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Article: Digital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an example

TitleDigital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an example
Authors
KeywordsDashboard
Dissemination
Influenza
Surveillance
Issue Date2011
PublisherJournal of Medical Internet Research. The Journal's web site is located at http://www.jmir.org/
Citation
Journal Of Medical Internet Research, 2011, v. 13 n. 4, article no. e85 How to Cite?
AbstractBackground: Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there has been less research in the area of dissemination. Proper dissemination of surveillance data can facilitate the end user's taking of appropriate actions, thus maximizing the utility of effort taken from upstream of the surveillance-to-action loop. Objective: The aims of the study were to develop a generic framework for a digital dashboard incorporating features of efficient dashboard design and to demonstrate this framework by specific application to influenza surveillance in Hong Kong. Methods: Based on the merits of the national websites and principles of efficient dashboard design, we designed an automated influenza surveillance digital dashboard as a demonstration of efficient dissemination of surveillance data. We developed the system to synthesize and display multiple sources of influenza surveillance data streams in the dashboard. Different algorithms can be implemented in the dashboard for incorporating all surveillance data streams to describe the overall influenza activity. Results: We designed and implemented an influenza surveillance dashboard that utilized self-explanatory figures to display multiple surveillance data streams in panels. Indicators for individual data streams as well as for overall influenza activity were summarized in the main page, which can be read at a glance. Data retrieval function was also incorporated to allow data sharing in standard format. Conclusions: The influenza surveillance dashboard serves as a template to illustrate the efficient synthesization and dissemination of multiple-source surveillance data, which may also be applied to other diseases. Surveillance data from multiple sources can be disseminated efficiently using a dashboard design that facilitates the translation of surveillance information to public health actions. © Calvin KY Cheng, Dennis KM Ip, Benjamin J Cowling, Lai Ming Ho, Gabriel M Leung, Eric HY Lau.
Persistent Identifierhttp://hdl.handle.net/10722/143816
ISSN
2015 Impact Factor: 4.532
2015 SCImago Journal Rankings: 1.648
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Research Fund for the Control of Infectious Diseases of the Health, Welfare and Food Bureau of the Hong Kong SAR Government08070662
Hong Kong University Grants CommitteeAoE/M-12/06
US National Institutes of Health1 U54 GM088558
Funding Information:

This research was funded by the Research Fund for the Control of Infectious Diseases of the Health, Welfare and Food Bureau of the Hong Kong SAR Government (grant number 08070662), the Area of Excellence Scheme of the Hong Kong University Grants Committee (grant number AoE/M-12/06), and the US National Institutes of Health Models of Infectious Disease Agent Study program (grant number 1 U54 GM088558). We gratefully acknowledge the Centre for Health Protection, Hong Kong; the Hospital Authority, Hong Kong; and BroadLearning Education (Asia) Ltd for providing influenza surveillance data. We thank Wing-Hong Seto for useful discussion, Xiu-Qing He for research support, and two anonymous reviewers for their comments and suggestions.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorCheng, CKYen_HK
dc.contributor.authorIp, DKMen_HK
dc.contributor.authorCowling, BJen_HK
dc.contributor.authorHo, LMen_HK
dc.contributor.authorLeung, GMen_HK
dc.contributor.authorLau, EHYen_HK
dc.date.accessioned2011-12-21T08:56:38Z-
dc.date.available2011-12-21T08:56:38Z-
dc.date.issued2011en_HK
dc.identifier.citationJournal Of Medical Internet Research, 2011, v. 13 n. 4, article no. e85en_HK
dc.identifier.issn1438-8871en_HK
dc.identifier.urihttp://hdl.handle.net/10722/143816-
dc.description.abstractBackground: Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there has been less research in the area of dissemination. Proper dissemination of surveillance data can facilitate the end user's taking of appropriate actions, thus maximizing the utility of effort taken from upstream of the surveillance-to-action loop. Objective: The aims of the study were to develop a generic framework for a digital dashboard incorporating features of efficient dashboard design and to demonstrate this framework by specific application to influenza surveillance in Hong Kong. Methods: Based on the merits of the national websites and principles of efficient dashboard design, we designed an automated influenza surveillance digital dashboard as a demonstration of efficient dissemination of surveillance data. We developed the system to synthesize and display multiple sources of influenza surveillance data streams in the dashboard. Different algorithms can be implemented in the dashboard for incorporating all surveillance data streams to describe the overall influenza activity. Results: We designed and implemented an influenza surveillance dashboard that utilized self-explanatory figures to display multiple surveillance data streams in panels. Indicators for individual data streams as well as for overall influenza activity were summarized in the main page, which can be read at a glance. Data retrieval function was also incorporated to allow data sharing in standard format. Conclusions: The influenza surveillance dashboard serves as a template to illustrate the efficient synthesization and dissemination of multiple-source surveillance data, which may also be applied to other diseases. Surveillance data from multiple sources can be disseminated efficiently using a dashboard design that facilitates the translation of surveillance information to public health actions. © Calvin KY Cheng, Dennis KM Ip, Benjamin J Cowling, Lai Ming Ho, Gabriel M Leung, Eric HY Lau.en_HK
dc.languageengen_US
dc.publisherJournal of Medical Internet Research. The Journal's web site is located at http://www.jmir.org/-
dc.relation.ispartofJournal of Medical Internet Researchen_HK
dc.subjectDashboarden_HK
dc.subjectDisseminationen_HK
dc.subjectInfluenzaen_HK
dc.subjectSurveillanceen_HK
dc.titleDigital dashboard design using multiple data streams for disease surveillance with influenza surveillance as an exampleen_HK
dc.typeArticleen_HK
dc.identifier.emailIp, DKM:dkmip@hku.hken_HK
dc.identifier.emailCowling, BJ:bcowling@hku.hken_HK
dc.identifier.emailHo, LM:lmho@hkucc.hku.hken_HK
dc.identifier.emailLeung, GM:gmleung@hku.hken_HK
dc.identifier.emailLau, EHY:ehylau@hku.hken_HK
dc.identifier.authorityIp, DKM=rp00256en_HK
dc.identifier.authorityCowling, BJ=rp01326en_HK
dc.identifier.authorityHo, LM=rp00360en_HK
dc.identifier.authorityLeung, GM=rp00460en_HK
dc.identifier.authorityLau, EHY=rp01349en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.2196/jmir.1658en_HK
dc.identifier.pmid22001082-
dc.identifier.pmcidPMC3222192-
dc.identifier.scopuseid_2-s2.0-81855173603en_HK
dc.identifier.hkuros198034en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-81855173603&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume13en_HK
dc.identifier.issue4en_HK
dc.identifier.spagee85en_US
dc.identifier.epagee85en_US
dc.identifier.isiWOS:000299313300041-
dc.publisher.placeCanada-
dc.relation.projectControl of Pandemic and Inter-pandemic Influenza-
dc.relation.projectDigital dashboard design for public health surveillance-
dc.identifier.scopusauthoridCheng, CKY=24474272100en_HK
dc.identifier.scopusauthoridIp, DKM=35117701600en_HK
dc.identifier.scopusauthoridCowling, BJ=8644765500en_HK
dc.identifier.scopusauthoridHo, LM=7402955625en_HK
dc.identifier.scopusauthoridLeung, GM=7007159841en_HK
dc.identifier.scopusauthoridLau, EHY=7103086074en_HK

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