Optimal surveillance against bioinvasions: The sample average
approximation method applied to an agent-based spread model
Abstract
Trade-offs exist between the point of early detection and the future
cost of controlling any invasive species. Finding optimal levels of
early detection, with post-border active surveillance, where time, space
and randomness are explicitly considered, is computationally
challenging. We use a stochastic programming model to find the optimal
level of surveillance and predict damages, easing the computational
challenge by combining the Sample Average Approximation approach and
parallel processing techniques. The model is applied to the case of
Asian Papaya Fruit Fly (PFF), a highly destructive pest, in Queensland,
Australia. To capture the non-linearity in PFF spread, we use an
agent-based model, which is calibrated to a highly detailed land-use
raster map (50m×50m) and weather-related data, validated against a
historical outbreak. We find that current surveillance levels are less
than optimal.