Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an...

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Bibliographic Details
Published in:	PLoS neglected tropical diseases 2017-03, Vol.11 (3), p.e0005354-e0005354
Main Authors:	Li, Zhihao, Liu, Tao, Zhu, Guanghu, Lin, Hualiang, Zhang, Yonghui, He, Jianfeng, Deng, Aiping, Peng, Zhiqiang, Xiao, Jianpeng, Rutherford, Shannon, Xie, Runsheng, Zeng, Weilin, Li, Xing, Ma, Wenjun
Format:	Article
Language:	eng
Subjects:	Aquatic insects Atmospheric models Biometeorology Care and treatment China - epidemiology Computer and Information Sciences Correlation coefficients Data search Database searching Demographics Dengue Dengue - epidemiology Dengue fever Disease control Disease prevention Distribution Early warning systems Earth Sciences Epidemics Epidemiological Monitoring Error analysis Internet Internet/Web search services Medical research Medicine and Health Sciences Medicine, Experimental Meteorological Concepts Models, Statistical Online searching Outbreaks People and Places Physical Sciences Prediction models Public health Research and Analysis Methods Root-mean-square errors Science Surveillance Technology application Trends Tropical diseases Viral diseases Websites
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Summary:	Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.
ISSN:	1935-2735 1935-2727 1935-2735