Pemetaan Risiko Relatif Kasus Demam Berdarah Dengue di Kota Makassar Menggunakan Model Bayesian Spasial
Abstract
Dengue Hemorrhagic Fever (DHF) is a disease that is still a main problem in public health in Indonesia. This study aims to map the relative risk (RR) of dengue cases in Makassar City using the Spatial Conditional Autoregressive (CAR) model with Bayesian approaches: Besag-York-Molliѐ (BYM) and Leroux models. The data used in this study is DHF case data from 2016 to 2018 for 15 sub-districts in Makassar City. The best model was based on the model fit criteria, namely Watanabe Akaike Information Criteria (WAIC) and Deviance Information Criteria (DIC). The results indicate that the best model used to map the RR for DHF cases in 2016 and 2017 is the BYM CAR model, while the best model for 2018 is the Leroux CAR model. Based on the results of the analysis, it was concluded that in 2016 the area with the highest RR was Manggala District and the lowest RR was Tamalate District. In 2017, the area with the highest RR was Ujung Pandang District and the lowest RR was Biringkanaya District. Meanwhile, in 2018, the area with the highest dan the lowest RR was Ujung Tanah and Tamalate Districts, respectively. The results of this study are expected to be able to assist the government in implementing the program to control dengue fever in Makassar City effectively and efficiently.
Keywords⎯ Dengue Hemorrhagic Fever, Relative Risk Mapping, CAR BYM, CAR Leroux.
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DOI: http://dx.doi.org/10.12962/j27213862.v6i2.15931
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