Optimal disease outbreak detection in a community using network observability

Alaeddini, Atiye; Morgansen, Kristi A.

doi:10.1109/acc.2016.7526833

Cited by 8 publications

(5 citation statements)

References 19 publications

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“…However, by employing system-theoretic notions such as observability 1 , one could address the aforementioned challenge by deploying as few sensors as possible. One of the earliest works in this direction is [27], where the problem of which subset of nodes in a network should be measured so as to improve observability of a SIS network is addressed; the condition therein involves checking the determinant of the inverse of the observability Grammian. Inspired by the work in [27], we aim to address the following question: under what conditions can we estimate the contamination levels in the infrastructure network by only measuring the infection levels of individuals in the population?…”

Section: Introductionmentioning

confidence: 99%

“…One of the earliest works in this direction is [27], where the problem of which subset of nodes in a network should be measured so as to improve observability of a SIS network is addressed; the condition therein involves checking the determinant of the inverse of the observability Grammian. Inspired by the work in [27], we aim to address the following question: under what conditions can we estimate the contamination levels in the infrastructure network by only measuring the infection levels of individuals in the population? Furthermore, given knowledge of such conditions, can we glean any insights into how the measurement matrix might be designed so that the contamination levels in the infrastructure network can be recovered purely by measuring the infection levels of individuals in the population?…”

Section: Introductionmentioning

confidence: 99%

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Multi-Layer SIS Model with an Infrastructure Network

Pare,

Janson,

Gracy

et al. 2021

Preprint

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This paper deals with the spread of diseases over both a population network and an infrastructure network. We develop a layered networked spread model for a susceptibleinfected-susceptible (SIS) pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-watersusceptible (SIWS) model. The "W" in SIWS represents any infrastructure network contamination, not necessarily restricted to a water distribution network. We say that the SIWS network is in the healthy state (also referred to as the diseasefree equilibrium) if none of the individuals in the population are infected nor is the infrastructure network contaminated; otherwise, we say that the network is in the endemic state (also referred to as the endemic equilibrium). First, we establish sufficient conditions for local exponential stability and global asymptotic stability (GAS) of the healthy state. Second, we provide sufficient conditions for existence, uniqueness, and GAS of the endemic state. Building off of these results, we provide a necessary, and sufficient, condition for the healthy state to be the unique equilibrium of our model. Third, we show that the endemic equilibrium of the SIWS model is worse than that of the networked SIS model without any infrastructure network, in the sense that at least one subpopulation has strictly larger infection proportion at the endemic equilibrium in the former model than that in the latter. Fourth, we study an observability problem, and, assuming that the measurements of the sickness-levels of the human contact network are available, provide a necessary and sufficient condition for estimation of the pathogen levels in the infrastructure network. Furthermore, we provide another sufficient, but not necessary, condition for estimation of pathogen levels in the infrastructure network. By leveraging the sufficient condition we finally provide insights in to how the measurement matrix could be designed so that the system is locally weakly observable.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Layer SIS Model with an Infrastructure Network

Pare,

Janson,

Gracy

et al. 2021

Preprint

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“…Observability-based design of a multi-agent network can be found in recent works [1]- [4]. The notion of observability has also been used in multi-robot localization [5]- [8], social networks [9], electric power grid management [10], and biological systems [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive communication networks with privacy guarantees

Alaeddini

Morgansen

Mesbahi

2017

2017 American Control Conference (ACC)

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Abstract-Utilizing the concept of observability, in conjunction with tools from graph theory and optimization, this paper develops an algorithm for network synthesis with privacy guarantees. In particular, we propose an algorithm for the selection of optimal weights for the communication graph in order to maximize the privacy of nodes in the network, from a control theoretic perspective. In this direction, we propose an observability-based design of the communication topology that improves the privacy of the network in presence of an intruder. The resulting adaptive network responds to the intrusion by changing the topology of the network-in an online manner-in order to reduce the information exposed to the intruder.

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“…disease eradication, at a specified probability (Roh and. When the population counts are sufficiently large, mean field theory can be used to give an approximate model, popular among researchers who study disease spread in a network (Alaeddini and Morgansen 2016, Preciado et al 2013, Miller et al 2012. While all of these algorithms are useful in specific situations, none address the challenge of finding the best inputs, especially for models that are stochastic and have many parameters.…”

Section: Introductionmentioning

confidence: 99%

Application of a second-order stochastic optimization algorithm for fitting stochastic epidemiological models

Alaeddini

Klein

2017

2017 Winter Simulation Conference (WSC)

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Epidemiological models have tremendous potential to forecast disease burden and quantify the impact of interventions. Detailed models are increasingly popular, however these models tend to be stochastic and very costly to evaluate. Fortunately, readily available high-performance cloud computing now means that these models can be evaluated many times in parallel. Here, we briefly describe PSPO, an extension to Spall's second-order stochastic optimization algorithm, Simultaneous Perturbation Stochastic Approximation (SPSA), that takes full advantage of parallel computing environments. The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany. Results indicate that PSPO far outperforms gradient ascent and SPSA on this challenging likelihood maximization problem.

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Optimal disease outbreak detection in a community using network observability

Cited by 8 publications

References 19 publications

Multi-Layer SIS Model with an Infrastructure Network

Multi-Layer SIS Model with an Infrastructure Network

Adaptive communication networks with privacy guarantees

Application of a second-order stochastic optimization algorithm for fitting stochastic epidemiological models

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