Summary

Objectives: We present a simulation software which allows studying the dynamics of a hypothetic infectious disease within a network of connected people. The software is aimed to facilitate the discrimination of stochastic factors governing the evolution of an infection in a network. In order to do this it provides simple tools to create networks of individuals and to set the epidemiological parameters of the outbreaks. Methods: Three popular models of infectious disease can be used (SI, SIS, SIR). The simulated networks are either the algorithm-based included ones (scale free, small-world, and random homogeneous networks), or provided by third party software. Results: It allows the simulation of a single or many outbreaks over a network, or outbreaks over multiple networks (with identical properties). Standard outputs are the evolution of the prevalence of the disease, on a single outbreak basis or by averaging many outbreaks. The user can also obtain customized outputs which address in detail different possible epidemiological questions about the spread of an infectious agent in a community. Conclusions: The presented software introduces sources of stochasticity present in real epidemics by simulating outbreaks on contact networks of individuals. This approach may help to understand the paths followed by outbreaks in a given community and to design new strategies for preventing and controlling them.