1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease
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SARS-CoV-2 (COVID-19) by the numbers

  1. Yinon M Bar-On
  2. Avi Flamholz
  3. Rob Phillips
  4. Ron Milo  Is a corresponding author
  1. The Weizmann Institute for Science, Israel
  2. University of California, Berkeley, United States
  3. California Institute of Technology, United States
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Cite this article as: eLife 2020;9:e57309 doi: 10.7554/eLife.57309

Abstract

The current SARS-CoV-2 pandemic is a harsh reminder of the fact that, whether in a single human host or a wave of infection across continents, viral dynamics is often a story about the numbers. In this snapshot, our aim is to provide a one-stop, curated graphical source for the key numbers that help us understand the virus driving our current global crisis. The discussion is framed around two broad themes: 1) the biology of the virus itself and 2) the characteristics of the infection of a single human host. Our one-page summary provides the key numbers pertaining to SARS-CoV-2, based mostly on peer-reviewed literature. The numbers reported in summary format are substantiated by the annotated references below. Readers are urged to remember that much uncertainty remains and knowledge of this pandemic and the virus driving it is rapidly evolving. In the paragraphs below we provide 'back of the envelope' calculations that exemplify the insights that can be gained from knowing some key numbers and using quantitative logic. These calculations serve to improve our intuition through sanity checks, but do not replace detailed epidemiological analysis.

Data availability

This article is a compilation of previously published data; no new data were generated in this study.

Article and author information

Author details

  1. Yinon M Bar-On

    Department of Plant and Environmental Sciences, The Weizmann Institute for Science, Rehovot, Israel
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8477-609X
  2. Avi Flamholz

    Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9278-5479
  3. Rob Phillips

    Department of Bioengineering, California Institute of Technology, Pasadena, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3082-2809
  4. Ron Milo

    Department of Plant and Environmental Sciences, The Weizmann Institute for Science, Rehovot, Israel
    For correspondence
    ron.milo@weizmann.ac.il
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1641-2299

Funding

National Institutes of Health (1R35 GM118043-01 (Maximizing Investigators Research Award))

  • Rob Phillips

Charles and Louise Gartner professional chair

  • Ron Milo

Azrieli Fellow

  • Yinon M Bar-On

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Michael B Eisen, HHMI, University of California, Berkeley, United States

Publication history

  1. Received: March 27, 2020
  2. Accepted: March 30, 2020
  3. Accepted Manuscript published: March 31, 2020 (version 1)
  4. Accepted Manuscript updated: April 1, 2020 (version 2)
  5. Accepted Manuscript updated: April 2, 2020 (version 3)
  6. Version of Record published: May 14, 2020 (version 4)

Copyright

© 2020, Bar-On et al.

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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Further reading

    1. Epidemiology and Global Health
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    ISARIC Clinical Characterisation Group et al.
    Research Article Updated

    Background:

    There is potentially considerable variation in the nature and duration of the care provided to hospitalised patients during an infectious disease epidemic or pandemic. Improvements in care and clinician confidence may shorten the time spent as an inpatient, or the need for admission to an intensive care unit (ICU) or high dependency unit (HDU). On the other hand, limited resources at times of high demand may lead to rationing. Nevertheless, these variables may be used as static proxies for disease severity, as outcome measures for trials, and to inform planning and logistics.

    Methods:

    We investigate these time trends in an extremely large international cohort of 142,540 patients hospitalised with COVID-19. Investigated are: time from symptom onset to hospital admission, probability of ICU/HDU admission, time from hospital admission to ICU/HDU admission, hospital case fatality ratio (hCFR) and total length of hospital stay.

    Results:

    Time from onset to admission showed a rapid decline during the first months of the pandemic followed by peaks during August/September and December 2020. ICU/HDU admission was more frequent from June to August. The hCFR was lowest from June to August. Raw numbers for overall hospital stay showed little variation, but there is clear decline in time to discharge for ICU/HDU survivors.

    Conclusions:

    Our results establish that variables of these kinds have limitations when used as outcome measures in a rapidly evolving situation.

    Funding:

    This work was supported by the UK Foreign, Commonwealth and Development Office and Wellcome [215091/Z/18/Z] and the Bill & Melinda Gates Foundation [OPP1209135]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

    1. Epidemiology and Global Health
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    Wilmer Cristobal Guzman-Vilca et al.
    Research Article Updated

    Global targets to reduce salt intake have been proposed, but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption at the population level based on simple predictors and applied this model to national surveys in 54 countries. We used 21 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on sex, age, weight, height, and systolic and diastolic blood pressure. We applied the ML model to 54 new surveys to quantify the mean salt consumption in the population. The pooled dataset in which we developed the ML model included 49,776 people. Overall, there were no substantial differences between the observed and ML-predicted mean salt intake (p<0.001). The pooled dataset where we applied the ML model included 166,677 people; the predicted mean salt consumption ranged from 6.8 g/day (95% CI: 6.8–6.8 g/day) in Eritrea to 10.0 g/day (95% CI: 9.9–10.0 g/day) in American Samoa. The countries with the highest predicted mean salt intake were in the Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence. An ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available.