利用搜索引擎、SNS、网上购物、问答服务及流感病人的经验进行流感监察
Influenza Surveillance using Search Engine, SNS, On-line Shopping, Q&A Service and Past Flu Patients
摘要：
流感是一种传染病，在世界范围内造成许多人死亡。在流行期间预测流感受害者是临床、医院和社区疫情准备的重要任务。在线用户生成内容（UGC）主要以社交媒体帖子或搜索查询日志的形式出现，通常用于预测对突发和不寻常疫情的反应。然而，大部分研究只依赖教资会作为资源，而没有使用各种教资会。我们的研究旨在解决这些关于流感预测的问题：哪种模型是最好的？什么样的多个UGC组合效果好？每个教资会的性质是什么？我们采用了一些模型，LASSO回归，Huber回归，线性核（SVR）和随机森林的支持向量机回归，以测试日本2015 - 2018年的流感数量预测。为此，我们使用了五种在线数据资源：（1）过去的流感患者，（2）SNS（Twitter），（3）搜索引擎（Yahoo！日本），（4）购物服务（雅虎！购物），和（5）问答服务（雅虎！Chiebukuro）作为每个模型的资源。然后，我们使用最佳模型Huber Regression验证各个资源的贡献，除了一个资源之外的所有资源。最后，我们使用贝叶斯变点方法来确定任何资源上的时间序列的趋势是否反映在流感患者计数的趋势中。实验表明，基于不同数据源的Huber回归模型产生的结果最准确。然后，从变化点分析，我们得到的结果是，搜索查询日志和社交媒体帖子的三年代表这些资源作为一个很好的预测。结论：我们表明，基于各种数据源的Huber回归对离群值有很强的预测能力，适用于流感预测。此外，我们指出了流感预测的每个资源的特征。
英文摘要：
Influenza, an infectious disease, causes many deaths worldwide. Predicting influenza victims during epidemics is an important task for clinical, hospital, and community outbreak preparation. On-line user-generated contents (UGC), primarily in the form of social media posts or search query logs, are generally used for prediction for reaction to sudden and unusual outbreaks. However, most studies rely only on the UGC as their resource and do not use various UGCs. Our study aims to solve these questions about Influenza prediction: Which model is the best? What combination of multiple UGCs works well? What is the nature of each UGC? We adapt some models, LASSO Regression, Huber Regression, Support Vector Machine regression with Linear kernel (SVR) and Random Forest, to test the influenza volume prediction in Japan during 2015 - 2018. For that, we use on-line five data resources: (1) past flu patients, (2) SNS (Twitter), (3) search engines (Yahoo! Japan), (4) shopping services (Yahoo! Shopping), and (5) Q&A services (Yahoo! Chiebukuro) as resources of each model. We then validate respective resources contributions using the best model, Huber Regression, with all resources except one resource. Finally, we use Bayesian change point method for ascertaining whether the trend of time series on any resources is reflected in the trend of flu patient count or not. Our experiments show Huber Regression model based on various data resources produces the most accurate results. Then, from the change point analysis, we get the result that search query logs and social media posts for three years represents these resources as a good predictor. Conclusions: We show that Huber Regression based on various data resources is strong for outliers and is suitable for the flu prediction. Additionally, we indicate the characteristics of each resource for the flu prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
