FluHMM: A simple and flexible Bayesian algorithm for sentinel influenza surveillance and outbreak detection.
Stat Methods Med Res
; 28(6): 1826-1840, 2019 06.
Article
in En
| MEDLINE
| ID: mdl-29869565
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Disease Outbreaks
/
Bayes Theorem
/
Sentinel Surveillance
/
Influenza, Human
Type of study:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
Stat Methods Med Res
Year:
2019
Document type:
Article
Affiliation country:
Grecia