MRC Centre for Global Infectious Disease Analysis and NIHR Health Protection Research Unit in Modelling and Health Economics, 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.
Centre for Public Health Data Science, Institute of Health Informatics, University College London, London, UK.
Epidemics. 2023 Sep;44:100713. doi: 10.1016/j.epidem.2023.100713. Epub 2023 Aug 7.
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.
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.
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).
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.
潜伏期是一种关键的流行病学度量，用于量化感染者-被感染者之间症状出现的时间间隔。它表明新病例出现的速度有多快，从而反映出疫情的传播速度。估计潜伏期需要确定感染者和被感染者对。然而，大多数研究都未能评估病例之间的传播方向，并假设感染的顺序——因此传播的顺序——严格遵循症状发作的顺序，从而使潜伏期呈阳性。由于 SARS-CoV-2 的潜伏期长且高度可变，情况并非总是如此（即被感染者可能在其感染者之前出现症状），并且可能会出现负潜伏期。本研究旨在估计不同 SARS-CoV-2 变体的潜伏期，同时考虑到负潜伏期。
本分析包括 2020 年 9 月至 2022 年 8 月期间在英格兰和威尔士的 2579 户家庭中，5842 名有症状的确诊 SARS-CoV-2 感染者。我们使用贝叶斯框架通过探索与观察到的症状日期一致的所有传播树来推断谁感染了谁，这些传播树基于与文献中报告的估计相匹配的广泛潜伏期和代时分布。潜伏期是从重建的传播对中得出的，按变体分层。
我们估计，在所有 VOC 中，有 22%（95%可信区间（CrI）为 8-32%）的潜伏期值为负。Omicron BA5 的平均潜伏期最短（2.02 天，1.26-2.84），Alpha 的最长（3.37 天，2.52-4.04）。
本研究强调了 SARS-CoV-2 变体中存在大量负潜伏期。由于潜伏期广泛用于估计传染性和预测病例，因此这些结果可能对疫情控制具有关键影响。
