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COVID-19 RISK MAPPING AND LIFE INSURANCE ESTIMATION: MARKOV CHAIN MODEL FOR PREMIUMS AND BENEFITS IN BANDUNG CITY

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 20 No. 1 (2025) · pp. 0239–0254

DOI
10.30598/barekengvol20iss1pp0239-0254
ISSN
2615-3017, 1978-7227
Published
2025-11-24
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The COVID-19 pandemic, first identified in China, rapidly spread worldwide and significantly impacted various sectors, including health and insurance. In Indonesia, regional disparities in case trends have highlighted the need for localized risk assessment. This study applies a Markov Chain model to estimate life insurance premiums and benefits by forecasting long-term COVID-19 transmission probabilities across 30 sub-districts in Bandung City. The analysis uses daily confirmed case data collected between September 18, 2020, and April 17, 2022, a period marked by multiple infection waves and heightened transmission risk. COVID-19 trends were categorized into discrete states—decrease, no change, and increase—and modeled to construct transition probability matrices and stationary distributions. These long-term probabilities were then used to generate a regional risk map and inform actuarial pricing of insurance products. The results reveal spatial heterogeneity in case increase probabilities, with Coblong, Arcamanik, and Antapani exhibiting the highest long-term risk. A strong correlation (R² = 0.9473) was found between case increase probabilities and projected insurance benefits and premiums. The practical implication of this study lies in its provision of a data-driven framework that enables insurance companies to align policy pricing with region-specific and evolving pandemic risks, including long-term health consequences such as post-COVID-19 conditions. This approach enhances both the fairness of premium structures and the financial resilience of insurers in managing future public health crises.

Collaboration network of the authors

17 authors · 34 co-authorship links · 7 across different organisations

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  • Bandung Institute of Technology
  • Tanjungpura University
  • STMIK IKMI Cirebon
  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Muhammad Rozzaq HamidiBandung Institute of Technology3
Kurnia Novita SariBandung Institute of Technology2
Nur’ainul Miftahul HudaTanjungpura University1
Nurfitri Imro’ahTanjungpura University1
Asrul IrfanullahBandung Institute of Technology1
Nuraziza ArfanBandung Institute of Technology1
Syifa Nurul FatimahBandung Institute of Technology1
Ahmad Fuad ZainnuddinAffiliation unknown1
Novi MardianaSTMIK IKMI Cirebon1
Nurhayati NurhayatiBandung Institute of Technology1
Riani UtamiBandung Institute of Technology1
Nabila MardiyahBandung Institute of Technology1
Yalela Sa’adahBandung Institute of Technology1
Erni WidyawatiBandung Institute of Technology1
Nurhayati NurhayatiUniversitas Halu Oleo1

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 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">COVID-19 RISK MAPPING AND LIFE INSURANCE ESTIMATION: MARKOV CHAIN MODEL FOR PREMIUMS AND BENEFITS IN BANDUNG CITY</dc:title>
	<dc:creator>Nadwah, Hamidah Qurrotun</dc:creator>
	<dc:creator>Mukhaiyar, Utriweni</dc:creator>
	<dc:description xml:lang="en-US">The COVID-19 pandemic, first identified in China, rapidly spread worldwide and significantly impacted various sectors, including health and insurance. In Indonesia, regional disparities in case trends have highlighted the need for localized risk assessment. This study applies a Markov Chain model to estimate life insurance premiums and benefits by forecasting long-term COVID-19 transmission probabilities across 30 sub-districts in Bandung City. The analysis uses daily confirmed case data collected between September 18, 2020, and April 17, 2022, a period marked by multiple infection waves and heightened transmission risk. COVID-19 trends were categorized into discrete states—decrease, no change, and increase—and modeled to construct transition probability matrices and stationary distributions. These long-term probabilities were then used to generate a regional risk map and inform actuarial pricing of insurance products. The results reveal spatial heterogeneity in case increase probabilities, with Coblong, Arcamanik, and Antapani exhibiting the highest long-term risk. A strong correlation (R² = 0.9473) was found between case increase probabilities and projected insurance benefits and premiums. The practical implication of this study lies in its provision of a data-driven framework that enables insurance companies to align policy pricing with region-specific and evolving pandemic risks, including long-term health consequences such as post-COVID-19 conditions. This approach enhances both the fairness of premium structures and the financial resilience of insurers in managing future public health crises.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2025-11-24</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/17824</dc:identifier>
	<dc:identifier>10.30598/barekengvol20iss1pp0239-0254</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application; 0239-0254</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol20iss1year2026</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/17824/11703</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Hamidah Qurrotun Nadwah, Utriweni Mukhaiyar</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>