Synergistic Effects in Networked Epidemic Spreading Dynamics

Ogura, Masaki; Mei, Wenjie; Sugimoto, Kenji

doi:10.1109/tcsii.2019.2912794

Cited by 12 publications

(4 citation statements)

References 28 publications

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“…[12]- [14], and positive realization [15]. Moreover, positive systems theory is also used in cooperative control [16], timedelay systems [17] and epidemic spreading dynamics [18].…”

Section: Introductionmentioning

confidence: 99%

Linear Quadratic Control of Positive Systems: A Projection-Based Approach

Yang

Tang

Wong

et al. 2023

IEEE Trans. Automat. Contr.

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This technical note investigates the linear quadratic regulator (LQR) design for continuous-time positive linear systems. Based on positive systems theory and Lyapunov theory, the solvability and optimality of the positivity-preserving LQR problem are analyzed through the lens of optimization, and two projection theorems are derived for single-input and multi-input positive systems, respectively, which paves the way for developing a projected gradient descent (PGD) algorithm. The proposed results fill the literature gap by considering both the optimality and positivity in LQR design, and numerical simulations are provided to verify the effectiveness of the results.

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“…[12]- [14], and positive realization [15]. Moreover, positive systems theory is also used in cooperative control [16], timedelay systems [17] and epidemic spreading dynamics [18].…”

Section: Introductionmentioning

confidence: 99%

Linear Quadratic Control of Positive Systems: A Projection-Based Approach

Yang

Tang

Wong

et al. 2023

IEEE Trans. Automat. Contr.

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“…For the past decades, complex network, as a complex system description method, has attracted much attention [10], [11]. Complex network provides new approaches of nonlinear time series analysis [12] and has been successfully used in network topology inference [13], multiphase flow [14], information propagation [15] and especially in brain science. A novel study [16], based on EEG signals, provides quantitative evidence that the disruption in the cortical inter-hemispheric connectivity and deficits in higher order cognitive functions are correlated with cognitive deficits.…”

Section: Introductionmentioning

confidence: 99%

Complex Network Analysis of Experimental EEG Signals for Decoding Brain Cognitive State

Gao

Gong

Cai

et al. 2021

IEEE Trans. Circuits Syst. II

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Depicting the relationship between brain cognitive state and task difficulty level constitutes a challenging problem of significant importance. In order to probe it, we design an experiment to gather EEG data from mental arithmetic task under different difficulty levels. We construct brain complex networks using a complex network method and information entropy theory. We then employ weighted clustering coefficient to characterize the networks generated from different brain cognitive states. The results show that with the increase in task difficulty level, the mean weighted clustering coefficients show a decrease. This is due to the lack of coordination of brain activity and the low efficiency of the network organization caused by the increase in task difficulty. In addition, we calculate the permutation entropy from the signals of each channel EEG signals to support the findings from our network analysis. These findings render our method particularly useful for depicting the relationship between brain cognitive state and difficulty level.

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“…Over the past few years, an increasing body of research has considered the synergistic effect in contagion processes [7,[10][11][12][13][14][15][16][17][18][19]. The synergistic effect represents a nonlinear cooperative effect in the transmission between an infected-susceptible pair, that is, infected neighbors around them enhance the transmission rate.…”

Section: Introductionmentioning

confidence: 99%

Synergistic epidemic spreading in correlated networks

Mizutaka¹,

Mori²,

Hasegawa³

2021

Preprint

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We investigate the effect of degree correlation on a susceptible-infected-susceptible (SIS) model with a nonlinear cooperative effect (synergy) in infectious transmissions. In a mean-field treatment of the synergistic SIS model on a bimodal network with tunable degree correlation, we identify a discontinuous transition that is independent of the degree correlation strength unless the synergy is absent or extremely weak. Regardless of synergy (absent or present), a positive and negative degree correlation in the model reduces and raises the epidemic threshold, respectively. For networks with a strongly positive degree correlation, the mean-field treatment predicts the emergence of two discontinuous jumps in the steady-state infected density. To test the mean-field treatment, we provide approximate master equations of the present model, which accurately describe the synergistic SIS dynamics. We quantitatively confirm all qualitative predictions of the mean-field treatment in numerical evaluations of the approximate master equations.

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Synergistic Effects in Networked Epidemic Spreading Dynamics

Cited by 12 publications

References 28 publications

Linear Quadratic Control of Positive Systems: A Projection-Based Approach

Linear Quadratic Control of Positive Systems: A Projection-Based Approach

Complex Network Analysis of Experimental EEG Signals for Decoding Brain Cognitive State

Synergistic epidemic spreading in correlated networks

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