A parallel approach to social network generation and agent-based epidemic simulation

Perrin, Dimitri & Ohsaki, Hiroyuki (2011) A parallel approach to social network generation and agent-based epidemic simulation. In Chen, Jinjun & Ranjan, Rajiv (Eds.) Proceedings of the 9th Australasian Symposium on Parallel and Distributed Computing (AusPDC 2011), Australian Computer Society Inc., Perth, WA, pp. 33-34.

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Abstract

Understanding the dynamics of disease spread is essential in contexts such as estimating load on medical services, as well as risk assessment and interven- tion policies against large-scale epidemic outbreaks. However, most of the information is available after the outbreak itself, and preemptive assessment is far from trivial. Here, we report on an agent-based model developed to investigate such epidemic events in a stylised urban environment. For most diseases, infection of a new individual may occur from casual contact in crowds as well as from repeated interactions with social partners such as work colleagues or family members. Our model therefore accounts for these two phenomena. Given the scale of the system, efficient parallel computing is required. In this presentation, we focus on aspects related to paralllelisation for large networks generation and massively multi-agent simulations.

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ID Code: 82674
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
ISBN: 9781920682989
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2011 Australian Computer Society, Inc.
Copyright Statement: This paper appeared at the 9th Australasian Symposium on Parallel and Distributed Computing (AusPDC 2011), Perth, Australia. Conferences in Research and Practice in Information
Technology (CRPIT), Vol. 118. J. Chen and R. Ranjan, Eds.
Reproduction for academic, not-for profit purposes permitted
provided this text is included.
Deposited On: 09 Apr 2015 23:37
Last Modified: 13 Apr 2015 16:36

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