Literature DB >> 27477202

Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape.

Vasthi Alonso Chavez1, Stephen Parnell2, Frank VAN DEN Bosch3.   

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.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Discovery-incidence; Epidemic; Monitoring; Tracking; model

Mesh:

Year:  2016        PMID: 27477202     DOI: 10.1016/j.jtbi.2016.07.041

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  6 in total

1.  Translating surveillance data into incidence estimates.

Authors:  Y Bourhis; T Gottwald; F van den Bosch
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

2.  A method of determining where to target surveillance efforts in heterogeneous epidemiological systems.

Authors:  Alexander J Mastin; Frank van den Bosch; Timothy R Gottwald; Vasthi Alonso Chavez; Stephen R Parnell
Journal:  PLoS Comput Biol       Date:  2017-08-28       Impact factor: 4.475

3.  Quantifying the hidden costs of imperfect detection for early detection surveillance.

Authors:  Alexander J Mastin; Frank van den Bosch; Femke van den Berg; Stephen R Parnell
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2019-07-08       Impact factor: 6.237

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

Authors:  Alexander J Mastin; Timothy R Gottwald; Frank van den Bosch; Nik J Cunniffe; Stephen Parnell
Journal:  PLoS Biol       Date:  2020-10-12       Impact factor: 8.029

5.  Epidemiologically-based strategies for the detection of emerging plant pathogens.

Authors:  Alexander J Mastin; Frank van den Bosch; Yoann Bourhis; Stephen Parnell
Journal:  Sci Rep       Date:  2022-06-29       Impact factor: 4.996

Review 6.  Promising Perspectives for Detection, Identification, and Quantification of Plant Pathogenic Fungi and Oomycetes through Targeting Mitochondrial DNA.

Authors:  Tomasz Kulik; Katarzyna Bilska; Maciej Żelechowski
Journal:  Int J Mol Sci       Date:  2020-04-10       Impact factor: 5.923

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.