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Volume 10, Issue 2

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Individual-Based Models of the Spread of Disease, Weeds, and Insects
Art Diggle
Moin Salam
Marta Monjardino

Conclusion

The focus on the behaviour of individual organisms in these models is well suited to representing the knowledge of scientists that study the organisms. The scientists tend to make observations of individuals or small groups of organisms, and as humans, they tend to be efficient at thinking about mechanisms and generating hypotheses at that scale. These models are able to represent the key processes that affect movement of individuals. Aggregation of the behaviour of individuals to predict the behaviour of entire populations is then a matter of repeated calculation and is well suited to computers.

The models are very computation intensive, and the examples presented here have taken hours or days to calculate on personal computers. Fortunately, understanding the dynamics of movement of organisms tends to be most important when populations are building up from low levels, so it is possible to produce valuable analyses. It is no doubt possible to develop more efficient algorithms to estimate spread. Also these problems are parallel in nature and consequently are adaptable to calculation on clusters of computers using gridMathematica. For these reasons much larger analyses will be possible in future.

These models are already proving useful in the management of crop diseases and incursions of serious weeds and insect pests in Western Australia. For example in the case of blackspot disease of peas a strategy whereby the farmers of the district cooperate to sow peas in bands across the prevailing wind and plant the next year's peas upwind of this peas markedly reduces the overall disease levels. The model is currently being adapted to predict spread of genes in established weed populations and will be applied to manage the buildup of herbicide resistance in weeds.



     
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