An accelerated hybrid framework for stochastic simulations of reaction–diffusion epidemic models
Bibliographic record
Abstract
Reaction–diffusion models have been widely used in mathematical epidemiology as a powerful tool for describing the spatiotemporal dynamics of an infectious disease. This paper presents a novel hybrid stochastic algorithm to simulate such models. Unlike existing hybrid methods, which are based on spatial coupling, our method temporally couples reactive multiparticle collision (RMPC) dynamics—a particle-based method—and the inhomogeneous stochastic simulation algorithm (ISSA)—a compartment-based method. The advantage of our hybrid algorithm is demonstrated on three benchmark epidemic models, with a focus on accuracy and computational cost. While the hybrid method has comparable accuracy, it is faster than full RMPC as long as the ISSA grid is not too refined. It is also found that the speed can be improved by either using a coarser ISSA grid or a smaller infectious switching threshold I*. Coupling RMPC with a spatial tau-leaping algorithm further improves the simulation times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".