Optimal strategies to protect a sub-population at risk due to an established epidemic

Bussell, E. H.; Cunniffe, Nik J.

doi:10.1098/rsif.2021.0718

Cited by 6 publications

(3 citation statements)

References 78 publications

(111 reference statements)

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“…Deterministic optimal control could be validly used in cases where neither global or local eradication of disease is feasible within the constraints. For example, in Bussell and Cunniffe 2022, there is a constant infectious pressure on the system. This situation will be more common in cases with low levels of resource although it may still be optimal to consider local eradication in more isolated subpopulations or groups.…”

Section: Discussionmentioning

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: Discussionmentioning

confidence: 99%

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

Russell,

Cunniffe

2024

Preprint

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“…Mathematical modelling is increasingly used to guide surveillance and intervention strategies for plant pathogens [11,14,[17][18][19], helping policy-makers understand how to direct limited resources for control to reduce transmission [20][21][22][23][24][25]. Multiple studies of different pathogens have focused on the question of how to optimise control measures when a pathogen is known to be in a particular host landscape ('reactive' control).…”

Section: Introductionmentioning

confidence: 99%

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

Lovell-Read

Parnell²,

Cunniffe³

et al. 2023

PLoS Comput Biol

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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, which requires effective surveillance through the implementation of appropriate monitoring programmes. 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 programmes 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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Section: Introductionmentioning

confidence: 99%

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

Lovell-Read

Parnell

Cunniffe

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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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Optimal strategies to protect a sub-population at risk due to an established epidemic

Cited by 6 publications

References 78 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

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

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