TY - JOUR AU - Nagar, Ruchit AU - Yuan, Qingyu AU - Freifeld, Clark C AU - Santillana, Mauricio AU - Nojima, Aaron AU - Chunara, Rumi AU - Brownstein，约翰年代PY - 2014 DA - 2014/10/20 TI -纽约的案例研究,2012 - 2013年流感季节日常地理编码Twitter数据从时间和时空角度乔- J地中海互联网Res SP - e236六世- 16 - 10 KW -流感KW - Twitter KW -纽约KW -时空KW -谷歌流感趋势KW - infodemiology KW - mHealth KW -社会媒体,自然语言处理KW -医学信息学AB -背景:Twitter已经显示出在多个国家和不同地理范围内每周预测流感病例的一些有用性。最近，Broniatowski和他的同事提出了Twitter在纽约市的城市层面上的相关性。在这里，我们希望通过从时间和时空角度分析每日Twitter数据来深入研究纽约市的案例。此外，通过对所有推文进行手动编码，我们希望获得定性的见解，这有助于指导未来的自动搜索。目的:本研究的目的首先是验证2012-2013年纽约市流感季节期间流感样疾病急诊科(ILI-ED)访问的每日Twitter数据与其他现有数据集(谷歌搜索查询或GSQ)的时间预测强度，其次，检查地理编码推文的空间分布和传播作为潜在病例的代理。方法:从Twitter Streaming API中，在纽约市地区收集了2972条匹配关键词“流感”、“流感”、“感冒”和“高烧”的推文。这些推文根据Lamb等人开发的方案进行分类。新的第四个类别被添加为评估者对受试者生病概率的猜测，以说明对陈述有效性的信心强度。 Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. Results: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay’s Center and the Atlantic Avenue Terminal. Conclusions: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter’s strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable. SN - 1438-8871 UR - //www.mybigtv.com/2014/10/e236/ UR - https://doi.org/10.2196/jmir.3416 UR - http://www.ncbi.nlm.nih.gov/pubmed/25331122 DO - 10.2196/jmir.3416 ID - info:doi/10.2196/jmir.3416 ER -