An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.

Journal: Journal of global health
PMID: 40146993

Abstract

BACKGROUND: Modelling can contribute to disease prevention and control strategies. Accurate predictions of future cases and mortality rates were essential for establishing appropriate policies during the COVID-19 pandemic. However, no single model yielded definite conclusions, with each having specific strengths and weaknesses. Here we propose an ensemble learning approach which can offset the limitations of each model and improve prediction performances.

Authors

  • Kyulhee Han
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, Korea.
  • Catherine Apio
    Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.
  • Hanbyul Song
    Interdisciplinary Program of Bioinformatics, Seoul National University, Seoul, Korea.
  • Bogyeom Lee
    Department of Industrial Engineering, Seoul National University, Seoul, Korea.
  • Xuwen Hu
    Department of Statistics, Seoul National University, Seoul, Korea.
  • Jiwon Park
    Department of Transportation and Logistics Engineering, Hanyang University Erica Campus, Ansan 15588, Korea.
  • Liu Zhe
    Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Kowloon, Hong Kong SAR.
  • Taewan Goo
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Taesung Park
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.

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