Abstract

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Data availability

- The source code of the pipeline is available at https://github.com/covid-19-Re/shiny-dailyRe ; this includes a script to download the required incidence data from public sources.- The resulting estimates (updated daily) are available at: https://github.com/covid-19-Re/dailyRe-Data- The code and data necessary to reproduce the figures in the paper is at: https://github.com/covid-19-Re/paper-codeThe Swiss estimates on our dashboard, and shown in Figs. 2, S9-S11 of the paper, use linelist data provided to us by the Federal Office of Public Health (FOPH) to inform the time-varying delay distributions. This data contains one row per infected individual, with information on their age, date of infection, postal code, etc. Although the data is anonymized, it could be linked directly to particular individuals, and this is a privacy concern. As such, we are not allowed to share the original data publicly. We are discussing with the FOPH whether we can share an aggregated form of the original data (for instance the time-varying delay distribution itself), but have already included the processed data (i.e. the estimates plotted in the figure) on https://github.com/covid-19-Re/paper-code for now.To obtain access to the original data, interested individuals should contact the FOPH directly. To the best of our knowledge, no official application or access granting procedure is in place, and applications will likely be assessed on a case by case basis.

Article and author information

Author details

  1. Jana Sanne Huisman

    Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
    For correspondence
    jana.huisman@env.ethz.ch
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1782-8109
  2. Jérémie Scire

    Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
    Competing interests
    No competing interests declared.
  3. Daniel C Angst

    Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6512-4595
  4. Jinzhou Li

    Department of Mathematics, ETH Zurich, Zurich, Switzerland
    Competing interests
    No competing interests declared.
  5. Richard A Neher

    Biozentrum, University of Basel, Basel, Switzerland
    Competing interests
    Richard A Neher, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2525-1407
  6. Marloes H Maathuis

    Department of Mathematics, ETH Zurich, Zurich, Switzerland
    Competing interests
    No competing interests declared.
  7. Sebastian Bonhoeffer

    Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8052-3925
  8. Tanja Stadler

    Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
    For correspondence
    tanja.stadler@bsse.ethz.ch
    Competing interests
    No competing interests declared.

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (31CA30_196267)

  • Tanja Stadler

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (200021_172603)

  • Marloes H Maathuis

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030B_176401)

  • Sebastian Bonhoeffer

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (407240-167121)

  • Sebastian Bonhoeffer
  • Tanja Stadler

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

Copyright

© 2022, Huisman 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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  1. Jana Sanne Huisman
  2. Jérémie Scire
  3. Daniel C Angst
  4. Jinzhou Li
  5. Richard A Neher
  6. Marloes H Maathuis
  7. Sebastian Bonhoeffer
  8. Tanja Stadler
(2022)
Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2
eLife 11:e71345.
https://doi.org/10.7554/eLife.71345

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https://doi.org/10.7554/eLife.71345