Code Availability

The mathematical models described in Part V are specified as systems of ordinary differential equations with explicit parameter definitions (this appendix). Implementation in any scientific computing environment (Python/SciPy, MATLAB, Julia/DifferentialEquations.jl, R/deSolve) is straightforward from the equations provided. No proprietary software is required. The parameter tables in this appendix provide all values needed to reproduce the models.

For reproducibility, the recommended implementation stack is:

• ODE integration: Julia with DifferentialEquations.jl (Radau5 solver) or Python with SciPy solve_ivp (Radau method)
• Bayesian inference: Stan (via CmdStan or PyStan) for NUTS sampling, or Julia with Turing.jl
• Sensitivity analysis: SALib (Python) for Sobol index computation
• Bifurcation analysis: AUTO-07p or MatCont (MATLAB) for continuation