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Inference of the SARS-CoV-2 generation time using UK household data
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 p...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967386/ https://www.ncbi.nlm.nih.gov/pubmed/35138250 http://dx.doi.org/10.7554/eLife.70767 |
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author | Hart, William S Abbott, Sam Endo, Akira Hellewell, Joel Miller, Elizabeth Andrews, Nick Maini, Philip K Funk, Sebastian Thompson, Robin N |
author_facet | Hart, William S Abbott, Sam Endo, Akira Hellewell, Joel Miller, Elizabeth Andrews, Nick Maini, Philip K Funk, Sebastian Thompson, Robin N |
author_sort | Hart, William S |
collection | PubMed |
description | 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 to 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. |
format | Online Article Text |
id | pubmed-8967386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-89673862022-03-31 Inference of the SARS-CoV-2 generation time using UK household data Hart, William S Abbott, Sam Endo, Akira Hellewell, Joel Miller, Elizabeth Andrews, Nick Maini, Philip K Funk, Sebastian Thompson, Robin N eLife Epidemiology and Global Health 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 to 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. eLife Sciences Publications, Ltd 2022-02-09 /pmc/articles/PMC8967386/ /pubmed/35138250 http://dx.doi.org/10.7554/eLife.70767 Text en © 2022, Hart et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Epidemiology and Global Health Hart, William S Abbott, Sam Endo, Akira Hellewell, Joel Miller, Elizabeth Andrews, Nick Maini, Philip K Funk, Sebastian Thompson, Robin N Inference of the SARS-CoV-2 generation time using UK household data |
title | Inference of the SARS-CoV-2 generation time using UK household data |
title_full | Inference of the SARS-CoV-2 generation time using UK household data |
title_fullStr | Inference of the SARS-CoV-2 generation time using UK household data |
title_full_unstemmed | Inference of the SARS-CoV-2 generation time using UK household data |
title_short | Inference of the SARS-CoV-2 generation time using UK household data |
title_sort | inference of the sars-cov-2 generation time using uk household data |
topic | Epidemiology and Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967386/ https://www.ncbi.nlm.nih.gov/pubmed/35138250 http://dx.doi.org/10.7554/eLife.70767 |
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