Spreading Dynamics of an SEIR Model with Delay on Scale-Free Networks

Kang, Hoon; Sun, Mengfeng; Yu, Yajuan; Fu, Xinchu; Bao, Bocheng

doi:10.1109/tnse.2018.2860988

Cited by 33 publications

(16 citation statements)

References 34 publications

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“…A stochastic ISIR epidemic model with immunization strategies was presented to simulate the epidemic spread on social contact networks [17]. The SEIR model with time delay on scale-free networks was described in [18] to investigate the reproduction number and transmission dynamics of the disease. A model that analyses a population with two diseases, infectious and noninfectious, was introduced in [19].…”

Section: Related Workmentioning

confidence: 99%

A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies

Kuzdeuov

Baimukashev

Karabay

et al. 2020

Preprint

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In this work, we present an open-source stochastic epidemic simulator, calibrated with extant epidemic experience of COVID-19. Our simulator incorporates information ranging from population demographics and mobility data to health care resource capacity, by region, with interactive controls of system variables to allow dynamic and interactive modeling of events. The simulator can be generalized to model the propagation of any disease, in any territory, but for this experiment was customized to model the spread of COVID-19 in the Republic of Kazakhstan, and estimate outcomes of policy options to inform deliberations on governmental interdiction policies.

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Section: Related Workmentioning

confidence: 99%

A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies

Kuzdeuov

Baimukashev

Karabay

et al. 2020

Preprint

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“…Shakya et al [26] presented a new susceptible-infectious-recovered model, which reflects spatial correlation characteristics of WSNs. Additionally, there are other representative epidemiology-based models for malware propagation in WSNs, such as a susceptible-exposedinfectious-recovered model reflecting time delay [27], a stochastic susceptible-infectious-susceptible model [28], and a susceptible-exposed-infectious-recovered model reflecting variable contact rates [29].…”

Section: Related Workmentioning

confidence: 99%

An Epidemiology-Based Model for Disclosing Dynamics of Malware Propagation in Heterogeneous and Mobile WSNs

et al. 2020

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Heterogeneous and mobile wireless sensor networks (HMWSNs) are generally practical in constructing smart Internet of Things. However, malware can easily propagate itself over HMWSNs and make harm such as data interception and unauthorized activities. To defend such malware, developing a model to disclose dynamics of malware propagation becomes urgently required. In this context, a heterogeneous and mobile vulnerable-compromised-quarantined-patched-scrapped (VCQPS) model is proposed by considering both the heterogeneity and mobility of HMSNs (heterogeneous and mobile sensor nodes). Then, differential equations of transition proportions among all states are achieved by analyzing the changeable quantities of HMSNs belonging to different states. Further, the existence of the stationary points of the VCQPS model is proved, upon which the malware propagation threshold is derived by calculating the reproduction number. The stability of the malware-free stationary-point is also proved. Experiments are performed to validate the stability of the malware-free stationary-point and show the effectiveness of the VCQPS model by comparing our model with traditional SIS and SIR models.INDEX TERMS Heterogeneous and mobile wireless sensor networks, malware, epidemic theory, heterogeneity, mobility.

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“…A discrete-time absorbing Markov process used in the SIS model of [36] characterizes the spread of malware across nontrivial topologies of large networks. In addition, typical epidemic models for WSN malware spread include a stochastic SIS model [37], an SEIR model considering time delay [38], a developed SEIRS model with a changeable infection rate [39], and an SEIR model with a variable contact rate [40].…”

Section: Related Workmentioning

confidence: 99%

SNIRD: Disclosing Rules of Malware Spread in Heterogeneous Wireless Sensor Networks

et al. 2019

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Heterogeneous wireless sensor networks (WSNs) are widely deployed, owing to their good capabilities in terms of network stability, dependability, and survivability. However, they are prone to the spread of malware because of the limited computational capabilities of sensor nodes. To suppress the spread of malware, a malware spread model is urgently required to discover the rules of malware spread. In this paper, a heterogeneous susceptible-iNsidious-infectious-recovered-dysfunctional (SNIRD) model was proposed, which not only considers the communication connectivity of heterogeneous sensor nodes but also reflects the characteristics of malware hiding and dysfunctional sensor nodes. Then, the fraction evolution equations of heterogeneous sensor nodes in different states in discrete time were obtained. Furthermore, the existence of equilibria for the heterogeneous SNIRD model was proved, and the malware spread threshold was obtained, which indicates whether malware will spread or fade out. Finally, the heterogeneous SNIRD model was simulated and it was contrasted with the conventional SIS and SIR models to validate its effectiveness. The results construct a theoretical guideline for administrators to suppress the spread of malware in heterogeneous WSNs.

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Spreading Dynamics of an SEIR Model with Delay on Scale-Free Networks

Cited by 33 publications

References 34 publications

A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies

A Network-Based Stochastic Epidemic Simulator: Controlling COVID-19 with Region-Specific Policies

An Epidemiology-Based Model for Disclosing Dynamics of Malware Propagation in Heterogeneous and Mobile WSNs

SNIRD: Disclosing Rules of Malware Spread in Heterogeneous Wireless Sensor Networks

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