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THE IMPLEMENTATION OF FINITE-STATES CONTINUOUS TIME MARKOV CHAIN ON DAILY CASES OF COVID-19 IN BANDUNG

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 1 (2023) · pp. 0085–0094

DOI
10.30598/barekengvol17iss1pp0085-0094
ISSN
2615-3017, 1978-7227
Published
2023-04-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Markov chain is a stochastic process to describe a phenomenon in the future based on a previous state. In practice, Markov chains are distinguished by time into two, namely discrete-time Markov chain and continuous-time Markov Chain. This research will discuss the continuous-time Markov chain with finite-state. COVID-19 phenomena can describe and predict using the continuous-time Markov chain. Authors use the data daily cases of COVID-19 in Greater Bandung including Bandung City, Bandung District, West Bandung District, Cimahi City and Sumedang District. Used data came from simulated data of daily cases of COVID-19 in Greater Bandung from August, 2020 until November 14, 2021 that recorded through the website COVID-19 of West Java. In terms of described and predicted the COVID-19 phenomenon in Greater Bandung for long-term probability, authors use stationary distribution and limit distribution. COVID-19 phenomenon is described into two states: state 0 (lower than average of data) and state 1 (higher than average of data). The result of continuous-time Markov chain with finite-state shows that the probability of the daily cases of COVID-19 for five locations in Greater Bandung is state 0 have a larger probability than state 1. It means that COVID-19 in Greater Bandung over the long-term will decrease.

Collaboration network of the authors

24 authors · 54 co-authorship links

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  • National Research and Innovation Agency
  • Affiliation unknown
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Co-authorOrganisationJoint articles
Dianne Amor KusumaAffiliation unknown4
Kankan ParmikantiAffiliation unknown3
Devi MunandarNational Research and Innovation Agency2
Mutik AlawiyahAffiliation unknown1
Salsabila Eka PutriAffiliation unknown1
Syifani AkmaliahAffiliation unknown1
Riza Andrian IbrahimAffiliation unknown1
Sri Adi WidodoAffiliation unknown1
Estiyan DwipriyokoAffiliation unknown1
Shabira A. AuliyazhafiraAffiliation unknown1
Fariza A. PutriAffiliation unknown1
Theresia S. NauliAffiliation unknown1
Aulia R. Al MadaniAffiliation unknown1
I Gede Nyoman Mindra JayaAffiliation unknown1
Annisa N. FalahAffiliation unknown1
Aulia Rahman Al MadaniAffiliation unknown1
Sandrina NajwaAffiliation unknown1
Gumgum DarmawanAffiliation unknown1
Ega SahertiAffiliation unknown1
Salwa Azzah ImtiyazAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">THE IMPLEMENTATION OF FINITE-STATES CONTINUOUS TIME MARKOV CHAIN ON DAILY CASES OF COVID-19  IN BANDUNG</dc:title>
	<dc:creator>Monika, Putri</dc:creator>
	<dc:creator>Soetikno, Christophorus</dc:creator>
	<dc:creator>Abdullah, Atje Setiawan</dc:creator>
	<dc:creator>Ruchjana, Budi Nurani</dc:creator>
	<dc:description xml:lang="en-US">Markov chain is a stochastic process to describe a phenomenon in the future based on a previous state. In practice, Markov chains are distinguished by time into two, namely discrete-time Markov chain and continuous-time Markov Chain. This research will discuss the continuous-time Markov chain with finite-state. COVID-19 phenomena can describe and predict using the continuous-time Markov chain. Authors use the data daily cases of COVID-19 in Greater Bandung including Bandung City, Bandung District, West Bandung District, Cimahi City and Sumedang District. Used data came from simulated data of daily cases of COVID-19 in Greater Bandung from August, 2020 until November 14, 2021 that recorded through the website COVID-19 of West Java. In terms of described and predicted the COVID-19 phenomenon in Greater Bandung for long-term probability, authors use stationary distribution and limit distribution. COVID-19 phenomenon is described into two states: state 0 (lower than average of data) and state 1 (higher than average of data). The result of continuous-time Markov chain with finite-state shows that the probability of the daily cases of COVID-19 for five locations in Greater Bandung is state 0 have a larger probability than state 1. It means that COVID-19 in Greater Bandung over the long-term will decrease.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-04-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6636</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss1pp0085-0094</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0085-0094</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss1year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6636/5512</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Putri Monika, Christophorus Soetikno, Atje Setiawan Abdullah, Budi Nurani Ruchjana</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>