Improving the computational efficiency of an agent-based spatiotemporal model of livestock disease spread and control

Title
Improving the computational efficiency of an agent-based spatiotemporal model of livestock disease spread and control
Publication Date
2016
Author(s)
Bradhurst, Richard A
Roche, S E
East, I J
Kwan, Paul H
Garner, M G
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Elsevier Ltd
Place of publication
United Kingdom
DOI
10.1016/j.envsoft.2015.11.015
UNE publication id
une:18495
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.
Link
Citation
Environmental Modelling & Software, v.77, p. 1-12
ISSN
1873-6726
1364-8152
Start page
1
End page
12

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