Optimizing sentinel surveillance in temporal network epidemiology
dc.contributor.author | Bai, Yuan | |
dc.contributor.author | Yang, Bo | |
dc.contributor.author | Lin, Lijuan | |
dc.contributor.author | Herrera, Jose L. [UNESP] | |
dc.contributor.author | Du, Zhanwei | |
dc.contributor.author | Holme, Petter | |
dc.contributor.institution | Jilin Univ | |
dc.contributor.institution | Univ Texas Austin | |
dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
dc.contributor.institution | Tokyo Inst Technol | |
dc.date.accessioned | 2018-11-26T15:44:38Z | |
dc.date.available | 2018-11-26T15:44:38Z | |
dc.date.issued | 2017-07-06 | |
dc.description.abstract | 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. | en |
dc.description.affiliation | Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China | |
dc.description.affiliation | Jilin Univ, Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China | |
dc.description.affiliation | Univ Texas Austin, Dept Integrat Biol, Austin, TX 78705 USA | |
dc.description.affiliation | Sao Paulo State Univ, ICTP South Amer Inst Fundamental Res, BR-03001000 Sao Paulo, Brazil | |
dc.description.affiliation | Tokyo Inst Technol, Inst Innovat Res, Tokyo 1528550, Japan | |
dc.description.affiliationUnesp | Sao Paulo State Univ, ICTP South Amer Inst Fundamental Res, BR-03001000 Sao Paulo, Brazil | |
dc.description.sponsorship | National Natural Science Foundation of China | |
dc.description.sponsorshipId | National Natural Science Foundation of China: 61572226 | |
dc.description.sponsorshipId | National Natural Science Foundation of China: 61373053 | |
dc.format.extent | 10 | |
dc.identifier | http://dx.doi.org/10.1038/s41598-017-03868-6 | |
dc.identifier.citation | Scientific Reports. London: Nature Publishing Group, v. 7, 10 p., 2017. | |
dc.identifier.doi | 10.1038/s41598-017-03868-6 | |
dc.identifier.file | WOS000404841100060.pdf | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.uri | http://hdl.handle.net/11449/159605 | |
dc.identifier.wos | WOS:000404841100060 | |
dc.language.iso | eng | |
dc.publisher | Nature Publishing Group | |
dc.relation.ispartof | Scientific Reports | |
dc.relation.ispartofsjr | 1,533 | |
dc.rights.accessRights | Acesso aberto | |
dc.source | Web of Science | |
dc.title | Optimizing sentinel surveillance in temporal network epidemiology | en |
dc.type | Artigo | |
dcterms.rightsHolder | Nature Publishing Group | |
unesp.author.orcid | 0000-0003-2156-1096[6] |