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Simulating long-term impacts of mortality shocks: learning from the cholera pandemic

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  • Nicole El Karoui
  • Kaouther Hadji
  • Sarah Kaakai

Abstract

The aim of this paper is to study the long-term consequence on longevity of a mortality shock. We adopt an historical and modeling approach to study how the population evolution following a mortality shock such as the COVID-19 pandemic could impact future mortality rates. In the first of part the paper, we study the several cholera epidemics in France and in England starting from the 1830s, and their impact on the major development of public health at the end of the nineteenth century. In the second part, we present the mathematical modeling of stochastic Individual-Based models. Using the R package IBMPopSim, this flexible framework is then applied to simulate the long-term impact of a mortality shock, using a toy model where nonlinear population compositional changes affect future mortality rates.

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  • Nicole El Karoui & Kaouther Hadji & Sarah Kaakai, 2021. "Simulating long-term impacts of mortality shocks: learning from the cholera pandemic," Papers 2111.08338, arXiv.org.
    • Handle: RePEc:arx:papers:2111.08338
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    References listed on IDEAS

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    1. David E. Bloom & Dara Lee Luca, 2016. "The Global Demography of Aging: Facts, Explanations, Future," PGDA Working Papers 13016, Program on the Global Demography of Aging.
    2. David Cutler & Angus Deaton & Adriana Lleras-Muney, 2006. "The Determinants of Mortality," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 97-120, Summer.
    3. John Bongaarts, 2014. "Trends in Causes of Death in Low-Mortality Countries: Implications for Mortality Projections," Population and Development Review, The Population Council, Inc., vol. 40(2), pages 189-212, June.
    4. David Cutler & Grant Miller, 2005. "The role of public health improvements in health advances: The twentieth-century United States," Demography, Springer;Population Association of America (PAA), vol. 42(1), pages 1-22, February.
    5. Kaakaï, Sarah & Labit Hardy, Héloïse & Arnold, Séverine & El Karoui, Nicole, 2019. "How can a cause-of-death reduction be compensated for by the population heterogeneity? A dynamic approach," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 16-37.
    6. Andrés Villegas & Steven Haberman, 2014. "On the Modeling and Forecasting of Socioeconomic Mortality Differentials: An Application to Deprivation and Mortality in England," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 168-193.
    7. Bloom, D.E. & Luca, D.L., 2016. "The Global Demography of Aging," Handbook of the Economics of Population Aging, in: Piggott, John & Woodland, Alan (ed.), Handbook of the Economics of Population Aging, edition 1, volume 1, chapter 0, pages 3-56, Elsevier.
    8. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
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    Cited by:

    1. Kaakai, Sarah & El Karoui, Nicole, 2023. "Birth Death Swap population in random environment and aggregation with two timescales," Stochastic Processes and their Applications, Elsevier, vol. 162(C), pages 218-248.

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