Developing a dengue forecast model using machine learning: A case study in China.

Journal: PLoS neglected tropical diseases
PMID: 29036169

Abstract

BACKGROUND: In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue.

Authors

  • Pi Guo
    Department of Preventive Medicine, Shantou University Medical College, Shantou, China.
  • Tao Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Qin Zhang
    Department of Burn, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Jianpeng Xiao
    Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
  • Qingying Zhang
    Department of Preventive Medicine, Shantou University Medical College, Shantou, China.
  • Ganfeng Luo
    Department of Preventive Medicine, Shantou University Medical College, Shantou, China.
  • Zhihao Li
    Heilongjiang University of CM, Harbin 150040, China.
  • Jianfeng He
    Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
  • Yonghui Zhang
    Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China.
  • Wenjun Ma
    Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China. mawenjun@bjmu.edu.cn.

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