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Lookup NU author(s): Dr Laura WadkinORCiD, Dr Andrew Golightly, Dr Andrew BaggaleyORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s)Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Computational individual-based models are a popular tool for describing and forecasting the spread of tree diseases due to their flexibility and ability to reveal collective behaviours. In this paper we present a versatile individual-based model with a Gaussian infectivity kernel to describe the spread of a generic tree disease through a synthetic treescape. We then explore several methods of calculating the basic reproduction number R0, a characteristic measurement of disease infectivity, defining the expected number of new infections resulting from one newly infected individual throughout their infectious period. It is a useful comparative summary parameter of a disease and can be used to explore the threshold dynamics of epidemics through mathematical models. We demonstrate several methods of estimating R0 through the individual-based model, including contact tracing, inferring the Kermack–McKendrick SIR model parameters using the linear noise approximation, and an analytical approximation. As an illustrative example, we then use the model and each of the methods to calculate estimates of R0 for the ash dieback epidemic in the UK.
Author(s): Wadkin LE, Holden J, Ettelaie R, Holmes MJ, Smith J, Golightly A, Parker NG, Baggaley AW
Publication type: Article
Publication status: Published
Journal: Ecological Modelling
Year: 2024
Volume: 489
Print publication date: 01/03/2024
Online publication date: 25/01/2024
Acceptance date: 16/01/2024
Date deposited: 20/02/2024
ISSN (print): 0304-3800
ISSN (electronic): 1872-7026
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.ecolmodel.2024.110630
DOI: 10.1016/j.ecolmodel.2024.110630
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