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. 2023 Sep:44:100713.
doi: 10.1016/j.epidem.2023.100713. Epub 2023 Aug 7.

Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals

Cyril Geismar  1 , Vincent Nguyen  2 , Ellen Fragaszy  3 , Madhumita Shrotri  2 , Annalan M D Navaratnam  2 , Sarah Beale  4 , Thomas E Byrne  2 , Wing Lam Erica Fong  2 , Alexei Yavlinsky  2 , Jana Kovar  5 , Susan Hoskins  2 , Isobel Braithwaite  2 , Robert W Aldridge  2 , Andrew C Hayward  5 , Peter J White  6 , Thibaut Jombart  6 , Anne Cori  6
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Free article

Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals

Cyril Geismar et al. Epidemics. 2023 Sep.
Free article

Abstract

Background: The serial interval is a key epidemiological measure that quantifies the time between the onset of symptoms in an infector-infectee pair. It indicates how quickly new generations of cases appear, thus informing on the speed of an epidemic. Estimating the serial interval requires to identify pairs of infectors and infectees. Yet, most studies fail to assess the direction of transmission between cases and assume that the order of infections - and thus transmissions - strictly follows the order of symptom onsets, thereby imposing serial intervals to be positive. Because of the long and highly variable incubation period of SARS-CoV-2, this may not always be true (i.e an infectee may show symptoms before their infector) and negative serial intervals may occur. This study aims to estimate the serial interval of different SARS-CoV-2 variants whilst accounting for negative serial intervals.

Methods: This analysis included 5 842 symptomatic individuals with confirmed SARS-CoV-2 infection amongst 2 579 households from September 2020 to August 2022 across England & Wales. We used a Bayesian framework to infer who infected whom by exploring all transmission trees compatible with the observed dates of symptoms, based on a wide range of incubation period and generation time distributions compatible with estimates reported in the literature. Serial intervals were derived from the reconstructed transmission pairs, stratified by variants.

Results: We estimated that 22% (95% credible interval (CrI) 8-32%) of serial interval values are negative across all VOC. The mean serial interval was shortest for Omicron BA5 (2.02 days, 1.26-2.84) and longest for Alpha (3.37 days, 2.52-4.04).

Conclusions: This study highlights the large proportion of negative serial intervals across SARS-CoV-2 variants. Because the serial interval is widely used to estimate transmissibility and forecast cases, these results may have critical implications for epidemic control.

Keywords: Bayesian modelling; Infectious Disease Epidemiology; Sars-cov-2; Serial interval; Variants of concern.

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Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Cyril Geismar: National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Modelling and Health Economics PhD funding, grant code: NIHR200908. Anne Cori: NIHR Grant paid to institution (HPRU in Modelling and Health Economics); Sergei Brin foundation Grant paid to institution (Mapping the Risk of International Infectious Disease Spread II); USAID Grant paid to institution (Mapping the Risk of International Infectious Disease Spread); Academy of Medical Sciences Grant paid to institution (Developing reliable epidemic forecasting using branching processes: Ebola as a case study); Pfizer Money paid to institution. Lecturing on a course on mathematical modelling of infectious disease transmission and vaccination. Peter J White: Medical Research CouncilFunding for the MRC Centre for Global Infectious Disease Analysis; Public Health England Salary; National Institute for Health Research, Funding for the Health Protection Research Unit in Modelling and Health Economics (2020–2025), Funding for the Health Protection, Research Unit in Modelling Methodology (2014–2020), Funding for work on tuberculosis; Pfizer Payment for teaching of mathematical modelling of infectious disease transmission and vaccination. Robert Aldridge: The research costs for the study have been supported by the MRC Grant Ref: MC_PC 19070 awarded to UCL on 30 March 2020 and MRC Grant Ref: MR/V028375/1 awarded on 17 August 2020. The study also received $15,000 of Facebook advertising credit to support a pilot social media recruitment campaign on 18th August 2020. This study was supported by the Wellcome Trust through a Wellcome Clinical Research Career Development Fellowship to RWA [206602]. Thibaut Jombart: SPI-M I have been a member of SPI-M in 2021 as an infectious disease modeller advising the response to COVID-19 in the UK. No funding received; RECON President of the R Epidemics Consortium, a non-profit organization developing free tools for outbreak analytics. All other authors declare no conflict of interests.

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