Open AccessEffects of Climatic Factors on Dengue Incidence: A Comparison of Bayesian Spatio-Temporal Models
Author(s) -
Aswi Aswi,
Sukarna Sukarna,
Susanna Cramb,
Kerrie Mengersen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1863/1/012050
Subject(s) - akaike information criterion , covariate , bayesian probability , deviance information criterion , bayesian information criterion , statistics , econometrics , context (archaeology) , bayesian inference , range (aeronautics) , autoregressive model , spatial analysis , computer science , geography , mathematics , materials science , archaeology , composite material
Considering only the spatial component of diseases can identify areas with reduced or elevated risk, but not capture anything about temporal variation of risk which could be more or equally crucial. Hence, both spatial and temporal components of diseases need to be considered. Bayesian methods are useful due to the ease of specifying additional information, including temporal or spatial structure, through prior distributions. Here, we examine a range of different Bayesian spatio-temporal models available using CARBayes. Combinations of model formulations and climatic covariates were compared using goodness-of-fit measures, such as Watanabe Akaike Information Criterion (WAIC). Comparisons were made in the context of a substantive case study, namely monthly dengue fever incidence from January 2013 to December 2017 and climatic covariates in 14 geographic areas of Makassar, Indonesia. A spatio-temporal conditional autoregressive adaptive model combining rainfall and average humidity provided the most suitable model.