Edge-based epidemic dynamics with multiple routes of transmission on random networks

Wang, Yi; Cao, Jinde; Li, Xiaodi

doi:10.1007/s11071-017-3877-3

Cited by 44 publications

(19 citation statements)

References 34 publications

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“…Therefore, approximations are often introduced to simplify the exact model and derive quantitative results. Most notably, well-known and widely used theoretical approaches include mean-field and higher order approximations 12 , 18 , edge-based dynamics 24 , 42 , percolation 10 , 23 , 25 and generating functions 29 , 33 .…”

Section: Introductionmentioning

confidence: 99%

Network inference from population-level observation of epidemics

Lauro

Croix

Dashti

et al. 2020

Sci Rep

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Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the class of the underlying network when epidemic data is only available at population-level (i.e., the number of infected individuals at a finite set of discrete times of a single realisation of the epidemic), the only information likely to be available in real world settings. To tackle this, epidemics on networks are approximated by a Birth-and-Death process which keeps track of the number of infected nodes at population level. The rates of this surrogate model encode both the structure of the underlying network and disease dynamics. We use extensive simulations over Regular, Erdős–Rényi and Barabási–Albert networks to build network class-specific priors for these rates. We then use Bayesian model selection to recover the most likely underlying network class, based only on a single realisation of the epidemic. We show that the proposed methodology yields good results on both synthetic and real-world networks.

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Section: Introductionmentioning

confidence: 99%

Network inference from population-level observation of epidemics

Lauro

Croix

Dashti

et al. 2020

Sci Rep

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“…On the one hand, it is natural to extend our study to multiple routes of rumors spread in heterogeneous networks. According to [55], the whole spreaders of two rumors with mutual promotion are regarded as an external virtual node that has connections with every other individual. The degree distribution of heterogeneous networks should be considered in 3SI3R model.…”

Section: Resultsmentioning

confidence: 99%

A 3SI3R model for the propagation of two rumors with mutual promotion

et al. 2020

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There are often many kinds of rumors spreading in new media after emergencies; however, the existing studies have focused on the rumors with competition, and little attention has been paid to those with mutual promotion. Therefore the mutual promotion between two rumors and the state transition rules between different groups are first analyzed. Then a 3SI3R model, extending the classical 2SI2R model, and mean-field equations of the proposed model are given. Further, the stability and influence of the mutual promotion on the final size of two rumors in the proposed model are derived. Finally, numerical simulations are carried out to investigate the influence of factors, that is, the mutual promotion, occurrence time differences, and the forgetting rate, on the propagation of two rumors. The results show that the enhancement of the mutual promotion can accelerate propagation of two rumors and enlarge their final size; the increase of forgetting rate helps to decrease the final size of two rumors; the decrease in occurrence time differences can result in a small increase in the final size of the later rumor, and the overall situation of two rumors becomes more difficult to control.

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“…Many great works in the community of control and engineering have reached towards this kind of systems. See [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] for details.…”

Section: Resultsmentioning

confidence: 99%

Modeling and Stability Analysis for Markov Jump Networked Evolutionary Games

Zhao

2018

Mathematical Problems in Engineering

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This paper investigates the algebraic formulation and stability analysis for a class of Markov jump networked evolutionary games by using the semitensor product method and presents a number of new results. Firstly, a proper algorithm is constructed to convert the given networked evolutionary games into an algebraic expression. Secondly, based on the algebraic expression, the stability of the given game is analyzed and an equivalent criterion is given. Finally, an example works out to support the new results.

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Edge-based epidemic dynamics with multiple routes of transmission on random networks

Cited by 44 publications

References 34 publications

Network inference from population-level observation of epidemics

Network inference from population-level observation of epidemics

A 3SI3R model for the propagation of two rumors with mutual promotion

Modeling and Stability Analysis for Markov Jump Networked Evolutionary Games

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