ArticlePLoS biology2020
Optimising risk-based surveillance for early detection of invasive plant pathogens.
Article in PLoS biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Unlocking plant health survey data: An approach to quantify the sensitivity and specificity of visual inspections.PLoS computational biology · 2025Article
- Modelling plant disease spread and containment: Simulation and approximate Bayesian Computation for Xylella fastidiosa in Puglia, Italy.PLoS computational biology · 2025Article
- Where to refine spatial data to improve accuracy in crop disease modelling: an analytical approach with examples for cassava.Royal Society open science · 2025Article
- Quantifying infectious disease epidemic risks: A practical approach for seasonal pathogens.PLoS computational biology · 2025Article
- Optimal control prevents itself from eradicating stochastic disease epidemics.PLoS computational biology · 2025Article
- Developing a spatio-temporal model for banana bunchy top disease: leveraging remote sensing and survey data.Frontiers in plant science · 2025Article
- Quantification of the fungal pathogen Didymella segeticola in Camellia sinensis using a DNA-based qRT-PCR assay.Plant methods · 2024Article
- Citizen science is a vital partnership for invasive alien species management and research.iScience · 2024Review
- Climate change impacts on plant pathogens, food security and paths forward.Nature reviews. Microbiology · 2023Review
- Bayesian inference for spatio-temporal stochastic transmission of plant disease in the presence of roguing: A case study to characterise the dispersal of Flavescence dorée.PLoS computational biology · 2023Article
- Using 'sentinel' plants to improve early detection of invasive plant pathogens.PLoS computational biology · 2023Article
- Assessing the effectiveness of environmental sampling for surveillance of foot-and-mouth disease virus in a cattle herd.Frontiers in veterinary science · 2023Article
- A framework for the design, implementation, and evaluation of output-based surveillance systems against zoonotic threats.Frontiers in public health · 2023Article
- Cattle transport network predicts endemic and epidemic foot-and-mouth disease risk on farms in Turkey.PLoS computational biology · 2022Article
- Risk-based serological survey of bluetongue and the first evidence of bluetongue virus serotype 26 circulation in Tunisia.Veterinary medicine and science · 2022Article
- Epidemiologically-based strategies for the detection of emerging plant pathogens.Scientific reports · 2022Article
- Optimal strategies to protect a sub-population at risk due to an established epidemic.Journal of the Royal Society, Interface · 2022Article
- Invasive Insect Species: Global Challenges, Strategies & Opportunities.Frontiers in insect science · 2021Article
- Optimal invasive species surveillance in the real world: practical advances from research.Emerging topics in life sciences · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.