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 forecast...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Diversity |
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| Online Access: | https://www.mdpi.com/1424-2818/15/4/496 |
| _version_ | 1797605784204345344 |
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| author | Laura E. Wadkin Andrew Golightly Julia Branson Andrew Hoppit Nick G. Parker Andrew W. Baggaley |
| author_facet | Laura E. Wadkin Andrew Golightly Julia Branson Andrew Hoppit Nick G. Parker Andrew W. Baggaley |
| author_sort | Laura E. Wadkin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:05:56Z |
| format | Article |
| id | doaj.art-5511c6acabe64cd1ae1044f1c32bc086 |
| institution | Directory Open Access Journal |
| issn | 1424-2818 |
| language | English |
| last_indexed | 2024-03-11T05:05:56Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diversity |
| spelling | doaj.art-5511c6acabe64cd1ae1044f1c32bc0862023-11-17T18:56:27ZengMDPI AGDiversity1424-28182023-03-0115449610.3390/d15040496Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network ModelLaura E. Wadkin0Andrew Golightly1Julia Branson2Andrew Hoppit3Nick G. Parker4Andrew W. Baggaley5School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UKDepartment of Mathematical Sciences, Durham University, Durham DH1 3LE, UKGeoData, Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UKForestry Commission England, Nobel House, London SW1P 3JR, UKSchool of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UKSchool of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne NE1 7RU, UKInvasive 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.https://www.mdpi.com/1424-2818/15/4/496invasive pestsnetwork epidemic modelscompartmental epidemic modelsoak processionary mothBayesian inferenceSIR model |
| spellingShingle | Laura E. Wadkin Andrew Golightly Julia Branson Andrew Hoppit Nick G. Parker Andrew W. Baggaley Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model Diversity invasive pests network epidemic models compartmental epidemic models oak processionary moth Bayesian inference SIR model |
| title | Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model |
| title_full | Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model |
| title_fullStr | Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model |
| title_full_unstemmed | Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model |
| title_short | Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model |
| title_sort | quantifying invasive pest dynamics through inference of a two node epidemic network model |
| topic | invasive pests network epidemic models compartmental epidemic models oak processionary moth Bayesian inference SIR model |
| url | https://www.mdpi.com/1424-2818/15/4/496 |
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