Effects of Climatic Factors on Dengue Incidence: A Comparison of Bayesian Spatio-Temporal Models

Abstract

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.

Pemodelan Bayesian Spasial Conditional Autoregressive (Car) Pada Kasus Demam Berdarah Dengue Di Indonesia

“…Selanjutnya, pemodelan DBD di Pamekasan, Provinsi Jawa Timur dengan menggunakan model Double Exponential CAR Frailty telah dilakukan [4]. Pemodelan Bayesian spasial survival [5,6], dan pemodelan menggunakan model Bayesian spatial atau spatio-temporal Conditional Autoregressive (CAR) [7][8][9][10][11] kasus DBD di Kota Makassar, Provinsi Sulawesi Selatan juga telah dilakukan oleh beberapa peneliti. Akan tetapi, penelitian tersebut masih fokus pada pemodelan DBD di salah satu provinsi yang ada di Indonesia.…”