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.

Metrics

  • 2,008
    views
  • 344
    downloads
  • 45
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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

Share this article

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

Further reading

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Felix Lankester, Tito J Kibona ... Sarah Cleaveland
    Research Article

    Lack of data on the aetiology of livestock diseases constrains effective interventions to improve livelihoods, food security and public health. Livestock abortion is an important disease syndrome affecting productivity and public health. Several pathogens are associated with livestock abortions but across Africa surveillance data rarely include information from abortions, little is known about aetiology and impacts, and data are not available to inform interventions. This paper describes outcomes from a surveillance platform established in Tanzania spanning pastoral, agropastoral and smallholder systems to investigate causes and impacts of livestock abortion. Abortion events were reported by farmers to livestock field officers (LFO) and on to investigation teams. Events were included if the research team or LFO could attend within 72 hr. If so, samples and questionnaire data were collected to investigate (a) determinants of attribution; (b) patterns of events, including species and breed, previous abortion history, and seasonality; (c) determinants of reporting, investigation and attribution; (d) cases involving zoonotic pathogens. Between 2017–2019, 215 events in cattle (n=71), sheep (n=44), and goats (n=100) were investigated. Attribution, achieved for 19.5% of cases, was significantly affected by delays in obtaining samples. Histopathology proved less useful than PCR due to rapid deterioration of samples. Vaginal swabs provided practical and sensitive material for pathogen detection. Livestock abortion surveillance, even at a small scale, can generate valuable information on causes of disease outbreaks, reproductive losses and can identify pathogens not easily captured through other forms of livestock disease surveillance. This study demonstrated the feasibility of establishing a surveillance system, achieved through engagement of community-based field officers, establishment of practical sample collection and application of molecular diagnostic platforms.

    1. Epidemiology and Global Health
    2. Genetics and Genomics
    Tianyu Zhao, Hui Li ... Li Chen
    Research Article

    Alzheimer’s disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.