Pemetaan Kasus Tuberkulosis di Provinsi Sulawesi Selatan Tahun 2020 Menggunakan Model Bayesian Spasial BYM dan Leroux
(1) Program Studi Statistika, Universitas Negeri Makassar
(2) Jurusan Matematika, Universitas Negeri Makassar
(3) Jurusan Fisika, Universitas Negeri Makassar
(*) Corresponding Author
DOI: https://doi.org/10.35580/jmathcos.v4i2.32755
Abstract
Tuberkulosis (TBC) merupakan penyakit menular yang merupakan salah satu dari sepuluh penyebab utama kematian di dunia. Indonesia merupakan negara yang menempati urutan tertinggi kedua penderita TBC di dunia. Tujuan dari penelitian ini adalah untuk mengidentifikasi area dengan risiko relatif (RR) tinggi TBC maupun rendah dengan menggunakan model Bayesian spasial Conditional Autoregressive (CAR) Besag-York-Molliѐ (BYM) dan Leroux. Data kasus TBC di setiap 24 kabupaten/kota di provinsi Sulawesi Selatan tahun 2020 digunakan. Model terbaik dipilih berdasarkan tiga kriteria yaitu Deviance Information Criteria (DIC) dan Watanabe Akaike Information Criteria (WAIC). Dari hasil analisis, diperoleh bahwa model Bayesian Spasial CAR BYM dan CAR Leroux dengan hyperprior IG (0,5; 0,0005) merupakan model terbaik yang memiliki nilai RR yang sama. Kota Makassar merupakan wilayah dengan nilai RR tertinggi (1,70) yang mengindikasikan bahwa Kota Makassar memiliki risiko TBC 70% lebih tinggi dari rata-rata. Sebaliknya, Kabupaten Toraja memiliki risiko TBC terendah (0,43) yang menunjukkan bahwa Toraja memiliki risiko TBC 43% lebih rendah dari rata-rata.
Kata Kunci: Tuberkulosis, Bayesian, spasial CAR, BYM, Leroux
Tuberculosis (TB) is an infectious disease that is one of the ten leading causes of death in the world. Indonesia is a country with the second-highest number of TB sufferers in the world. This study aims to identify areas with a high and low relative risk (RR) of TB by using the Bayesian Spatial Conditional Autoregressive (CAR) Besag-York-Molliѐ (BYM) and Leroux models. TB case data in every 24 districts/cities in South Sulawesi province in 2020 is used. The best model was selected based on three criteria, namely Deviance Information Criteria (DIC) and Watanabe Akaike Information Criteria (WAIC). The results show that the Bayesian Spatial CAR BYM and CAR Leroux with hyperprior IG (0.5; 0.0005) are the best models that have the same RR value. Makassar City is the area with the highest RR value (1.70) which indicates that Makassar City has a TB risk 70% higher than the average. On the other hand, the Toraja district has the lowest TB risk (0.43) which indicates that Toraja has a TB risk 43% lower than the average.
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