New release of epydemic
It’s been a while – about 15 months since v.1.13.1 – but today I
finally released v.1.14.1 of epydemic, a library for epidemic (and)
other simulation over complex networks.
This release include several bug-fixes and two major changes.
Better Newman-Ziff percolation
The Newman-Ziff algorithm for bond and site percolation is widely
used. The version that’s been in epydemic for a while emphasise
speed over everything. However, in the course of some work by one
of my MSc students on how grids of different dimensions respond to
random failure, we discovered that we needed to be able to do some
more flexible operations. In particular, we wanted to sample things
other than just the size of the largest connected component, and
wanted to be able to dig into to exactly how the network was deforming.
The problem was that this information wasn’t readily available. It
was encoded within the algorithm’s data structure, but it wasn’t
being reflected as an evolving network that was easy to get at. So
we upgraded the algorithm to build a working copy of the network as
it was constructed, so that it could be interrogated by normal networkx operations within the sampling process. This adds
some time penalty, but it’s acceptable slowdown for the extra capability.
Multiple process instances
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.
