Towards uncertainty quantification and inference in the stochastic SIR epidemic model.
Math Biosci
; 240(2): 250-9, 2012 Dec.
Article
em En
| MEDLINE
| ID: mdl-22989951
In this paper we address the problem of estimating the parameters of Markov jump processes modeling epidemics and introduce a novel method to conduct inference when data consists on partial observations in one of the state variables. We take the classical stochastic SIR model as a case study. Using the inverse-size expansion of van Kampen we obtain approximations for the first and second moments of the state variables. These approximate moments are in turn matched to the moments of an inputed Generic Discrete distribution aimed at generating an approximate likelihood that is valid both for low count or high count data. We conduct a full Bayesian inference using informative priors. Estimations and predictions are obtained both in a synthetic data scenario and in two Dengue fever case studies.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Epidemias
/
Modelos Biológicos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Math Biosci
Ano de publicação:
2012
Tipo de documento:
Article
País de afiliação:
México
País de publicação:
Estados Unidos