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

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• dc.contributor.author Lytras, Theodoros, 1987-
• dc.contributor.author Gkolfinopoulou, Kassiani
• dc.contributor.author Bonovas, Stefanos
• dc.contributor.author Nunes, Baltazar
• dc.date.accessioned 2023-10-24T07:13:14Z
• dc.date.available 2023-10-24T07:13:14Z
• dc.date.issued 2019
• dc.description.abstract Timely 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.
• dc.format.mimetype application/pdf
• dc.identifier.citation Lytras T, Gkolfinopoulou K, Bonovas S, Nunes B. FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection. Stat Methods Med Res. 2019 Jun;28(6):1826-40. DOI: 10.1177/0962280218776685
• dc.identifier.doi http://dx.doi.org/10.1177/0962280218776685
• dc.identifier.issn 0962-2802
• dc.identifier.uri http://hdl.handle.net/10230/58115
• dc.language.iso eng
• dc.publisher SAGE Publications
• dc.relation.ispartof Stat Methods Med Res. 2019 Jun;28(6):1826-40
• dc.rights Lytras T, Gkolfinopoulou K, Bonovas S, Nunes B. FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection. Stat Methods Med Res. 2019 Jun;28(6):1826-40. Copyright © The Author(s) 2018. DOI: 10.1177/0962280218776685.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Bayesian statistics
• dc.subject.keyword Influenza
• dc.subject.keyword Disease surveillance
• dc.subject.keyword Epidemics
• dc.subject.keyword Hidden Markov model
• dc.subject.keyword Outbreak detection
• dc.subject.keyword Seasonal influenza
• dc.title FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion