High infectiousness immediately before COVID-19 symptom onset highlights the importance of continued contact tracing

Abstract
Data availability
Article and author information
Metrics

Abstract

Background: Understanding changes in infectiousness during SARS-COV-2 infections is critical to assess the effectiveness of public health measures such as contact tracing.

Methods: Here, we develop a novel mechanistic approach to infer the infectiousness profile of SARS-COV-2 infected individuals using data from known infector-infectee pairs. We compare estimates of key epidemiological quantities generated using our mechanistic method with analogous estimates generated using previous approaches.

Results: The mechanistic method provides an improved fit to data from SARS-CoV-2 infector-infectee pairs compared to commonly used approaches. Our best-fitting model indicates a high proportion of presymptomatic transmissions, with many transmissions occurring shortly before the infector develops symptoms.

Conclusions: High infectiousness immediately prior to symptom onset highlights the importance of continued contact tracing until effective vaccines have been distributed widely, even if contacts from a short time window before symptom onset alone are traced.

Funding: Engineering and Physical Sciences Research Council (EPSRC).

Data availability

All data generated or analysed during this study are included in the manuscript and supporting files. A source data file has been provided for Figure 2, containing the SARS-CoV-2 transmission pair data used in our analyses. These data were originally reported in references (3,10,29-31), and the combined data were also considered in reference (4). Code for reproducing our results is available at https://github.com/will-s-hart/COVID-19-Infectiousness-Profile.

Article and author information

Author details

William Stephen Hart

Mathematical Institute, University of Oxford, Oxford, United Kingdom

For correspondence
william.hart@keble.ox.ac.uk

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-2504-6860
Philip K Maini

Mathematical Institute, University of Oxford, Oxford, United Kingdom

Competing interests
The authors declare that no competing interests exist.
Robin N Thompson

Mathematics Institute, University of Warwick, Coventry, United Kingdom

Competing interests
The authors declare that no competing interests exist.

Funding

Engineering and Physical Sciences Research Council (Excellence Award)

William Stephen Hart

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

Copyright

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.