MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
Lancet. 2022 Nov;400 Suppl 1:S40. doi: 10.1016/S0140-6736(22)02250-4. Epub 2022 Nov 24.
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.
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.
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.
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.
UK Medical Research Council and Wellcome Trust.
序列间隔是量化感染者和感染者症状出现之间时间的关键流行病学度量。这一措施有助于调查病例之间的流行病学联系，是用于估计传染性并为控制策略提供信息的传播模型中的一个重要参数。在 SARS-CoV-2 大流行期间，多种关注变异株（VOC）的出现导致 COVID-19 的序列间隔可能发生变化的不确定性增加。我们使用病毒观察研究收集的数据来估计多种 VOC 的家庭序列间隔。这项在线、前瞻性的社区队列研究自 2020 年 6 月中旬以来一直跟踪英格兰和威尔士的整个家庭。
本分析包括 2020 年 9 月 1 日至 2022 年 8 月 10 日期间，来自 2579 个家庭的 5842 名有症状的 SARS-CoV-2 感染确诊者。SARS-CoV-2 变异体的指定是基于国家监测数据，按日期和地理区域报告变异体的流行率。我们使用贝叶斯框架通过探索所有与观察到的症状日期一致的传播树来推断谁感染了谁，同时考虑到潜伏期和生成时间分布的假设，使用 R 包 outbreaker2。
我们根据 VOC 描述了 COVID-19 的序列间隔。奥密克戎 BA5 的平均序列间隔最短（2.02 天；95%可信区间 [CrI] 1.26-2.84），阿尔法的最长（3.37 天；2.52-4.04）。阿尔法（野生型）之前的平均序列间隔为 2.29 天（95% CrI 1.39-2.94），德尔塔为 3.11 天（2.28-3.90），奥密克戎 BA1 为 2.72 天（2.01-3.47），奥密克戎 BA2 为 2.67 天（1.90-3.46）。我们估计，所有变体的序列间隔值中有 17%（95% CrI 5-26）为负值。
大多数从发病时间序列估计繁殖数的方法在构建时不允许序列间隔为负。需要进一步研究来扩展这些方法，并评估不考虑负序列间隔引入的偏差。据我们所知，这是第一项使用贝叶斯框架从数千例确诊家庭病例中估计所有主要 SARS-CoV-2 VOC 的序列间隔的研究。
英国医学研究理事会和惠康信托基金会。
