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FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection

2018-06-05Journal article Restricted access
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dc.contributor.authorLytras, Theodore
dc.contributor.authorGkolfinopoulou, Kassiani
dc.contributor.authorBonovas, Stefanos
dc.contributor.authorNunes, Baltazar
dc.date.accessioned2019-03-07T16:58:48Z
dc.date.available2019-03-07T16:58:48Z
dc.date.issued2018-06-05
dc.description.abstractTimely detection of the seasonal influenza epidemic is important for public health action. We introduce FluHMM, a simple but flexible Bayesian algorithm to detect and monitor the seasonal epidemic on sentinel surveillance data. No comparable historical data are required for its use. FluHMM segments a typical influenza surveillance season into five distinct phases with clear interpretation (pre-epidemic, epidemic growth, epidemic plateau, epidemic decline and post-epidemic) and provides the posterior probability of being at each phase for every week in the period under surveillance, given the available data. An alert can be raised when the probability that the epidemic has started exceeds a given threshold. An accompanying R package facilitates the application of this method in public health practice. We apply FluHMM on 12 seasons of sentinel surveillance data from Greece, and show that it achieves very good sensitivity, timeliness and perfect specificity, thereby demonstrating its usefulness. We further discuss advantages and limitations of the method, providing suggestions on how to apply it and highlighting potential future extensions such as with integrating multiple surveillance data streams.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationStat Methods Med Res. 2019 Jun;28(6):1826-1840. doi: 10.1177/0962280218776685. Epub 2018 Jun 5.pt_PT
dc.identifier.doi10.1177/0962280218776685pt_PT
dc.identifier.issn0962-2802
dc.identifier.urihttp://hdl.handle.net/10400.18/6094
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSAGE Publicationspt_PT
dc.relation.publisherversionhttps://journals.sagepub.com/doi/10.1177/0962280218776685pt_PT
dc.subjectInfluenzapt_PT
dc.subjectSeasonal Influenzapt_PT
dc.subjectDisease Surveillancept_PT
dc.subjectHidden Markov Modelpt_PT
dc.subjectEpidemicspt_PT
dc.subjectOutbreak Detectionpt_PT
dc.subjectBayesian Statisticspt_PT
dc.subjectEstados de Saúde e de Doençapt_PT
dc.titleFluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage15pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleStatistical Methods in Medical Researchpt_PT
person.familyNameNunes
person.givenNameBaltazar
person.identifier.ciencia-idAB11-AD48-A8DF
person.identifier.orcid0000-0001-6230-7209
person.identifier.scopus-author-id9133723200
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationbe4efa0d-49df-4a93-bfdd-b76d9f7bf492
relation.isAuthorOfPublication.latestForDiscoverybe4efa0d-49df-4a93-bfdd-b76d9f7bf492

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