Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases

Zhang, Haifeng; Shu, Panpan; Wang, Zhen; Tang, Ming; Small, Michael

doi:10.1016/j.amc.2016.08.057

Cited by 52 publications

(26 citation statements)

References 46 publications

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“…It is widely used in identifying the spreading capacity of nodes by scholars and adopted in the study of vaccination strategy and infection control [27][28][29] as well. In principle, the SIR model detects the influential vertices due to the fact that key nodes are more likely to play an indispensable role in information Discrete Dynamics in Nature and Society 3 and viral transmission, and thereby an effective ranking is supposed to stand the test of real spreading coverage.…”

Section: Resultsmentioning

confidence: 99%

Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

2018

Discrete Dynamics in Nature and Society

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Identifying influential spreaders in complex networks is crucial for containing virus spread, accelerating information diffusion, and promoting new products. In this paper, inspired by the effect of leaders on social ties, we propose the most influential neighbors' -shell index that is the weighted sum of the products between -core values of itself and the node with the maximum -shell values. We apply the classical Susceptible-Infected-Recovered (SIR) model to verify the performance of our method. The experimental results on both real and artificial networks show that the proposed method can quantify the node influence more accurately than degree centrality, betweenness centrality, closeness centrality, and -shell decomposition method.

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Section: Resultsmentioning

confidence: 99%

Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

2018

Discrete Dynamics in Nature and Society

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“…After that, Zhang et al [37] demonstrated that the hubs may largely inhibit the outbreaks of infectious diseases under the voluntary vaccination policy. In the meantime, various subsidy policies on controlling the epidemic spreading have been determined from the socioeconomic perspectives within the well-mixed and networked population [38][39][40][41][42][43] . Furthermore, most previous works [44][45][46][47][48][49][50][51][52] often assume that the vaccine has a perfect efficacy, which will endow the complete immunity for the inoculated individuals, but an interesting topic is on how the epidemic spreads when the vaccine is not fully effective for the disease (i.e., 100% efficacy)?…”

Section: Introductionmentioning

confidence: 99%

Role of vaccine efficacy in the vaccination behavior under myopic update rule on complex networks

Huang

Wang

Xia

2020

Chaos, Solitons & Fractals

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a b s t r a c tHow to effectively prevent the diffusion of infectious disease has become an intriguing topic in the field of public hygienics. To be noted that, for the non-periodic infectious diseases, many people hope to obtain the vaccine of epidemics in time to be inoculated, rather than at the end of the epidemic. However, the vaccine may fail as a result of invalid storage, transportation and usage, and then vaccinated individuals may become re-susceptible and be infected again during the outbreak. To this end, we build a new framework that considers the imperfect vaccination during the one cycle of infectious disease within the spatially structured and heterogeneous population. Meanwhile, we propose a new vaccination update rule: myopic update rule, which is only based on one focal player's own perception regarding the disease outbreak, and one susceptible individual makes a decision to adopt the vaccine just by comparing the perceived payoffs vaccination with the perceived ones of being infected. Extensive Monte-Carlo simulations are performed to demonstrate the imperfect vaccination behavior under the myopic update rule in the spatially structured and heterogeneous population. The results indicate that healthy individuals are often willing to inoculate the vaccine under the myopic update rule, which can stop the infectious disease from being spread, in particular, it is found that the vaccine efficacy influences the fraction of vaccinated individuals much more than the relative cost of vaccination on the regular lattice, Meanwhile, vaccine efficacy is more sensitive on the heterogeneous scale-free network. Current results are helpful to further analyze and model the choice of vaccination strategy during the disease outbreaks.

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“…The spontaneous protection of the public, combined with some compulsory measures implemented by governments or departments of health and hygiene, effectively controlled the spread of the disease [1][2][3][4][5][6][7]. about the researches on the effect of individual behavior on the transmission of disease attracted much attention [8][9][10][11][12][13][14][15][16][17]. For example, Funk et al studied the effect of locally spreading awareness on the transmission of disease, and they found that in a well-mixed population, the spread of awareness can reduce the size of the outbreak, but cannot enhance the transmission of threshold [18].…”

Section: Introductionmentioning

confidence: 99%

Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks

Shi

Long

Pan

et al. 2019

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• We study the impact of asymptomatic infection on the epidemic spread dynamics in multiplex networks.• We assume infected can be isolation and non isolation, then compare the research results of these two cases.• We take the individual heterogeneity into consideration and study whether it affect research results. a b s t r a c tMultiplex network theory is widely introduced to deepen the understanding of the dynamical interplay between self-protective behavior and epidemic spreading. Most of the existing studies assumed that all infected individuals can transmit diseaserelated information or can be perceived by their neighbors. However, owing to lack of distinct symptoms for patients in the initial stage of infection, the disease information cannot be transmitted in the population, which may lead to the wrong perception of infection risk and inappropriate behavior response. In this work, we divide infected individuals into Exposed-state (without obvious clinical symptoms) individuals and Infected-state (with evident clinical symptoms) individuals, both of whom can spread disease, but only Infected-state individuals can diffuse disease information. Then, in this work we establish UAU-SEIS (Unaware-Aware-Unaware-Susceptible-Exposed-Infected-Susceptible) model in multiplex networks and analyze the effect of asymptomatic infection and the isolation of Infected-state individuals on the density of infection and the epidemic threshold. Furthermore, we extend the UAU-SEIS model by taking the individual heterogeneity into consideration. Combined Markov chain approach and Monte-Carlo Simulations, we find that asymptomatic infection has an effect on the density of infected individuals and the epidemic threshold, and the extent of the effect is influenced by whether Infected-state individuals are isolated or treated. In addition, results show that the individual heterogeneity can lower the density of infected individuals, but cannot enhance the epidemic threshold.

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Preferential imitation can invalidate targeted subsidy policies on seasonal-influenza diseases

Cited by 52 publications

References 46 publications

Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

Ranking Spreaders in Complex Networks Based on the Most Influential Neighbors

Role of vaccine efficacy in the vaccination behavior under myopic update rule on complex networks

Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks

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