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

Autor: Stephen Parnell, Frank van den Bosch, Nik J. Cunniffe, Timothy R. Gottwald, Alexander Mastin
Přispěvatelé: Perrings, C, Mastin, Alexander J [0000-0002-9536-3378], Gottwald, Timothy R [0000-0003-0885-8004], Cunniffe, Nik J [0000-0002-3533-8672], Apollo - University of Cambridge Repository
Rok vydání: 2019
Předmět:
0301 basic medicine
Citrus
Epidemiology
Disease
Pathology and Laboratory Medicine
Invasive species
Medical Conditions
0302 clinical medicine
Risk Factors
Medicine and Health Sciences
Cluster Analysis
Biology (General)
Disease surveillance
General Neuroscience
Explicit model
Eukaryota
Effective management
Plants
Cost savings
Infectious Diseases
Risk analysis (engineering)
Research Design
Physical Sciences
Pathogens
General Agricultural and Biological Sciences
Research Article
Optimization
Census
Infectious Disease Control
QH301-705.5
Early detection
Disease Surveillance
Biology
Research and Analysis Methods
Models
Biological
General Biochemistry
Genetics and Molecular Biology
Fruits
03 medical and health sciences
Computer Simulation
Epidemics
Environmental planning
Plant Diseases
Probability
Survey Research
General Immunology and Microbiology
Organisms
Biology and Life Sciences
Probability Theory
Probability Density
030104 developmental biology
Medical Risk Factors
Infectious Disease Surveillance
Sample Size
Targeted surveillance
Mathematics
030217 neurology & neurosurgery
Zdroj: PLoS Biology
PLoS Biology, Vol 18, Iss 10, p e3000863 (2020)
ISSN: 1545-7885
DOI: 10.1101/834705
Popis: 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.
Emerging infectious diseases of plants continue to devastate ecosystems and livelihoods worldwide. By linking a mathematical model of pest spread with a computational optimisation routine, this study identifies where to look for invasive pests if we wish to detect them at an early stage; this method improves upon conventional methods of risk-based surveillance and is robust to model misspecification.
Databáze: OpenAIRE
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