Quantifying the impact of quarantine duration on COVID-19 transmission
- ETH Zurich, Switzerland
- University of Bern, Switzerland
Abstract
The large number of individuals placed into quarantine because of possible SARS CoV-2 exposure has high societal and economic costs. There is ongoing debate about the appropriate duration of quarantine, particularly since the fraction of individuals who eventually test positive is perceived as being low. We use empirically determined distributions of incubation period, infectivity, and generation time to quantify how the duration of quarantine affects onward transmission from traced contacts of confirmed SARS-CoV-2 cases and from returning travellers. We also consider the roles of testing followed by release if negative (test-and-release), reinforced hygiene, adherence, and symptoms in calculating quarantine efficacy. We show that there are quarantine strategies based on a test-and-release protocol that, from an epidemiological viewpoint, perform almost as well as a 10 day quarantine, but with fewer person days spent in quarantine. The findings apply to both travellers and contacts, but the specifics depend on the context.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files. These files are available on github (https://github.com/ashcroftp/quarantine2020/) and archived at https://doi.org/10.5281/zenodo.4498169.
Article and author information
Author details
Sebastian Bonhoeffer
Funding
H2020 European Research Council (EpiPose,101003688)
- Nicola Low
Swiss National Science Foundation (176233)
- Nicola Low
Swiss National Science Foundation (176401)
- Sebastian Bonhoeffer
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Deborah Cromer, University of New South Wales, Australia
Version history
- Received: October 2, 2020
- Accepted: February 4, 2021
- Accepted Manuscript published: February 5, 2021 (version 1)
- Version of Record published: March 16, 2021 (version 2)
Copyright
© 2021, Ashcroft 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
-
- 12,249
- views
-
- 659
- downloads
-
- 62
- 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)
Quantifying the impact of quarantine duration on COVID-19 transmission
eLife 10:e63704.
https://doi.org/10.7554/eLife.63704
Further reading
-
- Epidemiology and Global Health
- Medicine
- Microbiology and Infectious Disease
eLife has published the following articles on SARS-CoV-2 and COVID-19.
-
- 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.
