metawards 0.10.0

MetaWards disease metapopulation modelling

MetaWards

This is a Python port of the MetaWards package originally written by Leon Danon. The port is kept in sync with the original C code, with checks in place to ensure that the two codes give identical results. This improves the robustness of both codes, as it minimises the footprint to bugs that can evade both C and Python.

The aim of this port is to make it easier for others to contribute to the program, to improve robustness by adding in unit and integration test, and to also open up scope for further optimisation and parallelisation.

The package makes heavy use of cython which is used with OpenMP to compile bottleneck parts of the code to parallelised C. This enables this Python port to run at approximately the same speed as the original C program on one core, and to run several times faster across multiple cores.

The program compiles on any system that has a working C compiler that supports OpenMP, and a working Python >= 3.7. This include X86-64 and ARM64 servers.

The software supports running over a cluster using MPI (via mpi4py) or via simple networking (via scoop).

Full instructions on how to use the program, plus example job submission scripts can be found on the project website.

Data

The data and input parameters needed to use this package are stored in the MetaWardsData repository. Please make sure that you clone this repository to your computer and supply the full path to that repository to the program when it runs. There are three ways to do this;

1. Set the METAWARDSDATA environment variable to point to this directory, e.g. export METAWARDSDATA=$HOME/GitHub/MetaWards

2. Pass the repository variable to the input data classes Disease, InputFiles and Parameters

3. Or simply make sure you clone into the directory $HOME/GitHub/MetaWardsData as this is the default path.

References

These are the references behind the original C code are;

• "Individual identity and movement networks for disease metapopulations" Matt J. Keeling, Leon Danon, Matthew C. Vernon, Thomas A. House Proceedings of the National Academy of Sciences May 2010, 107 (19) 8866-8870; DOI: 10.1073/pnas.1000416107

• "A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing" Leon Danon, Ellen Brooks-Pollock, Mick Bailey, Matt J Keeling medRxiv 2020.02.12.20022566; doi: 10.1101/2020.02.12.20022566

Dependencies

The code requires Python 3.7 or above, and requires no other dependencies to install. For development you will need cython to build the code, plus pytest for running the tests.

Installation

Full installation instructions are here.

As you are here, I guess you want to install the latest code from GitHub ;-)

To do that, type;

git clone https://github.com/metawards/MetaWards
cd MetaWards
pip install -r requirements-dev.txt
CYTHONIZE=1 python setup.py install

(assuming that python is version 3.7)

You can run tests using pytest, e.g.

METAWARDSDATA="/path/to/MetaWardsData" pytest tests

You can generate the docs using

cd docs
make

Running

Full usage instructions are here

You can either load and use the Python classes directly, or you can run the metawards front-end command line program that is automatically installed.

metawards --help

will print out all of the help for the program. For example;

metawards --input tests/data/ncovparams.csv --seed 15324 --nsteps 30 --nthreads 1

This will duplicate the run of the MetaWards C program that is bundled in this repository that was run using;

./original/metawards 15324 tests/data/ncovparams.csv 0 1.0

The original C code, command line and expected output are in the original directory that is bundled in this repo.

Running an ensemble

This program supports parallel running of an ensemble of jobs using multiprocessing for single-node jobs, and mpi4py or scoop for multi-node cluster jobs.

Full instructions for running on a cluster are here

Next stages...

The next stages of the program includes finishing up the custom extractor code, improving some of the analysis, providing more integrators, optimising the random number generator and finishing up the tutorial.

In addition, we plan to integrate the software into the Bayesian / MCMC package PyMC3.