Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London.
Proc Natl Acad Sci U S A
; 108(45): 18238-43, 2011 Nov 08.
Article
em En
| MEDLINE
| ID: mdl-22042838
ABSTRACT
The tracking and projection of emerging epidemics is hindered by the disconnect between apparent epidemic dynamics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly observed, system. Behavior changes compound this, altering both true dynamics and reporting patterns, particularly for diseases with nonspecific symptoms, such as influenza. We disentangle these effects to unravel the hidden dynamics of the 2009 influenza A/H1N1pdm pandemic in London, where surveillance suggests an unusual dominant peak in the summer. We embed an age-structured model into a bayesian synthesis of multiple evidence sources to reveal substantial changes in contact patterns and health-seeking behavior throughout the epidemic, uncovering two similar infection waves, despite large differences in the reported levels of disease. We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
/
Influenza Humana
/
Vírus da Influenza A Subtipo H1N1
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Ano de publicação:
2011
Tipo de documento:
Article