Epidemic spread in bipartite network by considering risk awareness

Han, Seung-Beom; Sun, Mei; Ampimah, Benjamin Chris; Han, Dun

doi:10.1016/j.physa.2017.11.107

Cited by 9 publications

(5 citation statements)

References 23 publications

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“…Based on Han et al [41], according to the network topology structure of the above consumers, the probability that potential consumers with the degree of k at time t have n buying consumer neighbors is Conformity degree of potential consumer j δ j Degree of interaction between potential consumer j and its neighbors X 0 (i, t)…”

Section: Network Topology Generation and Purchasementioning

confidence: 99%

Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity

Tang

Zhu

2020

Complexity

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When a new product enters the market, individual consumers’ decision-making behavior and purchase time are uncertain. Based on the dynamics of epidemic transmission theory and agent modeling technology, this study proposes a new coupling model through the combination of the improved SEIR epidemic model and the heterogeneous agent model. This model considers consumer heterogeneity resulting from three aspects in consumers’ sensitivity, network topology, and considerations of information flow received. It aims to analyze how consumer heterogeneity affects the scale and speed of new product diffusion. The proposed model showed that consumers’ characteristics and behavior combination at the microlevel lead to the diversity of nonlinear diffusion curves at the macrolevel for new products. Moreover, a pilot study is conducted to simulate this model and examine how to estimate the model’s parameters using aggregated data about film products. The pilot study results suggested that different consumer characteristics and behavior combinations affect the scale and speed of new product diffusion to varying degrees. In different scenarios, there were significant differences in the influence of the degree of consumer heterogeneity on diffusion, accompanied by the occurrence of threshold. The results of the empirical analysis in this study are in line with reality.

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Section: Network Topology Generation and Purchasementioning

confidence: 99%

Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity

Tang

Zhu

2020

Complexity

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“…Individuals tend to have herd mentality and free-riding when choosing vaccination strategies [3] . Individuals’ attitudes towards risks and their decision-making payoffs at previous moments will also have a great impact on their vaccination behavior [4] , [5] , [6] . Game theory is an effective means to quantify individual behavioral decisions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of epidemic vaccination strategies on heterogeneous networks: Based on SEIRV model and evolutionary game

Meng

Cai

et al. 2021

Applied Mathematics and Computation

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Nowadays, vaccination is the most effective way to control the epidemic spreading. In this paper, an epidemic SEIRV (susceptible-exposed-infected-removed -vaccinated) model and an evolutionary game model are established to analyze the difference between mandatory vaccination method and voluntary vaccination method on heterogeneous networks. Firstly, we divide the population into four categories, including susceptible individuals, exposed individuals, infected individuals and removed individuals. Based on the mean field approximation theory, differential equations are developed to characterize the changes of the proportions of the four groups over time under mandatory vaccination. Then through the analysis of the differential equations, the disease-free equilibrium point (DFE) and the endemic disease equilibrium point (EDE) are obtained. Also, the basic reproduction number is obtained by the next-generation matrix method and the stability analysis of the equilibrium points is performed. Next, by considering factors such as vaccination cost, treatment cost and government subsidy rate, differential equations are established to represent the change of vaccination rate over time. By analyzing the final vaccination coverage rate, we can get the minimum vaccination cost to make infectious disease disappear. Finally, the Monte Carlo method is used for numerical simulation to verify the results obtained from the theoretical analysis. Using the SARS-Cov-2 pandemic data from Wuhan, China, the experimental results show that when the effectiveness rate of vaccination is 0.75, the vaccination cost is not higher than 0.886 so that the vaccination strategy can be spread among the population. If mandatory vaccination is adopted, the minimum vaccination rate is 0.146.

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“…[34] for exceptions and Refs. [35][36][37][38][39] for epidemics on bipartite networks). However, many important biological and social systems are most naturally conceptualized as multipartite networks.…”

Section: Introductionmentioning

confidence: 99%

Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks

Lee,

Alves,

Amaral

2021

Preprint

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Many systems of scientific interest can be conceptualized as multipartite networks. Examples include the spread of sexually transmitted infections, scientific collaborations, human friendships, product recommendation systems, and metabolic networks. In practice, these systems are often studied after projection onto a single class of nodes, losing crucial information. Here, we address a significant knowledge gap by comparing transmission dynamics on temporal multipartite networks and on their time-aggregated unipartite projections to determine the impact of the lost information on our ability to predict the systems' dynamics. We show that the dynamics of transmission models can be dramatically dissimilar on multipartite networks and on their projections at three levels: final outcome, the magnitude of the variability from realization to realization, and overall shape of the temporal trajectory. We find that the ratio of the number of nodes to the number of active edges over the time aggregation scale determines the ability of projected networks to capture the dynamics on the multipartite network. Finally, we explore which properties of a multipartite network are crucial in generating synthetic networks that better reproduce the dynamical behavior observed in real multipartite networks.

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Epidemic spread in bipartite network by considering risk awareness

Cited by 9 publications

References 23 publications

Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity

Nonlinear Dynamic Analysis of New Product Diffusion considering Consumer Heterogeneity

Analysis of epidemic vaccination strategies on heterogeneous networks: Based on SEIRV model and evolutionary game

Spreader events and the limitations of projected networks for capturing dynamics on multipartite networks

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