Applying GIS and high performance agent-based simulation for managing an Old World Screwworm fly invasion of Australia.

Agent-based modelling has proven to be a promising approach for developing rich simulations for complex phenomena that provide decision support functions across a broad range of areas including biological, social and agricultural sciences. This paper demonstrates how high performance computing technologies, namely General-Purpose Computing on Graphics Processing Units (GPGPU), and commercial Geographic Information Systems (GIS) can be applied to develop a national scale, agent-based simulation of an incursion of Old World Screwworm fly (OWS fly) into the Australian mainland. The development of this simulation model leverages the combination of massively data-parallel processing capabilities supported by NVidia's Compute Unified Device Architecture (CUDA) and the advanced spatial visualisation capabilities of GIS. These technologies have enabled the implementation of an individual-based, stochastic lifecycle and dispersal algorithm for the OWS fly invasion. The simulation model draws upon a wide range of biological data as input to stochastically determine the reproduction and survival of the OWS fly through the different stages of its lifecycle and dispersal of gravid females. Through this model, a highly efficient computational platform has been developed for studying the effectiveness of control and mitigation strategies and their associated economic impact on livestock industries can be materialised.

[1]  J. Spradbery,et al.  Occurrence of the Old World screw-worm fly Chrysomya bezziana on livestock vessels and commercial aircraft. , 1989, Australian Veterinary Journal.

[2]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[3]  William Rand,et al.  Spatial process and data models: Toward integration of agent-based models and GIS , 2005, J. Geogr. Syst..

[4]  R. Keys.  Cubic convolution interpolation for digital image processing , 1981 .

[5]  Thomas Berger,et al.  Agent-based spatial models applied to agriculture: A simulation tool , 2001 .

[6]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[7]  D. A. Lindquist,et al.  The New World screwworm fly in Libya: a review of its introduction and eradication , 1992, Medical and veterinary entomology.

[8]  Mordechai Haklay,et al.  STREETS: an agent-based pedestrian model , 1999 .

[9]  P. Dawson,et al.  Comparison of an Agent-based Model of Disease Propagation with the Generalised SIR Epidemic Model , 2009 .

[10]  D. Butler,et al.  Spatial dispersal of exotic pests--the importance of extreme values , 1993 .

[11]  Darren J. Kriticos,et al.  CLIMEX Version 3: User's Guide , 2007 .

[12]  K. A. Anaman,et al.  Benefit-cost analysis of the use of sterile insect technique to eradicate screwworm fly in the event of an invasion of Australia , 1994 .

[13]  Christopher Bone,et al.  Evaluating Spatio-temporal Complexities of Forest Management: An Integrated Agent-based Modeling and GIS Approach , 2009 .

[14]  J. Slingenbergh,et al.  Seasonality of Old World screwworm myiasis in the Mesopotamia valley in Iraq , 2005, Medical and veterinary entomology.

[15]  D. Butler,et al.  Comparison of the predicted impact of a screwworm fly outbreak in Australia using a growth index model and a life‐cycle model , 1994, Medical and veterinary entomology.