[Zurück]

G. Zauner, N. Popper, P. Einzinger, F. Miksch, C. Urach, G. Endel, F. Breitenecker:
"Hybrid Combination and Modular Modelling Techniques in Health Technology Assessment - an Example Based Explanation for Communicable Diseases";
Vortrag: MATHMOD 2012 - 7th Vienna Conference on Mathematical Modelling, Wien; 14.02.2012 - 17.02.2012; in: "Preprints Mathmod 2012 Vienna - Full Paper Volume", F. Breitenecker, I. Troch (Hrg.); Argesim / Asim, 38 (2012), S. 365 - 366.

Introduction. Modelling and simulation of communicable diseases is an important field of interest in HTA.
There are different approaches to simulate epidemics but they have one characteristic in common: The more
details the modeller considers, the more complex models become. But standards are getting stricter and
requirements of stakeholders in the field of HTA are getting higher so there is a need for complex models.
A concept to deal with the increasing effort and complexity is to split up models into single parts, so called
modules. The intention is to make development easier, increase the efficiency of teamwork, make models more
flexible and reuse modules in other projects. In this work we concentrate on two different modular approaches for
simulation of epidemics.
The agent based approach. The first approach is agent based modelling. It deals with single persons with
attributes and individual behaviour. They have contacts in an environment, can get infected upon contacts, run
through individual sickness states, infect others and eventually recover. So an epidemic emerges. Additionally
people may require resources for prevention and treatment that sum up to economical evaluations. This structure
was further developed and improved within IFEDH (Innovative Framework for Evidence-Based Decision-Making
in Healthcare), a project funded by FFG. We underline this concept with three examples:
 A model for influenza where people move in different environments that look like lattice gas cellular
automata [1]
 A model for Streptococcus Pneumoniae that deals with complex infection rules [2]
 And another influenza-model with an extensive social environment where people move and meet each
other [3].
These examples are models in different projects but still they are able to share some of the modules while others
need to be developed separately.
The top-down approach. Top-down models for epidemics are a different approach than agent based models.
They do not consider single persons, instead they calculate the fractions of the population that are in certain states.
Common techniques are Markov models and differential equations. Since this approach works on a different basis
it splits up into three parts. It starts with the epidemiological part, where infections and incidences are calculated in
a general way. In the next step the demographical part maps the results from the first part on a real population of a
country. It ends with the economical part that calculates costs and other outcome values and evaluates economic
aspects. A model for HPV serves as an example [4]. The epidemiological modules uses an adopted model that has
been developed in Britain to receive epidemic outcome parameters. The demographic and the economic module
match these results on Austria´s situation to estimate the total costs caused by HPV in the long term.
Conclusions. The examples show that it is possible to split up models into the proposed modules. In general we
can conclude that modular modelling helps to structure modelling process and that reusability of tested and
validated modules makes projects more efficient and reliable.