Population-Wide Depression Incidence Forecasting Comparing Autoregressive Integrated Moving Average and Vector Autoregressive Integrated Moving Average to Temporal Fusion Transformers: Longitudinal Observational Study.

Journal: Journal of medical Internet research
PMID: 40354111

Abstract

BACKGROUND: Accurate prediction of population-wide depression incidence is vital for effective public mental health management. However, this incidence is often influenced by socioeconomic factors, such as abrupt events or changes, including pandemics, economic crises, and social unrest, creating complex structural break scenarios in the time-series data. These structural breaks can affect the performance of forecasting methods in various ways. Therefore, understanding and comparing different models across these scenarios is essential.

Authors

  • Deliang Yang
    Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Yiyi Tang
    Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Vivien Kin Yi Chan
    Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Qiwen Fang
    Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Sandra Sau Man Chan
    Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China (Hong Kong).
  • Hao Luo
    School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada.
  • Ian Chi Kei Wong
    Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Huang-Tz Ou
    Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Esther Wai Yin Chan
    Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • David Makram Bishai
    Division of Health Economics, Policy and Management, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Yingyao Chen
    National Health Commission Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China.
  • Martin Knapp
    Care Policy and Evaluation Centre, London School of Economics and Political Science, London, United Kingdom.
  • Mark Jit
    Laboratory of Data Discovery for Health (D24H), Hong Kong, China (Hong Kong).
  • Dawn Craig
    Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom.
  • Xue Li
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.

Keywords

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