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

Autor(es)

Schimit, P. H. T. ^[1] ; Pereira, F. H. ^{[1, 2]}

Afiliação do(s) autor(es)

^[1] Univ Nove Julho, Informat & Knowledge Management Grad Program, Rua Vergueiro 235-249, BR-01504000 Sao Paulo, SP - Brazil

^[2] Univ Nove Julho, Ind Engn Grad Program, Rua Vergueiro 235-249, BR-01504000 Sao Paulo, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: EXPERT SYSTEMS WITH APPLICATIONS; v. 97, p. 41-50, MAY 1 2018.
• Citações Web of Science: 6

Resumo

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 Erclos-Wenyi, Small-World, Scale-Free and Barabasi-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. (C) 2017 Elsevier Ltd. All rights reserved. (AU)

• Processo FAPESP: 17/12671-8 - Laboratório de dinâmicas populacionais
• Beneficiário: Pedro Henrique Triguis Schimit
• Modalidade de apoio: Auxílio à Pesquisa - Regular