Local risk perception enhances epidemic control
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Data de Publicação: | 2019 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1371/journal.pone.0225576 http://hdl.handle.net/11449/199763 |
Resumo: | As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population. |
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Local risk perception enhances epidemic controlAs infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)ICTP South American Institute for Fundamental ResearchIFT-UNESPCeSiMo Facultad de Ingeniería Universidad de Los AndesDepartment of Integrative Biology University of Texas at AustinIFT-UNESPFAPESP: 2016/01343-7FAPESP: 2017/00344-2ICTP South American Institute for Fundamental ResearchUniversidade Estadual Paulista (Unesp)Universidad de Los AndesUniversity of Texas at AustinHerrera-Diestra, José L. [UNESP]Meyers, Lauren Ancel2020-12-12T01:48:38Z2020-12-12T01:48:38Z2019-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pone.0225576PLoS ONE, v. 14, n. 12, 2019.1932-6203http://hdl.handle.net/11449/19976310.1371/journal.pone.02255762-s2.0-85076027950Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLoS ONEinfo:eu-repo/semantics/openAccess2021-10-23T09:34:06Zoai:repositorio.unesp.br:11449/199763Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:56:05.047798Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Local risk perception enhances epidemic control |
title |
Local risk perception enhances epidemic control |
spellingShingle |
Local risk perception enhances epidemic control Herrera-Diestra, José L. [UNESP] |
title_short |
Local risk perception enhances epidemic control |
title_full |
Local risk perception enhances epidemic control |
title_fullStr |
Local risk perception enhances epidemic control |
title_full_unstemmed |
Local risk perception enhances epidemic control |
title_sort |
Local risk perception enhances epidemic control |
author |
Herrera-Diestra, José L. [UNESP] |
author_facet |
Herrera-Diestra, José L. [UNESP] Meyers, Lauren Ancel |
author_role |
author |
author2 |
Meyers, Lauren Ancel |
author2_role |
author |
dc.contributor.none.fl_str_mv |
ICTP South American Institute for Fundamental Research Universidade Estadual Paulista (Unesp) Universidad de Los Andes University of Texas at Austin |
dc.contributor.author.fl_str_mv |
Herrera-Diestra, José L. [UNESP] Meyers, Lauren Ancel |
description |
As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-01 2020-12-12T01:48:38Z 2020-12-12T01:48:38Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1371/journal.pone.0225576 PLoS ONE, v. 14, n. 12, 2019. 1932-6203 http://hdl.handle.net/11449/199763 10.1371/journal.pone.0225576 2-s2.0-85076027950 |
url |
http://dx.doi.org/10.1371/journal.pone.0225576 http://hdl.handle.net/11449/199763 |
identifier_str_mv |
PLoS ONE, v. 14, n. 12, 2019. 1932-6203 10.1371/journal.pone.0225576 2-s2.0-85076027950 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLoS ONE |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128723999784960 |