Eysenbach Gunther
Centre for Global eHealth Innovation, University Health Network, Toronto M5G2C4 and Department of Health Policy, Management and Evaluation, University of Toronto, Canada.
AMIA Annu Symp Proc. 2006;2006:244-8.
Syndromic surveillance uses health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response.
While most syndromic surveillance systems rely on data from clinical encounters with health professionals, I started to explore in 2004 whether analysis of trends in Internet searches can be useful to predict outbreaks such as influenza epidemics and prospectively gathered data on Internet search trends for this purpose.
There is an excellent correlation between the number of clicks on a keyword-triggered link in Google with epidemiological data from the flu season 2004/2005 in Canada (Pearson correlation coefficient of current week clicks with the following week influenza cases r=.91). The "Google ad sentinel method" proved to be more timely, more accurate and - with a total cost of Can$365.64 for the entire flu-season - considerably cheaper than the traditional method of reports on influenza-like illnesses observed in clinics by sentinel physicians.
Systematically collecting and analyzing health information demand data from the Internet has considerable potential to be used for syndromic surveillance. Tracking web searches on the Internet has the potential to predict population-based events relevant for public health purposes, such as real outbreaks, but may also be confounded by "epidemics of fear". Data from such "infodemiology studies" should also include longitudinal data on health information supply.
症候群监测利用在诊断之前的与健康相关的数据，这些数据显示出发生病例或疫情的足够可能性，从而有必要采取进一步的公共卫生应对措施。
虽然大多数症候群监测系统依赖于与卫生专业人员临床接触的数据，但我在2004年开始探索分析互联网搜索趋势是否有助于预测诸如流感疫情等疫情，并为此前瞻性地收集互联网搜索趋势数据。
谷歌上关键词触发链接的点击次数与加拿大2004/2005流感季节的流行病学数据之间存在极佳的相关性（当前周点击次数与下周流感病例的皮尔逊相关系数r = 0.91）。“谷歌广告哨兵法”被证明更及时、更准确，而且整个流感季节的总成本为365.64加元，比传统的由哨兵医生在诊所观察流感样疾病报告的方法便宜得多。
系统地收集和分析来自互联网的健康信息需求数据在用于症候群监测方面具有相当大的潜力。追踪互联网上的网络搜索有潜力预测与公共卫生目的相关的基于人群的事件，如实际疫情，但也可能受到“恐惧疫情”的混淆。来自此类“信息流行病学研究”的数据还应包括健康信息供应的纵向数据。
