Impact of network topology on the spread of infectious diseases
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512020000100095 |
Resumo: | ABSTRACT The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease. |
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Impact of network topology on the spread of infectious diseasesscientific computingcomplex networksIndividual Based Modelepidemic infectious diseasesABSTRACT The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.Sociedade Brasileira de Matemática Aplicada e Computacional2020-04-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512020000100095TEMA (São Carlos) v.21 n.1 2020reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online)instname:Sociedade Brasileira de Matemática Aplicada e Computacionalinstacron:SBMAC10.5540/tema.2020.021.01.0095info:eu-repo/semantics/openAccessPINTO,E. R.NEPOMUCENO,E. G.CAMPANHARO,A. S. L. O.eng2020-04-28T00:00:00Zoai:scielo:S2179-84512020000100095Revistahttp://www.scielo.br/temaPUBhttps://old.scielo.br/oai/scielo-oai.phpcastelo@icmc.usp.br2179-84511677-1966opendoar:2020-04-28T00:00TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacionalfalse |
dc.title.none.fl_str_mv |
Impact of network topology on the spread of infectious diseases |
title |
Impact of network topology on the spread of infectious diseases |
spellingShingle |
Impact of network topology on the spread of infectious diseases PINTO,E. R. scientific computing complex networks Individual Based Model epidemic infectious diseases |
title_short |
Impact of network topology on the spread of infectious diseases |
title_full |
Impact of network topology on the spread of infectious diseases |
title_fullStr |
Impact of network topology on the spread of infectious diseases |
title_full_unstemmed |
Impact of network topology on the spread of infectious diseases |
title_sort |
Impact of network topology on the spread of infectious diseases |
author |
PINTO,E. R. |
author_facet |
PINTO,E. R. NEPOMUCENO,E. G. CAMPANHARO,A. S. L. O. |
author_role |
author |
author2 |
NEPOMUCENO,E. G. CAMPANHARO,A. S. L. O. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
PINTO,E. R. NEPOMUCENO,E. G. CAMPANHARO,A. S. L. O. |
dc.subject.por.fl_str_mv |
scientific computing complex networks Individual Based Model epidemic infectious diseases |
topic |
scientific computing complex networks Individual Based Model epidemic infectious diseases |
description |
ABSTRACT The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-04-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512020000100095 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-84512020000100095 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.5540/tema.2020.021.01.0095 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Matemática Aplicada e Computacional |
dc.source.none.fl_str_mv |
TEMA (São Carlos) v.21 n.1 2020 reponame:TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) instname:Sociedade Brasileira de Matemática Aplicada e Computacional instacron:SBMAC |
instname_str |
Sociedade Brasileira de Matemática Aplicada e Computacional |
instacron_str |
SBMAC |
institution |
SBMAC |
reponame_str |
TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
collection |
TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) |
repository.name.fl_str_mv |
TEMA (Sociedade Brasileira de Matemática Aplicada e Computacional. Online) - Sociedade Brasileira de Matemática Aplicada e Computacional |
repository.mail.fl_str_mv |
castelo@icmc.usp.br |
_version_ |
1752122220639944704 |