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Inference of the SARS-CoV-2 generation time using UK household data
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Hart, W. S., Abbott, S., Endo, A. , Hellewell, J., Miller, E. , Andrews, N., Maini, P. K., Funk, S. and Thompson, Robin N. (2022) Inference of the SARS-CoV-2 generation time using UK household data. eLife, 11 . e70767. doi:10.7554/eLife.70767 ISSN 2050-084X.
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Official URL: https://doi.org/10.7554/eLife.70767
Abstract
The distribution of the generation time (the interval between individuals becoming infected and transmitting the virus) characterises changes in the transmission risk during SARS-CoV-2 infections. Inferring the generation time distribution is essential to plan and assess public health measures. We previously developed a mechanistic approach for estimating the generation time, which provided an improved fit to data from the early months of the COVID-19 pandemic (December 2019-March 2020) compared to existing models (Hart et al., 2021). However, few estimates of the generation time exist based on data from later in the pandemic. Here, using data from a household study conducted from March-November 2020 in the UK, we provide updated estimates of the generation time. We considered both a commonly used approach in which the transmission risk is assumed to be independent of when symptoms develop, and our mechanistic model in which transmission and symptoms are linked explicitly. Assuming independent transmission and symptoms, we estimated a mean generation time (4.2 days, 95% credible interval 3.3-5.3 days) similar to previous estimates from other countries, but with a higher standard deviation (4.9 days, 3.0-8.3 days). Using our mechanistic approach, we estimated a longer mean generation time (5.9 days, 5.2-7.0 days) and a similar standard deviation (4.8 days, 4.0-6.3 days). As well as estimating the generation time using data from the entire study period, we also considered whether the generation time varied temporally. Both models suggest a shorter mean generation time in September-November 2020 compared to earlier months. Since the SARS-CoV-2 generation time appears to be changing, further data collection and analysis is necessary to continue to monitor ongoing transmission and inform future public health policy decisions.
| Item Type: | Journal Article | |||||||||||||||
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| Subjects: | R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine | |||||||||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | |||||||||||||||
| Library of Congress Subject Headings (LCSH): | COVID-19 (Disease) -- Transmission -- Mathematical models, Coronaviruses -- Transmission -- Mathematical models, Covid-19 Pandemic, 2020- | |||||||||||||||
| Journal or Publication Title: | eLife | |||||||||||||||
| Publisher: | eLife Sciences Publications Ltd. | |||||||||||||||
| ISSN: | 2050-084X | |||||||||||||||
| Official Date: | 9 February 2022 | |||||||||||||||
| Dates: |
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| Volume: | 11 | |||||||||||||||
| Article Number: | e70767 | |||||||||||||||
| DOI: | 10.7554/eLife.70767 | |||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||
| Publication Status: | Published | |||||||||||||||
| Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||
| Copyright Holders: | © 2022, Hart et al. | |||||||||||||||
| Date of first compliant deposit: | 9 February 2022 | |||||||||||||||
| Date of first compliant Open Access: | 18 February 2022 | |||||||||||||||
| RIOXX Funder/Project Grant: |
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