At the end of 2020, several new variants of SARS-CoV-2—designated variants of concern—were detected and quickly suspected to be associated with a higher transmissibility and possible escape of vaccine-induced immunity. In Belgium, this discovery has motivated the initiation of a more ambitious genomic surveillance program, which is drastically increasing the number of SARS-CoV-2 genomes to analyse for monitoring the circulation of viral lineages and variants of concern. In order to efficiently analyse the massive collection of genomic data that are the result of such increased sequencing efforts, streamlined analytical strategies are crucial. In this study, we illustrate how to efficiently map the spatio-temporal dispersal of target mutations at a regional level. As a proof of concept, we focus on the Belgian province of Liège that has been consistently sampled throughout 2020, but was also one of the main epicenters of the second European epidemic wave. Specifically, we employ a recently developed phylogeographic workflow to infer the regional dispersal history of viral lineages associated with three specific mutations on the spike protein (S98F, A222V and S477N) and to quantify their relative importance through time. Our analytical pipeline enables analysing large data sets and has the potential to be quickly applied and updated to track target mutations in space and time throughout the course of an epidemic.

到 2020 年底，检测到 SARS-CoV-2 的几种新变种（指定为关注变种），并很快怀疑它们与较高的传染性和疫苗诱导的免疫力可能逃逸有关。在比利时，这一发现推动了一项更雄心勃勃的基因组监测计划的启动，该计划正在大幅增加 SARS-CoV-2 基因组的数量，用于分析监测病毒谱系和相关变体的循环。为了有效地分析因测序工作增加而产生的大量基因组数据，简化的分析策略至关重要。在这项研究中，我们说明了如何在区域级别有效地绘制目标突变的时空分布图。作为概念证明，我们专注于比利时列日省，该省在整个 2020 年一直进行采样，但它也是第二次欧洲流行浪潮的主要震中之一。具体来说，我们采用最近开发的系统地理学工作流程来推断与刺突蛋白（S98F、A222V 和 S477N）上的三个特定突变相关的病毒谱系的区域传播历史，并量化它们随时间的相对重要性。我们的分析管道能够分析大型数据集，并有可能被快速应用和更新，以在整个流行病过程中在空间和时间上跟踪目标突变。我们采用最近开发的系统地理学工作流程来推断与刺突蛋白（S98F、A222V 和 S477N）上的三个特定突变相关的病毒谱系的区域传播历史，并量化它们随时间的相对重要性。我们的分析管道能够分析大型数据集，并有可能被快速应用和更新，以在整个流行病过程中在空间和时间上跟踪目标突变。我们采用最近开发的系统地理学工作流程来推断与刺突蛋白（S98F、A222V 和 S477N）上的三个特定突变相关的病毒谱系的区域传播历史，并量化它们随时间的相对重要性。我们的分析管道能够分析大型数据集，并有可能被快速应用和更新，以在整个流行病过程中在空间和时间上跟踪目标突变。