title: Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape.
abstract: The global increase in the movement of plant products in recent years has triggered an increase in the number of introduced plant pathogens. Plant nurseries importing material from abroad may play an important role in the introduction and spread of diseases such as ash dieback and sudden oak death which are thought to have been introduced through trade. The economic, environmental and social costs associated with the spread of invasive pathogens become considerably larger as the incidence of the pathogen increases. To control the movement of pathogens across the plant trade network it is crucial to develop monitoring programmes at key points of the network such as plant nurseries. By detecting the introduction of invasive pathogens at low incidence, the control and eradication of an epidemic is more likely to be successful. Equally, knowing the likelihood of having sold infected plants once a disease has been detected in a nursery can help designing tracing plans to control the onward spread of the disease. Here, we develop an epidemiological model to detect and track the movement of an invasive plant pathogen into and from a plant nursery. Using statistical methods, we predict the epidemic incidence given that a detection of the pathogen has occurred for the first time, considering that the epidemic has an asymptomatic period between infection and symptom development. Equally, we calculate the probability of having sold at least one infected plant during the period previous to the first disease detection. This analysis can aid stakeholder decisions to determine, when the pathogen is first discovered in a nursery, the need of tracking the disease to other points in the plant trade network in order to control the epidemic. We apply our method to high profile recent introductions including ash dieback and sudden oak death in the UK and citrus canker and Huanglongbing disease in Florida. These results provide new insight for the design of monitoring strategies at key points of the trade network.
authors.affiliation: Department of Computational and Systems Biology, Rothamsted Research, Harpenden, AL5 2JQ, UK. Electronic address: vasthi.alonso-chavez@rothamsted.ac.uk.
authors.forename: Vasthi
authors.initials: V
authors.lastname: Alonso Chavez
authors.affiliation: Department of Computational and Systems Biology, Rothamsted Research, Harpenden, AL5 2JQ, UK; University of Salford, School of Environment and Life Sciences, Manchester, M5 4WT, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Parnell
authors.affiliation: Department of Computational and Systems Biology, Rothamsted Research, Harpenden, AL5 2JQ, UK.
authors.forename: Frank
authors.initials: F
authors.lastname: VAN DEN Bosch
mesh_terms: D001419:Bacteria / Q000378:metabolism; D003198:Computer Simulation; D015994:Incidence; D058865:Introduced Species; D008954:Models, Biological; D010935:Plant Diseases / Q000382:microbiology* / Q000706:statistics & numerical data; D010944:Plants / Q000382:microbiology*; D011336:Probability
keywords: Discovery-incidence; Epidemic; Monitoring; Tracking; model