FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.18/6094 |
Resumo: | 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. |
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FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detectionInfluenzaSeasonal InfluenzaDisease SurveillanceHidden Markov ModelEpidemicsOutbreak DetectionBayesian StatisticsEstados de Saúde e de DoençaTimely 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.SAGE PublicationsRepositório Científico do Instituto Nacional de SaúdeLytras, TheodoreGkolfinopoulou, KassianiBonovas, StefanosNunes, Baltazar2019-03-07T16:58:48Z2018-06-052018-06-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.18/6094engStat Methods Med Res. 2019 Jun;28(6):1826-1840. doi: 10.1177/0962280218776685. Epub 2018 Jun 5.0962-280210.1177/0962280218776685info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-20T15:41:12Zoai:repositorio.insa.pt:10400.18/6094Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:40:44.827518Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
title |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
spellingShingle |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection Lytras, Theodore Influenza Seasonal Influenza Disease Surveillance Hidden Markov Model Epidemics Outbreak Detection Bayesian Statistics Estados de Saúde e de Doença |
title_short |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
title_full |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
title_fullStr |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
title_full_unstemmed |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
title_sort |
FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection |
author |
Lytras, Theodore |
author_facet |
Lytras, Theodore Gkolfinopoulou, Kassiani Bonovas, Stefanos Nunes, Baltazar |
author_role |
author |
author2 |
Gkolfinopoulou, Kassiani Bonovas, Stefanos Nunes, Baltazar |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Nacional de Saúde |
dc.contributor.author.fl_str_mv |
Lytras, Theodore Gkolfinopoulou, Kassiani Bonovas, Stefanos Nunes, Baltazar |
dc.subject.por.fl_str_mv |
Influenza Seasonal Influenza Disease Surveillance Hidden Markov Model Epidemics Outbreak Detection Bayesian Statistics Estados de Saúde e de Doença |
topic |
Influenza Seasonal Influenza Disease Surveillance Hidden Markov Model Epidemics Outbreak Detection Bayesian Statistics Estados de Saúde e de Doença |
description |
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. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-06-05 2018-06-05T00:00:00Z 2019-03-07T16:58:48Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.18/6094 |
url |
http://hdl.handle.net/10400.18/6094 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Stat Methods Med Res. 2019 Jun;28(6):1826-1840. doi: 10.1177/0962280218776685. Epub 2018 Jun 5. 0962-2802 10.1177/0962280218776685 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
eu_rights_str_mv |
embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
SAGE Publications |
publisher.none.fl_str_mv |
SAGE Publications |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132149537832960 |