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

IF 3 3区医学 Q2 INFECTIOUS DISEASES

Epidemics Pub Date : 2023-09-01 DOI:10.1016/j.epidem.2023.100713

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

{"title":"Bayesian reconstruction of SARS-CoV-2 transmissions highlights substantial proportion of negative serial intervals","authors":"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","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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).</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S175543652300049X","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFECTIOUS DISEASES","Score":null,"Total":0}

引用次数: 1

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.

Abstract Image

查看原文本刊更多论文

严重急性呼吸系统综合征冠状病毒2型传播的贝叶斯重建突出了负序列间隔的很大比例。

背景：序列间隔是一个关键的流行病学指标，它量化了感染者-感染者对出现症状之间的时间。它表明新一代病例出现的速度有多快，从而说明流行病的速度。估计序列间隔需要识别成对的感染者和被感染者。然而，大多数研究都没有评估病例之间的传播方向，并假设感染的顺序——以及传播的顺序——严格遵循症状出现的顺序，从而将连续的时间间隔强加为阳性。由于严重急性呼吸系统综合征冠状病毒2型的潜伏期长且高度可变，这可能并不总是正确的（即感染者可能在感染者之前出现症状），并且可能出现阴性序列间隔。本研究旨在估计不同严重急性呼吸系统综合征冠状病毒2型变异株的序列间隔，同时考虑负序列间隔。方法：该分析纳入了2020年9月至2022年8月英格兰和威尔士2 579户家庭中5 842名确诊感染严重急性呼吸系统综合征冠状病毒2型的有症状个体。我们使用贝叶斯框架，通过探索与观察到的症状日期兼容的所有传播树，基于与文献中报道的估计值兼容的广泛潜伏期和世代时间分布，来推断谁感染了谁。序列间隔是从重建的传输对中导出的，并按变量分层。结果：我们估计，在所有VOC中，22%（95%可信区间（CrI）8-32%）的序列区间值为阴性。奥密克戎BA5的平均序列间隔最短（2.02天，1.26-2.84），阿尔法最长（3.37天，2.52-4.04）。结论：本研究强调了严重急性呼吸系统综合征冠状病毒2型变异株中负序列间隔的比例很大。由于序列间隔被广泛用于估计传播性和预测病例，这些结果可能对疫情控制具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Epidemics INFECTIOUS DISEASES-

CiteScore

6.00

自引率

7.90%

发文量

审稿时长

140 days

期刊介绍： Epidemics publishes papers on infectious disease dynamics in the broadest sense. Its scope covers both within-host dynamics of infectious agents and dynamics at the population level, particularly the interaction between the two. Areas of emphasis include: spread, transmission, persistence, implications and population dynamics of infectious diseases; population and public health as well as policy aspects of control and prevention; dynamics at the individual level; interaction with the environment, ecology and evolution of infectious diseases, as well as population genetics of infectious agents.