C++ code for implementations of the temporal Gillespie algorithm for simulation of epidemic processes on time-varying networks. The algorithm is described in the paper: C.L. Vestergaard & M. Génois. "Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks". PLoS Computational Biology (2015) 11, e1004579 (http://arxiv.org/abs/1504.01298).
The programs need the boost library installed (http://www.boost.org/). Compile with the -O2 option for optimal speed, e.g., using g++ the code may be compiled as:
g++ "input name" -o "output name" -O2 -I"path of boost"
All programs simulate independent realizations of the given contagion process on a temporal network given on the form: (t i j) with one triple per line and t,i, and j separated by tabs. ("\t").
The programs found here are:
- SIR-Poisson-homogeneous.cpp : Constant-rate (Poissonian) SIR process in a homogeneous population (i.e., same infection/recovery rates for all nodes).
- SIR-Poisson-heterogeneous.cpp : Constant-rate (Poissonian) SIR process in a heterogeneous population (i.e., infection/recovery rates may differ between nodes).
- SIR-Poisson-homogeneous-contactRemoval.cpp : Constant-rate (Poissonian) SIR process in a homogeneous population where obsolete contacts are removed from the contact data as they occur.
- SIR-nonMarkovian.cpp : Non-Markovian SIR process with Weibull distributed (https://en.wikipedia.org/wiki/Weibull_distribution) recovery times of individual nodes.
- SIS-Poisson-homogeneous.cpp : Constant-rate (Poissonian) SIS process in a homogeneous population (i.e., same infection/recovery rates for all nodes).
- SIS-Poisson-heterogeneous.cpp : Constant-rate (Poissonian) SIS process in a heterogeneous population (i.e., infection/recovery rates may differ between nodes).
Sample data are found in:
- sampleData.txt : Activity-driven network consisting of 100 nodes simulated for 20,000 time-steps.
Empirical data of face-to-face interactions collected by the SocioPatterns collaboration can be found at:
The Pseudo-Random Number Generator (Mersenne Twister 19937) is initialized with the following seed: 9071982. It can be changed inline. Note however that simulations performed with randomly chosen different seeds are not guaranteed to be (pseudo)independent.