Optimising risk-based surveillance for early detection of invasive plant pathogens

Mastin, A; Gottwald, TR; van den Bosch, F; Cunniffe, NJ; Parnell, SR

Abstract

Emerging infectious diseases (EIDs) 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. That is, we find that spatial correlation in risk can make it suboptimal to focus solely on the highest-risk sites, meaning that it is best to avoid ‘putting all your eggs in one basket’. However, this depends on an interplay with other factors, such as the sensitivity of available detection methods. Using the economically important arboreal disease huanglongbing (HLB), we demonstrate how our approach leads to a significant performance gain and cost saving in comparison with conventional methods to targeted surveillance.

Citation

Mastin, A., Gottwald, T., van den Bosch, F., Cunniffe, N., & Parnell, S. (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

• Journal Article Type: Article
• Acceptance Date: Sep 14, 2020
• Publication Date: Oct 12, 2020
• Deposit Date: Oct 15, 2020
• Publicly Available Date: Oct 23, 2020
• Journal: PLOS Biology
• Print ISSN: 1544-9173
• Publisher: Public Library of Science
• Volume: 18
• Issue: 10
• Pages: e3000863
• DOI: https://doi.org/10.1371/journal.pbio.3000863
• Publisher URL: https://doi.org/10.1371/journal.pbio.3000863
• Related Public URLs: https://journals.plos.org/plosbiology/
• Additional Information: Funders : USDA APHIS farm bill grant;Defra;Biotechnology and Biosciences Sciences Research Council (BBSRC)