| Literature DB >> 35138250 |
Sebastian Funk1, Robin N Thompson2,3, William S Hart4, Sam Abbott1, Akira Endo1, Joel Hellewell1, Elizabeth Miller5,6, Nick Andrews7, Philip K Maini4.
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.Entities:
Keywords: COVID-19; SARS-CoV-2; epidemiology; generation interval; generation time; global health; mathematical modelling; presymptomatic transmission; viruses
Mesh:
Year: 2022 PMID: 35138250 PMCID: PMC8967386 DOI: 10.7554/eLife.70767
Source DB: PubMed Journal: Elife ISSN: 2050-084X Impact factor: 8.713