Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/18291
Title: Improving the computational efficiency of an agent-based spatiotemporal model of livestock disease spread and control
Contributor(s): Bradhurst, Richard A (author); Roche, S E (author); East, I J (author); Kwan, Paul H (author); Garner, M G (author)
Publication Date: 2016
DOI: 10.1016/j.envsoft.2015.11.015
Handle Link: https://hdl.handle.net/1959.11/18291
Abstract: Agent-based models (ABMs) are well suited to representing the spatiotemporal spread and control of disease in a population. The explicit modelling of individuals in a large population, however, can be computationally intensive, especially when models are stochastic and/or spatially-explicit. Large-scale ABMs often require a highly parallel platform such as a high-performance computing cluster, which tends to confine their utility to university, defence and scientific research environments. This poses a challenge for those interested in modelling the spread of disease on a large scale with access only to modest hardware platforms. The Australian Animal DISease (AADIS) model is a spatiotemporal ABM of livestock disease spread and control. The AADIS ABM is able to complete complex national-scale simulations of disease spread and control on a personal computer. Computational efficiency is achieved through a hybrid model architecture that embeds equation-based models inside herd agents, an asynchronous software architecture, and a grid-based spatial indexing scheme.
Publication Type: Journal Article
Source of Publication: Environmental Modelling & Software, v.77, p. 1-12
Publisher: Pergamon Press
Place of Publication: Oxford, United Kingdom
ISSN: 1873-6726
1364-8152
Field of Research (FOR): 070704 Veterinary Epidemiology
080102 Artificial Life
080110 Simulation and Modelling
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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