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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.
  • Hart WS; Mathematical Institute, University of Oxford, Oxford, United Kingdom.
  • Abbott S; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Endo A; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Hellewell J; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Miller E; Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
  • Andrews N; Immunisation and Countermeasures Division, UK Health Security Agency, London, United Kingdom.
  • Maini PK; Data and Analytical Sciences, UK Health Security Agency, London, United Kingdom.
  • Funk S; Mathematical Institute, University of Oxford, Oxford, United Kingdom.
  • Thompson RN; Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Elife ; 112022 02 09.
Article in English | MEDLINE | ID: covidwho-1742929
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 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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Topics: Long Covid Limits: Humans Country/Region as subject: Europa Language: English Year: 2022 Document Type: Article Affiliation country: ELIFE.70767

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Observational study / Prognostic study Topics: Long Covid Limits: Humans Country/Region as subject: Europa Language: English Year: 2022 Document Type: Article Affiliation country: ELIFE.70767