Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada.
Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, University of Melbourne at the Peter Doherty Institute for Infection & Immunity, Melbourne, VIC, Australia.
Nat Commun. 2023 Aug 10;14(1):4830. doi: 10.1038/s41467-023-40544-y.
Serial intervals - the time between symptom onset in infector and infectee - are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals' exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2-3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.
序列间隔 - 感染者和被感染者症状出现之间的时间 - 是传染病控制的基本数量。然而，它们的估计需要了解个人的接触情况，通常通过资源密集型的接触者追踪工作来获得。我们引入了一种使用病毒序列的替代框架来告知谁感染了谁，并据此估计序列间隔。我们将我们的技术应用于澳大利亚维多利亚州 COVID-19 前两波病例群中的 SARS-CoV-2 序列。我们发现，尽管不需要了解谁感染了谁，并且依赖于不完全采样的数据，但我们的方法提供了高分辨率、集群特异性的序列间隔估计，与从接触数据中获得的估计相当。与已发表的序列间隔相比，集群特异性序列间隔可以将有效繁殖数的估计值变化 2-3 倍。我们发现，在学校和肉类加工/包装厂等环境中的序列间隔估计值短于医疗保健设施中的序列间隔估计值。
