Research software for mechanistic simulation, statistical inference, and the design of experiments, studies, monitoring programs, and surveillance. Applications span human infectious disease, livestock and wildlife health, ecology, and earth and environmental systems, especially when observations are incomplete. Maintained by Joshua C. Macdonald.
Website · Publications · Projects
- Simulation and model specification. Mechanistic models, numerical solvers and scientific machine learning across within-host, population, spatial and ecological scales
- Inference and measurement. Parameter estimation, incomplete-data models and checks of what observations can distinguish
- Model evaluation and study design. Prediction checks and comparisons of experiments, interventions, monitoring programs and surveillance plans
Current methodological work includes empirical evaluation of scenario-model submissions, parameter inference and calibration, and learning from sparse observations. Declared consistency checks are evaluated for usefulness; they do not by themselves establish predictive accuracy. op_infer connects Bayesian fitting, simulation-based checks and replicated studies; the source is public, pre-alpha and unreleased on PyPI while broader interfaces and application-specific validation develop. See the research summaries for current work and available papers and artifacts.
| Package | Language | Description |
|---|---|---|
| trade-study | Python | Released framework for comparing model settings, experiments, studies and surveillance plans through replicated studies, paired comparisons, Pareto analysis and provenance-checked result bundles |
Public alpha release: v0.6.0 on PyPI. Cite the v0.6.0 archive; the concept DOI covers all archived versions.
The following repositories contain placeholder modules rather than usable implementations. They are retained only as possible starting points for future Julia work and should not be installed as packages.
| Repository | Status |
|---|---|
| TradeStudy.jl | Inactive scaffold for a possible trade-study port |
| OpSystem.jl | Inactive scaffold for a possible op_system port |
| OpEngine.jl | Inactive scaffold for a possible op_engine port |
| ScoringRules.jl | Inactive scaffold; no scoring-rule implementation is present |
| Package | Org | Description |
|---|---|---|
| VBPCApy | yoavram-lab | Variational Bayesian PCA for explicitly masked observations with approximate variational-posterior covariance summaries |
| pp-eigentest | yoavram-lab | Private pre-release work on posterior-predictive signal-rank selection |
| op_engine | ACCIDDA | Released PDE/field and nonlocal operator core with deterministic/stochastic reaction solvers, independent NumPy SSA banks, named observations and qualified CPU backends |
| op_system | ACCIDDA | Released PDE/field, nonlocal and reaction model compiler with Array API evaluators, reaction/observable metadata, forcing schedules and independent-block contracts |
| op_infer | ACCIDDA | Public pre-alpha statistical layer for Bayesian fitting, simulation-based calibration and replicated study-design checks; unreleased on PyPI |
| flepimop2 | ACCIDDA | Configuration-driven orchestration and provider framework for infectious-disease modeling campaigns |
The Python op_system and op_engine cores can run independently of FlepiMoP2; separate provider packages connect the layers. trade-study's worked examples show replicated evaluation and decision reporting. Qualification is specific to the model, method and backend; a Pareto front or model weighting does not by itself establish calibration or transfer to real data.