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A Comparative Analysis of the Forecasted Mortality Rate under Normal Conditions and the COVID‐19 Excess Mortality Rate in Malaysia

Author

Listed:
  • Robiaatul Adawiah Edrus
  • Zailan Siri
  • Mohd Azmi Haron
  • Muhammad Aslam Mohd Safari
  • Mohammed K. A. Kaabar

Abstract

SARS‐CoV‐2, known as COVID‐19, has affected the entire world, resulting in an unexpected death rate as compared to the death probability before the pandemic. Prior to the COVID‐19 pandemic, death probability has been assessed in a normal context that is different from those anticipated during the pandemic, particularly for the older population cluster. However, there is no such evidence of excess mortality in Malaysia to date. Therefore, this study determines the excess mortality rate for specific age groups during the pandemic outbreak in Malaysia. Before determining the excess mortality rate, this study aims to establish the efficiency of various parametrized mortality models in reference to the data set before the pandemic. This study employs the hold‐out, repeated hold‐out, and leave‐one‐out cross‐validation procedures to identify the optimal mortality law for fitting the mortality data. Based on the goodness‐of‐fit measures (mean absolute percentage error, mean absolute error, sum square error, and mean square error), the Heligman‐Pollard model for men and Rogers Planck model for women are considered as the optimal models. In assessing the excess mortality, both models favour the hold‐out technique. When the COVID‐19 mortality data are incorporated to forecast the mortality rate for people aged 60 and above, there is an excess mortality rate. However, the men’s mortality rate appears to be delayed and more prolonged than the women’s mortality rate. Consequently, the government is recommended to amend the existing policy to reflect the post COVID‐19 mortality forecast.

Suggested Citation

  • Robiaatul Adawiah Edrus & Zailan Siri & Mohd Azmi Haron & Muhammad Aslam Mohd Safari & Mohammed K. A. Kaabar, 2022. "A Comparative Analysis of the Forecasted Mortality Rate under Normal Conditions and the COVID‐19 Excess Mortality Rate in Malaysia," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:7715078
    DOI: 10.1155/2022/7715078
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    References listed on IDEAS

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    1. Cary Chi-Liang Tsai & Adelaide Di Wu, 2020. "Bühlmann Credibility-Based Approaches to Modeling Mortality Rates for Multiple Populations," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(2), pages 290-315, April.
    2. Stefano Mazzuco & Bruno Scarpa & Lucia Zanotto, 2018. "A mortality model based on a mixture distribution function," Population Studies, Taylor & Francis Journals, vol. 72(2), pages 191-200, May.
    3. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    4. Patrizio Vanella & Ugofilippo Basellini & Berit Lange, 2020. "Assessing Excess Mortality in Times of Pandemics Based on Principal Component Analysis of Weekly Mortality Data -- The Case of COVID-19," Working Papers axbhmxrs-o0viyh9z07m, French Institute for Demographic Studies.
    5. Cary Chi-Liang Tsai & Tzuling Lin, 2017. "A Bühlmann Credibility Approach to Modeling Mortality Rates," North American Actuarial Journal, Taylor & Francis Journals, vol. 21(2), pages 204-227, April.
    6. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    7. Tashman, Leonard J., 2000. "Out-of-sample tests of forecasting accuracy: an analysis and review," International Journal of Forecasting, Elsevier, vol. 16(4), pages 437-450.
    8. Angelos Alexopoulos & Petros Dellaportas & Jonathan J. Forster, 2019. "Bayesian forecasting of mortality rates by using latent Gaussian models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 689-711, February.
    9. Wan, Cheng & Bertschi, Ljudmila, 2015. "Swiss coherent mortality model as a basis for developing longevity de-risking solutions for Swiss pension funds: A practical approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 66-75.
    10. Peng Xu & Muhammad Aamir & Ani Shabri & Muhammad Ishaq & Adnan Aslam & Li Li, 2020. "A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-23, October.
    11. David Atance & Ana Debón & Eliseo Navarro, 2020. "A Comparison of Forecasting Mortality Models Using Resampling Methods," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    12. Tsai, Cary Chi-Liang & Cheng, Echo Sihan, 2021. "Incorporating statistical clustering methods into mortality models to improve forecasting performances," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 42-62.
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