Simonsen Lone, Gog Julia R, Olson Don, Viboud Cécile
Division of International Epidemiology and Population Studies, Fogarty International Center, US National Institutes of Health, Bethesda, Maryland.
Department of Public Health, University of Copenhagen, Denmark.
J Infect Dis. 2016 Dec 1;214(suppl_4):S380-S385. doi: 10.1093/infdis/jiw376.
While big data have proven immensely useful in fields such as marketing and earth sciences, public health is still relying on more traditional surveillance systems and awaiting the fruits of a big data revolution. A new generation of big data surveillance systems is needed to achieve rapid, flexible, and local tracking of infectious diseases, especially for emerging pathogens. In this opinion piece, we reflect on the long and distinguished history of disease surveillance and discuss recent developments related to use of big data. We start with a brief review of traditional systems relying on clinical and laboratory reports. We then examine how large-volume medical claims data can, with great spatiotemporal resolution, help elucidate local disease patterns. Finally, we review efforts to develop surveillance systems based on digital and social data streams, including the recent rise and fall of Google Flu Trends. We conclude by advocating for increased use of hybrid systems combining information from traditional surveillance and big data sources, which seems the most promising option moving forward. Throughout the article, we use influenza as an exemplar of an emerging and reemerging infection which has traditionally been considered a model system for surveillance and modeling.
虽然大数据在营销和地球科学等领域已被证明非常有用，但公共卫生仍依赖于更传统的监测系统，并期待大数据革命的成果。需要新一代的大数据监测系统来实现对传染病的快速、灵活和本地化追踪，特别是对于新出现的病原体。在这篇观点文章中，我们回顾了疾病监测悠久而卓越的历史，并讨论了与大数据使用相关的最新进展。我们首先简要回顾依赖临床和实验室报告的传统系统。然后我们研究大量医疗索赔数据如何以高时空分辨率帮助阐明局部疾病模式。最后，我们回顾基于数字和社会数据流开发监测系统的努力，包括谷歌流感趋势最近的兴衰。我们主张更多地使用结合传统监测和大数据源信息的混合系统，这似乎是未来最有前景的选择，以此作为结论。在整篇文章中，我们将流感作为一种新出现和再次出现的感染的范例，传统上它一直被视为监测和建模的模型系统。
