Using Stochastic Modeling to Predict the Effect of Culling and Colony Dispersal of Bats on Zoonotic Viral Epidemics
Bibliographic record
Abstract
Frequent outbreaks of emerging infectious diseases originating from wild animals have highlighted the necessity of managing wildlife populations to prevent zoonotic spillover, and the appropriate development of management protocols required attention on gaining a better understanding of viral dynamics in wild animal populations. In east Australia, there have been outbreaks of Hendra virus (HeV) infection in horses and humans following spillover from the virus's reservoir hosts, flying foxes (family Pteropodidae), and bat culling and colony dispersal have been proposed as appropriate management strategies. A key factor relating to flying fox population structure that influences HeV dynamics is that these bats form metapopulations, and consequently, to assess this factor, we designed an epidemic dynamics model of HeV transmission, focusing on bat metapopulation dynamics. Specifically, using flying fox movement data, we stochastically simulated models for a hypothetical metapopulation of flying foxes to examine the impact of metapopulation-related parameters, and subsequently simulated probable scenarios of culling and colony dispersal to estimate their effects on the probability of epidemic occurrence. Modeling of the hypothetical metapopulation revealed that a reduction in the number of large-sized colonies would lead to an increase in the probability of epidemic occurrence within the bat population, whereas the strong spatial coupling among colonies was found to dilute the effects of altering the number of colonies and the number of bats in each colony through culling or colony dispersal of bats on the probability that an epidemic within the bat population would occur. Culling and colony dispersal scenarios showed no significantly beneficial effect with respect to reducing the probability of an HeV epidemic occurring in flying foxes, and may indeed prove counterproductive. In conclusion, the modeling results indicate that bat culling and colony dispersal may not be an effective strategy to control HeV epidemics.
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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 | 0.000 | 0.002 |
| 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".