Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

  1. Jana Sanne Huisman  Is a corresponding author
  2. Jérémie Scire
  3. Daniel C Angst
  4. Jinzhou Li
  5. Richard A Neher
  6. Marloes H Maathuis
  7. Sebastian Bonhoeffer
  8. Tanja Stadler  Is a corresponding author
  1. ETH Zurich, Switzerland
  2. University of Basel, Switzerland

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.

Reviewing Editor

  1. Miles P Davenport, University of New South Wales, Australia

Publication history

  1. Preprint posted: November 30, 2020 (view preprint)
  2. Received: June 17, 2021
  3. Accepted: July 1, 2022
  4. Accepted Manuscript published: August 8, 2022 (version 1)
  5. Version of Record published: September 12, 2022 (version 2)

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.

Metrics

  • 618
    Page views
  • 200
    Downloads
  • 7
    Citations

Article citation count generated by polling the highest count across the following sources: PubMed Central, Crossref, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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
  1. Further reading

Further reading

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    James A Watson, Robert J Commons ... Nicholas J White
    Research Article Updated

    Tafenoquine is a newly licensed antimalarial drug for the radical cure of Plasmodium vivax malaria. The mechanism of action and optimal dosing are uncertain. We pooled individual data from 1102 patients and 72 healthy volunteers studied in the pre-registration trials. We show that tafenoquine dose is the primary determinant of efficacy. Under an Emax model, we estimate the currently recommended 300 mg dose in a 60 kg adult (5 mg/kg) results in 70% of the maximal obtainable hypnozoiticidal effect. Increasing the dose to 7.5 mg/kg (i.e. 450 mg) would result in 90% reduction in the risk of P. vivax recurrence. After adjustment for dose, the tafenoquine terminal elimination half-life, and day 7 methaemoglobin concentration, but not the parent compound exposure, were also associated with recurrence. These results suggest that the production of oxidative metabolites is central to tafenoquine’s hypnozoiticidal efficacy. Clinical trials of higher tafenoquine doses are needed to characterise their efficacy, safety and tolerability.

    1. Epidemiology and Global Health
    2. Medicine
    Qing Shen, Huan Song ... Unnur Valdimarsdóttir
    Research Article Updated

    Background:

    The association between cardiovascular disease (CVD) and selected psychiatric disorders has frequently been suggested while the potential role of familial factors and comorbidities in such association has rarely been investigated.

    Methods:

    We identified 869,056 patients newly diagnosed with CVD from 1987 to 2016 in Sweden with no history of psychiatric disorders, and 910,178 full siblings of these patients as well as 10 individually age- and sex-matched unrelated population controls (N = 8,690,560). Adjusting for multiple comorbid conditions, we used flexible parametric models and Cox models to estimate the association of CVD with risk of all subsequent psychiatric disorders, comparing rates of first incident psychiatric disorder among CVD patients with rates among unaffected full siblings and population controls.

    Results:

    The median age at diagnosis was 60 years for patients with CVD and 59.2% were male. During up to 30 years of follow-up, the crude incidence rates of psychiatric disorder were 7.1, 4.6, and 4.0 per 1000 person-years for patients with CVD, their siblings and population controls. In the sibling comparison, we observed an increased risk of psychiatric disorder during the first year after CVD diagnosis (hazard ratio [HR], 2.74; 95% confidence interval [CI], 2.62–2.87) and thereafter (1.45; 95% CI, 1.42–1.48). Increased risks were observed for all types of psychiatric disorders and among all diagnoses of CVD. We observed similar associations in the population comparison. CVD patients who developed a comorbid psychiatric disorder during the first year after diagnosis were at elevated risk of subsequent CVD death compared to patients without such comorbidity (HR, 1.55; 95% CI, 1.44–1.67).

    Conclusions:

    Patients diagnosed with CVD are at an elevated risk for subsequent psychiatric disorders independent of shared familial factors and comorbid conditions. Comorbid psychiatric disorders in patients with CVD are associated with higher risk of cardiovascular mortality suggesting that surveillance and treatment of psychiatric comorbidities should be considered as an integral part of clinical management of newly diagnosed CVD patients.

    Funding:

    This work was supported by the EU Horizon 2020 Research and Innovation Action Grant (CoMorMent, grant no. 847776 to UV, PFS, and FF), Grant of Excellence, Icelandic Research Fund (grant no. 163362-051 to UV), ERC Consolidator Grant (StressGene, grant no. 726413 to UV), Swedish Research Council (grant no. D0886501 to PFS), and US NIMH R01 MH123724 (to PFS).