Modelling and inference of tree pandemics in Great Britain
Lead Research Organisation:
Newcastle University
Department Name: Sch of Maths, Statistics and Physics
Abstract
The spread of contagious tree diseases threatens woodlands and urban trees in the UK and globally, and recent UK epidemics of ash and chestnut trees have been covered extensively in the news. The decimation of entire tree populations leads to dramatic ecological changes, and can also pose profound socio-economic challenges. Defra, the government Department for Environmental, Food and Rural Affairs have identified a lack of realistic models to describe and predict tree disease spread as a key gap in their ability to manage pests and diseases effectively. This could have a major influence on the planning and management of trees in both woodlands and urban settings. The proposed research will combine cutting edge techniques from applied mathematical modelling and statistical inference to develop a comprehensive modelling approach to predict tree disease in the UK. The framework we propose will allow the model to be trained on data of past outbreaks before being used to predict emerging pathogens such as sweet chestnut blight.
Publications
Golightly A
(2023)
Accelerating Bayesian inference for stochastic epidemic models using incidence data
in Statistics and Computing
Wadkin L
(2023)
Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model
in Diversity
Wadkin L
(2024)
Estimating the reproduction number, R 0 , from individual-based models of tree disease spread
in Ecological Modelling
Wadkin LE
(2022)
Inference for epidemic models with time-varying infection rates: Tracking the dynamics of oak processionary moth in the UK.
in Ecology and evolution
| Description | Invasive pests pose a great threat to forests, woodland and urban tree ecosystems. The oak processionary moth (OPM) is a destructive pest of oak trees, first reported in the UK in 2006. Despite great efforts to contain the outbreak within the original infested area of South-East England, OPM continues to spread. The main efforts of the funded project have present has been an analysis of the numbers of OPM nests removed each year from two parks in London between 2013 and 2020. Using a state-of-the-art Bayesian inference scheme we estimate the parameters for a stochastic compartmental SIR (susceptible, infested, removed) model with a time-varying infestation rate to describe the spread of OPM. We find that the infestation rate and subsequent basic reproduction number have remained constant since 2013. This shows further controls must be taken to reduce the reproduction rate of the moth below one and stop the advance of OPM into other areas of England. Our findings demonstrate the applicability of the SIR model to describing OPM spread and show that further controls are needed to reduce the infestation rate. The proposed statistical methodology is a powerful tool to explore the nature of a time-varying infestation rate, applicable to other partially observed time series epidemic data and so an important tool for the wider community. |
| Exploitation Route | We are currently disseminating our work to the community to ensure it is put to use by others |
| Sectors | Agriculture Food and Drink Environment |
| Description | Knowledge Exchange Fellowship: developing and sharing mathematical tools to protect urban trees and woodland from invasive pests |
| Amount | £158,322 (GBP) |
| Funding ID | NE/X000478/1 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2022 |
| End | 09/2025 |
| Title | Two node compartmental model of epidemic spread |
| Description | Compartmental modes are widely used to model and understand the spread of disease. Through the research funded by this grant, we have demonstrated their applicability to model the spread of invasive species. We have also gone further and considered a linked network to understand the spatial spread of invasives pest and pathogens. Increases in invasive pests (driven by climate change and enhanced global trade) are having a substantial ecological, economic and social impact, which highlights the value of our new approach. |
| Type Of Material | Data analysis technique |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | The paper has just been accepted and is in press. |
| Description | Fera |
| Organisation | Fera Science Limited |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Working on ABM of tree disease in collaboration with Fera Science Plc |
| Collaborator Contribution | Research time |
| Impact | Still in preparation |
| Start Year | 2023 |
| Description | Forestry Commission England |
| Organisation | Forestry Commission |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | In collaboration with Andrew Hoppit (Forestry Commission England), we have made significant steps in modelling the spread of Oak Processionary Moth (OPM) across the Royal Parks in London. This is with a view to developing larger regional level predictions of the spread across the next decade. We have developed Spatio-temporal agent-based models of the spread and have also performed time-series analysis of the aggregated data. Using a variation of and SIR model we used Bayesian inference to understand the time-varying 'infectivity' of the moth across an 8-year window and a first paper as a result of this partnership is under review at present. |
| Collaborator Contribution | Andrew has contributed to important data sets which underpin our work and also expert knowledge of the biology of OPM, its typical behaviour and information about human interventions (nest-removal) which guide our modelling efforts. |
| Impact | This is a multi-disciplinary partnership with a collaboration between mathematics & statistics researchers and a government agency |
| Start Year | 2021 |
| Title | An individual-based model with an infectious kernel describing the spatial spread of an epidemic, plus several approximations (both analytic and computational, including an inference scheme) of the reproduction number calculated for different epidemic... |
| Description | This code allows the user to recreate the figures from the Ecological Modelling paper: "Estimating the reproduction number, R0, from individual-based models of tree disease spread", DOI: XXXX, including model simulations and the inference scheme.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 $R_0$, 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 $R_0$ 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 $R_0$ for the ash dieback epidemic in the UK. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://data.ncl.ac.uk/articles/software/An_individual-based_model_with_an_infectious_kernel_describ... |
| Title | An individual-based model with an infectious kernel describing the spatial spread of an epidemic, plus several approximations (both analytic and computational, including an inference scheme) of the reproduction number calculated for different epidemic... |
| Description | This code allows the user to recreate the figures from the Ecological Modelling paper: "Estimating the reproduction number, R0, from individual-based models of tree disease spread", DOI: https://doi.org/10.1016/j.ecolmodel.2024.110630, including model simulations and the inference scheme. 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 $R_0$, 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 $R_0$ 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 $R_0$ for the ash dieback epidemic in the UK. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://data.ncl.ac.uk/articles/software/An_individual-based_model_with_an_infectious_kernel_describ... |
| Title | Quantifying invasive pest dynamics through inference of a two-node epidemic network model |
| Description | This code allows the user to recreate the figures from the Diversity paper: "Quantifying invasive pest dynamics through inference of a two-node epidemic network model" https://doi.org/10.3390/d15040496, including model simulations and the inference scheme. 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. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://data.ncl.ac.uk/articles/software/Quantifying_invasive_pest_dynamics_through_inference_of_a_t... |
| Title | Software supporting 'Quantifying invasive pest dynamics through inference of a two-node epidemic network model' |
| Description | This code allows the user to recreate the figures from the Diversity paper: "Quantifying invasive pest dynamics through inference of a two-node epidemic network model" https://doi.org/10.3390/d15040496, including model simulations and the inference scheme. 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. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://data.ncl.ac.uk/articles/software/Quantifying_invasive_pest_dynamics_through_inference_of_a_t... |
| Title | Software supporting 'Quantifying invasive pest dynamics through inference of a two-node epidemic network model' |
| Description | This code allows the user to recreate the figures from the Diversity paper: "Quantifying invasive pest dynamics through inference of a two-node epidemic network model" https://doi.org/10.3390/d15040496, including model simulations and the inference scheme. 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. |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://data.ncl.ac.uk/articles/software/Quantifying_invasive_pest_dynamics_through_inference_of_a_t... |