Abstract
Background
The serial interval is a key epidemiological measure that quantifies the time between an infector's and an infectee's onset of symptoms. This measure helps investigate epidemiological links between cases, and is an important parameter in transmission models used to estimate transmissibility and inform control strategies. The emergence of multiple variants of concern (VOC) during the SARS-CoV-2 pandemic has led to uncertainties about potential changes in the serial interval of COVID-19. We estimated the household serial interval of multiple VOC using data collected by the Virus Watch study. This online, prospective, community cohort study followed-up entire households in England and Wales since mid-June 2020.
Methods
This analysis included 5842 symptomatic individuals with confirmed SARS-CoV-2 infection among 2579 households from Sept 1, 2020, to Aug 10, 2022. SARS-CoV-2 variant designation was based upon national surveillance data of variant prevalence by date and geographical region. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, given assumptions on the incubation period and generation time distributions using the R package outbreaker2.
Findings
We characterised the serial interval of COVID-19 by VOC. The mean serial interval was shortest for omicron BA5 (2·02 days; 95% credible interval [CrI] 1·26–2·84) and longest for alpha (3·37 days; 2·52–4·04). The mean serial interval before alpha (wild-type) was 2·29 days (95% CrI 1·39–2·94), 3·11 days (2·28–3·90) for delta, 2·72 days (2·01–3·47) for omicron BA1, and 2·67 days (1·90–3·46) for omicron BA2. We estimated that 17% (95% CrI 5–26) of serial interval values are negative across all variants.
Interpretation
Most methods estimating the reproduction number from incidence time series do not allow for a negative serial interval by construction. Further research is needed to extend these methods and assess biases introduced by not accounting for negative serial intervals. To our knowledge, this study is the first to use a Bayesian framework to estimate the serial interval of all major SARS-CoV-2 VOC from thousands of confirmed household cases.
Funding
UK Medical Research Council and Wellcome Trust.
Contributors
Declaration of interests
Acknowledgments
Acknowledgments
The research costs for the study have been supported by the UK Medical Research Council (MC_PC 19070, awarded to University College London [UCL] on March 30, 2020; and MR/V028375/1, awarded on Aug 17, 2020). The study also received US$15 000 of Facebook advertising credit to support a pilot social media recruitment campaign on Aug 18, 2020. This study was supported by the Wellcome Trust through a Wellcome Clinical Research Career Development Fellowship (206602) to RWA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the Abstract. Data were collected by the UCL Virus Watch study.
Acknowledgments
CG, VN, EF, MS, AMDN, SB, TEB, WLEF, AY, JK, IB, RWA, and ACH are part of the Virus Watch Core Team. CG, VN, EF, MS, and AMDN processed the data. CG, AC, TJ, and PW did the data analysis. CG, AC, TJ, and PW wrote the Abstract.
CG is supported by a PhD studentship at Imperial College London funded by the National Institute for Health Research Health Protection and Research Unit in Modelling and Health Economics, which is a partnership between the UK Health Security Agency, Imperial College London, and the London School of Hygiene & Tropical Medicine (NIHR200908). ACH serves on the UK New and Emerging Respiratory Virus Threats Advisory Group. All other authors declare no competing interests.