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Age-specific mortality and immunity patterns of SARS-CoV-2

Author

Listed:
  • Megan O’Driscoll

    (University of Cambridge
    Institut Pasteur, UMR2000, CNRS)

  • Gabriel Ribeiro Dos Santos

    (University of Cambridge
    Institut Pasteur, UMR2000, CNRS)

  • Lin Wang

    (University of Cambridge
    Institut Pasteur, UMR2000, CNRS)

  • Derek A. T. Cummings

    (University of Florida)

  • Andrew S. Azman

    (Johns Hopkins Bloomberg School of Public Health
    Geneva University Hospitals)

  • Juliette Paireau

    (Institut Pasteur, UMR2000, CNRS
    Institut Pasteur)

  • Arnaud Fontanet

    (Institut Pasteur
    PACRI Unit, Conservatoire National des Arts et Métiers)

  • Simon Cauchemez

    (Institut Pasteur, UMR2000, CNRS)

  • Henrik Salje

    (University of Cambridge
    Institut Pasteur, UMR2000, CNRS)

Abstract

Estimating the size of the coronavirus disease 2019 (COVID-19) pandemic and the infection severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is made challenging by inconsistencies in the available data. The number of deaths associated with COVID-19 is often used as a key indicator for the size of the epidemic, but the observed number of deaths represents only a minority of all infections1,2. In addition, the heterogeneous burdens in nursing homes and the variable reporting of deaths of older individuals can hinder direct comparisons of mortality rates and the underlying levels of transmission across countries3. Here we use age-specific COVID-19-associated death data from 45 countries and the results of 22 seroprevalence studies to investigate the consistency of infection and fatality patterns across multiple countries. We find that the age distribution of deaths in younger age groups (less than 65 years of age) is very consistent across different settings and demonstrate how these data can provide robust estimates of the share of the population that has been infected. We estimate that the infection fatality ratio is lowest among 5–9-year-old children, with a log-linear increase by age among individuals older than 30 years. Population age structures and heterogeneous burdens in nursing homes explain some but not all of the heterogeneity between countries in infection fatality ratios. Among the 45 countries included in our analysis, we estimate that approximately 5% of these populations had been infected by 1 September 2020, and that much higher transmission rates have probably occurred in a number of Latin American countries. This simple modelling framework can help countries to assess the progression of the pandemic and can be applied in any scenario for which reliable age-specific death data are available.

Suggested Citation

  • Megan O’Driscoll & Gabriel Ribeiro Dos Santos & Lin Wang & Derek A. T. Cummings & Andrew S. Azman & Juliette Paireau & Arnaud Fontanet & Simon Cauchemez & Henrik Salje, 2021. "Age-specific mortality and immunity patterns of SARS-CoV-2," Nature, Nature, vol. 590(7844), pages 140-145, February.
  • Handle: RePEc:nat:nature:v:590:y:2021:i:7844:d:10.1038_s41586-020-2918-0
    DOI: 10.1038/s41586-020-2918-0
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    Cited by:

    1. Carl Bonander & Mats Ekman & Niklas Jakobsson, 2023. "When do default nudges work?," Oxford Open Economics, Oxford University Press, vol. 2, pages 391-425.
    2. Seres, Gyula & Balleyer, Anna & Cerutti, Nicola & Friedrichsen, Jana & Süer, Müge, 2021. "Face mask use and physical distancing before and after mandatory masking: No evidence on risk compensation in public waiting lines," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 765-781.
    3. Chauvin, Juan Pablo & Tricaud, Clemence, 2022. "Gender and Electoral Incentives: Evidence from Crisis Response," IDB Publications (Working Papers) 12411, Inter-American Development Bank.
    4. Sebastian Sund & Lars H. Sendstad & Jacco J. J. Thijssen, 2022. "Kalman filter approach to real options with active learning," Computational Management Science, Springer, vol. 19(3), pages 457-490, July.
    5. Camille Delbrouck & Jennifer Alonso-García, 2024. "COVID-19 and Excess Mortality: An Actuarial Study," Risks, MDPI, vol. 12(4), pages 1-27, March.
    6. Julien Bergeot & Florence Jusot, 2024. "Risk, time preferences, trustworthiness and COVID-19 preventive behavior: evidence from France," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(1), pages 91-101, February.
    7. Kalewold Hailu Kalewold, 2023. "Lockdowns and the ethics of intergenerational compensation," Politics, Philosophy & Economics, , vol. 22(3), pages 271-289, August.
    8. Anikó Bíró & Réka Branyiczki & Péter Elek, 2022. "Time patterns of precautionary health behaviours during an easing phase of the COVID-19 pandemic in Europe," European Journal of Ageing, Springer, vol. 19(4), pages 837-848, December.
    9. Fanny Velardo & Verity Watson & Pierre Arwidson & François Alla & Stéphane Luchini & Michaël Schwarzinger, 2021. "Regional Differences in COVID-19 Vaccine Hesitancy in December 2020: A Natural Experiment in the French Working-Age Population," Post-Print hal-03513452, HAL.
    10. Maddalena Ferranna & JP Sevilla & David E. Bloom, 2021. "Addressing the COVID-19 Pandemic: Comparing Alternative Value Frameworks," NBER Working Papers 28601, National Bureau of Economic Research, Inc.
    11. Walmsley, Terrie & Rose, Adam & John, Richard & Wei, Dan & Hlávka, Jakub P. & Machado, Juan & Byrd, Katie, 2023. "Macroeconomic consequences of the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 120(C).
    12. Ferranna, Maddalena & Sevilla, J.P. & Bloom, David E., 2021. "Addressing the COVID-19 Pandemic: Comparing Alternative Value Frameworks," IZA Discussion Papers 14181, Institute of Labor Economics (IZA).
    13. M. Kate Bundorf & Jill DeMatteis & Grant Miller & Maria Polyakova & Jialu L. Streeter & Jonathan Wivagg, 2021. "Risk Perceptions and Protective Behaviors: Evidence from COVID-19 Pandemic," NBER Working Papers 28741, National Bureau of Economic Research, Inc.

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