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Change point detection for COVID-19 excess deaths in Belgium

Author

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  • Han Lin Shang

    (Macquarie University
    Australian National University)

  • Ruofan Xu

    (Australian National University)

Abstract

Emerging at the end of 2019, COVID-19 has become a public health threat to people worldwide. Apart from deaths with a positive COVID-19 test, many others have died from causes indirectly related to COVID-19. Therefore, the COVID-19 confirmed deaths underestimate the influence of the pandemic on society; instead, the measure of ‘excess deaths’ is a more objective and comparable way to assess the scale of the epidemic and formulate lessons. One common practical issue in analysing the impact of COVID-19 is to determine the ‘pre-COVID-19′ period and the ‘post-COVID-19′ period. We apply a change point detection method to identify any change points using excess deaths in Belgium.

Suggested Citation

  • Han Lin Shang & Ruofan Xu, 2022. "Change point detection for COVID-19 excess deaths in Belgium," Journal of Population Research, Springer, vol. 39(4), pages 557-565, December.
    • Handle: RePEc:spr:joprea:v:39:y:2022:i:4:d:10.1007_s12546-021-09256-2
    DOI: 10.1007/s12546-021-09256-2
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    References listed on IDEAS

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    1. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    2. David S. Matteson & Nicholas A. James, 2014. "A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 334-345, March.
    3. Gabor J. Szekely & Maria L. Rizzo, 2005. "Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 151-183, September.
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    Cited by:

    1. Zhe Michelle Dong & Han Lin Shang & Aaron Bruhn, 2022. "Air Pollution and Mortality Impacts," Risks, MDPI, vol. 10(6), pages 1-21, June.
    2. Shaokang Wang & Han Lin Shang & Leonie Tickle & Han Li, 2024. "Forecasting Age- and Sex-Specific Survival Functions: Application to Annuity Pricing," Risks, MDPI, vol. 12(7), pages 1-15, July.

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