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Article: Situational awareness of influenza activity based on multiple streams of surveillance data using multivariate dynamic linear model

TitleSituational awareness of influenza activity based on multiple streams of surveillance data using multivariate dynamic linear model
Authors
KeywordsAbsenteeism
Awareness
Biostatistics
Correlation analysis
Disease activity
Issue Date2012
PublisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.action
Citation
PLoS One, 2012, v. 7 n. 5, article no. e38346 How to Cite?
AbstractBACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (rho = 0.82, p
Persistent Identifierhttp://hdl.handle.net/10722/151777
ISSN
2015 Impact Factor: 3.057
2015 SCImago Journal Rankings: 1.395
PubMed Central ID
ISI Accession Number ID
Funding AgencyGrant Number
Research Fund for the Control of Infectious Diseases of the Food and Health Bureau of the Hong Kong Special Administrative Region Government11101092
Area of Excellence Scheme of the University Grants CommitteeAoE/M-12/06
Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical SciencesU54 GM088558
Funding Information:

This research was in part funded by the Research Fund for the Control of Infectious Diseases of the Food and Health Bureau of the Hong Kong Special Administrative Region Government (grant no. 11101092), the Area of Excellence Scheme of the University Grants Committee (grant no. AoE/M-12/06), and the Harvard Center for Communicable Disease Dynamics from the National Institute of General Medical Sciences (grant number U54 GM088558). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of General Medical Sciences or the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References
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DC FieldValueLanguage
dc.contributor.authorLau, EHYen_US
dc.contributor.authorCheng, CKYen_US
dc.contributor.authorIp, DKMen_US
dc.contributor.authorCowling, BJen_US
dc.date.accessioned2012-06-26T06:28:21Z-
dc.date.available2012-06-26T06:28:21Z-
dc.date.issued2012en_US
dc.identifier.citationPLoS One, 2012, v. 7 n. 5, article no. e38346en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://hdl.handle.net/10722/151777-
dc.description.abstractBACKGROUND: Multiple sources of influenza surveillance data are becoming more available; however integration of these data streams for situational awareness of influenza activity is less explored. METHODS AND RESULTS: We applied multivariate time-series methods to sentinel outpatient and school absenteeism surveillance data in Hong Kong during 2004-2009. School absenteeism data and outpatient surveillance data experienced interruptions due to school holidays and changes in public health guidelines during the pandemic, including school closures and the establishment of special designated flu clinics, which in turn provided 'drop-in' fever counts surveillance data. A multivariate dynamic linear model was used to monitor influenza activity throughout epidemics based on all available data. The inferred level followed influenza activity closely at different times, while the inferred trend was less competent with low influenza activity. Correlations between inferred level and trend from the multivariate model and reference influenza activity, measured by the product of weekly laboratory influenza detection rates and weekly general practitioner influenza-like illness consultation rates, were calculated and compared with those from univariate models. Over the whole study period, there was a significantly higher correlation (rho = 0.82, p</=0.02) for the inferred trend based on the multivariate model compared to other univariate models, while the inferred trend from the multivariate model performed as well as the best univariate model in the pre-pandemic and the pandemic period. The inferred trend and level from the multivariate model was able to match, if not outperform, the best univariate model albeit with missing data plus drop-in and drop-out of different surveillance data streams. An overall influenza index combining level and trend was constructed to demonstrate another potential use of the method. CONCLUSIONS: Our results demonstrate the potential use of multiple streams of influenza surveillance data to promote situational awareness about the level and trend of seasonal and pandemic influenza activity.en_US
dc.languageengen_US
dc.publisherPublic Library of Science. The Journal's web site is located at http://www.plosone.org/home.actionen_US
dc.relation.ispartofPLoS Oneen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectAbsenteeism-
dc.subjectAwareness-
dc.subjectBiostatistics-
dc.subjectCorrelation analysis-
dc.subjectDisease activity-
dc.titleSituational awareness of influenza activity based on multiple streams of surveillance data using multivariate dynamic linear modelen_US
dc.typeArticleen_US
dc.identifier.emailLau, EHY: ehylau@hku.hken_US
dc.identifier.emailCheng, CKY: chengkyc@hkucc.hku.hken_US
dc.identifier.emailIp, DKM: dkmip@hku.hken_US
dc.identifier.emailCowling, BJ: bcowling@hku.hk-
dc.identifier.authorityLau, EHY=rp01349en_US
dc.identifier.authorityIp, DKM=rp00256en_US
dc.identifier.authorityCowling, BJ=rp01326en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1371/journal.pone.0038346en_US
dc.identifier.pmid22675456-
dc.identifier.pmcidPMC3364986-
dc.identifier.scopuseid_2-s2.0-84861653379en_US
dc.identifier.hkuros200469-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-84861653379&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume7en_US
dc.identifier.issue5, article no. e38346en_US
dc.identifier.isiWOS:000305338500137-
dc.publisher.placeUnited Statesen_US
dc.relation.projectDevelopment and evaluation of an electronic school absenteeism system for influenza-like-illness surveillance in Hong Kong-
dc.relation.projectControl of Pandemic and Inter-pandemic Influenza-
dc.identifier.scopusauthoridCowling, BJ=8644765500en_US
dc.identifier.scopusauthoridIp, DKM=35117701600en_US
dc.identifier.scopusauthoridCheng, CKY=24474272100en_US
dc.identifier.scopusauthoridLau, EHY=7103086074en_US

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