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50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds)
12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie (dae)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie
Deutsche Arbeitsgemeinschaft für Epidemiologie

12. bis 15.09.2005, Freiburg im Breisgau

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Stochastic modelling of infection spread and interventions

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  • Markus Schwehm - Institut für Medizinische Biometrie, Tübingen
  • Martin Eichner - Institut für Medizinische Biometrie, Tübingen

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. Deutsche Arbeitsgemeinschaft für Epidemiologie. 50. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (gmds), 12. Jahrestagung der Deutschen Arbeitsgemeinschaft für Epidemiologie. Freiburg im Breisgau, 12.-15.09.2005. Düsseldorf, Köln: German Medical Science; 2005. Doc05gmds501

The electronic version of this article is the complete one and can be found online at: http://www.egms.de/en/meetings/gmds2005/05gmds019.shtml

Published: September 8, 2005

© 2005 Schwehm et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc-nd/3.0/deed.en). You are free: to Share – to copy, distribute and transmit the work, provided the original author and source are credited.

Outline

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Introduction

In order to understand the dynamics of infections spread and the effects of intervention strategies, individual-based modelling has become rather widespread. We present a modelling framework that allows to develop epidemic models from basic modules in an efficient and intuitive way.

Methods

The Epidemic model is implemented as an extension of the SBTools package, a Java plugin to the Eclipse platform. SBTools is a general modelling toolkit for systems biology and provides abstract concepts like entities, suites, events and processes. SBTools allows to integrate deterministic and stochastic discrete event models and provides a variety of data management and visualisation tools. The extensions of the SBTools package for epidemic modelling contribute concepts like individuals, populations and intervention strategies. In particular we have extensions for individuals with different behaviour (age groups, health care workers), and modules for intervention strategies like seclusion, isolation, quarantine, vaccination and treatment. A large variety of contact networks including random, small world, scale-free, social and bipartite networks has been implemented.

Applications

The epidemic modelling tools are intended to be used for the modelling of emerging diseases. So far, the tools have been applied to directly transmitted infectious diseases including smallpox, polio, SARS and influenza. The individual-based model allows to plan resource usage and to detect bottlenecks in intervention plans.

Conclusions

The SBTools project and the epidemic modelling extensions are open source projects which are planned to be released in Summer 2005. It is our intention that they will find a wide audience since they can also be used for educational purposes in infectious disease epidemiology.

Acknowledgements

This work is supported by eu-projects MODELREL, INFTRANS and SARScontrol.

Outline

References

1.
http://www.uni-tuebingen.de/modeling