Simon Dobson (Posts about epydemic)

New release of epydemic

Simon Dobson — Fri, 29 Nov 2024 16:35:46 GMT

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.

epydemic hits 100,000 downloads

Simon Dobson — Wed, 08 May 2024 15:21:29 GMT

epydemic hits 100,000 downloads

Downloads of epydemic, my library for epidemic (and other) process simulation on networks (and hopefully other combinatorial structures soon…) recently passed 100,000.

That number comes from the project's PePy page, which tracks downloads from the main PyPi page as used by pip. I can't say whether or not that number is accurate. Quite honestly it's at least 98,000 more than I ever expected, but 100,000 feels like something of a milestone to be pleased about.

epydemic came about because of a lack of standard tooling for doing epidemic simulation. This involves a lot of stochastic simulation, which is quite tricky code to write and to make efficient. Testing the system actually involved us thinking more deeply about the effectiveness of unit testing for stochastic code, which then led to a presentation at UK Systems in 2021 explaining the problems we'd had.

Notes on using Jupyter in the cloud

Simon Dobson — Fri, 02 Dec 2022 16:36:55 GMT

Notes on using Jupyter in the cloud

I've been thinking about running Jupyter notebooks in the cloud for some fairly compute-intensive simulation. Specifically I want to do epidemic and other simulations over complex networks. These are CPU-intensive and don't make use of GPU acceleration (yet, anyway). Using the cloud would make things easier to scale-out, especially for those without access to a local compute cluster.

Talk at UK Systems on unit testing stochastic code

Simon Dobson — Mon, 06 Dec 2021 16:49:30 GMT

Last week I went to my first scientific meeting since February 2021 to talk about unit testing of stochastic code.

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Generating functions for epydemic

Simon Dobson — Thu, 10 Jun 2021 17:22:16 GMT

The next version of epydemic has just been released. It now includes a generating functions library.

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New, faster, release of epydemic

Simon Dobson — Fri, 14 May 2021 08:33:17 GMT

I just made a new release of epydemic.

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A talk on "Exploring epidemic spreading using network models"

Simon Dobson — Sat, 19 Dec 2020 09:10:00 GMT

I gave a talk by Zoom to the Institute of Mathematics and its Applications arising from my epidemic modelling book.

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Backporting Python type annotations

Simon Dobson — Wed, 09 Dec 2020 10:45:43 GMT

I recently added type annotations to my epyc and epydemic libraries. Making these work while not sacrificing interoperability a wide range of Python versions is quite delicate.