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

Cyril Geismar; Vincent Nguyen; Ellen Fragaszy; Madhumita Shrotri; Annalan M.D. Navaratnam; Sarah Beale; Thomas E. Byrne; Wing Lam Erica Fong; Alexei Yavlinsky; Jana Kovar; Susan Hoskins; Isobel Braithwaite; Robert W. Aldridge; Andrew C. Hayward; Peter J. White; Thibaut Jombart; Anne Cori

doi:10.1016/j.epidem.2023.100713

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

Cyril Geismar^*, Vincent Nguyen, Ellen Fragaszy, Madhumita Shrotri, Annalan M.D. Navaratnam, Sarah Beale, Thomas E. Byrne, Wing Lam Erica Fong, Alexei Yavlinsky, Jana Kovar, Susan Hoskins, Isobel Braithwaite, Robert W. Aldridge, Andrew C. Hayward, Peter J. White, Thibaut Jombart, Anne Cori

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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.

Original language	English
Article number	100713
Journal	Epidemics
Volume	44
DOIs	https://doi.org/10.1016/j.epidem.2023.100713
Publication status	Published - Sept 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2023 The Authors

Keywords

Bayesian modelling
Infectious Disease Epidemiology
Sars-cov-2
Serial interval
Variants of concern

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.epidem.2023.100713

Cite this

Geismar, C., Nguyen, V., Fragaszy, E., Shrotri, M., Navaratnam, A. M. D., Beale, S., Byrne, T. E., Fong, W. L. E., Yavlinsky, A., Kovar, J., Hoskins, S., Braithwaite, I., Aldridge, R. W., Hayward, A. C., White, P. J., Jombart, T., & Cori, A. (2023). Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals. Epidemics, 44, Article 100713. https://doi.org/10.1016/j.epidem.2023.100713

@article{59ebb841b8134fcba5e515b50333f435,

title = "Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals",

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",

author = "Cyril Geismar and Vincent Nguyen and Ellen Fragaszy and Madhumita Shrotri and Navaratnam, {Annalan M.D.} and Sarah Beale and Byrne, {Thomas E.} and Fong, {Wing Lam Erica} and Alexei Yavlinsky and Jana Kovar and Susan Hoskins and Isobel Braithwaite and Aldridge, {Robert W.} and Hayward, {Andrew C.} and White, {Peter J.} and Thibaut Jombart and Anne Cori",

note = "Publisher Copyright: {\textcopyright} 2023 The Authors",

year = "2023",

month = sep,

doi = "10.1016/j.epidem.2023.100713",

language = "English",

volume = "44",

journal = "Epidemics",

issn = "1755-4365",

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Geismar, C, Nguyen, V, Fragaszy, E, Shrotri, M, Navaratnam, AMD, Beale, S, Byrne, TE, Fong, WLE, Yavlinsky, A, Kovar, J, Hoskins, S, Braithwaite, I, Aldridge, RW, Hayward, AC, White, PJ, Jombart, T & Cori, A 2023, 'Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals', Epidemics, vol. 44, 100713. https://doi.org/10.1016/j.epidem.2023.100713

TY - JOUR

T1 - Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals

AU - Geismar, Cyril

AU - Nguyen, Vincent

AU - Fragaszy, Ellen

AU - Shrotri, Madhumita

AU - Navaratnam, Annalan M.D.

AU - Beale, Sarah

AU - Byrne, Thomas E.

AU - Fong, Wing Lam Erica

AU - Yavlinsky, Alexei

AU - Kovar, Jana

AU - Hoskins, Susan

AU - Braithwaite, Isobel

AU - Aldridge, Robert W.

AU - Hayward, Andrew C.

AU - White, Peter J.

AU - Jombart, Thibaut

AU - Cori, Anne

PY - 2023/9

Y1 - 2023/9

N2 - 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.

AB - 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.

KW - Bayesian modelling

KW - Infectious Disease Epidemiology

KW - Sars-cov-2

KW - Serial interval

KW - Variants of concern

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U2 - 10.1016/j.epidem.2023.100713

DO - 10.1016/j.epidem.2023.100713

M3 - Article

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AN - SCOPUS:85170111264

SN - 1755-4365

VL - 44

JO - Epidemics

JF - Epidemics

M1 - 100713

ER -

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

Abstract

Bibliographical note

Keywords

UN SDGs

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