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Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK

Author

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  • Harwood, Thomas D.
  • Xu, Xiangming
  • Pautasso, Marco
  • Jeger, Mike J.
  • Shaw, Michael W.

Abstract

We developed a stochastic simulation model incorporating most processes likely to be important in the spread of Phytophthora ramorum and similar diseases across the British landscape (covering Rhododendron ponticum in woodland and nurseries, and Vaccinium myrtillus in heathland). The simulation allows for movements of diseased plants within a realistically modelled trade network and long-distance natural dispersal. A series of simulation experiments were run with the model, representing an experiment varying the epidemic pressure and linkage between natural vegetation and horticultural trade, with or without disease spread in commercial trade, and with or without inspections-with-eradication, to give a 2×2×2×2 factorial started at 10 arbitrary locations spread across England. Fifty replicate simulations were made at each set of parameter values. Individual epidemics varied dramatically in size due to stochastic effects throughout the model. Across a range of epidemic pressures, the size of the epidemic was 5–13 times larger when commercial movement of plants was included. A key unknown factor in the system is the area of susceptible habitat outside the nursery system. Inspections, with a probability of detection and efficiency of infected-plant removal of 80% and made at 90-day intervals, reduced the size of epidemics by about 60% across the three sectors with a density of 1% susceptible plants in broadleaf woodland and heathland. Reducing this density to 0.1% largely isolated the trade network, so that inspections reduced the final epidemic size by over 90%, and most epidemics ended without escape into nature. Even in this case, however, major wild epidemics developed in a few percent of cases. Provided the number of new introductions remains low, the current inspection policy will control most epidemics. However, as the rate of introduction increases, it can overwhelm any reasonable inspection regime, largely due to spread prior to detection.

Suggested Citation

  • Harwood, Thomas D. & Xu, Xiangming & Pautasso, Marco & Jeger, Mike J. & Shaw, Michael W., 2009. "Epidemiological risk assessment using linked network and grid based modelling: Phytophthora ramorum and Phytophthora kernoviae in the UK," Ecological Modelling, Elsevier, vol. 220(23), pages 3353-3361.
  • Handle: RePEc:eee:ecomod:v:220:y:2009:i:23:p:3353-3361
    DOI: 10.1016/j.ecolmodel.2009.08.014
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    Citations

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    Cited by:

    1. Stanaway, M.A. & Reeves, R. & Mengersen, K.L., 2011. "Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal," Ecological Modelling, Elsevier, vol. 222(19), pages 3531-3540.
    2. Silvia Traversari & Sonia Cacini & Angelica Galieni & Beatrice Nesi & Nicola Nicastro & Catello Pane, 2021. "Precision Agriculture Digital Technologies for Sustainable Fungal Disease Management of Ornamental Plants," Sustainability, MDPI, vol. 13(7), pages 1-22, March.
    3. Douma, J.C. & Pautasso, M. & Venette, R.C. & Robinet, C. & Hemerik, L. & Mourits, M.C.M. & Schans, J. & van der Werf, W., 2016. "Pathway models for analysing and managing the introduction of alien plant pests—an overview and categorization," Ecological Modelling, Elsevier, vol. 339(C), pages 58-67.
    4. Vuilleumier, S. & Buttler, A. & Perrin, N. & Yearsley, J.M., 2011. "Invasion and eradication of a competitively superior species in heterogeneous landscapes," Ecological Modelling, Elsevier, vol. 222(3), pages 398-406.
    5. Thompson, Robin N. & Cobb, Richard C. & Gilligan, Christopher A. & Cunniffe, Nik J., 2016. "Management of invading pathogens should be informed by epidemiology rather than administrative boundaries," Ecological Modelling, Elsevier, vol. 324(C), pages 28-32.
    6. David C. Cook & Shuang Liu & Brendan Murphy & W. Mark Lonsdale, 2010. "Adaptive Approaches to Biosecurity Governance," Risk Analysis, John Wiley & Sons, vol. 30(9), pages 1303-1314, September.

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