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Weibull Regression Model on Hospitalization Time Data of COVID-19 Patients at Abdul Wahab Sjahranie Hospital Samarinda

Model Regresi Weibull pada Data Waktu Rawat Inap Pasien COVID-19 di RSUD Abdul Wahab Sjahranie Samarinda

Jurnal Matematika, Statistika dan Komputasi · Vol. 19 No. 2 (2023) · pp. 286–303

DOI
10.20956/j.v19i2.22266
ISSN
2614-8811, 1858-1382
Published
2023-01-05
Publisher
Department of Mathematics, Hasanuddin University
Language
en
License
creativecommons.org/licenses/by/4.0

Abstract

English

The Weibull regression model is a Weibull distribution that is directly influenced by covariates. The Weibull regression model discussed in this study was the Weibull survival and the Weibull hazard regression model. The Weibull regression model in this study was applied to the hospitalization time data of COVID-19 patients from May to September 2021 at the RSUD Abdul Wahab Sjahranie Samarinda. The event of the study is recovery of patient. This study aims to obtain Weibull survival and hazard regression model to the hospitalization time data of Covid-19 patients, to obtain the factors that affect the chance of not recovering (survive) and the recovery rate of Covid-19 patients, and also to interpret Weibull survival and hazard regression models based on the obtained model. In this study, the Maximum Likelihood Estimation (MLE) was used as the parameter estimation method. The closed form of the Maximum Likelihood (ML) estimator cannot be found analytically, and the approximation of ML estimator was found using Newton-Raphson iterative method. Based on the test results, the factors that influence the chance of not recovering and the recovery rate of COVID-19 patients were comorbidities history. The chance of not not recovering (survive) for patients who have a history of comorbidities is greater than the chance of not recovering (survive) for patients who have no history of comorbidities. The recovery rate for COVID-19 patients who have a history of comorbidities is 0,5358 times the recovery rate for patients without a history of comorbidities.

Bahasa Indonesia

Model regresi Weibull adalah distribusi Weibull yang dipengaruhi langsung oleh kovariat. Model regresi Weibull yang dibahas pada penelitian ini adalah model regresi survival Weibull dan model regresi hazard Weibull. Model regresi Weibull pada penelitian ini diaplikasikan pada data waktu rawat inap pasien COVID-19 di RSUD Abdul Wahab Sjahranie Samarinda periode Mei-September 2021. Event pada penelitian ini adalah kesembuhan pasien. Tujuan penelitian ini adalah memperoleh model regresi survival dan regresi hazard Weibull pada data waktu rawat inap pasien COVID-19, mengetahui faktor-faktor yang memengaruhi peluang tidak sembuh dan laju kesembuhan pasien COVID-19, serta menginterpretasikan model regresi survival dan model regresi hazard Weibull berdasarkan model yang diperoleh. Metode penaksiran parameter adalah Maximum Likelihood Estimation (MLE). Kesimpulan penelitian ini adalah penaksir eksak Maximum Likelihood (ML) tidak dapat ditemukan secara analitikal dan hampiran penaksir ML ditemukan menggunakan metode iteratif Newton-Raphson. Berdasarkan hasil pengujian hipotesis ditemukan bahwa faktor-faktor yang berpengaruh terhadap peluang tidak sembuh dan laju kesembuhan pasien COVID-19 adalah riwayat komorbiditas.

