sispread: A Software to Simulate Infectious Diseases Spreading on Contact Networks

F.P. Alvarez; P. Crépey; M. Barthélemy; A.-J. Valleron

doi:10.1055/s-0038-1627827

Methods of Information in Medicine, Table of Contents

Methods Inf Med 2007; 46(01): 19-26
DOI: 10.1055/s-0038-1627827

Original Articles

Schattauer GmbH

sispread: A Software to Simulate Infectious Diseases Spreading on Contact Networks

F.P. Alvarez

¹INSERM, U707, ESIM, Paris, France

²Université Pierre et Marie Curie – Paris 6, UMR S 707, Paris, France

,

P. Crépey

¹INSERM, U707, ESIM, Paris, France

²Université Pierre et Marie Curie – Paris 6, UMR S 707, Paris, France

,

M. Barthélemy

²Université Pierre et Marie Curie – Paris 6, UMR S 707, Paris, France

⁴School of Informatics, Indiana University, Bloomington, IN, USA

,

A.-J. Valleron

¹INSERM, U707, ESIM, Paris, France

²Université Pierre et Marie Curie – Paris 6, UMR S 707, Paris, France

⁵Hôpital Saint-Antoine, Unité de Santé Publique, Paris, France

› Author Affiliations

Abstract

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.

Keywords

Public health - epidemiological methods - contact tracing - computer models - outbreaks

Full Text

References

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