A novel model for malaria prediction based on ensemble algorithms.

Journal: PloS one
Published Date: Dec 26, 2019

Abstract

BACKGROUND AND OBJECTIVE: Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can solve this problem by combining distinct algorithms and models. This study compares the performance of traditional time series models and deep learning algorithms in malaria case prediction and explores the application value of stacking methods in the field of infectious disease prediction.

Authors

  • Mengyang Wang
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Jiao Wang
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Hongwei Liu
    Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia.
  • Rui Lu
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Tongqing Duan
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Xiaowen Gong
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Siyuan Feng
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Yuanyuan Liu
    College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
  • Zhuang Cui
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Changping Li
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Jun Ma
    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.

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