An Epidemiological Modeling and Data Integration Framework

B. Pfeifer; M. Wurz; F. Hanser; M. Seger; M. Netzer; M. Osl; R. Modre-Osprian; G. Schreier; C. Baumgartner

doi:10.3414/ME09-02-0025

Methods of Information in Medicine, Inhaltsverzeichnis

Methods Inf Med 2010; 49(03): 290-293
DOI: 10.3414/ME09-02-0025

Special Topic – Original Articles

Schattauer GmbH

An Epidemiological Modeling and Data Integration Framework

B. Pfeifer^*

¹UMIT, Institute of Electrical, Electronic and Bioengineering, Hall, Austria

,

M. Wurz^*

¹UMIT, Institute of Electrical, Electronic and Bioengineering, Hall, Austria

,

F. Hanser

¹UMIT, Institute of Electrical, Electronic and Bioengineering, Hall, Austria

,

M. Seger

¹UMIT, Institute of Electrical, Electronic and Bioengineering, Hall, Austria

,

M. Netzer

¹UMIT, Institute of Electrical, Electronic and Bioengineering, Hall, Austria

,

M. Osl

¹UMIT, Institute of Electrical, Electronic and Bioengineering, Hall, Austria

,

R. Modre-Osprian

²AIT Austrian Institute of Technology GmbH, Safety and Security Department, Graz, Austria

,

G. Schreier

²AIT Austrian Institute of Technology GmbH, Safety and Security Department, Graz, Austria

,

C. Baumgartner

²AIT Austrian Institute of Technology GmbH, Safety and Security Department, Graz, Austria

› Institutsangaben

Abstract

Summary

Objectives: In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria.

Methods: The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD³).

Results: The obtained simulation results were used for generating prediction models for all nine federal states of Austria.

Conclusion: The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Keywords

Computer models - computer simulation - infectious disease transmission

Volltext

Referenzen

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