Transmission Dynamics of an SIS Model with Age Structure on Heterogeneous Networks

Chen, Shanshan; Small, Michael; Tao, Yizhou; Fu, Xinchu

doi:10.1007/s11538-018-0445-z

Cited by 21 publications

(12 citation statements)

References 53 publications

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“…In order to better describe the dynamics of disease, we will model the spread of disease in heterogeneous networks. Chen et al [3] took into account that not only could nodes be in states S and I, but also they belong to different connectivity classes k and built a mean-field equations which was degreedependent. They showed that basic reproduction number and the disease dynamics relied not only on the network structure, but also on an age-dependent factor.…”

Section: Introductionmentioning

confidence: 99%

SIQR dynamics in a random network with heterogeneous connections with infection age

Yan¹,

Li²

2020

J. Nonlinear Sci. Appl.

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In this paper, an SIQR-Epidemic transmission model of the non-Markovian infection process and quarantine process in a heterogeneous complex network is established, in which the infection rate and quarantine rate are related to infection age. Next, we use the method of characteristics to transform the model into an integro-differential equation and derive the epidemic threshold of the model. Finally, we focus on the impact of three different infection or quarantine time distributions on the disease transmission and show that infection or quarantine time distribution has a significant effect on the disease dynamics.

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Section: Introductionmentioning

confidence: 99%

SIQR dynamics in a random network with heterogeneous connections with infection age

Yan¹,

Li²

2020

J. Nonlinear Sci. Appl.

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“…The explored nodes are added into the basic susceptible-infected-recovered (SIR) framework, and the immune loss area and death zone are inserted into the basic SIR framework to establish the susceptible-explored-infected-recovered-susceptible (SEIRS) model. References [ 12 , 13 ] introduced that the threshold property of the basic reproduction number was introduced for the propagation model of age-dependent SIS (susceptible-infection-susceptible) diseases in heterogeneous networks. The introduction of a popular threshold can accurately grasp the control effect of the network.…”

Section: Introductionmentioning

confidence: 99%

Broken-Edge Decision-Making Strategy for COVID-19 over Air Railway Composite Network

Sun

Qin

et al. 2022

Computational Intelligence and Neuroscience

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In order to control the spread of the COVID-19 virus, this study proposes an ARCN-SUTS (air railway composite network susceptible-untested-tested-susceptible) model based on the correlation characteristics of the air railway composite network in mainland China. Furthermore, this study also puts forward a broken-edge decision-making strategy for the purpose of making decision about the edge efficiently broken and avoiding the second outbreak of the virus spread to minimize the economic losses for railway and civil aviation companies. Finally, simulation results demonstrate that the proposed strategy can effectively control the spread of the virus with minimal economic losses.

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“…Duan et al employed heterogeneous and stochastic agent-based models to analyze the characteristics of infectious diseases’ super spreaders [ 18 ]. Researchers also paid attention to epidemic dynamics in heterogeneous networks with different connectivities [ 19 , 20 , 21 ], and went forward and investigated epidemic spread in weighted networks representing the heterogeneity of individual interaction strengths [ 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

Matrix-Based Formulation of Heterogeneous Individual-Based Models of Infectious Diseases: Using SARS Epidemic as a Case Study

Duan

2021

IJERPH

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Heterogeneities of individual attributes and behaviors play an important role in the complex process of epidemic spreading. Compared to differential equation-based system dynamical models of infectious disease transmission, individual-based epidemic models exhibit the advantage of providing a more detailed description of realities to capture heterogeneities across a population. However, the higher granularity and resolution of individual-based epidemic models comes with the cost of increased computational complexities, which result in difficulty in formulating individual-based epidemic models with mathematics. Furthermore, it requires great effort to understand and reproduce existing individual-based epidemic models presented by previous researchers. We proposed a mathematical formulation of heterogeneous individual-based epidemic models using matrices. Matrices and vectors were applied to represent individual attributes and behaviors. We derived analytical results from the matrix-based formulations of individual epidemic models, and then designed algorithms to force the computation of matrix-based individual epidemic models. Finally, we used a SARS epidemic control as a case study to verify the matrix-based formulation of heterogeneous individual-based epidemic models.

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Transmission Dynamics of an SIS Model with Age Structure on Heterogeneous Networks

Cited by 21 publications

References 53 publications

SIQR dynamics in a random network with heterogeneous connections with infection age

SIQR dynamics in a random network with heterogeneous connections with infection age

Broken-Edge Decision-Making Strategy for COVID-19 over Air Railway Composite Network

Matrix-Based Formulation of Heterogeneous Individual-Based Models of Infectious Diseases: Using SARS Epidemic as a Case Study

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