Keywords

Collaboration network of the authors

15 authors · 27 co-authorship links

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Co-authorOrganisationJoint articles
Sifriyani SifriyaniAffiliation unknown2
Frans Karta Sayoga SitohangAffiliation unknown1
Ghina Fadhilla MumtazAffiliation unknown1
Memi Nor HayatiAffiliation unknown1
Ineu SintiaAffiliation unknown1
Meiliyani SiringoringoAffiliation unknown1
Ni Made Shantia AnandaAffiliation unknown1
SuyitnoAffiliation unknown1
Meirinda FauziyahAffiliation unknown1
Adelia MirandaAffiliation unknown1
Darnah DarnahAffiliation unknown1
Azkadiaannzumi AzkadiaannzumiAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.unhas.ac.id/index.php/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Weibull Regression Model on Hospitalization Time Data of COVID-19 Patients at Abdul Wahab Sjahranie Hospital Samarinda</dc:title>
	<dc:title xml:lang="id">Model Regresi Weibull pada Data Waktu Rawat Inap Pasien COVID-19 di RSUD Abdul Wahab Sjahranie Samarinda </dc:title>
	<dc:creator xml:lang="en">Khairunnisa, Siti Fatimah </dc:creator>
	<dc:creator xml:lang="en">Suyitno, Suyitno</dc:creator>
	<dc:creator xml:lang="en">Mahmuda, Siti </dc:creator>
	<dc:subject xml:lang="en">Newton-Raphson</dc:subject>
	<dc:subject xml:lang="en">Weibull hazard regression model</dc:subject>
	<dc:subject xml:lang="en">Weibull survival regression model</dc:subject>
	<dc:subject xml:lang="id">Newton-Raphson</dc:subject>
	<dc:subject xml:lang="id">Model regresi hazard Weibull</dc:subject>
	<dc:subject xml:lang="id">Model regresi survival Weibull</dc:subject>
	<dc:description xml:lang="en">The Weibull regression model is a Weibull distribution that is directly influenced by covariates. The Weibull regression model discussed in this study was the Weibull survival and the Weibull hazard regression model. The Weibull regression model in this study was applied to the hospitalization time data of COVID-19 patients from May to September 2021 at the RSUD Abdul Wahab Sjahranie Samarinda. The event of the study is recovery of patient. This study aims to obtain Weibull survival and hazard regression model to the hospitalization time data of Covid-19 patients, to obtain the factors that affect the chance of not recovering (survive) and the recovery rate of Covid-19 patients, and also to interpret Weibull survival and hazard regression models based on the obtained model. In this study, the Maximum Likelihood Estimation (MLE) was used as the parameter estimation method. The closed form of the Maximum Likelihood (ML) estimator cannot be found analytically, and the approximation of ML estimator was found using Newton-Raphson iterative method. Based on the test results, the factors that influence the chance of not recovering and the recovery rate of COVID-19 patients were comorbidities history. The chance of not not recovering (survive) for patients who have a history of comorbidities is greater than the chance of not recovering (survive) for patients who have no history of comorbidities. The recovery rate for COVID-19 patients who have a history of comorbidities is 0,5358 times the recovery rate for patients without a history of comorbidities.</dc:description>
	<dc:description xml:lang="id">Model regresi Weibull adalah distribusi Weibull yang dipengaruhi langsung oleh kovariat. Model regresi Weibull yang dibahas pada penelitian ini adalah model regresi survival Weibull dan model regresi hazard &amp;nbsp;Weibull. Model regresi Weibull pada penelitian ini diaplikasikan pada data waktu rawat inap pasien COVID-19 di RSUD Abdul Wahab Sjahranie Samarinda periode Mei-September 2021. Event pada penelitian ini adalah kesembuhan pasien. Tujuan penelitian ini adalah memperoleh model regresi survival dan regresi hazard Weibull pada data waktu rawat inap pasien COVID-19, mengetahui faktor-faktor yang memengaruhi peluang tidak sembuh dan laju kesembuhan pasien COVID-19, serta menginterpretasikan model regresi survival dan model regresi hazard Weibull berdasarkan model yang diperoleh. Metode penaksiran parameter adalah Maximum Likelihood Estimation (MLE). Kesimpulan penelitian ini adalah penaksir eksak Maximum Likelihood (ML) tidak dapat ditemukan secara analitikal dan hampiran penaksir ML ditemukan menggunakan metode iteratif Newton-Raphson. Berdasarkan hasil pengujian hipotesis ditemukan bahwa faktor-faktor yang berpengaruh terhadap peluang tidak sembuh dan laju kesembuhan pasien COVID-19 adalah riwayat komorbiditas.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2023-01-05</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/22266</dc:identifier>
	<dc:identifier>10.20956/j.v19i2.22266</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 19 No. 2 (2023): JANUARY 2023; 286-303</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/22266/9148</dc:relation>
	<dc:relation>10.20956/j.v19i2.22266.g9148</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Author and publisher</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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