Optimal surveillance against bioinvasions: a 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 a sample average approximation (SAA) 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 (ABM), which is calibrated to a highly detailed land-use raster map (50 m × 50 m) and weather-

SUBMITTER: Nguyen HT 

PROVIDER: S-EPMC9285032 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature