F. Miksch, C. Urach, G. Zauner, I. Schiller-Frühwirth, G. Endel, P. Einzinger:
"How agent-based models reveal the dynamic of epidemics - a case study on influenza";
Value In Health, 15 (2012), 7; S. 288 - 289.

Kurzfassung englisch:
OBJECTIVES: Influenza is a disease that occurs every year for a few months in
winter season. Predictions on vaccination strategies require a deep understanding
of current influenza epidemics. The aim of this work is the reproduction of a past
influenza season through a model, its examination and to make its dynamics
transparent. METHODS: We used an agent based epidemic model to simulate the
spread of influenza. It belongs to the class of dynamic transmission models and
simulates single persons with individual behavior who live in an environment,
meet each other and spread the virus from person to person upon contacts. Contacts
are based on statistical data and social studies; epidemiological parameters
are found in clinical studies and through calibration. RESULTS: Estimates say that
about 5% of the population fall sick with influenza every year in Austria. The model
shows clearly that this number is highly implausible under naive assumptions
because the epidemic would not behave like this; instead it would be much stronger
or die out - depending on the parameters. This reveals that our knowledge on
influenza is insufficient. Three additional assumptions might solve the problem:
First, that the influenza season highly depends on the seasonal climate, second,
that many people are generally resistant for the whole season and third, that many
people undergo infections without symptoms. Simulation of these assumptions
reveal three different possible propagations of the influenza that all result in 5%
sick people. CONCLUSIONS: The model cannot answer all questions about influenza.
But it is able to show clearly where we need more information and it provides
the possibility to test different assumptions and evaluate them. In other words, the
model can lead to a deeper understanding of the real world by examining assumptions
that could not be observed directly so far.

Erstellt aus der Publikationsdatenbank der Technischen Universitšt Wien.