FluHMM

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

Theodore Lytras, Kassiani Gkolfinopoulou, Stefanos Bonovas, Baltazar Nunes

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)1826-1840
JournalStatistical Methods In Medical Research
Volume28
Issue number6
DOIs
Publication statusPublished - 2019

Fingerprint

Sentinel Surveillance
Influenza
Surveillance
Human Influenza
Disease Outbreaks
Public Health
Public Health Practice
Greece
Historical Data
Posterior Probability
Data Streams
Specificity
Exceed
Monitor
Distinct

Keywords

  • Bayesian statistics
  • disease surveillance
  • epidemics
  • hidden Markov model
  • Influenza
  • outbreak detection
  • seasonal influenza

Cite this

Lytras, Theodore ; Gkolfinopoulou, Kassiani ; Bonovas, Stefanos ; Nunes, Baltazar. / FluHMM : a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection. In: Statistical Methods In Medical Research. 2019 ; Vol. 28, No. 6. pp. 1826-1840.
@article{25662403e2b041abb3066bad7e00c87f,
title = "FluHMM: a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection",
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.",
keywords = "Bayesian statistics, disease surveillance, epidemics, hidden Markov model, Influenza, outbreak detection, seasonal influenza",
author = "Theodore Lytras and Kassiani Gkolfinopoulou and Stefanos Bonovas and Baltazar Nunes",
year = "2019",
doi = "10.1177/0962280218776685",
language = "English",
volume = "28",
pages = "1826--1840",
journal = "Statistical Methods In Medical Research",
issn = "0962-2802",
publisher = "Sage Publications Ltd",
number = "6",

}

FluHMM : a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection. / Lytras, Theodore; Gkolfinopoulou, Kassiani; Bonovas, Stefanos; Nunes, Baltazar.

In: Statistical Methods In Medical Research, Vol. 28, No. 6, 2019, p. 1826-1840.

Research output: Contribution to journalArticle

TY - JOUR

T1 - FluHMM

T2 - a simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection

AU - Lytras, Theodore

AU - Gkolfinopoulou, Kassiani

AU - Bonovas, Stefanos

AU - Nunes, Baltazar

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Bayesian statistics

KW - disease surveillance

KW - epidemics

KW - hidden Markov model

KW - Influenza

KW - outbreak detection

KW - seasonal influenza

UR - http://www.scopus.com/inward/record.url?scp=85048071014&partnerID=8YFLogxK

U2 - 10.1177/0962280218776685

DO - 10.1177/0962280218776685

M3 - Article

VL - 28

SP - 1826

EP - 1840

JO - Statistical Methods In Medical Research

JF - Statistical Methods In Medical Research

SN - 0962-2802

IS - 6

ER -