Hierarchical statistical modelling of influenza epidemic dynamics in space and time.
Stat Med
; 21(18): 2703-21, 2002 Sep 30.
Article
em En
| MEDLINE
| ID: mdl-12228886
ABSTRACT
An infectious disease typically spreads via contact between infected and susceptible individuals. Since the small-scale movements and contacts between people are generally not recorded, available data regarding infectious disease are often aggregations in space and time, yielding small-area counts of the number infected during successive, regular time intervals. In this paper, we develop a spatially descriptive, temporally dynamic hierarchical model to be fitted to such data. Disease counts are viewed as a realization from an underlying multivariate autoregressive process, where the relative risk of infection incorporates the space-time dynamic. We take a Bayesian approach, using Markov chain Monte Carlo to compute posterior estimates of all parameters of interest. We apply the methodology to an influenza epidemic in Scotland during the years 1989-1990.
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Base de dados:
MEDLINE
Assunto principal:
Surtos de Doenças
/
Modelos Estatísticos
/
Influenza Humana
/
Modelos Biológicos
Tipo de estudo:
Etiology_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País como assunto:
Europa
Idioma:
En
Ano de publicação:
2002
Tipo de documento:
Article