Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
Department of Biosystems Science and Engineering, ETH Zürich, Basel, CH 4058, Switzerland; Swiss Institute of Bioinformatics, Lausanne, CH 1015, Switzerland.
Epidemics. 2022 Jun;39:100576. doi: 10.1016/j.epidem.2022.100576. Epub 2022 May 14.
The SARS-CoV-2 pandemic led to a huge increase in global pathogen genome sequencing efforts, and the resulting data are becoming increasingly important to detect variants of concern, monitor outbreaks, and quantify transmission dynamics. However, this rapid up-scaling in data generation brought with it many IT infrastructure challenges. In this paper, we report about developing an improved system for genomic epidemiology. We (i) highlight key challenges that were exacerbated by the pandemic situation, (ii) provide data infrastructure design principles to address them, and (iii) give an implementation example developed by the Swiss SARS-CoV-2 Sequencing Consortium (S3C) in response to the COVID-19 pandemic. Finally, we discuss remaining challenges to data infrastructure for genomic epidemiology. Improving these infrastructures will help better detect, monitor, and respond to future public health threats.
SARS-CoV-2 大流行导致了病原体基因组测序工作的大幅增加，这些数据对于发现令人关注的变体、监测疫情爆发以及量化传播动态变得越来越重要。然而，这种数据生成的快速扩展带来了许多 IT 基础设施挑战。在本文中，我们报告了一种用于基因组流行病学的改进系统的开发。我们：（i）强调了大流行情况下加剧的关键挑战；（ii）提供了解决这些挑战的数据基础设施设计原则；（iii）给出了瑞士 SARS-CoV-2 测序联盟（S3C）针对 COVID-19 大流行开发的一个实施示例。最后，我们讨论了基因组流行病学的数据基础设施面临的剩余挑战。改进这些基础设施将有助于更好地发现、监测和应对未来的公共卫生威胁。
