File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Risk-informed decisions for epidemics

TitleRisk-informed decisions for epidemics
Authors
KeywordsRisk-informed decisions
Social media
Epidemics
Influenza-like illness
Issue Date2016
PublisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/tjds
Citation
Proceedings of the 2016 Open Conference of the International Federation for Information Processing Working Group 8.3: Decision Support Systems (IFIP WG 8.3 DSS): Big Data, Better Decisions, Brighter Future, Cork, Ireland, 24-26 June 2016. In Journal of Decision Systems, 2016, v. 25 n. suppl.1, p. 240-247 How to Cite?
AbstractSocial media, an open and free platform containing large volume of user-generated content (UGC) is an ideal data source to achieve risk-informed decisions for epidemics. The probability and predictive value of how social systems deal with epidemics can be conceptually and empirically studied by monitoring social media data for formulating risk-informed decisions in improving preparedness and response to epidemics. ILI (influenza-like illness) surveillance by monitoring social media data offers opportunity to provide early warning signs for improving public health interventions. In this research, we monitored Weibo, a Chinese social media data on swine flu in 2011 to analyse the post content, the correlation with official surveillance data as well as geography distribution in order to verify whether Weibo is an effective platform for conducting risk-informed decision for epidemics.
DescriptionThis journal suppl. entitled: Proceedings of the 2016 Open Conference of the IFIP WG 8.3
Persistent Identifierhttp://hdl.handle.net/10722/235207
ISSN
2023 Impact Factor: 2.8
2023 SCImago Journal Rankings: 0.746
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorFeng, S-
dc.contributor.authorHossain, L-
dc.date.accessioned2016-10-14T13:51:55Z-
dc.date.available2016-10-14T13:51:55Z-
dc.date.issued2016-
dc.identifier.citationProceedings of the 2016 Open Conference of the International Federation for Information Processing Working Group 8.3: Decision Support Systems (IFIP WG 8.3 DSS): Big Data, Better Decisions, Brighter Future, Cork, Ireland, 24-26 June 2016. In Journal of Decision Systems, 2016, v. 25 n. suppl.1, p. 240-247-
dc.identifier.issn1246-0125-
dc.identifier.urihttp://hdl.handle.net/10722/235207-
dc.descriptionThis journal suppl. entitled: Proceedings of the 2016 Open Conference of the IFIP WG 8.3-
dc.description.abstractSocial media, an open and free platform containing large volume of user-generated content (UGC) is an ideal data source to achieve risk-informed decisions for epidemics. The probability and predictive value of how social systems deal with epidemics can be conceptually and empirically studied by monitoring social media data for formulating risk-informed decisions in improving preparedness and response to epidemics. ILI (influenza-like illness) surveillance by monitoring social media data offers opportunity to provide early warning signs for improving public health interventions. In this research, we monitored Weibo, a Chinese social media data on swine flu in 2011 to analyse the post content, the correlation with official surveillance data as well as geography distribution in order to verify whether Weibo is an effective platform for conducting risk-informed decision for epidemics.-
dc.languageeng-
dc.publisherTaylor & Francis. The Journal's web site is located at http://www.tandfonline.com/tjds-
dc.relation.ispartofJournal of Decision Systems-
dc.subjectRisk-informed decisions-
dc.subjectSocial media-
dc.subjectEpidemics-
dc.subjectInfluenza-like illness-
dc.titleRisk-informed decisions for epidemics-
dc.typeConference_Paper-
dc.identifier.emailHossain, L: lhossain@hku.hk-
dc.identifier.authorityHossain, L=rp01858-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1080/12460125.2016.1187813-
dc.identifier.scopuseid_2-s2.0-84976477543-
dc.identifier.hkuros267933-
dc.identifier.hkuros267294-
dc.identifier.hkuros302118-
dc.identifier.hkuros302122-
dc.identifier.volume25-
dc.identifier.issuesuppl.1-
dc.identifier.spage240-
dc.identifier.epage247-
dc.identifier.isiWOS:000378231800018-
dc.publisher.placeUnited Kingdom-
dc.customcontrol.immutablesml 161124-
dc.identifier.issnl1246-0125-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats