The large-scale real-time sequencing of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes has allowed for rapid identification of concerning variants through phylogenetic analysis. However, the nature of phylogenetic reconstruction is typically static, in that the relationships between taxonomic units, once defined, are not subject to alterations. Furthermore, most phylogenetic methods are intrinsically batch mode in nature, requiring the presence of the entire data set. Finally, the emphasis of phylogenetics is on relating taxonomical units. These characteristics complicate the application of classical phylogenetics methods to represent relationships in molecular data collected from rapidly evolving strains of an etiological agent, such as SARS-CoV-2, since the molecular landscape is updated continuously as samples are collected. In such settings, variant definitions are subject to epistemological constraints and may change as data accumulate. Furthermore, representing within-variant molecular relationships may be as important as representing between variant relationships. This article describes a novel data representation framework called dynamic epidemiological networks (DENs) along with algorithms that underpin its construction to address these issues. The proposed representation is applied to study the molecular development underlying the spread of the COVID-19 (coronavirus disease 2019) pandemic in two countries: Israel and Portugal spanning a 2-year period from February 2020 to April 2022. The results demonstrate how this framework could be used to provide a multiscale representation of the data by capturing molecular relationships between samples as well as those between variants, automatically identifying the emergence of high frequency variants (lineages), including variants of concern such as Alpha and Delta, and tracking their growth. Additionally, we show how analyzing the evolution of the DEN can help identify changes in the viral population that could not be readily inferred from phylogenetic analysis.

中文翻译：

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动态流行病学网络：用于建模和跟踪 SARS-CoV-2 变体的数据表示框架。

严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 基因组的大规模实时测序允许通过系统发育分析快速识别相关变异。然而，系统发育重建的本质通常是静态的，因为分类单位之间的关系一旦定义，就不会发生变化。此外，大多数系统发育方法本质上都是批处理模式，需要整个数据集的存在。最后，系统发育学的重点是关联分类单位。这些特征使经典系统发育学方法的应用复杂化，以表示从快速进化的病原体菌株（如 SARS-CoV-2）收集的分子数据中的关系，因为随着样本的收集，分子景观不断更新。在这种情况下，变体定义受到认识论的约束，并可能随着数据的积累而改变。此外，表示变体内的分子关系可能与表示变体之间的关系一样重要。本文介绍了一种称为动态流行病学网络 (DEN) 的新型数据表示框架，以及支持其构建以解决这些问题的算法。拟议的表示法用于研究 2020 年 2 月至 2022 年 4 月 2 年期间 COVID-19（2019 年冠状病毒病）大流行在以色列和葡萄牙这两个国家传播的潜在分子发展。结果证明了该框架如何用于通过捕获样本之间以及变体之间的分子关系来提供数据的多尺度表示，自动识别高频变体（谱系）的出现，包括关注的变体，例如 Alpha 和Delta，并跟踪他们的成长。此外，我们展示了分析 DEN 的进化如何帮助识别无法从系统发育分析中轻易推断出的病毒种群变化。