Optimising risk-based surveillance for early detection of invasive plant pathogens

Mastin, Alexander; Gottwald, T. R.; Bosch, Frank van den; Cunniffe, Nik J.; Parnell, Stephen

doi:10.1101/834705

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…In realistic, plant focused systems, the subpopulations would often be formed from a raster grid over a landscape (Cunniffe et al 2016, Mastin et al 2020, Godding et al 2023, Nguyen et al 2023, Meentemeyer et al 2011, Harwood et al 2011 and so this can be viewed as a minimal version suitable for easy experimentation with MPC. In real use cases, iterations on the control strategy might occur every few weeks at most and so the runtimes of MPC iterations on larger systems could be acceptable.…”

Section: Potential Challengesmentioning

confidence: 99%

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

Russell,

Cunniffe

2024

Preprint

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The resources available for managing disease epidemics -- whether in animals, plants or humans -- are limited by a range of practical and financial constraints. This motivates study of where these resources should be allocated to maximise their impact. Optimal control has been widely explored for optimising management of epidemics. The most common approach assumes a deterministic, continuous model based on differential equations to approximate the epidemic dynamics. However, real systems are stochastic and so a range of outcomes are possible for any given epidemic situation and choice of control. The deterministic models are also known to be poor approximations in cases where the number of infected hosts is low -- either globally or within a subset of the population -- and these cases are highly relevant in the context of control. Hence, this work explores the effectiveness of disease management strategies derived using optimal control theory when applied to a more realistic, stochastic form of disease model. We demonstrate that solutions to the deterministic optimal control are not optimal in cases where the disease is eradicated or close to eradication. The range of potential outcomes in the stochastic models means that optimising the deterministic case will not reliably eradicate disease, even when it is possible. For effective eradication, the rate of control must be higher than optimal control as applied to the deterministic model would predict. Using Model Predictive Control, in which the optimisation is performed repeatedly as the system evolves to correct for deviations from the optimal control predictions, improves performance but does not fix the underlying issue and the level of control calculated at each repeated optimisation is still insufficient. To demonstrate this, we present several very simple heuristics to identify which sites to control which can outperform the strategies calculated by MPC when the control budget is sufficient for eradication to be possible. Our illustration uses examples based on stochastic simulation of the spatial spread of plant disease but similar issues would be expected in any deterministic model where infection is driven close to zero or targeted to remain below some critical value.

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Section: Potential Challengesmentioning

confidence: 99%

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

Russell,

Cunniffe

2024

Preprint

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“…For example, we could extend the transmission model considered here to account for the spatial structure of the population, allowing the likelihood of transmission between any two plants to depend on the distance between them [19,21,[26][27][28]30]. Considering a spatially heterogeneous model would raise additional questions regarding the optimal spatial placement of sentinel plants and their selection as part of a monitoring programme, particularly if the risk of pathogen invasion also varied in space [65]. In addition to spatial heterogeneities, we could also consider temporal heterogeneities in the probability of invasion and detection that arise due to seasonal effects that impact vector dynamics [55,66] and the level of symptoms displayed by infected hosts [36,67,68].…”

Section: S18)mentioning

confidence: 99%

Using ‘sentinel’ plants to improve early detection of invasive plant pathogens

Lovell-Read

Parnell

Cunniffe

et al. 2022

Preprint

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Infectious diseases of plants present an ongoing and increasing threat to international biosecurity, with wide-ranging implications. An important challenge in plant disease management is achieving early detection of invading pathogens in new locations, which requires effective surveillance through the implementation of appropriate monitoring programs. However, when monitoring relies on visual inspection as a means of detection, surveillance is often hindered by a long incubation period (delay from infection to symptom onset) during which plants may be infectious but not displaying visible symptoms. "Sentinel" plants - alternative susceptible host species that display visible symptoms of infection more rapidly - could be introduced to at-risk populations and included in monitoring programs to act as early warning beacons for infection. However, while sentinel hosts exhibit faster disease progression and so allow pathogens to be detected earlier, this often comes at a cost: faster disease progression typically promotes earlier onward transmission. Here, we construct a computational model of pathogen transmission to explore this trade-off and investigate how including sentinel plants in monitoring programmes could facilitate earlier detection of invasive plant pathogens. Using Xylella fastidiosa infection in Olea europaea (European olive) as a current high profile case study, for which Catharanthus roseus (Madagascan periwinkle) is a candidate sentinel host, we apply a Bayesian optimisation algorithm to determine the optimal number of sentinel hosts to introduce for a given sampling effort, as well as the optimal division of limited surveillance resources between crop and sentinel plants. Our results demonstrate that including sentinel plants in monitoring programmes can reduce the expected prevalence of infection upon outbreak detection substantially, increasing the feasibility of local outbreak containment.

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“…Emerging infectious diseases of plants are an increasing threat to wild and cultivated plants worldwide. After a pathogen or pest has been found in an area, the implementation of delimiting surveillance actions is pivotal to establish management actions such as eradication or containment programs to prevent further spread (Mastin et al 2020). European Union Regulation (EU) 2016/2031 (EU 2016b) provides the general framework for plant pest surveys under a riskbased approach.…”

Section: Introductionmentioning

confidence: 99%

Tracking the outbreak: an optimized sequential adaptive strategy for Xylella fastidiosa delimiting surveys

Lázaro

López‐Quílez

et al. 2021

Biol Invasions

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The EU plant health legislation enforces the implementation of intensive surveillance programs for quarantine pests. After an outbreak, surveys are implemented to delimit the extent of the infested zone and to manage disease control. Surveillance in agricultural and natural environments can be enhanced by increasing the survey efforts. Budget constraints often limit inspection and sampling intensities, thus making it necessary to adapt and optimize surveillance strategies. A sequential adaptive delimiting survey involving a three-phase and a two-phase design with increasing spatial resolution was developed and implemented for the Xylella fastidiosa demarcated area in Alicante, Spain. Inspection and sampling intensities were optimized using simulation-based methods. Sampling intensity thresholds were evaluated by quantifying their effect on the estimation of X. fastidiosa incidence. This strategy made it possible to sequence inspection and sampling taking into account increasing spatial resolutions, and to adapt the inspection and sampling intensities according to the information obtained in the previous, coarser, spatial resolution. The proposed strategy was able to efficiently delimit the extent of Xylella fastidiosa, while improving on the efficiency and maintaining the efficacy of the official survey campaign. From a methodological perspective, our approach provides new insights into alternative delimiting designs and new reference sampling intensity values.

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Optimising risk-based surveillance for early detection of invasive plant pathogens

Cited by 4 publications

References 44 publications

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

Using ‘sentinel’ plants to improve early detection of invasive plant pathogens

Tracking the outbreak: an optimized sequential adaptive strategy for Xylella fastidiosa delimiting surveys

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