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A dengue fever predicting model based on Baidu search index data and climate data in South China

Author

Listed:
  • Dan Liu
  • Songjing Guo
  • Mingjun Zou
  • Cong Chen
  • Fei Deng
  • Zhong Xie
  • Sheng Hu
  • Liang Wu

Abstract

With the acceleration of global urbanization and climate change, dengue fever is spreading worldwide. Different levels of dengue fever have also occurred in China, especially in southern China, causing enormous economic losses. Unfortunately, there is no effective treatment for dengue, and the most popular dengue vaccine does not exhibit good curative effects. Therefore, we developed a Generalized Additive Mixed Model (GAMM) that gathered climate factors (mean temperature, relative humidity and precipitation) and Baidu search data during 2011–2015 in Guangzhou city to improve the accuracy of dengue fever prediction. Firstly, the time series dengue fever data were decomposed into seasonal, trend and remainder components by the seasonal-trend decomposition procedure based on loess (STL). Secondly, the time lag of variables was determined in cross-correlation analysis and the order of autocorrelation was estimated using autocorrelation (ACF) and partial autocorrelation functions (PACF). Finally, the GAMM was built and evaluated by comparing it with Generalized Additive Mode (GAM). Experimental results indicated that the GAMM (R2: 0.95 and RMSE: 34.1) has a superior prediction capability than GAM (R2: 0.86 and RMSE: 121.9). The study could help the government agencies and hospitals respond early to dengue fever outbreak.

Suggested Citation

  • Dan Liu & Songjing Guo & Mingjun Zou & Cong Chen & Fei Deng & Zhong Xie & Sheng Hu & Liang Wu, 2019. "A dengue fever predicting model based on Baidu search index data and climate data in South China," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0226841
    DOI: 10.1371/journal.pone.0226841
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    References listed on IDEAS

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    1. Andrea Freyer Dugas & Mehdi Jalalpour & Yulia Gel & Scott Levin & Fred Torcaso & Takeru Igusa & Richard E Rothman, 2013. "Influenza Forecasting with Google Flu Trends," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-7, February.
    2. Samir Bhatt & Peter W. Gething & Oliver J. Brady & Jane P. Messina & Andrew W. Farlow & Catherine L. Moyes & John M. Drake & John S. Brownstein & Anne G. Hoen & Osman Sankoh & Monica F. Myers & Dylan , 2013. "The global distribution and burden of dengue," Nature, Nature, vol. 496(7446), pages 504-507, April.
    3. Zhihao Li & Tao Liu & Guanghu Zhu & Hualiang Lin & Yonghui Zhang & Jianfeng He & Aiping Deng & Zhiqiang Peng & Jianpeng Xiao & Shannon Rutherford & Runsheng Xie & Weilin Zeng & Xing Li & Wenjun Ma, 2017. "Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 11(3), pages 1-13, March.
    4. Shaowei Sang & Shaohua Gu & Peng Bi & Weizhong Yang & Zhicong Yang & Lei Xu & Jun Yang & Xiaobo Liu & Tong Jiang & Haixia Wu & Cordia Chu & Qiyong Liu, 2015. "Predicting Unprecedented Dengue Outbreak Using Imported Cases and Climatic Factors in Guangzhou, 2014," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 9(5), pages 1-12, May.
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    Cited by:

    1. Zhichao Li, 2022. "Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil," IJERPH, MDPI, vol. 19(20), pages 1-16, October.

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