Inventory and review of quantitative models for spread of plant pests for use in pest risk assessment for the EU territory

Chapman, Daniel S.; White, Steven M.; Hooftman, Danny A. P.; Bullock, James M.

doi:10.2903/sp.efsa.2015.en-795

Cited by 15 publications

(26 citation statements)

References 115 publications

(83 reference statements)

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“…We model the spread of X. fastidiosa using a spatially explicit simulation model, building upon the spread model presented in Chapman et al (2015) (see Case Study 5), which we briefly describe in the sections below. The model runs over the Apulian region at a 1 km 2 gridded resolution and at a yearly temporal scale to correspond with the seasonality of the vector which only feeds on olive trees in the summer months when host grasses dry-out (EFSA PLH Panel (EFSA Panel on Plant Health) 2015).…”

Section: Methodsmentioning

confidence: 99%

“…progression of disease within the grid cell as a fraction of trees infected); and dispersal between grid cells. This approach is well suited to the underlying epidemiological mechanisms of vector spread and phenology (Chapman et al 2015; EFSA PLH Panel (EFSA Panel on Plant Health) 2015). Chapman et al (2015) showed that the model reproduces the qualitative patterns of X. fastidiosa spread in Apulia, Italy.…”

Section: Methodsmentioning

confidence: 99%

“…This approach is well suited to the underlying epidemiological mechanisms of vector spread and phenology (Chapman et al 2015; EFSA PLH Panel (EFSA Panel on Plant Health) 2015). Chapman et al (2015) showed that the model reproduces the qualitative patterns of X. fastidiosa spread in Apulia, Italy. In this paper, we use this framework to provide estimates of the accuracy of the underlying spread model by comparing risk analysis with spatial infection data.…”

Section: Methodsmentioning

confidence: 99%

“…In this paper, we build upon a novel mechanistic model for the spread of X. fastidiosa in Apulia (Chapman et al 2015) and show that it qualitatively and quantitatively fits the observed pattern of spread. Using the spread model we test the control strategies currently being employed, namely the eradication zone (EZ) and buffer zone (BZ).…”

Section: Introductionmentioning

confidence: 93%

“…Parnell et al (2015) use a simple spread model to reveal a rule of thumb for early detection surveillance strategies for EIDs of plants. However, it is rare to use spread models in plant health risk assessment in contrast to non-mechanistic species distribution models (Chapman et al 2015).…”

Section: Introductionmentioning

confidence: 99%

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Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

et al. 2017

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Xylella fastidiosa is an important plant pathogen that attacks several plants of economic importance. Once restricted to the Americas, the bacterium, which causes olive quick decline syndrome, was discovered near Lecce, Italy in 2013. Since the initial outbreak, it has invaded 23,000 ha of olives in the Apulian Region, southern Italy, and is of great concern throughout Mediterranean basin. Therefore, predicting its spread and estimating the efficacy of control are of utmost importance. As data on this invasive infectious disease are poor, we have developed a spatially-explicit simulation model for X. fastidiosa to provide guidance for predicting spread in the early stages of invasion and inform management strategies. The model qualitatively and quantitatively predicts the patterns of spread. We model control zones currently employed in Apulia, showing that increasing buffer widths decrease infection risk beyond the control zone, but this may not halt the spread completely due to stochastic long-distance jumps caused by vector dispersal. Therefore, management practices should aim to reduce vector longdistance dispersal. We find optimal control scenarios that minimise control effort while reducing X. fastidiosa spread maximally-suggesting that increasing buffer zone widths should be favoured over surveillance efforts as control budgets increase. Our model highlights the importance of non-olive hosts which increase the spread rate of the disease and may lead to an order of magnitude increase in risk. Many aspects of X. fastidiosa disease invasion remain uncertain and hinder forecasting; we recommend future studies investigating quantification of the infection growth rate, and short and long distance dispersal.Keywords Buffer zone Á CoDiRO Á Olea europaea Á Olive quick decline syndrome Á Pierce's disease Á X. fastidiosa Electronic supplementary material The online version of this article

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

et al. 2017

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Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model

et al. 2019

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Invasion of new territories by alien organisms is of primary concern for environmental and health agencies and has been a core topic in mathematical modeling, in particular in the intents of reconstructing the past dynamics of the alien organisms and predicting their future spatial extents. Partial differential equations offer a rich and flexible modeling framework that has been applied to a large number of invasions. In this article, we are specifically interested in dating and localizing the introduction that led to an invasion using mathematical modeling, post-introduction data and an adequate statistical inference procedure. We adopt a mechanistic-statistical approach grounded on a coupled reaction–diffusion–absorption model representing the dynamics of an organism in an heterogeneous domain with respect to growth. Initial conditions (including the date and site of the introduction) and model parameters related to diffusion, reproduction and mortality are jointly estimated in the Bayesian framework by using an adaptive importance sampling algorithm. This framework is applied to the invasion of Xylella fastidiosa , a phytopathogenic bacterium detected in South Corsica in 2015, France. Electronic supplementary material The online version of this article (10.1007/s00285-019-01376-x) contains supplementary material, which is available to authorized users.

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Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa

et al. 2023

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Forecasting invasive-pathogen dynamics is paramount to anticipate eradication and containment strategies. Such predictions can be obtained using a model grounded on partial differential equations (PDE; often exploited to model invasions) and fitted to surveillance data. This framework allows the construction of phenomenological but concise models relying on mechanistic hypotheses and real observations. However, it may lead to models with overly rigid behavior and possible data-model mismatches. Hence, to avoid drawing a forecast grounded on a single PDE-based model that would be prone to errors, we propose to apply Bayesian model averaging (BMA), which allows us to account for both parameter and model uncertainties. Thus, we propose a set of different competing PDE-based models for representing the pathogen dynamics, we use an adaptive multiple importance sampling algorithm (AMIS) to estimate parameters of each competing model from surveillance data in a mechanistic-statistical framework, we evaluate the posterior probabilities of models by comparing different approaches proposed in the literature, and we apply BMA to draw posterior distributions of parameters and a posterior forecast of the pathogen dynamics. This approach is applied to predict the extent of Xylella fastidiosa in South Corsica, France, a phytopathogenic bacterium detected in situ in Europe less than 10 years ago (Italy 2013, France 2015). Separating data into training and validation sets, we show that the BMA forecast outperforms competing forecast approaches. Supplementary Information The online version contains supplementary material available at 10.1007/s11538-023-01169-w.

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Inventory and review of quantitative models for spread of plant pests for use in pest risk assessment for the EU territory

Cited by 15 publications

References 115 publications

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy

Dating and localizing an invasion from post-introduction data and a coupled reaction–diffusion–absorption model

Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa

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