JAGS model specification for spatiotemporal epidemiological modelling.
Spat Spatiotemporal Epidemiol
; 49: 100645, 2024 Jun.
Article
en En
| MEDLINE
| ID: mdl-38876555
ABSTRACT
Bayesian inference in modelling infectious diseases using Bayesian inference using Gibbs Sampling (BUGS) is notable in the last two decades in parallel with the advancements in computing and model development. The ability of BUGS to easily implement the Markov chain Monte Carlo (MCMC) method brought Bayesian analysis to the mainstream of infectious disease modelling. However, with the existing software that runs MCMC to make Bayesian inferences, it is challenging, especially in terms of computational complexity, when infectious disease models become more complex with spatial and temporal components, in addition to the increasing number of parameters and large datasets. This study investigates two alternative subscripting strategies for creating models in Just Another Gibbs Sampler (JAGS) environment and their performance in terms of run times. Our results are useful for practitioners to ensure the efficiency and timely implementation of Bayesian spatiotemporal infectious disease modelling.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Cadenas de Markov
/
Teorema de Bayes
/
Análisis Espacio-Temporal
Límite:
Humans
Idioma:
En
Revista:
Spat Spatiotemporal Epidemiol
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Países Bajos