Markovian approach to tackle competing pathogens in simplicial complex

Nie, Yanyi; Li, Wenyao; Pan, Liming; Lin, Tao; Wang, Wei

doi:10.1016/j.amc.2021.126773

Cited by 29 publications

(8 citation statements)

References 50 publications

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“…By extending the Microscopic Markov Chain Approach [31] , [33] , [56] , [57] , the dynamical process of our model can be described as the following time evolution equations.…”

Section: Theoretical Analysismentioning

confidence: 99%

“…The different sub-populations are linked by population movement. Unlike the contact network model [30] , [31] , [32] , [33] , the meta-population network is a coarse-grained description, where each node in the network represents a region (city). Individuals within the same node can react, for example, e.g., transmitting diseases, and they move to neighboring nodes according to certain rules.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pathogen diversity in meta-population networks

Nie¹,

Zhong²,

Lin³

et al. 2023

Chaos, Solitons & Fractals

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“…By extending the Microscopic Markov Chain Approach [31] , [33] , [56] , [57] , the dynamical process of our model can be described as the following time evolution equations.…”

Section: Theoretical Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pathogen diversity in meta-population networks

Nie¹,

Zhong²,

Lin³

et al. 2023

Chaos, Solitons & Fractals

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“…In recent years, with the development of network science theory, network scientists propose to use hypergraph networks to characterize real-world network systems further [23][24][25][26][27][28] . Compared with simple graphs with pairwise interactions, hypergraphs have higher-order interactions and are better able to express some scenes that appear in daily life [29][30][31][32][33][34][35] . For example, when several co-authors jointly submit a paper, these authors have a cooperative relationship if the article is accepted.…”

Section: Inroductionmentioning

confidence: 99%

Message passing approach to analyze the robustness of hypergraph

Peng¹,

Qian²,

Zhao³

et al. 2023

Preprint

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Hypergraph networks are closer to real life because they can reflect higher-order interactions, so researchers have begun using them to build models for real-world networks. The mean-field approach is the current tool for studying the percolation problem on hypergraph networks. However, we found that when there is a loop in the hypergraph network, the calculated results using this approach deviate from the real results. Therefore, in this paper, we rephrase the percolation on the hypergraph network as a message passing process, thus obtaining a message passing approach. Our proposed approach has been tested in several hypergraph networks with loops, and the experimental results are more accurate than those under the mean-field approach. This is helpful to analyze and understand the robustness of hypergraph networks with loops. In addition, we also specifically analyzed how four different types of loops affect the accuracy of the experiment. Our proposed message passing approach also provides another way to study percolation on hypergraph networks.

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“…They presented an abrupt transition and showed that ELE can give a more accurate prediction than MMCA about the susceptible-infected-susceptible (SIS) model in simplicial complexes. Nie et al [39] and Li et al [40] focused on the influence of simplicial complex on the competing spreading. However, the influence of the higher-order properties on the epidemic spreading has not been fully understood.…”

Section: Introductionmentioning

confidence: 99%

Epidemic spreading on coupling network with higher-order information layer

Zhu,

Li,

2023

New J. Phys.

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Epidemic tends to break out with information spreading which occurs between pairwise individuals or in groups. In active social online platform, three or more individuals can share information or exchange opinion, which could be modeled as a clique beyond pairwise interaction. This work studies the influence of information with higher-order cliques whose closure probability is described by higher-order clustering coefficient on epidemic spreading. The coupled spreading process of disease and awareness follows an unaware-aware-unaware-susceptible-infected-susceptible (UAU-SIS) model on multiplex networks. We deduce the epidemic threshold of multiplex network with higher-order cliques, and explore the effects of cliques on epidemic spreading process at slow, middle and fast pairwise information spreading rates. The results show that the epidemic threshold and higher-order clustering coefficient are positively correlated. Moreover, we find that the effect of higher-order cliques could be ignored when pairwise information spreading rate is large enough. Finally, we show that the steady infection ratio decreases with the increase of the higher-order clustering coefficient.

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Markovian approach to tackle competing pathogens in simplicial complex

Cited by 29 publications

References 50 publications

Pathogen diversity in meta-population networks

Pathogen diversity in meta-population networks

Message passing approach to analyze the robustness of hypergraph

Epidemic spreading on coupling network with higher-order information layer

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