Location: Subtropical Plant Pathology ResearchTitle: Optimising risk-based surveillance for early detection of invasive plant pathogens
|MASTIN, ALEXANDER - University Of Salford|
|VAN DEN BOSCH, FRANK - University Of Salford|
|CUNNIFFE, NIK - University Of Cambridge|
|PARNELL, STEPHEN - University Of Salford|
Submitted to: PLoS Biology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/14/2020
Publication Date: 10/12/2020
Citation: Mastin, A.J., Gottwald, T.R., van den Bosch, F., Cunniffe, N.J., Parnell, S.R. 2020. Optimising risk-based surveillance for early detection of invasive plant pathogens. PLoS Biology. 18(10): e3000863. https://doi.org/10.1371/journal.pbio.3000863.
Interpretive Summary: Emerging infectious diseases of plants continue to devastate agricultural commodities and ecosystems worldwide. Effective management requires the development of survey strategies to detect epidemics at an early stage before they spread and become unmanageable. Many surveillance methods have been developed but it remains unclear how best to target available surveillance resources to achieve adequate management. We demonstrate a method that captures characteristics of pathogen entry and subsequent spread combined with a mathematical model for optimal detection. The model determines where to best deploy surveyors to maximize the probability of detecting an invading epidemic. We use the example of the severe citrus disease, huanglongbing, currently devastating citrus industries worldwide. We show how our new approach outperforms conventional methods of surveillance. This method will be useful to federal, state and international regulatory agencies faced with the daunting task of how best to optimally survey for new and emerging plant disease with minimal resources.
Technical Abstract: 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.