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.

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.