Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model.
Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model.
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DOI：
10.1038/srep46469
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发表时间：
2017-04-19
影响因子：
4.6
通讯作者：
Zhang Q
中科院分区：
文献类型：
--
作者：
Guo P;Zhang J;Wang L;Yang S;Luo G;Deng C;Wen Y;Zhang Q
季节性流感流行在中国造成严重的公共卫生问题。最近提出了基于搜索查询的监测，以补充流感流行的传统监测方法。然而，开发强大的搜索查询选择技术和提高流感流行的可预测性仍然是一个挑战。本研究的目的是开发一种新的集成框架，以改善惩罚回归模型检测流感流行，使用百度搜索引擎查询数据从中国。该集成框架采用自举聚集（装袋）和秩聚集方法的组合来优化惩罚回归模型。通过使用百度搜索引擎查询，对lasso、ridge、弹性网等不同算法以及所提出的集成框架中的算法进行了比较。大多数选定的检索词捕捉到流感病例时间序列曲线的波峰和波谷。该集成框架提高了传统惩罚回归模型的可预测性。弹性网络回归模型的预测误差最小，优于比较模型。我们建立了一个基于百度搜索引擎查询的流感疫情监测模型，该模型为流感等传染病的公共卫生应对提供了一个有用的工具。
Seasonal influenza epidemics cause serious public health problems in China. Search queries-based surveillance was recently proposed to complement traditional monitoring approaches of influenza epidemics. However, developing robust techniques of search query selection and enhancing predictability for influenza epidemics remains a challenge. This study aimed to develop a novel ensemble framework to improve penalized regression models for detecting influenza epidemics by using Baidu search engine query data from China. The ensemble framework applied a combination of bootstrap aggregating (bagging) and rank aggregation method to optimize penalized regression models. Different algorithms including lasso, ridge, elastic net and the algorithms in the proposed ensemble framework were compared by using Baidu search engine queries. Most of the selected search terms captured the peaks and troughs of the time series curves of influenza cases. The predictability of the conventional penalized regression models were improved by the proposed ensemble framework. The elastic net regression model outperformed the compared models, with the minimum prediction errors. We established a Baidu search engine queries-based surveillance model for monitoring influenza epidemics, and the proposed model provides a useful tool to support the public health response to influenza and other infectious diseases.