ANALISIS KUMULATIF COVID-19 PROVINSI PAPUA TAHUN 2020 MENGGUNAKAN MODEL DISTRIBUSI JOHNSON SB

Felix Reba, Alvian Sroyer

Abstract


Coronavirus belongs to the coronaviridae family. The coronavirus family groups are alpha (α), beta (β), gamma (γ) and delta (δ) coronavirus. Although research related to covid-19 in several provinces in Indonesia has been conducted by several researchers so far there has been no research related to the Covid-19 model in Papua province. One of the obstacles faced by some researchers is related to the Covid-19 data parameters which are difficult to estimate, so that the model formulated could not describe the outbreak well. Therefore the aim of this study is to conduct a cumulative analysis of the 2020 Papua province Covid-19 using the Johnson SB distribution model. The methods used to perform the analysis are Kolmogorov Smirnov for testing the suitability of the Covid-19 data to the model, Johnson SB to show the data distribution model, Maximum Likelihood to estimate the parameters and the Johnson SB cumulative distribution function to describe the probability of Covid-19 data. 19 Papua Province in 2020. The secondary data on the number of Covid-19 cases in Papua, obtained from the Papua Provincial Health Office is used in this research. The results showed that, the highest increase in the number of patients every day, starting from September 1 2020 to October 31, 2020 for infected cases was on 16-17 September, by 274 patients. Meanwhile, most recovery (308 patients) happened to be on 30-31 October and the highest death (5 people) was on 27-28 September. The highest cumulative probability for cases of infection, recovery and death were (Confirmed <4965) = 0.3, Prob(Cured <6408) = 0.9 and Prob(died <91) = 0.4 respectively.


Keywords


Covid-19 data, KS, Johnson SB, MLE, Cumulative Analysis

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References


Akhter, M. F. and Abbas, S. (2020) ‘Rainfall Pattern in Pakistan in the Perspective of Probability Distribution distributions on data and performs three tests’, 57(April 2019), pp. 31–38.

Aldila, D. et al. (2020) ‘A mathematical study on the spread of COVID-19 considering social distancing and rapid assessment: The case of Jakarta, Indonesia’, Chaos, Solitons and Fractals, 139, p. 110042. doi: 10.1016/j.chaos.2020.110042.

Alvina Felicia Watratan, Arwini Puspita. B and Dikwan Moeis (2020) ‘Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia’, Journal of Applied Computer Science and Technology. doi: 10.52158/jacost.v1i1.9.

Annas, S. et al. (2020) ‘Stability analysis and numerical simulation of SEIR model for pandemic COVID-19 spread in Indonesia’, Chaos, Solitons and Fractals, 139. doi: 10.1016/j.chaos.2020.110072.

Cristea, G. and Constantinescu, D. M. (2018) ‘Comparative analysis through probability distributions of a data set’, AIP Conference Proceedings, 1932. doi: 10.1063/1.5024158.

George, F. and Ramachandran, K. M. (2011) ‘Estimation of parameters of johnson’s system of distributions’, Journal of Modern Applied Statistical Methods, 10(2), pp. 494–504. doi: 10.22237/jmasm/1320120480.

Karmakar, S. and Oceanographic, N. (2020) ‘GENERALIZED EXTREME VALUE ( GEV ) DISTRIBUTION TO MEASURE THE CHANGING PROBABILITIES OF WINTER TEMPERATURE IN DHAKA MEASURE THE CHANGING PROBABILITIES OF WINTER’, (October).

Lintuman, A. et al. (2020) ‘Keefektifan model pembelajaran berbasis inkuiri ditinjau dari prestasi belajar dan kepercayaan diri dalam belajar matematika siswa SMP’, Jurnal Riset Pendidikan Matematika.

Liu, Y. et al. (2020) ‘Family companion between patients with coronavirus disease 2019: a retrospective observational study’, Chinese medical journal, 133(20), pp. 2507–2509. doi: 10.1097/CM9.0000000000001114.

Menezes, A. F. B. and Mazucheli, J. (2020) ‘Improved maximum likelihood estimators for the parameters of the Johnson SB distribution’, Communications in Statistics: Simulation and Computation. doi: 10.1080/03610918.2018.1498892.

Parhusip, H. A. (2020) ‘Study on COVID-19 in the World and Indonesia Using Regression Model of SVM, Bayesian Ridge and Gaussian’, Jurnal Ilmiah Sains, 20(2), p. 49. doi: 10.35799/jis.20.2.2020.28256.

Pogoda, P., Ochał, W. and Orzeł, S. (2020) ‘Performance of Kernel estimator and Johnson SB function for modeling diameter distribution of black alder (Alnus glutinosa (L.) Gaertn.) stands’, Forests, 11(6). doi: 10.3390/F11060634.

Pratikto, F. R. (2020) ‘Prediksi Akhir Pandemi COVID-19 di Indonesia dengan Simulasi Berbasis Model Pertumbuhan Parametrik’, Jurnal Rekayasa Sistem Industri, 9(2), pp. 63–68. doi: 10.26593/jrsi.v9i2.4018.63-68.

Shereen, M. A. et al. (2020) ‘COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses’, Journal of Advanced Research, 24, pp. 91–98. doi: 10.1016/j.jare.2020.03.005.

Soyinka, A. T. et al. (2022) ‘On the Parameter Estimation of Johnson ’ s System of Distribution’, 3(2020), pp. 82–90.

Sroyer, A. M. et al. (2018) ‘Comparison of Parameter Estimation Methods to Determine the Frequency Data Magnitude of Aftershock in Nabire, Papua’, Journal of Science & Science Education, 2(2), pp. 17–21. Available at: http://ejournal.uksw.edu/josse/article/view/1969.

Satgas Covid Papua, “COVID-19, Sampai Kapan ?” bappeda.papua, Jayapura, 11-14-2020. https://bappeda.papua.go.id/berita/covid-19-sampai-kapan [Diakses : 03 April 2021]

Toharudin, T. et al. (2020) ‘Bayesian Poisson Model for COVID-19 in West Java Indonesia Spatial Analysis View project Bayesian Poisson Model for COVID-19 in West Java Indonesia’, 164(6). Available at: https://www.researchgate.net/publication/342466492.

Vina Fadhrotul Mukaromah, “Update Virus Corona di Dunia: 331.273 Orang Terinfeksi, 97.847 Orang Sembuh”, Kompas, 23/03/2020, 07:26 WIB. https://www.kompas.com/tren/read/2020/03/23/072649465/update-virus-corona-di-dunia-331273-orang-terinfeksi-97847-orang-sembuh?page=2 [Diakses : 03 April 2021]




DOI: https://doi.org/10.29313/jstat.v21i1.7820

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