A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale

Viet, Anne-France; Krebs, Stéphane; Rat-Aspert, Olivier; Jeanpierre, Laurent; Belloc, Catherine; Ezanno, Pauline

doi:10.1371/journal.pone.0197612

Cited by 7 publications

(4 citation statements)

References 47 publications

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“…For example, the number of states in a metapopulation SIR model with N hosts in each of M subpopulations scales as O ( N (3 M ) ). This limits applications to cases where status is abstracted to an infection status per subpopulation rather than a detailed host count per compartment (Viet et al 2018).…”

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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“…Only recently have more sophisticated models emerged. An interesting example is the work of Viet et al [30], in which the authors use an MDP to model the spread of the Porcine Reproductive and Respiratory Syndrome (PPRS) and use it to synthesise a regional policy for its containment. A similar attempt has been made also for the SARS-CoV-2 by Nasir et al [25].…”

Section: Related Workmentioning

confidence: 99%

A Markovian Model for the Spread of the SARS-CoV-2 Virus

Palopoli¹,

Fontanelli²,

Frego³

et al. 2022

Preprint

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We propose a Markovian stochastic approach to model the spread of a SARS-CoV-2-like infection within a closed group of humans. The model takes the form of a Partially Observable Markov Decision Process (POMDP), whose states are given by the number of subjects in different health conditions. The model also exposes the different parameters that have an impact on the spread of the disease and the various decision variables that can be used to control it (e.g, social distancing, number of tests administered to single out infected subjects). The model describes the stochastic phenomena that underlie the spread of the epidemic and captures, in the form of deterministic parameters, some fundamental limitations in the availability of resources (hospital beds and test swabs). The model lends itself to different uses. For a given control policy, it is possible to verify if it satisfies an analytical property on the stochastic evolution of the state (e.g., to compute probability that the hospital beds will reach a fill level, or that a specified percentage of the population will die). If the control policy is not given, it is possible to apply POMDP techniques to identify an optimal control policy that fulfils some specified probabilistic goals. Whilst the paper primarily aims at the model description, we show with numeric examples some of its potential applications.

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“…However, most of the available models do not explicitly integrate human decision-making, while control decisions are often made by farmers (e.g., unregulated diseases), with sometimes large-scale health and decision-making consequences (e.g., pathogen spread, dissemination of information and rumours, area of influence). Recent work aims to integrate humans and their decisions by mobilising optimal control and adaptive strategies from AI [ 7 , 85 ] or health economics methods [ 86 , 87 ]. A challenge is to propose clear and context-adapted control policies [ 88 ].…”

Section: Targeted Interventions Model Of Human Decisions and Suppormentioning

confidence: 99%

Research perspectives on animal health in the era of artificial intelligence

Ezanno

Picault

Beaunée

et al. 2021

Vet Res

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Leveraging artificial intelligence (AI) approaches in animal health (AH) makes it possible to address highly complex issues such as those encountered in quantitative and predictive epidemiology, animal/human precision-based medicine, or to study host × pathogen interactions. AI may contribute (i) to diagnosis and disease case detection, (ii) to more reliable predictions and reduced errors, (iii) to representing more realistically complex biological systems and rendering computing codes more readable to non-computer scientists, (iv) to speeding-up decisions and improving accuracy in risk analyses, and (v) to better targeted interventions and anticipated negative effects. In turn, challenges in AH may stimulate AI research due to specificity of AH systems, data, constraints, and analytical objectives. Based on a literature review of scientific papers at the interface between AI and AH covering the period 2009–2019, and interviews with French researchers positioned at this interface, the present study explains the main AH areas where various AI approaches are currently mobilised, how it may contribute to renew AH research issues and remove methodological or conceptual barriers. After presenting the possible obstacles and levers, we propose several recommendations to better grasp the challenge represented by the AH/AI interface. With the development of several recent concepts promoting a global and multisectoral perspective in the field of health, AI should contribute to defract the different disciplines in AH towards more transversal and integrative research.

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A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale

Cited by 7 publications

References 47 publications

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

Optimal Control Prevents Itself from Eradicating Stochastic Disease Epidemics

A Markovian Model for the Spread of the SARS-CoV-2 Virus

Research perspectives on animal health in the era of artificial intelligence

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