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DOI: 10.1055/s-0038-1627827
sispread: A Software to Simulate Infectious Diseases Spreading on Contact Networks
Publication History
Received:
22 September 2005
Accepted:
24 March 2006
Publication Date:
24 January 2018 (online)
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.
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References
- 1 Bauch CT. et al. Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present, and future. Epidemiology 2005; 16: 791-801.
- 2 Meyers LA. et al. Network theory and SARS: predicting outbreak diversity. J Theor Biol 2005; 232: 71-81.
- 3 Lloyd-Smith JO. et al. Superspreading and the effect of individual variation on disease emergence. Nature 2005; 438: 355-9.
- 4 Shen Z. et al. Superspreading SARS events, Beijing, 2003. Emerg Infect Dis 2004; 10: 256-60.
- 5 Eubank S. et al. Modelling disease outbreaks in realistic urban social networks. Nature 2004; 429: 180-4.
- 6 Ferguson NM. et al. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 2005; 437: 209-14.
- 7 Koopman J. Modeling infection transmission. Annu Rev Public Health 2004; 25: 303-26.
- 8 Longini IM. Jr., et al. Containing pandemic influenza at the source. Science 2005; 309: 1083-7.
- 9 Anderson RM, May RM. Infectious Disease of Humans: Dynamics and Control. Oxford: Oxford University Press;; 1991
- 10 Diekmann O, Heesterbeek JAP. Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. Chichester, UK: John Wiley & Sons;; 2000
- 11 Valleron A-J. Les rôles de la modélisation en épidémiologie. Comptes Rendus de l’Academie des Sciences III 2000; 323: 429-33.
- 12 Barthelemy M. et al. Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. Phys Rev Lett 2004; 92: 178701.
- 13 Barthelemy M. et al. Dynamical patterns of epidemic outbreaks in complex heterogeneous networks. J Theor Biol 2005; 235: 275-88.
- 14 Lloyd AL, May RM. How Viruses Spread Among Computers and People. Science 2001; 292: 1316-7.
- 15 May RM, Lloyd AL. Infection dynamics on scalefree networks. Phys Rev E 2001; 64: 066112.
- 16 Moreno Y. et al. Epidemic outbreaks in complex heterogeneous networks. Eur Phys J B 2002; 26: 521-29.
- 17 Newman MEJ. Spread of epidemic disease on networks. Phys Rev E 2002; 66: 016128.
- 18 Pastor-Satorras R, Vespignani A. Epidemic dynamics and endemic states in complex networks. Phys Rev E 2001; 63: 066117.
- 19 Pastor-Satorras R, Vespignani A. Epidemic spreading in scale-free networks. Phys Rev Lett 2001; 86: 3200-3.
- 20 Saramaki J, Kaski K. Modelling development of epidemics with dynamic small-world networks. J Theor Biol 2005; 234: 413-21.
- 21 Schneeberger A. et al. Scale-free networks and sexually transmitted diseases: a description of observed patterns of sexual contacts in Britain and Zimbabwe. Sex Transm Dis 2004; 31: 380-7.
- 22 Newman MEJ. The structure and function of complex networks. SIAM Rev 2003; 45: 167-256.
- 23 Erdös P, Renyi A. On the evolution of random graphs. Publ Math Inst Hung Acad Sci 1960; 5: 17-61.
- 24 Watts DJ, Strogatz SH. Collective dynamics of ‘small-world’networks. Nature 1998; 393: 440-2.
- 25 Amaral LA. et al. Classes of small-world networks. Proc Natl Acad Science USA 2000; 97: 11149-52.
- 26 Barabási A-L. et al. Scale-free characteristics of random networks: the topology of the world-wide web. Physica A 2000; 281: 69-77.
- 27 Liljeros F. et al. The web of human sexual contacts. Nature 2001; 411: 907-8.
- 28 Pastor-Satorras R, Vespignani A. Epidemic dynamics in finite size scale-free networks. Phys Rev E 2002; 65: 035108.
- 29 Newman MEJ, Park J. Why social networks are different from other types of networks. Phys Rev E 2003; 68: 036122.
- 30 Kleinberg JM. Navigation in a small world. Nature 2002; 406: 805.
- 31 Barabási A-L, Bonabeau E. Scale-free networks. Sci Am 2003; 288: 60-9.
- 32 yEd – Java Graph Editor. yWorks; 2005; last accessed: October 2006 Available from: http://www.yworks.com/en/products_yed_about.htm.
- 33 GML: A portable Graph File Format.. Graphlet; 1997; last accessed: October 2006 Available from: http://infosun.fmi.uni-passau.de/GraphletGML.
- 34 Grundmann H, Hellriegel B. Mathematical modelling: a tool for hospital infection control. Lancet Infect Dis 2006; 6: 39-45.
- 35 Cassa CA. et al. A software tool for creating simulated outbreaks to benchmark surveillance systems. BMC Med Inform Decis Mak 2005; 5: 22.
- 36 Ghys PD. et al. The UNAIDS Estimation and Projection Package: a software package to estimate and project national HIV epidemics. Sex Transm Infect 2004; 80 (Suppl. 01) i5-9.
- 37 FluAid Home. National Vaccine Program Office, CDC; 2000; last accessed: October 2006 Available from: http://www2a.cdc.gov/od/fluaid.
- 38 Radespiel-Troeger M, Daugs A, Meyer M. A simulation model for small-area cancer incidence rates. Methods Inf Med 2004; 43: 493-8.
- 39 EpiQuest – Making Time For Prevention.. Epi- Quest LLC; 2001; last accessed: October 2006 Available from: http://www.epiquest.com.
- 40 VIGI@ct. bioMérieux SA; 2005; last accessed: October 2006 Available from: http://www.biomerieux.com/servlet/srt/bio/portail/dynPage?open=PRT_PRD_CLN_CTL&doc=PRT_PRD_ CLN_CTL_BCT_VGC.