Discrete-time layered-network epidemics model with time-varying transition rates and multiple resources

Cui, Shaoxuan; Liu, Fangzhou; Jardón-Kojakhmetov, Hildeberto; Cao, Ming

doi:10.1016/j.automatica.2023.111303

Cited by 10 publications

(3 citation statements)

References 26 publications

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“…Wang et al applied the heterogeneous NIMFA framework to analyze and assess interest Negative ACKnowledgments on mitigating interest flooding attacks in Named Data Networking (NDN) [8]. Cui et al studied a susceptible-infected-water-susceptible (SIWS) model which takes in to account disease diffusion in various mediums with different infection rates [9]. Pagliara and Leonard studied the susceptible-infected-recovered-infected (SIRI) model which assumes that the a recovering individual would change their susceptibility to reinfection [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Variance of the Infection Number of Heterogeneous Malware Spread in Network

Guo,

Jiao,

Jiao

et al. 2024

Applied Sciences

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The Susceptible–Infected–Susceptible (SIS) model in complex networks is one of the critical models employed in the modeling of virus spread. The study of the heterogeneous SIS model with a non-homogeneous nodal infection rate in finite-size networks has attracted little attention. Investigating the statistical properties of heterogeneous SIS epidemic dynamics in finite networks is thus intriguing. In this paper, we focus on the measure of variability in the number of infected nodes for the heterogeneous SIS epidemic dynamics in finite-size bipartite graphs and star graphs. Specifically, we investigate the metastable-state variance of the number of infected nodes for the SIS epidemic process in finite-size bipartite graphs and star graphs with heterogeneous nodal infection rates. We employ an extended individual-based mean-field approximation to analyze the heterogeneous SIS epidemic process in finite-size bipartite networks and star graphs. We derive the approximation solutions of the variance of the infected number. We verify the proposed theory by simulations. The proposed theory has the potential to help us better understand the fluctuations of SIS models like epidemic dynamics with a non-homogeneous infection rate.

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Section: Literature Reviewmentioning

confidence: 99%

Variance of the Infection Number of Heterogeneous Malware Spread in Network

Guo,

Jiao,

Jiao

et al. 2024

Applied Sciences

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“…Virus layer 1 + Virus layer 2 [43,48,49] Information layer + Virus layer [50][51][52][53][54][55][56][57][58][59] Virus layer + Resource layer [44] Three layers Information layer + Virus layer + Resource layer [45,60] Anti-virus layer + Virus layer 1 + Virus layer 2 [61] Table 1. Cont.…”

Section: Two Layersmentioning

confidence: 99%

“…Information layer + Virus layer + Resource layer [45,60] Anti-virus layer + Virus layer 1 + Virus layer 2 [61] More than three layers Virus layer 1 + Virus layer 2 + … + Virus layer 6 [46] Information layer + Virus layer 1 + … + Virus layer D [47] Remarks: (1) "Information layer" is named differently in various papers, such as "virtual layer" and "communication layer". Here, we collectively refer to it as the "information layer".…”

Section: Three Layersmentioning

confidence: 99%

Navigating Resource Challenges in Health Emergencies: The Role of Information Diffusion and Virus Spread in Demand Dynamics

Zhou,

Zhang,

Yang

2024

Systems

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The dynamics of medical resource demand during public health crises pose significant challenges to emergency supply chain management, particularly within an evolving and complex social environment. To explore this, the interactive effects of information diffusion and virus spreading on medical resource demand are investigated using a novel three-layer coevolution “information–epidemic–resource” model through Markov process simulations. The study firstly identifies eight factors influencing demand fluctuations in terms of some city characteristics, such as media exposure, consistency of public opinion, self-protection level, and restrictive protection level, while categorizing resources into individual holdings and centralized storage. Then, extensive simulations are examined to elucidate the impact of these factors. The results reveal that various city characteristics can affect fluctuation in demand for both individual holdings and centralized storage. Inaccurate media information tends to inflate fluctuations, while higher public opinion consistency can reduce it. Reinforcing self-protection decreases the demand fluctuations of individuals, and effective restrictive protections can reduce fluctuations in centralized resource storage. Moreover, an analytical simulation of various city scenarios, underpinned by statistical data from selected Chinese and German cities, demonstrates that distinct city characteristics significantly influence medical resource demand changes during epidemics. This underscores the importance of tailoring emergency medical supply strategies to the specific developmental traits of different countries and cities. This study provides valuable insights to researchers, governments, and enterprises, enhancing their preparedness and response for emergency supply chain disruptions.

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