GEM: a domain-specific language for epidemic modelling

GEM is a toolkit for epidemic analysis. It is a probabilistic programming language that allows users to define an epidemic process in a clear, repeatable language, and embed that process into a higher-order probabilistic model. It provides for both simulation and inference processes.

GEM documentation: https://gem.readthedocs.io

Quickstart

To install GEM:

$ pip install git+http://fhm-chicas-code.lancs.ac.uk/GEM/gem.git

Example usage:

from gem import GEM
from gem.plotting import plot_timeseries, traceplot

prog = """
beta ~ Gamma(2.0, 10.0)
gamma ~ Gamma(1.0, 10.0)

Epidemic MyEpidemic() {

    S = State(init=999)
    I = State(init=1)
    R = State(init=0)
    
    [S -> I] = beta * I / 1000.0
    [I -> R] = gamma
}
epi ~ MyEpidemic()
"""

model = GEM(prog)

# Simulate
sim = model.sample(1, condition_vars={'beta': [0.4], 'gamma': [0.14]})
plot_timeseries(model.random_variables['epi'], sim['epi'])

# Inference
posterior, accept = model.fit(observed={'epi': sim['epi']}, n_samples=5000,
                              init=[0.001, 0.001], burnin=2000)
traceplot(posterior)

Status

GEM is currently very much in alpha testing, with the interface likely to change without warning. So far, the software is able to perform inference on epidemic models with known transition times. It can also perform inference on a range of non-epidemic probabilistic models.

Watch this space in the coming year for developments in GEM, specifically around partially-observed epidemic processes and hidden-Markov models.

Get involved

If you like the idea of GEM, get involved! Clone the Git repo, and/or contact the project leader, Chris Jewell c.jewell@lancaster.ac.uk.

Acknowledgements

I'd very much like to thank the University of Lancaster for providing me a base from which to develop GEM, CHICAS my group at Lancaster, and most of all The Wellcome Trust for providing funding funding to take GEM to the next level.