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Optimising risk-based surveillance for early detection of invasive plant pathogens
by
Mastin, Alexander J.
, Parnell, Stephen
, Gottwald, Timothy R.
, Cunniffe, Nik J.
, van den Bosch, Frank
in
Analysis
/ Biology and Life Sciences
/ Citrus
/ Citrus fruits
/ Cluster Analysis
/ Computer Simulation
/ disease detection
/ Disease management
/ disease outbreaks
/ disease surveillance
/ Diseases and pests
/ Economic importance
/ Ecosystem management
/ Ecosystems
/ Eggs
/ emerging diseases
/ Epidemics
/ Epidemiology
/ Flowers & plants
/ greening disease
/ Infectious diseases
/ Invasive plants
/ Mathematical models
/ Medicine and Health Sciences
/ Methods
/ microbial detection
/ Models, Biological
/ Observations
/ Optimization
/ Pathogenic microorganisms
/ Pathogens
/ Physical Sciences
/ Plant bacterial diseases
/ Plant diseases
/ Plant Diseases - microbiology
/ Plant introduction
/ plant pathogenic bacteria
/ Probability
/ Research and Analysis Methods
/ Risk
/ Risk assessment
/ Risk Factors
/ Sample Size
/ Software
/ Statistical analysis
/ Statistical models
/ Stochasticity
/ Strategic management
/ Supervision
/ Surveillance
2020
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Optimising risk-based surveillance for early detection of invasive plant pathogens
by
Mastin, Alexander J.
, Parnell, Stephen
, Gottwald, Timothy R.
, Cunniffe, Nik J.
, van den Bosch, Frank
in
Analysis
/ Biology and Life Sciences
/ Citrus
/ Citrus fruits
/ Cluster Analysis
/ Computer Simulation
/ disease detection
/ Disease management
/ disease outbreaks
/ disease surveillance
/ Diseases and pests
/ Economic importance
/ Ecosystem management
/ Ecosystems
/ Eggs
/ emerging diseases
/ Epidemics
/ Epidemiology
/ Flowers & plants
/ greening disease
/ Infectious diseases
/ Invasive plants
/ Mathematical models
/ Medicine and Health Sciences
/ Methods
/ microbial detection
/ Models, Biological
/ Observations
/ Optimization
/ Pathogenic microorganisms
/ Pathogens
/ Physical Sciences
/ Plant bacterial diseases
/ Plant diseases
/ Plant Diseases - microbiology
/ Plant introduction
/ plant pathogenic bacteria
/ Probability
/ Research and Analysis Methods
/ Risk
/ Risk assessment
/ Risk Factors
/ Sample Size
/ Software
/ Statistical analysis
/ Statistical models
/ Stochasticity
/ Strategic management
/ Supervision
/ Surveillance
2020
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Optimising risk-based surveillance for early detection of invasive plant pathogens
by
Mastin, Alexander J.
, Parnell, Stephen
, Gottwald, Timothy R.
, Cunniffe, Nik J.
, van den Bosch, Frank
in
Analysis
/ Biology and Life Sciences
/ Citrus
/ Citrus fruits
/ Cluster Analysis
/ Computer Simulation
/ disease detection
/ Disease management
/ disease outbreaks
/ disease surveillance
/ Diseases and pests
/ Economic importance
/ Ecosystem management
/ Ecosystems
/ Eggs
/ emerging diseases
/ Epidemics
/ Epidemiology
/ Flowers & plants
/ greening disease
/ Infectious diseases
/ Invasive plants
/ Mathematical models
/ Medicine and Health Sciences
/ Methods
/ microbial detection
/ Models, Biological
/ Observations
/ Optimization
/ Pathogenic microorganisms
/ Pathogens
/ Physical Sciences
/ Plant bacterial diseases
/ Plant diseases
/ Plant Diseases - microbiology
/ Plant introduction
/ plant pathogenic bacteria
/ Probability
/ Research and Analysis Methods
/ Risk
/ Risk assessment
/ Risk Factors
/ Sample Size
/ Software
/ Statistical analysis
/ Statistical models
/ Stochasticity
/ Strategic management
/ Supervision
/ Surveillance
2020
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Optimising risk-based surveillance for early detection of invasive plant pathogens
Journal Article
Optimising risk-based surveillance for early detection of invasive plant pathogens
2020
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Overview
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.
Publisher
Public Library of Science,Public Library of Science (PLoS)
Subject
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