Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals
Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:44 |
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| Enthalten in: |
Epidemics - 44(2023) vom: 23. Sept., Seite 100713 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Geismar, Cyril [VerfasserIn] |
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| Links: |
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| Themen: |
Bayesian modelling |
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| Anmerkungen: |
Date Completed 08.09.2023 Date Revised 26.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.epidem.2023.100713 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Bayesian modelling | |
| 650 | 4 | |a Infectious Disease Epidemiology | |
| 650 | 4 | |a Sars-cov-2 | |
| 650 | 4 | |a Serial interval | |
| 650 | 4 | |a Variants of concern | |
| 700 | 1 | |a Nguyen, Vincent |e verfasserin |4 aut | |
| 700 | 1 | |a Fragaszy, Ellen |e verfasserin |4 aut | |
| 700 | 1 | |a Shrotri, Madhumita |e verfasserin |4 aut | |
| 700 | 1 | |a Navaratnam, Annalan M D |e verfasserin |4 aut | |
| 700 | 1 | |a Beale, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Byrne, Thomas E |e verfasserin |4 aut | |
| 700 | 1 | |a Fong, Wing Lam Erica |e verfasserin |4 aut | |
| 700 | 1 | |a Yavlinsky, Alexei |e verfasserin |4 aut | |
| 700 | 1 | |a Kovar, Jana |e verfasserin |4 aut | |
| 700 | 1 | |a Hoskins, Susan |e verfasserin |4 aut | |
| 700 | 1 | |a Braithwaite, Isobel |e verfasserin |4 aut | |
| 700 | 1 | |a Aldridge, Robert W |e verfasserin |4 aut | |
| 700 | 1 | |a Hayward, Andrew C |e verfasserin |4 aut | |
| 700 | 1 | |a White, Peter J |e verfasserin |4 aut | |
| 700 | 1 | |a Jombart, Thibaut |e verfasserin |4 aut | |
| 700 | 1 | |a Cori, Anne |e verfasserin |4 aut | |
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