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Unveiling mortality dynamics: a novel mathematical framework across adulthood and aging

Author

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  • Begüm Çığşar

  • Deniz Ünal

    (Comenius University)

Abstract

Life tables, which present data on the number of newborns and individuals surviving within a community over the years, serve as essential tools in demography. These tables provide a detailed record of survival rates and mortality events for various age groups within a given year. By analyzing and modeling the structure of life tables, it becomes possible to make informed predictions about population dynamics. To achieve this, mathematical mortality functions are employed. Mathematical models of life tables can vary based on factors such as country, gender, and different life stages. This study underscores the importance of distinguishing between the mortality probabilities of adults and the elderly by employing different distributions. Furthermore, exponential distribution, the Kannisto model, and the Gompertz function, all of which exhibit an exponential structure, are incorporated into the mathematical models defined in this study. In this study, after presenting the structure of the proposed models, a synthetic dataset is generated using the Monte Carlo simulation method, which is based on a lognormal distribution. The method is used to demonstrate the behavior of proposed models in a larger dataset. A genetic algorithm is employed to determine the initial parameter values of the proposed models. In another part, the mortality probabilities derived from these functions are estimated using age-specific life tables from the Turkish Statistical Institute for the years 2017–2019. Finally, the goodness-of-fit of the predicted mathematical models is visually assessed through graphical representations, and their performances are compared using Root Mean Squared Error, Mean Absolute Percentage Error, and adjusted R² criteria.

Suggested Citation

  • Begüm Çığşar & Deniz Ünal, 2026. "Unveiling mortality dynamics: a novel mathematical framework across adulthood and aging," Computational Statistics, Springer, vol. 41(1), pages 1-21, January.
    • Handle: RePEc:spr:compst:v:41:y:2026:i:1:d:10.1007_s00180-025-01693-4
    DOI: 10.1007/s00180-025-01693-4
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    References listed on IDEAS

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    1. Dmitri A. Jdanov & Domantas Jasilionis & Eugeny L. Soroko & Roland Rau & James W. Vaupel, 2008. "Beyond the Kannisto-Thatcher Database on Old Age Mortality: an assessment of data quality at advanced ages," MPIDR Working Papers WP-2008-013, Max Planck Institute for Demographic Research, Rostock, Germany.
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