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

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.

Automated summary

Invasive woodland pests can have significant impacts on ecology, economy, and society, causing harm to biodiversity and ecosystem services. Bayesian-informed mathematical modeling can enhance our understanding of invasive pest behaviors and provide predictive tools for future spread. The oak processionary moth (OPM) is a major invasive pest in the UK, harmful to both oak trees and humans, with its infestation expanding continuously. In this study, a computational inference scheme is used to estimate parameters for a two-node network epidemic model, describing the temporal dynamics of OPM in two geographically neighboring parks (Bushy Park and Richmond Park, London). Results suggest that the infestation in Richmond Park has largely driven the infestation in Bushy Park, showcasing the applicability of network models in describing invasive pest dynamics.