Abstract
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.
Highlights
According to Yuan, Q. et al.[11], the construction of the prediction model involved compositing many search keywords into a single index according to different weights
Results of this study indicated that the ensemble elastic net regression model outperformed the compared models in monitoring seasonal influenza activity by using Baidu search engine query data
117 predictors were used for building the prediction models
Summary
According to Yuan, Q. et al.[11], the construction of the prediction model involved compositing many search keywords into a single index according to different weights. Beyond the use of a linear regression model for prediction, we explored an ensemble framework that incorporated different penalized regression algorithms including lasso, ridge and elastic net[12] to avoid the over-fitting problem with various keywords, identify informative predictors from a pool of candidate keywords, and estimate the parameters of the model with low variability. We sought to develop a Baidu search engine query data-based prediction model whose performance was optimized with respect to a set of measures. New ensemble penalized regression models using the lasso, ridge and elastic net algorithms were constructed, and applied to predict seasonal influenza activity. Results of this study indicated that the ensemble elastic net regression model outperformed the compared models in monitoring seasonal influenza activity by using Baidu search engine query data
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