and temporal variability in host populations play a significant role influencing the spread and persistence of pathogens, their impact on the effectiveness of disease control are not well understood. Field studies are impractical for many zoonotic diseases; thus, simulation modelling is an alternative. Some research has experimented with metapopulation models of host–pathogen systems, with discrete host populations distributed on a network of connections or on a one‐dimensional transect of contiguous cells. Little attention has been paid to treating geographic space as a fine‐grained two‐dimensional continuum, a more appropriate spatial model for many generalist host and vector species. Using raccoon rabies as an example, we apply an individual‐based spatially explicit stochastic simulation model to evaluate effectiveness of vaccination barrier strategies to control rabies. Barrier width and immunization levels are varied over landscapes with habitats of varying quality and spatial heterogeneity, resulting in varying degrees of host connectivity. Our results demonstrate that spatial heterogeneity in the landscape does affect vaccination efficacy. The probability that rabies will breach a vaccination barrier is greater and rabies incidence is higher in landscapes with (i) overall good‐quality homogeneous habitat and (ii) overall poor‐quality habitat with high spatial heterogeneity, than in landscapes with overall good‐quality habitat and high spatial heterogeneity. The influence of landscape conditions on disease dynamics decreases with increasing population immunity. Synthesis and applications . Using a spatially explicit stochastic simulation model, we demonstrated that landscape spatial heterogeneity and vaccination control will interact to influence the success of controlling infectious disease outbreaks. Further, under some landscape conditions, insufficient vaccination is counter‐productive because immunized individuals (i) reduce the number of disease transmitting contacts, preventing the disease from growing rapidly thus depleting the susceptible population; and (ii) survive to replenish the stock of susceptible animals through reproduction, facilitating disease persistence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".