About: Disease spreading in complex networks: A numerical study with Principal Component Analysis

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Disease spreading in complex networks: A numerical study with Principal Component Analysis
Creator	Pereira, F Schimit, P
Source	Elsevier; Medline; PMC
abstract	Abstract Disease spreading models need a population model to organize how individuals are distributed over space and how they are connected. Usually, disease agent (bacteria, virus) passes between individuals through these connections and an epidemic outbreak may occur. Here, complex networks models, like Erdös–Rényi, Small-World, Scale-Free and Barábasi–Albert will be used for modeling a population, since they are used for social networks; and the disease will be modeled by a SIR (Susceptible–Infected–Recovered) model. The objective of this work is, regardless of the network/population model, analyze which topological parameters are more relevant for a disease success or failure. Therefore, the SIR model is simulated in a wide range of each network model and a first analysis is done. By using data from all simulations, an investigation with Principal Component Analysis (PCA) is done in order to find the most relevant topological and disease parameters.
has issue date	2018-05-01(xsd:dateTime)
bibo:doi	10.1016/j.eswa.2017.12.021
bibo:pmid	32288338
has license	els-covid
sha1sum (hex)	3a2039049a4c65892b962dbd5c90e170aea42ddb
schema:url	https://doi.org/10.1016/j.eswa.2017.12.021
resource representing a document's title	Disease spreading in complex networks: A numerical study with Principal Component Analysis
has PubMed Central identifier	PMC7126495
has PubMed identifier	32288338
schema:publication	Expert Systems with Applications
resource representing a document's body	covid:3a2039049a4c65892b962dbd5c90e170aea42ddb#body_text
is schema:about of	named entity 'PRINCIPAL COMPONENT ANALYSIS' named entity 'NETWORKS' named entity 'ANALYSIS' named entity 'OUTBREAK' named entity 'POPULATION' named entity 'DATA' named entity 'FREE' named entity 'EPIDEMIC' named entity 'SOCIAL NETWORKS' named entity 'VIRUS' named entity 'USING' named entity 'USED' named entity 'AGENT' named entity 'SIR' named entity 'NETWORKS' named entity 'COMPLEX' named entity 'SPREADING' named entity 'HERE' named entity 'DISEASE' named entity 'ORDER' named entity 'ERD' named entity 'SIMULATIONS' named entity 'NUMERICAL STUDY' named entity 'COMPLEX' named entity 'DISEASE' named entity 'PRINCIPAL COMPONENT ANALYSIS' named entity 'RECOVERED' named entity 'INVESTIGATION' named entity 'NETWORK' named entity 'ORGANIZE' named entity 'SPREADING' named entity 'INFECTED' named entity 'DISTRIBUTED' named entity 'HOW' named entity 'SPACE' named entity 'MODELING' named entity 'MODELS' named entity 'SUCCESS' named entity 'POPULATION MODEL' named entity 'SMALL' named entity 'RANGE' named entity 'WORK' named entity 'FAILURE' named entity 'MODELED' named entity 'NEED' named entity 'OCCUR' named entity 'USUALLY' named entity 'CONNECTIONS' named entity 'PARAMETERS' named entity 'OBJECTIVE' named entity 'BAR' named entity 'INDIVIDUALS' named entity 'RELEVANT' named entity 'WIDE' named entity 'LIKE' named entity 'REGARDLESS' named entity 'SCALE' named entity 'MODEL' named entity 'WORLD' named entity 'NETWORK MODEL' named entity 'OF EACH' named entity 'THESE' named entity 'SIMULATED' named entity 'virus' named entity 'population model' named entity 'maximum degree' named entity 'scale-free networks' named entity 'ODE' named entity 'network model' named entity 'power law' named entity 'clustering coefficient' named entity 'small-world' named entity 'degree distribution' named entity 'basic reproduction number' named entity 'multivariate analysis' named entity 'clustering coefficient' named entity 'ODE' named entity 'eigenvalue decomposition' named entity 'Control strategies' named entity 'dimensionality reduction'

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