School of Public Health and Community Medicine, University of New South Wales, Australia.
School of Public Health and Community Medicine, University of New South Wales, Australia; College of Public Service and Community Solutions, Arizona State University, Phoenix, USA.
Int J Infect Dis. 2017 Oct;63:77-87. doi: 10.1016/j.ijid.2017.07.020. Epub 2017 Jul 29.
Rapid epidemic detection is an important objective of surveillance to enable timely intervention, but traditional validated surveillance data may not be available in the required timeframe for acute epidemic control. Increasing volumes of data on the Internet have prompted interest in methods that could use unstructured sources to enhance traditional disease surveillance and gain rapid epidemic intelligence. We aimed to summarise Internet-based methods that use freely-accessible, unstructured data for epidemic surveillance and explore their timeliness and accuracy outcomes.
Steps outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist were used to guide a systematic review of research related to the use of informal or unstructured data by Internet-based intelligence methods for surveillance.
We identified 84 articles published between 2006-2016 relating to Internet-based public health surveillance methods. Studies used search queries, social media posts and approaches derived from existing Internet-based systems for early epidemic alerts and real-time monitoring. Most studies noted improved timeliness compared to official reporting, such as in the 2014 Ebola epidemic where epidemic alerts were generated first from ProMED-mail. Internet-based methods showed variable correlation strength with official datasets, with some methods showing reasonable accuracy.
The proliferation of publicly available information on the Internet provided a new avenue for epidemic intelligence. Methodologies have been developed to collect Internet data and some systems are already used to enhance the timeliness of traditional surveillance systems. To improve the utility of Internet-based systems, the key attributes of timeliness and data accuracy should be included in future evaluations of surveillance systems.
快速发现疫情是监测的一个重要目标，可实现及时干预，但传统的经过验证的监测数据可能无法在急性疫情控制所需的时间范围内提供。互联网上不断增加的数据引发了人们对可能利用非结构化来源来增强传统疾病监测并快速获取疫情情报的方法的兴趣。我们旨在总结使用互联网上免费获取的非结构化数据进行疫情监测的方法，并探讨其及时性和准确性结果。
按照系统评价和荟萃分析的首选报告项目（PRISMA）清单中的步骤，对使用互联网智能方法基于非正规或非结构化数据进行监测的相关研究进行了系统评价。
我们确定了 2006 年至 2016 年间发表的 84 篇与互联网公共卫生监测方法相关的文章。这些研究使用搜索查询、社交媒体帖子以及从现有的互联网系统中衍生的方法，以实现早期疫情警报和实时监测。与官方报告相比，大多数研究都注意到了及时性的提高，例如在 2014 年埃博拉疫情中，ProMED-mail 首先发出了疫情警报。互联网方法与官方数据集的相关性强度各不相同，一些方法的准确性也相当合理。
互联网上公开信息的大量涌现为疫情情报提供了新途径。已经开发了收集互联网数据的方法，并且一些系统已经被用于增强传统监测系统的及时性。为了提高基于互联网的系统的实用性，应该在未来的监测系统评估中包含及时性和数据准确性等关键属性。
