Episimlab

Episimlab is a Python package for rapid development and execution of contagion models. It provides a set of lightweight, reusable, and extensible Python processes which can be combined into models with arbitrary complexity. While Episimlab was originally written with epidemiological use cases in mind, its applications extend to other fields such as computer security. This package specifically supports development of models that:

• Are Markovian
• Are stochastic or deterministic
• Are compartmental, with arbitrary network structure
• Run in discrete (as opposed to continuous) time
• Incorporate diverse data sources, such as census and cell phone mobility data
• Use parameters that are highly heterogeneous along multiple user-defined axes, such as age, risk group, and zip code
• Include dynamic interventions, such as starting vaccination when number of infections in an age group exceeds some limit
• Are performant, thanks to Dask integration and accelerated matrix math in xarray and numpy
• Can be easily visualized in Jupyter or Google Colab notebooks

While Episimlab provides defaults for these behaviors, users can easily customize their models thanks to integration with the xarray-simlab package. For more details, please consult the documentation for Episimlab and xarray-simlab.

Example: A Basic Compartmental SIR Model

See the full example here.

import xsimlab
# Import an existing SIR model 
from episimlab.models import ExampleSIR

@xsimlab.process
class CustomRecoveryRate:
    """We modify the model by writing a single custom process. This process
    calculates a recovery rate (`rate_I2R`)."""
    # Variables output by this process (intent='out')
    rate_I2R = xsimlab.variable(
        global_name='rate_I2R', groups=['edge_weight'], intent='out', 
        description="rate of change from compartments I to R")

    # Variables ingested by this process (intent='in'). 
    # Setting a `global_name` here allows this process to ingest from an 
    # upstream CalculateGamma process that would define gamma like:
    # gamma = xsimlab.global_ref('gamma', intent='out')
    gamma = xsimlab.variable(global_name='gamma', intent='in')

    # The state is the N-D array of every individual in the simulation,
    # stratified by age, risk group, geospatial identifier, compartment, etc.
    state = xsimlab.global_ref('state', intent='in')

    def run_step(self):
        """Re-calculate `rate_I2R` at every step of the simulation. We can
        instead decide to calculate only once at the beginning by renaming
        this method `initialize`. We calculate recovery rate as the product
        of `gamma` and the current state of the `I` compartment.
        """
        self.rate_I2R = self.gamma * self.state.loc[{'compt': 'I'}]

# Instantiate the included model
model = ExampleSIR()
# Edit the model so it uses our custom process
model.update_processes({'recovery_rate': CustomRecoveryRate})
results = model.run(input_vars={'gamma': 0.6})
model.plot()

# The final state is an N-D array represented in Python as an xarray.DataArray
# object (like a numpy array, but with labeled axes)
final_state = results['compt_model__state']
print(final_state)
# <xarray.DataArray 'compt_model__state' (step: 15, vertex: 4, compt: 4, age: 5, risk: 2)>
# array([[[[[200., 200.],
#           [200., 200.],
#           ...
#           [  0.,   0.],
#           [  0.,   0.]]]]])
# Coordinates:
#   * age      (age) object '0-4' '5-17' '18-49' '50-64' '65+'
#   * compt    (compt) object 'S' 'I' 'R' 'V'
#   * risk     (risk) object 'low' 'high'
#   * step     (step) datetime64[ns] 2020-03-01 2020-03-02 ... 2020-03-15
#   * vertex   (vertex) object 'Austin' 'Houston' 'San Marcos' 'Dallas'

Results

Compartment populations over time

Compartment graph generated by CustomSetupComptGraph in the full example

Installation

From PyPI

pip install episimlab

Latest Release

To install the most recent (and potentially less stable) development release, install directly from the main branch of the GitHub repository:

pip install git+https://github.com/UT-Covid/episimlab

Testing

GitHub Actions

Pushing commits to a Pull Request against the main branch will automatically trigger pytests in GitHub Actions. Tests run in the poetry environment defined by the pyproject.toml file.

Local Testing

Preferred testing environment runs poetry virtual env within tox.

1. Install tox and poetry
2. Run tox from repository root:

   # Default args
   tox
   # Pass args to pytest. In this case, we run only the model tests
   tox -- tests/test_models.py

Contributing

If you are interested in contibuting to Episimlab, please see the contribution guide.

Troubleshooting

If you are having trouble using Episimlab, please first refer to the documentation and examples. If you're still having trouble, please submit a GitHub issue.