Statistical Inference and Computational Efficiency for Spatial Infectious Disease Models with Plantation Data
Bibliographic record
Abstract
Summary The paper considers data from an aphid infestation on a sugar cane plantation and illustrates the use of an individual level infectious disease model for making inference on the biological process underlying these data. The data are interval censored, and the practical issues involved with the use of Markov chain Monte Carlo algorithms with models of this sort are explored and developed. As inference for spatial infectious disease models is complex and computationally demanding, emphasis is put on a minimal parsimonious model and speed of code execution. With careful coding we can obtain highly efficient Markov chain Monte Carlo algorithms based on a simple random-walk Metropolis-within-Gibbs routine. An assessment of model fit is provided by comparing the predicted numbers of weekly infections from the data to the trajectories of epidemics simulated from the posterior distributions of model parameters. This assessment shows that the data have periods where the epidemic proceeds more slowly and more quickly than the (temporally homogeneous) model predicts.
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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 | 0.017 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".