PhyloView: A System to Visualize the Ecology of Infectious Diseases Using Phylogenetic Data
PhyloView: A System to Visualize the Ecology of Infectious Diseases Using Phylogenetic Data
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DOI：
10.1109/mdm55031.2022.00051
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
M. T. Le;D. Attaway;T. Anderson;H. Kavak;A. Roess;Andreas Züfle
中科院分区：
文献类型：
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作者：
M. T. Le;D. Attaway;T. Anderson;H. Kavak;A. Roess;Andreas Züfle
自2019冠状病毒病大流行以来，数以百万计的冠状病毒序列已迅速储存在公共信息库中。这些序列主要用于监测病毒的进化和传播。此外，这些数据可以与时空信息相结合，并在空间和时间上进行映射，以进一步了解传输动态。例如，澳大利亚的第一批COVID-19病例与中国武汉的主要菌株有遗传关系，并通过国际旅行传播。这些数据目前可通过共享禽流感数据全球倡议（GISAID）获得，但对于数据科学家来说，分析这种多维数据通常仍然是一种未开发的资源。因此，在本研究中，我们展示了一个名为Phyloview的系统，这是一个高度交互式的视觉环境，可用于研究COVID-19随时间（从-到）的时空演变，并以美国路易斯安那州为例进行了研究。PhyloView（由ArcGIsInsights提供支持）促进了系统发育数据的不同维度的可视化和探索，并且可以与其他类型的时空数据分层，以便进一步研究。我们的系统有可能作为一个模型被卫生官员共享，他们可以通过GISAID访问相关数据，对其进行可视化和分析。这些数据对于更好地理解、预测和应对传染病至关重要。
Since the onset of the COVID-19 pandemic, mil-lions of coronavirus sequences have been rapidly deposited in publicly available repositories. The sequences have been used primarily to monitor the evolution and transmission of the virus. In addition, the data can be combined with spatiotemporal information and mapped over space and time to understand transmission dynamics further. For example, the first COVID-19 cases in Australia were genetically related to the dominant strain in Wuhan, China, and spread via international travel. These data are currently available through the Global Initiative on Sharing Avian Influenza Data (GISAID) yet generally remains an untapped resource for data scientists to analyze such multi-dimensional data. Therefore, in this study, we demonstrate a system named Phyloview, a highly interactive visual environment that can be used to examine the spatiotemporal evolution of COVID-19 (from-to) over time using the case study of Louisiana, USA. PhyloView (powered by ArcGIsInsights) facilitates the visualization and exploration of the different dimensions of the phylogenetic data and can be layered with other types of spatiotemporal data for further investigation. Our system has the potential to be shared as a model to be used by health officials that can access relevant data through GISAID, visualize, and analyze it. Such data is essential for a better understanding, predicting, and responding to infectious diseases.