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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

The colors were used as following: green describes individuals in the state susceptible (S), gentle-pink marks individuals as in state infective (I) and dark blue marks the individuals as to be in state recovered (R). The first graphic depicts the simulation after 20 days. Although the simulation has started in the capital of Tyrol, in Innsbruck, after 20 days there are some outbreaks in the west of the state as well as in the east, which results form the fact that individuals are moving from cell to cell. Furthermore, unknown activities, which are denoted as spontaneous infection, are responsible for these characteristics. The second image shows a snapshot 80 days later at the time point 100 days after the infection started. It is clearly visible that in the capital where the disease spread started most people are in state recovered. Individual who did not die from the disease do have a temporary immunity. More than 50% of the individuals are in state infected. The last image shows a snapshot at time point 200 days after outbreak where individuals may get infected again. This represents the second outbreak wave with smaller amplitude.
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Figure 4: The colors were used as following: green describes individuals in the state susceptible (S), gentle-pink marks individuals as in state infective (I) and dark blue marks the individuals as to be in state recovered (R). The first graphic depicts the simulation after 20 days. Although the simulation has started in the capital of Tyrol, in Innsbruck, after 20 days there are some outbreaks in the west of the state as well as in the east, which results form the fact that individuals are moving from cell to cell. Furthermore, unknown activities, which are denoted as spontaneous infection, are responsible for these characteristics. The second image shows a snapshot 80 days later at the time point 100 days after the infection started. It is clearly visible that in the capital where the disease spread started most people are in state recovered. Individual who did not die from the disease do have a temporary immunity. More than 50% of the individuals are in state infected. The last image shows a snapshot at time point 200 days after outbreak where individuals may get infected again. This represents the second outbreak wave with smaller amplitude.

Mentions: Fig. (4) depicts the spatial results of the disease spread for scenario A. When analyzing the spreading, one can see that the geographical properties and the different densities in the valleys of Tyrol are responsible for slowing down the outbreak. But the geographical conditions are unable to stop the outbreak completely.


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)

The colors were used as following: green describes individuals in the state susceptible (S), gentle-pink marks individuals as in state infective (I) and dark blue marks the individuals as to be in state recovered (R). The first graphic depicts the simulation after 20 days. Although the simulation has started in the capital of Tyrol, in Innsbruck, after 20 days there are some outbreaks in the west of the state as well as in the east, which results form the fact that individuals are moving from cell to cell. Furthermore, unknown activities, which are denoted as spontaneous infection, are responsible for these characteristics. The second image shows a snapshot 80 days later at the time point 100 days after the infection started. It is clearly visible that in the capital where the disease spread started most people are in state recovered. Individual who did not die from the disease do have a temporary immunity. More than 50% of the individuals are in state infected. The last image shows a snapshot at time point 200 days after outbreak where individuals may get infected again. This represents the second outbreak wave with smaller amplitude.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The colors were used as following: green describes individuals in the state susceptible (S), gentle-pink marks individuals as in state infective (I) and dark blue marks the individuals as to be in state recovered (R). The first graphic depicts the simulation after 20 days. Although the simulation has started in the capital of Tyrol, in Innsbruck, after 20 days there are some outbreaks in the west of the state as well as in the east, which results form the fact that individuals are moving from cell to cell. Furthermore, unknown activities, which are denoted as spontaneous infection, are responsible for these characteristics. The second image shows a snapshot 80 days later at the time point 100 days after the infection started. It is clearly visible that in the capital where the disease spread started most people are in state recovered. Individual who did not die from the disease do have a temporary immunity. More than 50% of the individuals are in state infected. The last image shows a snapshot at time point 200 days after outbreak where individuals may get infected again. This represents the second outbreak wave with smaller amplitude.
Mentions: Fig. (4) depicts the spatial results of the disease spread for scenario A. When analyzing the spreading, one can see that the geographical properties and the different densities in the valleys of Tyrol are responsible for slowing down the outbreak. But the geographical conditions are unable to stop the outbreak completely.

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