Optimizing sentinel surveillance in temporal network epidemiology
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1038/s41598-017-03868-6 http://hdl.handle.net/11449/159605 |
Resumo: | To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but this is a first step study to explore designing surveillance systems for early detection on temporal networks. This paper is based on the idea that vaccination strategies can serve as a method to identify sentinels. The vaccination problem is a related question that is much more well studied for temporal networks. To assess the ability to detect epidemic outbreaks early, we calculate the time difference (lead time) between the surveillance set and whole population in reaching 1% prevalence. We find that the optimal selection of sentinels depends on both the network's temporal structures and the infection probability of the disease. We find that, for a mild infectious disease (low infection probability) on a temporal network in relation to potential disease spreading (the Prostitution network), the strategy of selecting latest contacts of random individuals provide the most amount of lead time. And for a more uniform, synthetic network with community structure the strategy of selecting frequent contacts of random individuals provide the most amount of lead time. |
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Optimizing sentinel surveillance in temporal network epidemiologyTo help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but this is a first step study to explore designing surveillance systems for early detection on temporal networks. This paper is based on the idea that vaccination strategies can serve as a method to identify sentinels. The vaccination problem is a related question that is much more well studied for temporal networks. To assess the ability to detect epidemic outbreaks early, we calculate the time difference (lead time) between the surveillance set and whole population in reaching 1% prevalence. We find that the optimal selection of sentinels depends on both the network's temporal structures and the infection probability of the disease. We find that, for a mild infectious disease (low infection probability) on a temporal network in relation to potential disease spreading (the Prostitution network), the strategy of selecting latest contacts of random individuals provide the most amount of lead time. And for a more uniform, synthetic network with community structure the strategy of selecting frequent contacts of random individuals provide the most amount of lead time.National Natural Science Foundation of ChinaJilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R ChinaJilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R ChinaUniv Texas Austin, Dept Integrat Biol, Austin, TX 78705 USASao Paulo State Univ, ICTP South Amer Inst Fundamental Res, BR-03001000 Sao Paulo, BrazilTokyo Inst Technol, Inst Innovat Res, Tokyo 1528550, JapanSao Paulo State Univ, ICTP South Amer Inst Fundamental Res, BR-03001000 Sao Paulo, BrazilNational Natural Science Foundation of China: 61572226National Natural Science Foundation of China: 61373053Nature Publishing GroupJilin UnivUniv Texas AustinUniversidade Estadual Paulista (Unesp)Tokyo Inst TechnolBai, YuanYang, BoLin, LijuanHerrera, Jose L. [UNESP]Du, ZhanweiHolme, Petter2018-11-26T15:44:38Z2018-11-26T15:44:38Z2017-07-06info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article10http://dx.doi.org/10.1038/s41598-017-03868-6Scientific Reports. London: Nature Publishing Group, v. 7, 10 p., 2017.2045-2322http://hdl.handle.net/11449/15960510.1038/s41598-017-03868-6WOS:000404841100060WOS000404841100060.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengScientific Reports1,533info:eu-repo/semantics/openAccess2021-10-23T16:51:38Zoai:repositorio.unesp.br:11449/159605Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:11:37.746369Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Optimizing sentinel surveillance in temporal network epidemiology |
title |
Optimizing sentinel surveillance in temporal network epidemiology |
spellingShingle |
Optimizing sentinel surveillance in temporal network epidemiology Bai, Yuan |
title_short |
Optimizing sentinel surveillance in temporal network epidemiology |
title_full |
Optimizing sentinel surveillance in temporal network epidemiology |
title_fullStr |
Optimizing sentinel surveillance in temporal network epidemiology |
title_full_unstemmed |
Optimizing sentinel surveillance in temporal network epidemiology |
title_sort |
Optimizing sentinel surveillance in temporal network epidemiology |
author |
Bai, Yuan |
author_facet |
Bai, Yuan Yang, Bo Lin, Lijuan Herrera, Jose L. [UNESP] Du, Zhanwei Holme, Petter |
author_role |
author |
author2 |
Yang, Bo Lin, Lijuan Herrera, Jose L. [UNESP] Du, Zhanwei Holme, Petter |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Jilin Univ Univ Texas Austin Universidade Estadual Paulista (Unesp) Tokyo Inst Technol |
dc.contributor.author.fl_str_mv |
Bai, Yuan Yang, Bo Lin, Lijuan Herrera, Jose L. [UNESP] Du, Zhanwei Holme, Petter |
description |
To help health policy makers gain response time to mitigate infectious disease threats, it is essential to have an efficient epidemic surveillance. One common method of disease surveillance is to carefully select nodes (sentinels, or sensors) in the network to report outbreaks. One would like to choose sentinels so that they discover the outbreak as early as possible. The optimal choice of sentinels depends on the network structure. Studies have addressed this problem for static networks, but this is a first step study to explore designing surveillance systems for early detection on temporal networks. This paper is based on the idea that vaccination strategies can serve as a method to identify sentinels. The vaccination problem is a related question that is much more well studied for temporal networks. To assess the ability to detect epidemic outbreaks early, we calculate the time difference (lead time) between the surveillance set and whole population in reaching 1% prevalence. We find that the optimal selection of sentinels depends on both the network's temporal structures and the infection probability of the disease. We find that, for a mild infectious disease (low infection probability) on a temporal network in relation to potential disease spreading (the Prostitution network), the strategy of selecting latest contacts of random individuals provide the most amount of lead time. And for a more uniform, synthetic network with community structure the strategy of selecting frequent contacts of random individuals provide the most amount of lead time. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-07-06 2018-11-26T15:44:38Z 2018-11-26T15:44:38Z |
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/10.1038/s41598-017-03868-6 Scientific Reports. London: Nature Publishing Group, v. 7, 10 p., 2017. 2045-2322 http://hdl.handle.net/11449/159605 10.1038/s41598-017-03868-6 WOS:000404841100060 WOS000404841100060.pdf |
url |
http://dx.doi.org/10.1038/s41598-017-03868-6 http://hdl.handle.net/11449/159605 |
identifier_str_mv |
Scientific Reports. London: Nature Publishing Group, v. 7, 10 p., 2017. 2045-2322 10.1038/s41598-017-03868-6 WOS:000404841100060 WOS000404841100060.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Scientific Reports 1,533 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
10 |
dc.publisher.none.fl_str_mv |
Nature Publishing Group |
publisher.none.fl_str_mv |
Nature Publishing Group |
dc.source.none.fl_str_mv |
Web of Science 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 |
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1808128477709205504 |