Generalized Compartmental Modeling Framework

This documentation describes a modular, configurable framework for defining, simulating, and calibrating compartmental epidemiological models (SIR, SEIR, SIS, SIRS, SEIRS, and more).

The framework is driven by a YAML configuration file and supports: - Arbitrary compartment structures and transition expressions - Dynamic ODE generation from transition rules - Simulation with scipy.integrate.odeint - Noise injection and sub-sampling for realistic observations - Parameter estimation using classical optimizers (Nelder–Mead, BFGS, L-BFGS-B, Basin-Hopping) - Bayesian calibration via MCMC (emcee) - Diagnostics, loss landscapes, and posterior analysis

Use the left navigation to explore the YAML specification, usage examples, extensibility notes, and debugging tips.

View the source code on GitHub