Interaction between epidemic spread and collective behavior in scale-free networks with community structure

Xu, Zhongpu; Li, Kezan; Sun, Mengfeng; Fu, Xinchu

doi:10.1016/j.jtbi.2018.11.003

Cited by 24 publications

(9 citation statements)

References 40 publications

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“…Expanding out the derivative of the function V k1 (t) The derivative of this function is divided into three parts. Since the value at the disease-free equilibrium point satisfies equation (14), substituting the first equation of (14) into the first part of the derivative, and the following equation can be obtained where…”

Section: Stability Analysismentioning

confidence: 99%

“…Since the endemic disease equilibrium point E * satisfies equation (14), that is b = λkΘ(t−τ ) 1+αΘ(t−τ ) S * k − bS * k −δS * k , plugging the first formula into and further simplifying…”

Section: Consequently the Lyapunov Function Below Is Definedmentioning

confidence: 99%

“…Especially after Barabśi and ALbert proposing that the nodes distribution of many real networks (such as transportation network, cooperative paper network, and computer integrated network) conforms to power-law characteristics [13], many scholars have begun to study the propagation dynamics in such heterogeneous networks (scale-free networks). Xu et al research the influence of community structure on disease transmission in scale-free networks and apply the comparison principle and Gergorin theorem to solve the threshold of the transmission dynamics model [14]. Because of the characteristics of drug resistance in some parts of the system, Wan further draws the inoculation population V into the SIR model and explores the system dynamics characteristics of the SVIRS model by using the theory of mean-field equation, it is revealed that the existence of the disease-free equilibrium point in the system depends on the basic reproduction number.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Dynamic Behavior of a Class of Heterogeneous Network Systems With Time Delay

Wang

Cui

2020

IEEE Access

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Successful treatment of COVID-19 that outbroke worldwide since the beginning of 2020 has demonstrated the importance of effective isolation, which is aimed at asymptomatic and symptomatic infected persons in the incubation period. In this paper, to further analyze the transmission dynamics behavior of epidemics with the latent state, we construct a class of health state-latent state-infectionrecovery state (SEIR) infectious disease model with heterogeneity and time delay characteristic based on considering the nonlinear incidence rate formed by psychological inhibition factors. Also, the dynamics of the epidemic, the threshold condition, and stability are studied by creating Lyapunov functions reasonably, applying LaSalle's Invariance Principle and mean-field equation theory. The research shows that, the basic reproduction number R 0 of the system depends on birth rate, death rate, recovery rate, disease transmission rate, and network topology. If R 0 < 1, the system is stable at the disease-free equilibrium point E 0 , and if R 0 > 1, the system is sound at the endemic equilibrium point E *. Moreover, it is also proved that latent delay and psychological inhibitory factors can influence the peak and rate of the infected nodes in the system before their convergence to the equilibrium point, but not the system's global stability. Meanwhile, the theoretical results are verified by numerical simulation finally.

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Section: Stability Analysismentioning

confidence: 99%

Section: Consequently the Lyapunov Function Below Is Definedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Study on the Dynamic Behavior of a Class of Heterogeneous Network Systems With Time Delay

Wang

Cui

2020

IEEE Access

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“…In a complex network, the phenomenon that the degree is divided into two different segments implies the existence of a nuclear dictionary [33]. Despite the small portion of nodes, they are connected to a large number of other nodes, and most of the remaining nodes are connected to only a few nodes.…”

Section: Scale-free Effectmentioning

confidence: 99%

Author Cooperation Network in Biology and Chemistry Literature during 2014–2018: Construction and Structural Characteristics

et al. 2019

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How to explore the interaction between an individual researcher and others in scientific research, find out the degree of association among individual researchers, and evaluate the contribution of researchers to the whole according to the mechanism and law of interaction, is of great significance to grasp the overall trend of the field. Scholars mostly use bibliometrics to solve these problems and analyze the citation and cooperation among academic achievements from the dimension of "quantity". However, there is still no mature method for scholars to explore the evolution of knowledge and the relationship between authors; this paper tries to fill this gap. We narrow down the scope of research and focus the research content on the literature in biology and chemistry, collect all the papers from PubMed system (a very comprehensive authoritative database of biomedical papers) during 2014–2018, and take year as a specific analysis unit so as to improve the accuracy of the analysis. Then, we construct the author cooperation networks. Finally, through the above methods and steps, we identify the core authors of each year, analyze the recent cooperative relationships among authors, and predict some changes in the cooperative relationship among the authors based on the networks’ analytical data, evaluating and estimating the role that authors play in the overall field. Therefore, we expect that the cooperative authorship networks supported by the complex network theory can better explain the author's cooperative relationship.

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“…There are some similar transmission mechanism between the disease and the information, such as the rumor. A lot of the rumor spreading model were proposed based on the disease model [2], [3]. The series began in 1960s, the classical rumor spreading DK model was introduced by the Daley and Kendal [4], in which the population has divided…”

Section: Introductionmentioning

confidence: 99%

Dynamical Analysis of Rumor Spreading Model With Incubation Mechanism and Activity of Nodes

et al. 2019

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In this paper, incubation mechanism and activity of nodes are introduced to study the dynamics mechanism of rumor spreading in complex network. It is assumed that when the susceptible interacts with the rumor believers, the susceptible need time to think about the rumor before they believe it. The susceptible individual will first go through a incubation period after being infected before becoming believer or stifler. The nodes have active and the inactive states, it can convert each other with a certain probability, the active nodes can interact with others which directs linking, and the inactive nodes just response the active neighbors. The dynamic rumor spreading model was built by mean-field equations in heterogeneous network, and then the numerical simulations were proposed which reveals that the joint efforts of activity rate and spreading rate have negative effects on final rumor spreading size, and the simulations results also confirm the theoretical analysis in the modified model. INDEX TERMS Rumor spreading, incubation mechanism, activity of nodes, heterogeneous networks.

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Interaction between epidemic spread and collective behavior in scale-free networks with community structure

Cited by 24 publications

References 40 publications

Study on the Dynamic Behavior of a Class of Heterogeneous Network Systems With Time Delay

Study on the Dynamic Behavior of a Class of Heterogeneous Network Systems With Time Delay

Author Cooperation Network in Biology and Chemistry Literature during 2014–2018: Construction and Structural Characteristics

Dynamical Analysis of Rumor Spreading Model With Incubation Mechanism and Activity of Nodes

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