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

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    Author(s)
    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
    Griffith University Author(s)
    Rutherford, Shannon
    Year published
    2017
    Metadata
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    Abstract
    Background: 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. Methodology and principal findings: 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. ...
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    Background: 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. Methodology and principal findings: 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). Conclusions: Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou.
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    Journal Title
    PloS Neglected Tropical Diseases
    Volume
    11
    Issue
    3
    DOI
    https://doi.org/10.1371/journal.pntd.0005354
    Copyright Statement
    © 2017 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
    Subject
    Biological sciences
    Biomedical and clinical sciences
    Publication URI
    http://hdl.handle.net/10072/342651
    Collection
    • Journal articles

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