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关于疾病监测的搜索查询和社交媒体的范围审查:创新年表

Scoping review on search queries and social media for disease surveillance: a chronology of innovation.

作者信息

Bernardo Theresa Marie, Rajic Andrijana, Young Ian, Robiadek Katie, Pham Mai T, Funk Julie A

机构信息

College of Veterinary Medicine, Michigan State University, East Lansing, MI, United States.

出版信息

J Med Internet Res. 2013 Jul 18;15(7):e147. doi: 10.2196/jmir.2740.

Abstract

BACKGROUND

The threat of a global pandemic posed by outbreaks of influenza H5N1 (1997) and Severe Acute Respiratory Syndrome (SARS, 2002), both diseases of zoonotic origin, provoked interest in improving early warning systems and reinforced the need for combining data from different sources. It led to the use of search query data from search engines such as Google and Yahoo! as an indicator of when and where influenza was occurring. This methodology has subsequently been extended to other diseases and has led to experimentation with new types of social media for disease surveillance.

OBJECTIVE

The objective of this scoping review was to formally assess the current state of knowledge regarding the use of search queries and social media for disease surveillance in order to inform future work on early detection and more effective mitigation of the effects of foodborne illness.

METHODS

Structured scoping review methods were used to identify, characterize, and evaluate all published primary research, expert review, and commentary articles regarding the use of social media in surveillance of infectious diseases from 2002-2011.

RESULTS

Thirty-two primary research articles and 19 reviews and case studies were identified as relevant. Most relevant citations were peer-reviewed journal articles (29/32, 91%) published in 2010-11 (28/32, 88%) and reported use of a Google program for surveillance of influenza. Only four primary research articles investigated social media in the context of foodborne disease or gastroenteritis. Most authors (21/32 articles, 66%) reported that social media-based surveillance had comparable performance when compared to an existing surveillance program. The most commonly reported strengths of social media surveillance programs included their effectiveness (21/32, 66%) and rapid detection of disease (21/32, 66%). The most commonly reported weaknesses were the potential for false positive (16/32, 50%) and false negative (11/32, 34%) results. Most authors (24/32, 75%) recommended that social media programs should primarily be used to support existing surveillance programs.

CONCLUSIONS

The use of search queries and social media for disease surveillance are relatively recent phenomena (first reported in 2006). Both the tools themselves and the methodologies for exploiting them are evolving over time. While their accuracy, speed, and cost compare favorably with existing surveillance systems, the primary challenge is to refine the data signal by reducing surrounding noise. Further developments in digital disease surveillance have the potential to improve sensitivity and specificity, passively through advances in machine learning and actively through engagement of users. Adoption, even as supporting systems for existing surveillance, will entail a high level of familiarity with the tools and collaboration across jurisdictions.

摘要

背景

H5N1流感(1997年)和严重急性呼吸综合征(SARS,2002年)疫情爆发构成全球大流行威胁,这两种人畜共患病引发了人们对改进早期预警系统的兴趣,并强化了整合不同来源数据的必要性。这促使人们将谷歌和雅虎等搜索引擎的搜索查询数据用作流感发生时间和地点的指标。该方法随后被扩展到其他疾病,并引发了利用新型社交媒体进行疾病监测的试验。

目的

本范围综述的目的是正式评估关于利用搜索查询和社交媒体进行疾病监测的现有知识状况,以便为未来关于食源性疾病早期检测和更有效减轻其影响的工作提供信息。

方法

采用结构化范围综述方法,识别、描述和评估2002年至2011年期间所有已发表的关于利用社交媒体进行传染病监测的原始研究、专家评论和评论文章。

结果

确定了32篇原始研究文章以及19篇综述和案例研究为相关文献。大多数相关引用是2010 - 2011年发表的同行评审期刊文章(29/32,91%)(28/32,88%),并报告了使用谷歌程序进行流感监测。只有4篇原始研究文章在食源性疾病或肠胃炎背景下研究了社交媒体。大多数作者(21/32篇文章,66%)报告称,与现有监测项目相比,基于社交媒体的监测表现相当。社交媒体监测项目最常被提及的优势包括其有效性(21/32,66%)和疾病快速检测能力(21/32,66%)。最常被提及的劣势是可能出现假阳性(16/32,50%)和假阴性(11/32,34%)结果。大多数作者(24/32,75%)建议社交媒体项目应主要用于支持现有监测项目。

结论

利用搜索查询和社交媒体进行疾病监测是相对较新的现象(首次报道于2006年)。工具本身及其利用方法都在随着时间不断发展。虽然它们在准确性、速度和成本方面与现有监测系统相比具有优势,但主要挑战是通过减少周围噪声来优化数据信号。数字疾病监测的进一步发展有可能通过机器学习的进步被动地提高敏感性和特异性,并通过用户参与主动地提高敏感性和特异性。即使作为现有监测的支持系统被采用,也需要对这些工具高度熟悉并跨辖区进行协作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86e5/3785982/fc07eca999ed/jmir_v15i7e147_fig1.jpg

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