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.
翻译:流感是一种传染性疾病,它在全世界造成许多死亡。在流行病期间预测流感受害者是临床、医院和社区疾病爆发准备的重要任务。在线用户生成的内容(UGC),主要以社交媒体站或搜索查询日志的形式,通常用于预测对突发和异常爆发的反应。然而,大多数研究都只依靠UGC作为资源,而不使用各种UGC。我们的研究旨在解决关于Influenza预测的问题:哪个模型是最佳的?多个UGC的组合是什么?每个UGC的性质是什么?我们调整了一些模型,LASOS Regrestition,Huber Regrestition,HUGC(UGC)和Rand Forest Forest Formation(UGC),用来测试2015-2018年日本流感数量模型模型预测。我们使用5个数据资源:(1) 过去的流感病人,(2) SNS(Twitter),(3) 搜索引擎(Yahhoo!日本),(4) 购物服务(Yahhoo!Swick), 和 ⁇ A服务(Yah Rebe Regal Regro) Resour ser real real res reser reser reser reser resour resour resul resulation resulation resulation ress ress ress) ex ex resulation resulation ex ex res res ress ress res res ress resut the resut the sour resut the ress ress resutus resut the resut the the the the four resut the resut the the four resut the four resut the fours fours fours fours fours fours, resm resm resm ress fours ress rests, ress mal ress fow) rests, ress fe, resutusm resm ress, ress fows, ress, ress, ress fours feal ress fours mutts, ress, ress, resutesutes, ex res