1. Epidemiology and Global Health
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Tracking excess mortality across countries during the COVID-19 pandemic with the World Mortality Dataset

  1. Ariel Karlinsky  Is a corresponding author
  2. Dmitry Kobak  Is a corresponding author
  1. Hebrew University, Israel
  2. University of Tübingen, Germany
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Cite this article as: eLife 2021;10:e69336 doi: 10.7554/eLife.69336

Abstract

Comparing the impact of the COVID-19 pandemic between countries or across time is difficult because the reported numbers of cases and deaths can be strongly affected by testing capacity and reporting policy. Excess mortality, defined as the increase in all-cause mortality relative to the expected mortality, is widely considered as a more objective indicator of the COVID-19 death toll. However, there has been no global, frequently-updated repository of the all-cause mortality data across countries. To fill this gap, we have collected weekly, monthly, or quarterly all-cause mortality data from 94 countries and territories, openly available as the regularly-updated World Mortality Dataset. We used this dataset to compute the excess mortality in each country during the COVID-19 pandemic. We found that in several worst-affected countries (Peru, Ecuador, Bolivia, Mexico) the excess mortality was above 50% of the expected annual mortality. At the same time, in several other countries (Australia, New Zealand) mortality during the pandemic was below the usual level, presumably due to social distancing measures decreasing the non-COVID infectious mortality. Furthermore, we found that while many countries have been reporting the COVID-19 deaths very accurately, some countries have been substantially underreporting their COVID-19 deaths (e.g. Nicaragua, Russia, Uzbekistan), sometimes by two orders of magnitude (Tajikistan). Our results highlight the importance of open and rapid all-cause mortality reporting for pandemic monitoring.

Data availability

Full data is publicly available at: https://github.com/akarlinsky/world_mortality

The following data sets were generated

Article and author information

Author details

  1. Ariel Karlinsky

    Economics, Hebrew University, Jerusalem, Israel
    For correspondence
    ariel.karlinsky@mail.huji.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0966-5837
  2. Dmitry Kobak

    Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
    For correspondence
    dmitry.kobak@uni-tuebingen.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5639-7209

Funding

DK was supported by the Deutsche Forschungsgemeinschaft (BE5601/4-1 and the Cluster of Excellence ``Machine Learning --- New Perspectives for Science', EXC 2064, project number 390727645), the Federal Ministry of Education and Research (FKZ 01GQ1601 and 01IS18039A) and the National Institute of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Reviewing Editor

  1. Marc Lipsitch, Harvard TH Chan School of Public Health, United States

Publication history

  1. Received: April 13, 2021
  2. Accepted: June 29, 2021
  3. Accepted Manuscript published: June 30, 2021 (version 1)
  4. Accepted Manuscript updated: July 7, 2021 (version 2)

Copyright

© 2021, Karlinsky & Kobak

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Further reading

Further reading

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    Humans and other group-living animals tend to distribute their social effort disproportionately. Individuals predominantly interact with a small number of close companions while maintaining weaker social bonds with less familiar group members. By incorporating this behavior into a mathematical model, we find that a single parameter, which we refer to as social fluidity, controls the rate of social mixing within the group. Large values of social fluidity correspond to gregarious behavior, whereas small values signify the existence of persistent bonds between individuals. We compare the social fluidity of 13 species by applying the model to empirical human and animal social interaction data. To investigate how social behavior influences the likelihood of an epidemic outbreak, we derive an analytical expression of the relationship between social fluidity and the basic reproductive number of an infectious disease. For species that form more stable social bonds, the model describes frequency-dependent transmission that is sensitive to changes in social fluidity. As social fluidity increases, animal-disease systems become increasingly density-dependent. Finally, we demonstrate that social fluidity is a stronger predictor of disease outcomes than both group size and connectivity, and it provides an integrated framework for both density-dependent and frequency-dependent transmission.

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    Background:

    Sanofi-Pasteur’s CYD-TDV is the only licensed dengue vaccine. Two phase three trials showed higher efficacy in seropositive than seronegative recipients. Hospital follow-up revealed increased hospitalisation in 2–5- year-old vaccinees, where serostatus and age effects were unresolved.

    Methods:

    We fit a survival model to individual-level data from both trials, including year 1 of hospital follow-up. We determine efficacy by age, serostatus, serotype and severity, and examine efficacy duration and vaccine action mechanism.

    Results:

    Our modelling indicates that vaccine-induced immunity is long-lived in seropositive recipients, and therefore that vaccinating seropositives gives higher protection than two natural infections. Long-term increased hospitalisation risk outweighs short-lived immunity in seronegatives. Independently of serostatus, transient immunity increases with age, and is highest against serotype 4. Benefit is higher in seropositives, and risk enhancement is greater in seronegatives, against hospitalised disease than against febrile disease.

    Conclusions:

    Our results support vaccinating seropositives only. Rapid diagnostic tests would enable viable ‘screen-then-vaccinate’ programs. Since CYD-TDV acts as a silent infection, long-term safety of other vaccine candidates must be closely monitored.

    Funding:

    Bill & Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust, Royal Society.

    Clinical trial number:

    NCT01373281 and NCT01374516.