Suppressing Epidemics in Networks Using Priority Planning

Scaman, Kevin; Kalogeratos, Argyris; Vayatis, Nicolas

doi:10.1109/tnse.2016.2600029

Cited by 24 publications

(29 citation statements)

References 31 publications

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“…holds. However, (13) is not the best approximation that can be derived from the solutions of (10). The optimal approximation which can be obtained from propagating the dynamics (10) can be found efficiently via linear programming, as we state formally in the following result and its proof.…”

Section: Decay Constraint Approximationmentioning

confidence: 99%

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Watkins

Nowzari

Pappas

2020

IEEE Trans. Automat. Contr.

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In this paper, we develop a robust economic model predictive controller for the containment of stochastic Susceptible-Exposed-Infected-Vigilant pSEIV q epidemic processes which drives the process to extinction quickly, while minimizing the rate at which control resources are used. The work we present here is significant in that it addresses the problem of efficiently controlling general stochastic epidemic systems without relying on mean-field approximation, which is an important issue in the theory of stochastic epidemic processes. This enables us to provide rigorous convergence guarantees on the stochastic epidemic model itself, improving over the mean-field type convergence results of most prior work. There are two primary technical difficulties addressed in treating this problem: (i) constructing a means of tractably approximating the evolution of the process, so that the designed approximation is robust to the modeling error introduced by the applied moment closure, and (ii) guaranteeing that the designed controller causes the closed-loop system to drive the SEIV process to extinction quickly. As an application, we use the developed framework for optimizing the use of quarantines in containing an SEIV epidemic outbreak.

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Section: Decay Constraint Approximationmentioning

confidence: 99%

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Watkins

Nowzari

Pappas

2020

IEEE Trans. Automat. Contr.

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“…The Dynamic Resource Allocation (DRA) [11], [12] dynamically determines the resource allocation vector…”

Section: A Setting and Scoring Functionmentioning

confidence: 99%

“…Dynamic Resource Allocation (DRA) [11], [12] is a model for network control, originally developed for SISlike processes [10] (the nodes are either infected or healthy without permanent immunity) that distributes a limited budget of available treatment resources on infected nodes in order to speed-up their recovery. The resources are non-cumulable at nodes (i.e.…”

Section: Introductionmentioning

confidence: 99%

Sequential Dynamic Resource Allocation for Epidemic Control

Fekom¹,

Vayatis²,

Kalogeratos³

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Under the Dynamic Resource Allocation (DRA) model, an administrator has the mission to allocate dynamically a limited budget of resources to the nodes of a network in order to reduce a diffusion process (DP) (e.g. an epidemic). The standard DRA assumes that the administrator has constantly full information and instantaneous access to the entire network. Towards bringing such strategies closer to real-life constraints, we first present the Restricted DRA model extension where, at each intervention round, the access is restricted to only a fraction of the network nodes, called sample. Then, inspired by sequential selection problems such as the well-known Secretary Problem, we propose the Sequential DRA (SDRA) model. Our model introduces a sequential aspect in the decision process over the sample of each round, offering a completely new perspective to the dynamic DP control. Finally, we incorporate several sequential selection algorithms to SDRA control strategies and compare their performance in SIS epidemic simulations.The authors are with CMLA -ENS Paris-Saclay,

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“…In recent years, epidemic theory has found applications in various different fields, covering virus/disease spreading (both biological and digital ones) (e.g., [1] [5]) and corresponding immunization strategies (e.g., [26] [27] [28] [29]), information dissemination in (online) social networks (e.g., [30]), communication protocol design (e.g., [31] [32] [33]) and cascading failure prediction/protection (e.g., [34]) as well as in more general contexts, analysis on stability of spreading processes over time-varying networks (e.g., [35]) and iden-tification of influential seeds/spreaders in networks (e.g., [36]). …”

Section: Background Basics and Related Workmentioning

confidence: 99%

Modelling Spreading Process Induced by Agent Mobility in Complex Networks

Chai

2018

IEEE Trans. Netw. Sci. Eng.

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Abstract-Most conventional epidemic models assume contact-based contagion process. We depart from this assumption and study epidemic spreading process in networks caused by agents acting as carrier of infection. These agents traverse from origins to destinations following specific paths in a network and in the process, infecting the sites they travel across. We focus our work on the Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents' vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters' activities in the system. We highlight the key differences in the spreading patterns between the contact-based vs. agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based model due to agents' movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of the network and vice versa.

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Suppressing Epidemics in Networks Using Priority Planning

Cited by 24 publications

References 31 publications

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Robust Economic Model Predictive Control of Continuous-Time Epidemic Processes

Sequential Dynamic Resource Allocation for Epidemic Control

Modelling Spreading Process Induced by Agent Mobility in Complex Networks

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