DESModel

models.DESModel(
    process,
    population=None,
    interventions,
    resources=None,
    horizon,
    discount_rate=0.03,
    n_individuals=1000,
    effect='qaly',
    independent_streams=False,
)

Discrete-event simulation engine wrapping SimPy.

Each process is the user’s own SimPy code with signature process(env, entity, params, intervention, toolkit). entity is that individual’s attribute row, params the iteration’s draw merged with the intervention decision levers, intervention the intervention name, and toolkit the _DESToolkit that accrues cost and effect and logs the trajectory. Per iteration and intervention the engine builds the environment, samples entities, creates the shared resources, runs every process to horizon, collects the per-entity discounted accruals, averages them, and writes one Outcomes row.

Parameters

Name	Type	Description	Default
process	ProcessFn	The SimPy process factory, fn(env, entity, params, intervention, toolkit) returning a generator.	required
population	PopulationSpec	Attribute sampler fn(rng, n) -> DataFrame, an int count for a featureless population, or None to use n_individuals with no attributes.	None
interventions	InterventionSpec	A sequence of intervention names or heormodel.models.Intervention objects; an Intervention may carry parameter decision levers merged into params for that intervention. Order is preserved in Outcomes.	required
resources	ResourceFn | None	fn(env, params, intervention) -> dict[str, simpy.Resource], built fresh for each run and shared by every entity in it. None for a model with no constrained resources.	None
horizon	float	Time horizon in the environment’s unit; the run stops here. Each process reads it back as toolkit.horizon.	required
discount_rate	float	Annual (per-unit-time) discount rate for costs and effects (0.03 by default). Discounting is continuous (exp(-rate * t)).	0.03
n_individuals	int	Population size when population is a sampler or None.	1000
effect	str	Name of the effect column (QALYs by default).	'qaly'
independent_streams	bool	Give each intervention its own population and streams instead of common random numbers.	False

Randomness is supplied by heormodel.run.run_psa at run time, not at construction; the engine holds no seed of its own.

Example

import numpy as np, pandas as pd from heormodel.models import DESModel def process(env, entity, params, intervention, toolkit): … wait = toolkit.rng.exponential(params[“los”]) … toolkit.accrue_rate(params[“day_cost”], 1.0, wait) … yield env.timeout(wait) engine = DESModel( … process=process, population=200, interventions=[“ward”], … horizon=30.0) draws = pd.DataFrame({“los”: [3.0], “day_cost”: [500.0]}, … index=pd.RangeIndex(1, name=“iteration”)) engine.evaluate(draws).interventions [‘ward’]