Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation.
Stat Med
; 38(21): 4146-4158, 2019 09 20.
Article
em En
| MEDLINE
| ID: mdl-31290184
ABSTRACT
Disease incidence reported directly within health systems frequently reflects a partial observation relative to the true incidence in the population. State-space models present a general framework for inferring both the dynamics of infectious disease processes and the unobserved burden of disease in the population. Here, we present a state-space model of measles transmission and vaccine-based interventions at the country-level and a particle filter-based estimation procedure. Our dynamic transmission model builds on previous work by incorporating population age-structure to allow explicit representation of age-targeted vaccine interventions. We illustrate the performance of estimators of model parameters and predictions of unobserved states on simulated data from two dynamic models one on the annual time-scale of observations and one on the biweekly time-scale of the epidemiological dynamics. We show that our model results in approximately unbiased estimates of unobserved burden and the underreporting rate. We further illustrate the performance of the fitted model for prediction of future disease burden in the next one to 15 years.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Funções Verossimilhança
/
Métodos Epidemiológicos
/
Sarampo
Tipo de estudo:
Incidence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Panamá