COVID-19 RISK MAPPING AND LIFE INSURANCE ESTIMATION: MARKOV CHAIN MODEL FOR PREMIUMS AND BENEFITS IN BANDUNG CITY - Hamidah Qurrotun Nadwah; Utriweni Mukhaiyar (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2026)
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.