Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling.
Bayesian
Climatic factors
Spatio-temporal conditional autoregressive
Journal
Spatial and spatio-temporal epidemiology
ISSN: 1877-5853
Titre abrégé: Spat Spatiotemporal Epidemiol
Pays: Netherlands
ID NLM: 101516571
Informations de publication
Date de publication:
06 2020
06 2020
Historique:
received:
10
06
2019
revised:
10
11
2019
accepted:
04
12
2019
entrez:
7
5
2020
pubmed:
7
5
2020
medline:
5
8
2021
Statut:
ppublish
Résumé
A range of Bayesian models have been used to describe spatial and temporal patterns of disease in areal unit data. In this study, we applied two Bayesian spatio-temporal conditional autoregressive (ST CAR) models, one of which allows discontinuities in risk between neighbouring areas (creating 'groups'), to examine dengue fever patterns. Data on annual (2002-2017) and monthly (January 2013 - December 2017) dengue cases and climatic factors over 14 geographic areas were obtained for Makassar, Indonesia. Combinations of covariates and model formulations were compared considering credible intervals, overall goodness of fit, and the grouping structure. For annual data, an ST CAR localised model incorporating average humidity provided the best fit, while for monthly data, a single-group ST CAR autoregressive model incorporating rainfall and average humidity was preferred. Using appropriate Bayesian spatio-temporal models enables identification of different groups of areas and the impact of climatic covariates which may help inform policy decisions.
Identifiants
pubmed: 32370940
pii: S1877-5845(20)30013-7
doi: 10.1016/j.sste.2020.100335
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100335Informations de copyright
Crown Copyright © 2020. Published by Elsevier Ltd. All rights reserved.