Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2
- ETH Zurich, Switzerland
- 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
Jana Sanne Huisman
Sebastian Bonhoeffer
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
- Miles P Davenport, University of New South Wales, Australia
Version history
- Preprint posted: November 30, 2020 (view preprint)
- Received: June 17, 2021
- Accepted: July 1, 2022
- Accepted Manuscript published: August 8, 2022 (version 1)
- 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
-
- 1,770
- views
-
- 318
- downloads
-
- 32
- 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)
Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2
eLife 11:e71345.
https://doi.org/10.7554/eLife.71345
Further reading
-
- Epidemiology and Global Health
We discuss 12 misperceptions, misstatements, or mistakes concerning the use of covariates in observational or nonrandomized research. Additionally, we offer advice to help investigators, editors, reviewers, and readers make more informed decisions about conducting and interpreting research where the influence of covariates may be at issue. We primarily address misperceptions in the context of statistical management of the covariates through various forms of modeling, although we also emphasize design and model or variable selection. Other approaches to addressing the effects of covariates, including matching, have logical extensions from what we discuss here but are not dwelled upon heavily. The misperceptions, misstatements, or mistakes we discuss include accurate representation of covariates, effects of measurement error, overreliance on covariate categorization, underestimation of power loss when controlling for covariates, misinterpretation of significance in statistical models, and misconceptions about confounding variables, selecting on a collider, and p value interpretations in covariate-inclusive analyses. This condensed overview serves to correct common errors and improve research quality in general and in nutrition research specifically.
-
- Ecology
- Epidemiology and Global Health
Zoonotic disease dynamics in wildlife hosts are rarely quantified at macroecological scales due to the lack of systematic surveys. Non-human primates (NHPs) host Plasmodium knowlesi, a zoonotic malaria of public health concern and the main barrier to malaria elimination in Southeast Asia. Understanding of regional P. knowlesi infection dynamics in wildlife is limited. Here, we systematically assemble reports of NHP P. knowlesi and investigate geographic determinants of prevalence in reservoir species. Meta-analysis of 6322 NHPs from 148 sites reveals that prevalence is heterogeneous across Southeast Asia, with low overall prevalence and high estimates for Malaysian Borneo. We find that regions exhibiting higher prevalence in NHPs overlap with human infection hotspots. In wildlife and humans, parasite transmission is linked to land conversion and fragmentation. By assembling remote sensing data and fitting statistical models to prevalence at multiple spatial scales, we identify novel relationships between P. knowlesi in NHPs and forest fragmentation. This suggests that higher prevalence may be contingent on habitat complexity, which would begin to explain observed geographic variation in parasite burden. These findings address critical gaps in understanding regional P. knowlesi epidemiology and indicate that prevalence in simian reservoirs may be a key spatial driver of human spillover risk.
