S. Kopman, M.I. Akbas, and D. Turgut

EpidemicSim: Epidemic Simulation System with Realistic Mobility


Cite as:

S. Kopman, M.I. Akbas, and D. Turgut. EpidemicSim: Epidemic Simulation System with Realistic Mobility. In Proceedings of IEEE P2MNet'12, pp. 663–669, October 2012.

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Abstract:

Much attention has been paid to modeling human behavior and social interactions for epidemic and pandemic predictions. Nearly all of these models and predictive simulations rely on synthetic individuals to simulate social patterns using data gathered from databases such as census and transportation information. At the same time the ubiquity of mobile devices and online social networks have created an opportunity for real-life simulations of disease transmissions. In this paper, we lay the groundwork for a mobile epidemic simulation framework by creating a simulation model of individuals walking in a defined space with varying durations. The population size and walking area are varied to determine the best parameters for simulating disease spread. The effects of different infection probabilities are also investigated for a more realistic simulation framework. The resulting information is employed to create disease social networking maps used to determine the importance of each individual in the network and the connections between the ground zero source of the disease and the total infected population at the end of the simulation.

BibTeX:

@inproceedings{Kopman-2012-P2MNet,
   author = "S. Kopman and M.I. Akbas and D. Turgut",
   title = "EpidemicSim: Epidemic Simulation System with Realistic Mobility",
   booktitle = "Proceedings of IEEE P2MNet'12",
   month = "October",
   year = "2012",
   pages = "663-669",
   abstract = {Much attention has been paid to modeling human behavior and social interactions for epidemic and pandemic predictions. Nearly all of these models and predictive simulations rely on synthetic individuals to simulate social patterns using data gathered from databases such as census and transportation information. At the same time the ubiquity of mobile devices and online social networks have created an opportunity for real-life simulations of disease transmissions. In this paper, we lay the groundwork for a mobile epidemic simulation framework by creating a simulation model of individuals walking in a defined space with varying durations. The population size and walking area are varied to determine the best parameters for simulating disease spread. The effects of different infection probabilities are also investigated for a more realistic simulation framework. The resulting information is employed to create disease social networking maps used to determine the importance of each individual in the network and the connections between the ground zero source of the disease and the total infected population at the end of the simulation. },
}

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