Bayesian Markov switching models for the early detection of influenza epidemics.
Stat Med
; 27(22): 4455-68, 2008 Sep 30.
Article
em En
| MEDLINE
| ID: mdl-18618414
ABSTRACT
The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In this paper, a Markov switching model is introduced to determine the epidemic and non-epidemic periods from influenza surveillance data the process of differenced incidence rates is modelled either with a first-order autoregressive process or with a Gaussian white-noise process depending on whether the system is in an epidemic or in a non-epidemic phase. The transition between phases of the disease is modelled as a Markovian process. Bayesian inference is carried out on the former model to detect influenza epidemics at the very moment of their onset. Moreover, the proposal provides the probability of being in an epidemic state at any given moment. In order to validate the methodology, a comparison of its performance with other alternatives has been made using influenza illness data obtained from the Sanitary Sentinel Network of the Comunitat Valenciana, one of the 17 autonomous regions in Spain.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Surtos de Doenças
/
Cadeias de Markov
/
Teorema de Bayes
/
Influenza Humana
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Incidence_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Ano de publicação:
2008
Tipo de documento:
Article