Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data
Graph-Based Reconstruction and Analysis of Disease Transmission Networks Using Viral Genomic Data
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DOI：
10.1089/cmb.2022.0373
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发表时间：
2023-06-22
影响因子：
1.7
通讯作者：
Vikalo，Haris
中科院分区：
文献类型：
--
作者：
Ke，Ziqi;Vikalo，Haris
了解病毒性疾病传播模式有助于制定公共卫生政策，并有助于控制和结束疾病爆发。研究疾病传播动态的经典方法依赖于流行病学数据，例如样本采集时间和暴露间隔的持续时间，由于此类数据的信息有限，很难提供所需的见解。可以从揭示患者样本中病毒基因组之间遗传距离的测序数据中获得疾病传播的更精确特征。事实上，宿主中存在的病毒株之间的遗传距离包含有关传播历史的宝贵信息，从而激发了依赖基因组数据来重建定向疾病传播网络、检测传播簇并识别重要网络节点（例如超级传播者）的方法的设计。在本文中，我们提出了一种新颖的端到端框架，用于利用病毒基因组（测序）数据分析病毒传播。所提出的框架根据感染宿主的重建病毒株将受感染的宿主分组为传播簇；使用地球移动者距离计算一对主机之间的遗传距离，并进一步用于推断主机之间的传输方向。为了量化传输网络中主机的重要性，通过图卷积自动编码器计算重要性得分。病毒传播网络由有向最小生成树表示，利用埃德蒙德算法进行修改，以纳入对主机重要性分数的约束。在半实验和实验数据的多项实验中，所提出的框架优于最先进的病毒传播动力学分析技术。
Understanding the patterns of viral disease transmissions helps establish public health policies and aids in controlling and ending a disease outbreak. Classical methods for studying disease transmission dynamics that rely on epidemiological data, such as times of sample collection and duration of exposure intervals, struggle to provide desired insight due to limited informativeness of such data. A more precise characterization of disease transmissions may be acquired from sequencing data that reveal genetic distance between viral genomes in patient samples. Indeed, genetic distance between viral strains present in hosts contains valuable information about transmission history, thus motivating the design of methods that rely on genomic data to reconstruct a directed disease transmission network, detect transmission clusters, and identify significant network nodes (e.g., super-spreaders). In this article, we present a novel end-to-end framework for the analysis of viral transmissions utilizing viral genomic (sequencing) data. The proposed framework groups infected hosts into transmission clusters based on the reconstructed viral strains infecting them; the genetic distance between a pair of hosts is calculated using Earth Mover's Distance, and further used to infer transmission direction between the hosts. To quantify the significance of a host in the transmission network, the importance score is calculated by a graph convolutional autoencoder. The viral transmission network is represented by a directed minimum spanning tree utilizing the Edmond's algorithm modified to incorporate constraints on the importance scores of the hosts. The proposed framework outperforms state-of-the-art techniques for the analysis of viral transmission dynamics in several experiments on semiexperimental as well as experimental data.