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A cellular automaton framework for infectious disease spread simulation.

Pfeifer B, Kugler K, Tejada MM, Baumgartner C, Seger M, Osl M, Netzer M, Handler M, Dander A, Wurz M, Graber A, Tilg B - Open Med Inform J (2008)

Bottom Line: The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans.The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data.The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease.Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework.In this study, six different scenarios were simulated.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Austria.

ABSTRACT
In this paper, a cellular automaton framework for processing the spatiotemporal spread of infectious diseases is presented. The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans. In this study, the outbreak of an avian flu like virus was modeled in the state of Tyrol, and various scenarios such as quarantine, effect of different medications on viral spread and changes of social behavior were simulated.The proposed framework is implemented using the programming language Java. The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data.The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease. Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework.In this study, six different scenarios were simulated. It showed that geographical barriers may help to slow down the spread of an infectious disease, however, when an aggressive and deadly communicable disease spreads, only quarantine and controlled medical treatment are able to stop the outbreak, if at all.

No MeSH data available.


Related in: MedlinePlus

population density of state Tyrol. The used colors (from white to red) for the population densities specify the density steps from 0, 200, 400, 600, 800 and 1000 inhabitants per square kilometer. The color black was used to describe the non-state area.
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Figure 1: population density of state Tyrol. The used colors (from white to red) for the population densities specify the density steps from 0, 200, 400, 600, 800 and 1000 inhabitants per square kilometer. The color black was used to describe the non-state area.

Mentions: For the simulation, the state Tyrol was chosen. Tyrol is one of nine states of Austria. Tyrol has 660.000 inhabitants, where about 115.000 inhabitants are living in the capital named Innsbruck, and a total area of 10.628 square kilometers. The area of settlement is about 1.600 square kilometers [17]. Fig. (1) depicts the area of Tyrol and the population density using colors from white, light yellow up to red.


A cellular automaton framework for infectious disease spread simulation.

Pfeifer B, Kugler K, Tejada MM, Baumgartner C, Seger M, Osl M, Netzer M, Handler M, Dander A, Wurz M, Graber A, Tilg B - Open Med Inform J (2008)

population density of state Tyrol. The used colors (from white to red) for the population densities specify the density steps from 0, 200, 400, 600, 800 and 1000 inhabitants per square kilometer. The color black was used to describe the non-state area.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2666960&req=5

Figure 1: population density of state Tyrol. The used colors (from white to red) for the population densities specify the density steps from 0, 200, 400, 600, 800 and 1000 inhabitants per square kilometer. The color black was used to describe the non-state area.
Mentions: For the simulation, the state Tyrol was chosen. Tyrol is one of nine states of Austria. Tyrol has 660.000 inhabitants, where about 115.000 inhabitants are living in the capital named Innsbruck, and a total area of 10.628 square kilometers. The area of settlement is about 1.600 square kilometers [17]. Fig. (1) depicts the area of Tyrol and the population density using colors from white, light yellow up to red.

Bottom Line: The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans.The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data.The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease.Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework.In this study, six different scenarios were simulated.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Austria.

ABSTRACT
In this paper, a cellular automaton framework for processing the spatiotemporal spread of infectious diseases is presented. The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans. In this study, the outbreak of an avian flu like virus was modeled in the state of Tyrol, and various scenarios such as quarantine, effect of different medications on viral spread and changes of social behavior were simulated.The proposed framework is implemented using the programming language Java. The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data.The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease. Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework.In this study, six different scenarios were simulated. It showed that geographical barriers may help to slow down the spread of an infectious disease, however, when an aggressive and deadly communicable disease spreads, only quarantine and controlled medical treatment are able to stop the outbreak, if at all.

No MeSH data available.


Related in: MedlinePlus