Load, visualise and model shedding data — and simulate shedding kinetics from parameters estimated on real studies
The shedding-hub package is how you use this repository from Python.
It loads any curated dataset, summarises and plots it, fits shedding curves by
censored maximum likelihood, and simulates synthetic individuals from fitted
parameters — the input a transmission or wastewater model consumes.
pip install shedding-hubAny curated dataset by its identifier, pinned to a commit or pull request when an analysis has to stay reproducible.
Time courses, heatmaps, clearance curves and per-participant panels, returned as matplotlib figures you can restyle or save.
Censored maximum-likelihood fits of rise-and-decay curves, handling non-detects properly instead of dropping or substituting them. A catalog of precomputed fits ships with the package.
Monte Carlo trajectories for synthetic individuals, drawn from the fitted parameter distributions so a cohort carries inter-individual variability rather than tracing one average curve.
Full documentation lives on Read the Docs, generated from the package source so it cannot drift from the code. Every public function carries a worked example that is executed as a test on every commit.
The whole arc in one page: load a study, summarise it, plot it, fit a curve, and simulate a cohort.
OpenSimulating shedding for synthetic individuals end to end, including choosing between competing studies and overriding that choice.
OpenWhat the fitted estimates mean, and what they do not support. Read this before publishing anything built on them.
OpenEvery public function and class, with its full signature, arguments and a runnable example.
OpenWe welcome contributions to the shedding-hub package! Whether you're fixing bugs, adding new features, improving documentation, or suggesting enhancements, your help is appreciated.
Please visit our Contributing Guidelines to get started. For major changes, please open an issue first to discuss what you would like to change.