Publications in Scientific Journals:

P. Pichler, F. Miksch:
"Calibration: A Usecase on the Influenza season 2006/07 in Austria";
Simulation Notes Europe, 24 (2014), 1; 39 - 46.

English abstract:
Calibration deals with finding of unknown parameter values. In this paper a possible calibration approach for agent-based models is defined. After a general explanation the approach is used to calibrate an agent-based model that was developed for the Influenza Season 2006/07 in Austria. This can not only help to fit the simulation to given data, but increase model credibility.
A crucial task in the process of modelling an simulation is called parametrization. This is the finding of parameter values to feed the model. Usually parametrization goes along with the development of the model/simulation. First of all the system is analysed, usually this results in a huge collection of every data set that is known about the system under study, including publications, studies, other models, surveys, and others. Generally data can be split into two different groups: (1) Input Data: Everything that the model needs to be executed and (2) Output Data: Representations to the real word system after simulation runtime.
The further the modelling process (see Balci (2) or (3) progresses the more precise the parameters have to be defined. This task fais if there is a lack of data or if values can not be measured in reality. Calibration helps to fill this gap. What happens is that the model qutput ist fit to real word data.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

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Created from the Publication Database of the Vienna University of Technology.