laser.measles.compartmental.model

`laser.measles.compartmental.model`

Compartmental SEIR model for measles transmission with daily timesteps.

`laser.measles.compartmental.model.CompartmentalModel(scenario, params, name='compartmental')`

Bases: BaseLaserModel

Population-level SEIR model for measles transmission with daily timesteps.

Tracks compartment counts (S, E, I, R) per patch using deterministic difference equations. This model is the fastest option for calibrating transmission parameters to surveillance data because it avoids stochastic noise. For individual-level tracking, see ABMModel; for 14-day timesteps, see BiweeklyModel.

This is the first object created in the build model stage of the researcher workflow. After construction, attach components with add_component or by setting the components property, then call model.run().

Parameters:

Name	Type	Description	Default
`scenario`	`DataFrame \| BaseCompartmentalScenario`	Metapopulation patch data. Required columns: `id` (str), `pop` (int), `lat` (Float64), `lon` (Float64), `mcv1` (Float64). A plain `polars.DataFrame` is automatically wrapped.	required
`params`	`CompartmentalParams`	Simulation parameters including `num_ticks`, `seed`, and `start_time`.	required
`name`	`str`	Display name for log messages. Defaults to `"compartmental"`.	`'compartmental'`

Example:

```python
import laser.measles as lm
from laser.measles.compartmental.components import (
    InfectionSeedingProcess,
    InfectionProcess,
)

params = lm.CompartmentalParams(num_ticks=365, seed=42, start_time="2000-01")
model = lm.CompartmentalModel(scenario=df, params=params)
model.components = [InfectionSeedingProcess, InfectionProcess]
model.run()
```

`laser.measles.compartmental.model.CompartmentalModel.expose(indices, num_exposed)`

Exposes the given nodes with the given number of exposed individuals. Moves individuals from Susceptible to Exposed compartment.

Parameters:

Name	Type	Description	Default
`indices`	`int \| ndarray`	The indices of the nodes to expose.	required
`num_exposed`	`int \| ndarray`	The number of exposed individuals.	required

`laser.measles.compartmental.model.CompartmentalModel.from_snapshot(path, params, components=None, verbose=True)` `classmethod`

Load a CompartmentalModel from an HDF5 snapshot.

Convenience wrapper around load_snapshot. Use this to resume a simulation from a checkpoint saved with save_snapshot.

Parameters:

Name	Type	Description	Default
`path`	`str \| Path`	Path to the HDF5 file written by `save_snapshot`.	required
`params`	`CompartmentalParams`	`CompartmentalParams` for the resumed segment.	required
`components`	`list \| None`	Ordered list of component classes — same as the original model, minus `InfectionSeedingProcess`.	`None`
`verbose`	`bool`	Print a loading summary.	`True`

Returns:

Type	Description
`CompartmentalModel`	A configured `CompartmentalModel` ready for `model.run()`.

Example:

```python
import laser.measles as lm
from laser.measles.compartmental.components import InfectionProcess

params2 = lm.CompartmentalParams(num_ticks=365, seed=42, start_time="2001-01")
model2 = lm.CompartmentalModel.from_snapshot(
    "checkpoint.h5", params2, components=[InfectionProcess]
)
model2.run()
```

`laser.measles.compartmental.model.CompartmentalModel.infect(indices, num_infected)`

Infects the given nodes with the given number of infected individuals. Moves individuals from Exposed to Infected compartment.

Parameters:

Name	Type	Description	Default
`indices`	`int \| ndarray`	The indices of the nodes to infect.	required
`num_infected`	`int \| ndarray`	The number of infected individuals.	required

`laser.measles.compartmental.model.CompartmentalModel.recover(indices, num_recovered)`

Recovers the given nodes with the given number of recovered individuals. Moves individuals from Infected to Recovered compartment.

Parameters:

Name	Type	Description	Default
`indices`	`int \| ndarray`	The indices of the nodes to recover.	required
`num_recovered`	`int \| ndarray`	The number of recovered individuals.	required

laser.measles.compartmental.model