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

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About: Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks
Creator	Dong, Chao Long, Ting Pan, Yaohui Shi, Tianyu Yin, Qiuju Zhang, Wensi
Source	Elsevier; Medline; PMC
abstract	Abstract Multiplex 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 disease- related 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.
has issue date	2019-12-15(xsd:dateTime)
bibo:doi	10.1016/j.physa.2019.04.266
bibo:pmid	32288109
has license	els-covid
sha1sum (hex)	4c4aa83e7c5b990b8b14fa2f9cf017f3f5b5ffb7
schema:url	https://doi.org/10.1016/j.physa.2019.04.266
resource representing a document's title	Effects of asymptomatic infection on the dynamical interplay between behavior and disease transmission in multiplex networks
has PubMed Central identifier	PMC7125818
has PubMed identifier	32288109
schema:publication	Physica A: Statistical Mechanics and its Applications
resource representing a document's body	covid:4c4aa83e7c5b990b8b14fa2f9cf017f3f5b5ffb7#body_text
is schema:about of	named entity 'MULTIPLEX' named entity 'ASYMPTOMATIC INFECTION' named entity 'MULTIPLEX' named entity 'SPREAD' named entity 'EPIDEMIC' named entity 'ASYMPTOMATIC INFECTION' named entity '80%' named entity 'STUDY' named entity 'DYNAMICS' named entity 'DISEASE TRANSMISSION' covid:arg/4c4aa83e7c5b990b8b14fa2f9cf017f3f5b5ffb7 named entity 'BEHAVIOR' named entity 'NETWORKS' named entity 'NETWORKS' named entity 'EFFECTS' named entity 'IMPACT' named entity 'transmission of disease' named entity 'Monte-Carlo' named entity 'epidemic' named entity 'viruses' named entity 'physical contact' named entity 'clinical symptoms' named entity 'epidemic' named entity 'transmission of disease' named entity 'transmission of disease' named entity 'disease transmission' named entity 'epidemic' named entity 'epidemic' named entity 'exponential function' named entity 'HIV' named entity 'infection' named entity 'physical contact' named entity 'MMCA' named entity 'adaptive behaviors' named entity 'infection' named entity 'MMCA' named entity 'epidemic' named entity 'SARS' named entity 'spread of the disease' named entity 'epidemic' named entity 'order parameter' named entity 'spread of disease' named entity 'epidemic' named entity 'viruses' named entity 'asymptomatic infection' named entity 'clinical symptoms' named entity 'physical contact' named entity 'susceptible individual' named entity 'epidemic' named entity 'infection' named entity 'identity matrix' named entity 'epidemic' named entity 'degree distribution' named entity 'SARS' named entity 'viruses' named entity 'MMCA' named entity 'MMCA' named entity 'susceptible individual' named entity 'viruses' named entity 'clinical symptoms' named entity 'asymptomatic' named entity 'epidemic' named entity 'viruses' named entity 'clinical symptoms' named entity 'probability' named entity 'steady state' named entity 'stationary state' named entity 'epidemic' named entity 'medical examination' named entity 'epidemic' named entity 'viruses' named entity 'asymptomatic infection' named entity 'disease transmission' named entity 'washing hands' named entity 'physical contact' named entity 'epidemic'

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