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Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model

Lookup NU author(s): Dr Laura WadkinORCiD, Andrew Golightly, Professor Nick ParkerORCiD, Dr Andrew BaggaleyORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Invasive woodland pests have substantial ecological, economic, and social impacts, harming biodiversity and ecosystem services. Mathematical modelling informed by Bayesian inference can deepen our understanding of the fundamental behaviours of invasive pests and provide predictive tools for forecasting future spread. A key invasive pest of concern in the UK is the oak processionary moth (OPM). OPM was established in the UK in 2006; it is harmful to both oak trees and humans, and its infestation area is continually expanding. Here, we use a computational inference scheme to estimate the parameters for a two-node network epidemic model to describe the temporal dynamics of OPM in two geographically neighbouring parks (Bushy Park and Richmond Park, London). We show the applicability of such a network model to describing invasive pest dynamics and our results suggest that the infestation within Richmond Park has largely driven the infestation within Bushy Park.

Publication metadata

Author(s): Wadkin LE, Golightly A, Branson J, Hoppit A, Parker NG, Baggaley AW

Publication type: Article

Publication status: Published

Journal: Diversity

Year: 2023

Volume: 15

Issue: 4

Print publication date: 01/04/2023

Online publication date: 28/03/2023

Acceptance date: 21/03/2023

Date deposited: 24/04/2023

ISSN (electronic): 1424-2818

Publisher: MDPI AG


DOI: 10.3390/d15040496


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