Optimising risk-based surveillance for early detection of invasive plant pathogens.
Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. Effective management requires surveillance to detect epidemics at an early stage. However, despite the increasing use of risk-based surveillance programs in plant health, it remains unclear how best to target surveillance resources to achieve this. We combine a spatially explicit model of pathogen entry and spread with a statistical model of detection and use a stochastic optimisation routine to identify which arrangement of surveillance sites maximises the probability of detecting an invading epidemic. Our approach reveals that it is not always optimal to target the highest risk sites, and that the optimal strategy differs depending on, not only patterns of pathogen entry and spread, but also the choice of detection method. We use the example of the economically important arboreal disease huanglongbing to demonstrate how our approach outperforms conventional methods of targeted surveillance.