Dion M, AbdelMalik P, Mawudeku A
Schulich School of Family Medicine and Dentistry, University of Western Ontario, London, ON.
Centre for Emergency Preparedness and Response, Public Health Agency of Canada, Ottawa, ON.
Can Commun Dis Rep. 2015 Sep 3;41(9):209-214. doi: 10.14745/ccdr.v41i09a02.
Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events.
To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN.
Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN's capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development.
The GPHIN's early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN's capability for timely detection and early warning.
全球化以及新发传染病迅速传播的可能性增加了持续监测和早期发现的必要性。全球公共卫生情报网络（GPHIN）的建立是为了提高态势感知能力以及早期发现新发公共卫生事件的能力。
描述GPHIN如何将大数据用作全球传染病暴发的有效早期检测技术，并确定GPHIN未来的潜在发展方向。
GPHIN每天分析全球9种语言的20000多篇在线新闻报道（来自30000多个来源）。一个基于网络的程序根据一种算法汇总数据，该算法提供新发公共卫生事件的潜在信号，然后由一个多语言、多学科团队进行审查。如果识别出潜在风险，就会发出警报。这一过程在严重急性呼吸综合征（SARS）暴发期间被证明是有用的，并且在SARS暴发后不久被一些国家采用，以满足新的《国际卫生条例》的要求，即每个国家都要有早期发现和报告的能力。GPHIN在中国发现了早期的SARS疫情，因首次发出中东呼吸综合征冠状病毒（MERS-CoV）警报而受到赞誉，并在监测西非埃博拉疫情中发挥了重要作用。鉴于社交媒体等其他大数据来源及其在算法开发方面的分析能力，正在考虑未来的发展，以提高GPHIN的能力。
GPHIN对大数据的早期采用提高了全球检测国际传染病暴发和其他公共卫生事件的能力。整合更多大数据来源以及提高分析能力可以进一步加强GPHIN及时发现和早期预警的能力。
