epydemic defines different epidemic processes (and indeed
non-disease processes like pulse-coupled oscillators). Until now
these have been usable alone in a simulation, but not together: one
couldn’t run two diseases in the same simulation over the same
population simultaneously. Doing so is obviously very desirable,
especially if you want to explore co-infecting diseases.
Co-infection is a difficult problem. As a first step we’ve added
multiple process instances which can have their own parameters and
results – or can share parameters if required. This involves
assigning distinct names to each instance, and then optionally
using them to decorate parameter/result names.
This is fiddly if done manually, so we also added some methods on the Process class to get and set parameters and results using any
instance name on the calling process. For example:
params = dict()
# network
N = 10000
kmean = 100
params[ERNetwork.N] = N
params[ERNetwork.KMEAN] = kmean
# first infection
p1 = SIR("Disease1")
p1.setParameters(params,
{SIR.P_INFECT: 0.1,
SIR.P_INFECTED: 5.0 / N
})
# second infection
p2 = SIR("Disease2")
p2.setParameters(params,
{SIR.P_INFECT: 0.3,
SIR.P_INFECTED: 5.0 / N
})
# common removal rate
params[SIR.P_REMOVE] = 0.005
# run the processes together
ps = ProcessSequence([p1, p2])
e = StochasticDynamics(ps, ERNetwork())
rc = e.set(params).run(fatal=True)
The setParameters call sets the parameters decorated with the name
of the process, if it has one. There are other operations for
extracting the parameters, and for interacting with experimental
results without getting into the details of decoration.
See the project documentation for more details, as well as an
updated tutorial and a cookbook recipe for co-infection (which is
based around the code above). The Github repo is also available. To
upgrade, just run:
pip install --upgrade epydemic
or delete and re-build any virtual environments.
