Pemodelan Spasial Kasus Kematian Akibat Covid-19 di Provinsi Jawa Timur Tahun 2020
DOI:
https://doi.org/10.33006/ji-kes.v6i1.412Abstract
Abstrak
Provinsi Jawa Timur merupakan provinsi yang mempunyai jumlah kasus kematian yang tinggi dibanding dengan provinsi lain di Indonesia. Tujuan penelitian adalah melakukan pemodelan kematian akibat Covid-19 di Provinsi Jawa Timur pada tahun 2020, menggunakan analisis Geographically Weighted Regression (GWR). Analisis GWR merupakan pengembangan dari regresi linier dengan parameter model yang berbeda di setiap pengamatan (wilayah). Data yang dianalisis adalah data sekunder dari Dinas Kesehatan Provinsi Jawa Timur dan Badan Pusat Statistik (BPS) Provinsi Jawa Timur. Jumlah kematian akibat covid-19 di Jawa Timur mencapai 84.152 jiwa sampai dengan bulan Desember 2020. Hasil analisis menunjukkan bahwa faktor yang mempengaruhi kematian akibat Covid-19 di sebagian besar wilayah Provinsi Jawa Timur adalah jumlah dokter umum di Rumah Sakit dan jumlah pelayanan kesehatan penyakit Diabetes Melitus, sedangkan variable lainnya yang berpengaruh signifikan adalah pelayanan kesehatan penderita Hipertensi dan jumlah Rumah Sakit Umum. Analisis GWR menghasilkan pemodelan jumlah kematian akibat Covid-19 di Jawa Timur dengan koefisien determinasi yang lebih tinggi dibanding pemodelan secara global. Pemodelan secara geografis menghasilkan 5 kelompok kabupaten/kota, dengan variabel jumlah dokter umum di Rumah Sakit dan pelayanan kesehatan Diabetes Melitus yang cukup berpengaruh di sebagian besar kabupaten/kota di Jawa Timur.
Kata kunci: Covid-19, Jawa Timur, Geographically Weighted Regression
Abstract
East Java Province was a province that had a high number of deaths compared to other provinces in Indonesia. Using Geographically Weighted Regression (GWR) analysis, the study's goal was to predict the number of deaths caused by COVID-19 in the East Java Province in 2020. With various model parameters in each observation, GWR analysis was a progression of linear regression (region). Secondary data from the East Java Province's Statistics Agency and Health Agency were used in the analysis. As of December 2020, 84.152 individuals had perished in East Java as a result of COVID-19. The analysis's findings indicate that the number of public hospitals and the availability of healthcare for people with diabetes mellitus were the two health factors that had the greatest impact on Covid-19 deaths in the majority of East Java Province. Other factors that had a significant impact included the number of public hospitals and the availability of healthcare for people with hypertension.The analysis of the GWR model on the number of deaths due to Covid-19 in East Java resulted in a higher coefficient of determination than linear regression modeling. Geographical modeling resulted in 5 groups of districts/cities, with the variable number of general doctors in hospitals and diabetes mellitus health services being quite influential in most districts/cities in East Java.
Keyword: Covid-19, East Java Provinces, Geographically Weighted Regression
References
Ariawan, I. et al. (2021) Proyeksi COVID-19 di Indonesia. Jakarta: Direktorat Kesehatan dan Gizi Masyarakat, Kedeputian Pembangunan Manusia, Masyarakat dan Kebudayaan, Kementerian PPN/Bappenas.
Du, Y. et al. (2021) ‘Hypertension is a Clinically Important Risk Factor for Critical Illness and Mortality in COVID-19: A Meta-analysis’, Nutrition, Metabolism and Cardiovascular Diseases, 31(3), pp. 745–755. doi: 10.1016/j.numecd.2020.12.009.
Fitrial, N. H. and Fatikhurrizqi, A. (2020) ‘Pemodelan Jumlah Kasus Covid-19 Di Indonesia Dengan Pendekatan Regresi Poisson Dan Regresi Binomial Negatif’, Prosiding Seminar Nasional Official Statistics 2020 ‘Statistics in the New Normal: A Challenge of Big Data and Official Statistics’, 2020(1), pp. 65–72. doi: 10.34123/semnasoffstat.v2020i1.465.
Fotheringham, A. S., Brunsdon, C. and Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley and Sons Ltd. doi: 10.1353/geo.2003.0008.
Lu, B. et al. (2014) ‘Geographically Weighted Regression with A Non-Euclidean Distance Metric: A Case Study Using Hedonic House Price Data’, International Journal of Geographical Information Science, 28(4), pp. 660–681. doi: 10.1080/13658816.2013.865739.
Mahdy, I. F. (2020) ‘Pemodelan Jumlah Kasus Covid-19 Di Jawa Barat Menggunakan Geographically Weighted Regression’, Prosiding Seminar Nasional Official Statistics 2020 ‘Statistics in the New Normal: A Challenge of Big Data and Official Statistics’, 2020(1), pp. 138–145. doi: 10.34123/semnasoffstat.v2020i1.642.
Mansour, S. et al. (2021) ‘Sociodemographic Determinants of COVID-19 Incidence Rates in Oman: Geospatial Modelling Using Multiscale Geographically Weighted Regression (MGWR)’, Sustainable Cities and Society, 65(January). doi: 10.1016/j.scs.2020.102627.
Mohammadi, Farzaneh et al. (2021) ‘Artificial Neural Network and Logistic Regression Modelling to Characterize COVID-19 Infected Patients in Local Areas of Iran’, Biomedical Journal, 44(3), pp. 304–316. doi: 10.1016/j.bj.2021.02.006.
Purhadi and Yasin, H. (2012) ‘Mixed Geographically Weighted Regression Model (Case Study: The Percentage of Poor Households in Mojokerto 2008)’, European Journal of Scientific Research, 69(2), pp. 188–196.
Rodriguez-Villamizar, L. A. et al. (2021) ‘Air Pollution, Sociodemographic and Health Conditions Effects on COVID-19 Mortality in Colombia: An Ecological Study’, Science of the Total Environment, 756(February), pp. 5–7. doi: 10.1016/j.scitotenv.2020.144020.
Sannigrahi, S. et al. (2020) ‘Examining the Association Between Socio-demographic Composition and COVID-19 Fatalities in the European Region Using Spatial Regression Approach’, Sustainable Cities and Society, 62(May), p. 102418. doi: 10.1016/j.scs.2020.102418.
Sen-Crowe, B. et al. (2021) ‘A Closer Look Into Global Hospital Beds Capacity and Resource Shortages During the COVID-19 Pandemic’, Journal of Surgical Research, 260(20), pp. 56–63. doi: 10.1016/j.jss.2020.11.062.
Zhang, H. et al. (2021) ‘The Effect of Sociodemographic Factors on COVID-
Incidence of 342 Cities in China: A Geographically Weighted Regression Model Analysis’, BMC Infectious Diseases, 21(1), pp. 1–8. doi: 10.1186/s12879-021-06128-1.
Downloads
Published
Issue
Section
Citation Check
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.