使用贝叶斯变化点分析评估谷歌、Twitter和维基百科作为流感监测工具卡塔尔世界杯8强波胆分析的作用比较分析%A Sharpe,J Danielle %A Hopkins,Richard S %A Cook,Robert L %A Striley,Catherine W %+ Rollins公共卫生学院，美国埃默里大学流行病学系，美国亚特兰大克利夫顿路1518号，30322，美国，1 912 399 2811,danielle.sharpe@emory.edu %K互联网%K社交媒体%K贝叶斯理论%K公共卫生监测%K流感，人类%D 2016 %7 20.10.2016 %9原文%J JMIR公共卫生监测%G英文%X背景:传统的流感监测依赖卫生保健提供者报告的流感样疾病(ILI)综合征。它主要捕获那些寻求医疗保健的人，而忽略了那些不寻求医疗保健的人。最近，由于越来越多的人在寻求医疗护理之前搜索、发布和tweet自己的疾病，人们研究了基于web的数据源，以便将其应用于公共卫生监测。现有研究显示，利用谷歌、Twitter和Wikipedia的数据补充传统的ILI监测具有一定的前景。然而，过去的研究对这些基于web的资源进行了单独或双重评估，而没有对所有3种资源进行比较，因此了解哪一种基于web的资源表现最好，以便被认为是对传统方法的补充，将是有益的。目的:本研究的目的是比较分析谷歌，Twitter和Wikipedia，通过检查最符合疾病控制和预防中心(CDC) ILI数据。假设维基百科最符合CDC ILI数据，因为之前的研究发现，与谷歌和Twitter相比，维基百科受媒体高覆盖率的影响最小。方法:从疾病预防控制中心、谷歌流感趋势、HealthTweets和维基百科收集2012-2015年流感季节的公开、未识别的数据。 Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC’s change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package “bcp” version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92% was found for Google, whereas the PPV for Google was 85%. A low sensitivity of 50% was calculated for Twitter; a low PPV of 43% was found for Twitter also. Wikipedia had the lowest sensitivity of 33% and lowest PPV of 40%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. %M 27765731 %R 10.2196/publichealth.5901 %U http://publichealth.www.mybigtv.com/2016/2/e161/ %U https://doi.org/10.2196/publichealth.5901 %U http://www.ncbi.nlm.nih.gov/pubmed/27765731