SIR

class bayesflow.simulators.SIR(N: float = 1000000.0, T: int = 160, I0: float = 1.0, R0: float = 0.0, subsample: int = None, total_count: int = 1000, scale_by_total: bool = True, rng: Generator = None)

Bases: BenchmarkSimulator

SIR simulated benchmark See: https://arxiv.org/pdf/2101.04653.pdf, Task T.9

NOTE: the simulator scales outputs between 0 and 1.

Parameters:

N: float, optional, default: 1e6

The size of the simulated population.

T: int, optional, default: 160

The duration (time horizon) of the simulation.

I0: float, optional, default: 1.0

The number of initially infected individuals.

R0: float, optional, default: 0.0

The number of initially recovered individuals.

subsample: int or None, optional, default: 10

The number of evenly spaced time points to return. If None, no subsampling will be performed and all T timepoints will be returned.

total_count: int, optional, default: 1000

The N parameter of the binomial noise distribution. Used just for scaling the data and magnifying the effect of noise, such that max infected == total_count.

scale_by_total: bool, optional, default: True

Scales the outputs by total_count if set to True.

rng: np.random.Generator or None, optional, default: None

An optional random number generator to use.

prior()

Generates a random draw from a 2-dimensional (independent) lognormal prior which represents the contact and recovery rate parameters of a basic SIR model.

Returns:

paramsnp.ndarray of shape (2, )

A single draw from the 2-dimensional prior.

__call__(**kwargs) → dict[str, ndarray]

Call self as a function.

observation_model(params: ndarray)

Runs a basic SIR model simulation for T time steps and returns subsample evenly spaced points from the simulated trajectory, given disease parameters (contact and recovery rate) params.

Parameters:

paramsnp.ndarray of shape (2,)

The 2-dimensional vector of disease parameters.

Returns:

xnp.ndarray of shape (subsample, ) or (T, 1) if subsample=None

The time series of simulated infected individuals. A trailing dimension of 1 should be added by a BayesFlow configurator if the data is (properly) to be treated as time series.

rejection_sample(batch_shape: tuple[int, ...], predicate: Callable[[dict[str, ndarray]], ndarray], *, axis: int = 0, sample_size: int = None, **kwargs) → dict[str, ndarray]

sample(batch_shape: tuple[int, ...], **kwargs) → dict[str, ndarray]

Runs simulated benchmark and returns batch_size parameter and observation batches

Parameters:

batch_shape: tuple

Number of parameter-observation batches to simulate.

Returns:

dict[str, np.ndarray]: simulated parameters and observables

with shapes (batch_size, …)

sample_batched(batch_shape: tuple[int, ...], *, sample_size: int, **kwargs)

Sample the desired number of simulations in smaller batches.

Limited resources, especially memory, can make it necessary to run simulations in smaller batches. The number of samples per simulated batch is specified by sample_size.

Parameters:

batch_shapeShape

The desired output shape, as in sample(). Will be rounded up to the next complete batch.

sample_sizeint

The number of samples in each simulated batch.

kwargs

Additional keyword arguments passed to sample().