Please click GitHub to find the code used in the generation of the results in:
F. Di Lauro, J.-C. Croix, M. Dashti, L. Berthouze & I.Z. Kiss (2019) Network Inference from Population-Level Observation of Epidemics. [arxiv]
If you use this code, please make sure to cite the above paper.
Please email any bug report, correction, suggestion should go to Jean-Charles Croix and Istvan Kiss.
PGF_equation_generator: Generating networks with arbitrary subgraph composition followed by the automatic generation of an ODE system approximating a stochastic SIR model on the network
Please click here for a zip file containing all code necessary to generate (a) networks with arbitrary subgraph distributions, (b) a set of ODEs corresponding to SIR dynamics on the generated network and (c) the numerical solution of these equations. The code is written in MATLAB. It makes use of the Symbolic Math toolbox of Matlab as well as a number of freely available functions (see credit in the README file). Usage instructions and a number of examples are provided in the README file as well as PGF_equation_generator.m . See this for an explanation of the code.
For full details regarding the method, please see:
Beyond clustering: Mean-field dynamics on networks with arbitrary subgraph composition,
Ritchie M, Berthouze L, Kiss IZ. (in revision)
Available as arXiv:1405.6234 [math.DS]
If you use this code, please make sure to cite the above paper.
Please email any bug report, correction, suggestion should go to Martin Ritchie and Istvan Kiss .